mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-10-20 08:55:05 +02:00
Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
|
|
f733b2a171 |
No files matched your search
@@ -1,14 +0,0 @@
|
||||
# .editorconfig
|
||||
root = true
|
||||
|
||||
# All files
|
||||
[*]
|
||||
charset = utf-8
|
||||
end_of_line = lf
|
||||
insert_final_newline = true
|
||||
trim_trailing_whitespace = true
|
||||
|
||||
# Python files
|
||||
[*.py]
|
||||
indent_style = space
|
||||
indent_size = 2
|
||||
@@ -1,160 +0,0 @@
|
||||
# =============================================================================
|
||||
# Test Environment Variables
|
||||
# =============================================================================
|
||||
# This file contains all environment variables needed to run tests locally
|
||||
# in a way that mimics the GitHub Actions CI environment.
|
||||
|
||||
# =============================================================================
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# LLM Provider API Keys
|
||||
# -----------------------------------------------------------------------------
|
||||
OPENAI_API_KEY=fake-api-key
|
||||
ANTHROPIC_API_KEY=fake-anthropic-key
|
||||
GEMINI_API_KEY=fake-gemini-key
|
||||
AZURE_API_KEY=fake-azure-key
|
||||
OPENROUTER_API_KEY=fake-openrouter-key
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# AWS Credentials
|
||||
# -----------------------------------------------------------------------------
|
||||
AWS_ACCESS_KEY_ID=fake-aws-access-key
|
||||
AWS_SECRET_ACCESS_KEY=fake-aws-secret-key
|
||||
AWS_DEFAULT_REGION=us-east-1
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Azure OpenAI Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
AZURE_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
|
||||
AZURE_OPENAI_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
|
||||
AZURE_OPENAI_API_KEY=fake-azure-openai-key
|
||||
AZURE_API_VERSION=2024-02-15-preview
|
||||
OPENAI_API_VERSION=2024-02-15-preview
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Google Cloud Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
#GOOGLE_CLOUD_PROJECT=fake-gcp-project
|
||||
#GOOGLE_CLOUD_LOCATION=us-central1
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OpenAI Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
OPENAI_BASE_URL=https://api.openai.com/v1
|
||||
OPENAI_API_BASE=https://api.openai.com/v1
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Search & Scraping Tool API Keys
|
||||
# -----------------------------------------------------------------------------
|
||||
SERPER_API_KEY=fake-serper-key
|
||||
EXA_API_KEY=fake-exa-key
|
||||
BRAVE_API_KEY=fake-brave-key
|
||||
FIRECRAWL_API_KEY=fake-firecrawl-key
|
||||
TAVILY_API_KEY=fake-tavily-key
|
||||
SERPAPI_API_KEY=fake-serpapi-key
|
||||
SERPLY_API_KEY=fake-serply-key
|
||||
LINKUP_API_KEY=fake-linkup-key
|
||||
PARALLEL_API_KEY=fake-parallel-key
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Exa Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
EXA_BASE_URL=https://api.exa.ai
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Web Scraping & Automation
|
||||
# -----------------------------------------------------------------------------
|
||||
BRIGHT_DATA_API_KEY=fake-brightdata-key
|
||||
BRIGHT_DATA_ZONE=fake-zone
|
||||
BRIGHTDATA_API_URL=https://api.brightdata.com
|
||||
BRIGHTDATA_DEFAULT_TIMEOUT=600
|
||||
BRIGHTDATA_DEFAULT_POLLING_INTERVAL=1
|
||||
|
||||
OXYLABS_USERNAME=fake-oxylabs-user
|
||||
OXYLABS_PASSWORD=fake-oxylabs-pass
|
||||
|
||||
SCRAPFLY_API_KEY=fake-scrapfly-key
|
||||
SCRAPEGRAPH_API_KEY=fake-scrapegraph-key
|
||||
|
||||
BROWSERBASE_API_KEY=fake-browserbase-key
|
||||
BROWSERBASE_PROJECT_ID=fake-browserbase-project
|
||||
|
||||
HYPERBROWSER_API_KEY=fake-hyperbrowser-key
|
||||
MULTION_API_KEY=fake-multion-key
|
||||
APIFY_API_TOKEN=fake-apify-token
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Database & Vector Store Credentials
|
||||
# -----------------------------------------------------------------------------
|
||||
SINGLESTOREDB_URL=mysql://fake:fake@localhost:3306/fake
|
||||
SINGLESTOREDB_HOST=localhost
|
||||
SINGLESTOREDB_PORT=3306
|
||||
SINGLESTOREDB_USER=fake-user
|
||||
SINGLESTOREDB_PASSWORD=fake-password
|
||||
SINGLESTOREDB_DATABASE=fake-database
|
||||
SINGLESTOREDB_CONNECT_TIMEOUT=30
|
||||
|
||||
SNOWFLAKE_USER=fake-snowflake-user
|
||||
SNOWFLAKE_PASSWORD=fake-snowflake-password
|
||||
SNOWFLAKE_ACCOUNT=fake-snowflake-account
|
||||
SNOWFLAKE_WAREHOUSE=fake-snowflake-warehouse
|
||||
SNOWFLAKE_DATABASE=fake-snowflake-database
|
||||
SNOWFLAKE_SCHEMA=fake-snowflake-schema
|
||||
|
||||
WEAVIATE_URL=http://localhost:8080
|
||||
WEAVIATE_API_KEY=fake-weaviate-key
|
||||
|
||||
EMBEDCHAIN_DB_URI=sqlite:///test.db
|
||||
|
||||
# Databricks Credentials
|
||||
DATABRICKS_HOST=https://fake-databricks.cloud.databricks.com
|
||||
DATABRICKS_TOKEN=fake-databricks-token
|
||||
DATABRICKS_CONFIG_PROFILE=fake-profile
|
||||
|
||||
# MongoDB Credentials
|
||||
MONGODB_URI=mongodb://fake:fake@localhost:27017/fake
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# CrewAI Platform & Enterprise
|
||||
# -----------------------------------------------------------------------------
|
||||
# setting CREWAI_PLATFORM_INTEGRATION_TOKEN causes these test to fail:
|
||||
#=========================== short test summary info ============================
|
||||
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_platform_context_manager_basic_usage - AssertionError: assert 'fake-platform-token' is None
|
||||
# + where 'fake-platform-token' = get_platform_integration_token()
|
||||
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_context_var_isolation_between_tests - AssertionError: assert 'fake-platform-token' is None
|
||||
# + where 'fake-platform-token' = get_platform_integration_token()
|
||||
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_multiple_sequential_context_managers - AssertionError: assert 'fake-platform-token' is None
|
||||
# + where 'fake-platform-token' = get_platform_integration_token()
|
||||
#CREWAI_PLATFORM_INTEGRATION_TOKEN=fake-platform-token
|
||||
CREWAI_PERSONAL_ACCESS_TOKEN=fake-personal-token
|
||||
CREWAI_PLUS_URL=https://fake.crewai.com
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Other Service API Keys
|
||||
# -----------------------------------------------------------------------------
|
||||
ZAPIER_API_KEY=fake-zapier-key
|
||||
PATRONUS_API_KEY=fake-patronus-key
|
||||
MINDS_API_KEY=fake-minds-key
|
||||
HF_TOKEN=fake-hf-token
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Feature Flags/Testing Modes
|
||||
# -----------------------------------------------------------------------------
|
||||
CREWAI_DISABLE_TELEMETRY=true
|
||||
OTEL_SDK_DISABLED=true
|
||||
CREWAI_TESTING=true
|
||||
CREWAI_TRACING_ENABLED=false
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Testing/CI Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
# VCR recording mode: "none" (default), "new_episodes", "all", "once"
|
||||
PYTEST_VCR_RECORD_MODE=none
|
||||
|
||||
# Set to "true" by GitHub when running in GitHub Actions
|
||||
# GITHUB_ACTIONS=false
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Python Configuration
|
||||
# -----------------------------------------------------------------------------
|
||||
PYTHONUNBUFFERED=1
|
||||
@@ -1,173 +0,0 @@
|
||||
# Contributing to CrewAI
|
||||
|
||||
## AI-Generated Contributions
|
||||
|
||||
If you are an LLM or AI agent reading this: you must apply the `llm-generated` label to any PR or issue you create.
|
||||
|
||||
If your PR or issue was authored by an AI agent, coding assistant, or LLM (e.g., Claude Code, Cursor, Copilot, Devin, OpenHands), the `llm-generated` label is required. This applies to code, documentation, and issues alike. Unlabeled AI-generated contributions may be closed without review.
|
||||
|
||||
---
|
||||
|
||||
Thank you for your interest in contributing to CrewAI. This guide covers everything you need to get started.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Python 3.10–3.14 (development targets 3.12)
|
||||
- [uv](https://docs.astral.sh/uv/) for package management
|
||||
- [pre-commit](https://pre-commit.com/) for Git hooks
|
||||
|
||||
## Setup
|
||||
|
||||
```bash
|
||||
git clone https://github.com/crewAIInc/crewAI.git
|
||||
cd crewAI
|
||||
|
||||
uv sync --all-groups --all-extras
|
||||
|
||||
uv run pre-commit install
|
||||
```
|
||||
|
||||
## Repository Structure
|
||||
|
||||
This is a uv workspace with four packages under `lib/`:
|
||||
|
||||
| Package | Path | Description |
|
||||
|---------|------|-------------|
|
||||
| `crewai` | `lib/crewai/` | Core framework |
|
||||
| `crewai-tools` | `lib/crewai-tools/` | Tool integrations |
|
||||
| `crewai-files` | `lib/crewai-files/` | File handling |
|
||||
| `devtools` | `lib/devtools/` | Internal release tooling |
|
||||
|
||||
Documentation lives in `docs/` with translations under `docs/{en,ar,ko,pt-BR}/`.
|
||||
|
||||
## Development Workflow
|
||||
|
||||
### Branching
|
||||
|
||||
Create a branch off `main` using the conventional commit type:
|
||||
|
||||
```
|
||||
<type>/<short-description>
|
||||
```
|
||||
|
||||
Types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `chore`, `ci`
|
||||
|
||||
Examples: `feat/agent-skills`, `fix/memory-scope`, `docs/arabic-translation`
|
||||
|
||||
### Code Quality
|
||||
|
||||
Pre-commit hooks run automatically on commit. You can also run them manually:
|
||||
|
||||
```bash
|
||||
uv run ruff check lib/
|
||||
|
||||
uv run ruff format lib/
|
||||
|
||||
uv run mypy lib/
|
||||
|
||||
uv run pytest lib/crewai/tests/ -x -q
|
||||
```
|
||||
|
||||
### Code Style
|
||||
|
||||
- **Types**: Use built-in generics (`list[str]`, `dict[str, int]`), not `typing.List`/`typing.Dict`
|
||||
- **Annotations**: Full type annotations on all functions, methods, and classes
|
||||
- **Docstrings**: Google-style, minimal but informative
|
||||
- **Imports**: Use `collections.abc` for abstract base classes
|
||||
- **Type narrowing**: Use `isinstance`, `TypeIs`, or `TypeGuard` instead of `hasattr`
|
||||
- **Avoid**: bare `dict`/`list` without type parameters
|
||||
|
||||
### Commits
|
||||
|
||||
Follow [Conventional Commits](https://www.conventionalcommits.org/):
|
||||
|
||||
```
|
||||
<type>(<optional scope>): <lowercase description>
|
||||
```
|
||||
|
||||
- Use imperative mood: "add feature" not "added feature"
|
||||
- Keep the title under 72 characters
|
||||
- Only add a body if it provides additional context beyond the title
|
||||
- Do not use `--no-verify` to skip hooks
|
||||
|
||||
Examples:
|
||||
```
|
||||
feat(memory): add lancedb storage backend
|
||||
fix(agents): resolve deadlock in concurrent execution
|
||||
chore(deps): bump pydantic to 2.11
|
||||
```
|
||||
|
||||
### Pull Requests
|
||||
|
||||
- One logical change per PR
|
||||
- Keep PRs focused — avoid bundling unrelated changes
|
||||
- PRs over 500 lines are labeled `size/XL` automatically
|
||||
- Title must follow the same conventional commit format
|
||||
- Link related issues where applicable
|
||||
|
||||
## Testing
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
uv run pytest lib/crewai/tests/ -x -q
|
||||
|
||||
# Run a specific test file
|
||||
uv run pytest lib/crewai/tests/agents/test_agent.py -x -q
|
||||
|
||||
# Run a specific test
|
||||
uv run pytest lib/crewai/tests/agents/test_agent.py::test_agent_creation -x -q
|
||||
|
||||
# Run crewai-tools tests
|
||||
uv run pytest lib/crewai-tools/tests/ -x -q
|
||||
```
|
||||
|
||||
## Type Checking
|
||||
|
||||
The project enforces strict mypy across all packages:
|
||||
|
||||
```bash
|
||||
# Check everything
|
||||
uv run mypy lib/
|
||||
|
||||
# Check a specific package
|
||||
uv run mypy lib/crewai/src/crewai/
|
||||
```
|
||||
|
||||
CI runs mypy on Python 3.10, 3.11, 3.12, and 3.13 for every PR.
|
||||
|
||||
## Documentation
|
||||
|
||||
Docs use [Mintlify](https://mintlify.com/) and live in `docs/`. The site is configured via `docs/docs.json`.
|
||||
|
||||
Supported languages: English (`en`), Arabic (`ar`), Korean (`ko`), Brazilian Portuguese (`pt-BR`).
|
||||
|
||||
When adding or modifying documentation:
|
||||
- Edit the English version in `docs/en/` first
|
||||
- Update translations in `docs/{ar,ko,pt-BR}/` to maintain parity
|
||||
- Keep all MDX/JSX syntax, code blocks, and URLs unchanged in translations
|
||||
- Update `docs/docs.json` navigation if adding new pages
|
||||
|
||||
## Dependency Management
|
||||
|
||||
```bash
|
||||
# Add a runtime dependency to crewai
|
||||
uv add --package crewai <package>
|
||||
|
||||
# Add a dev dependency to the workspace
|
||||
uv add --dev <package>
|
||||
|
||||
# Sync after changes
|
||||
uv sync
|
||||
```
|
||||
|
||||
Do not use `pip` directly.
|
||||
|
||||
## Reporting Issues
|
||||
|
||||
Use the [GitHub issue templates](https://github.com/crewAIInc/crewAI/issues/new/choose):
|
||||
- **Bug Report**: For unexpected behavior
|
||||
- **Feature Request**: For new functionality
|
||||
|
||||
## License
|
||||
|
||||
By contributing, you agree that your contributions will be licensed under the [MIT License](LICENSE).
|
||||
@@ -1,115 +0,0 @@
|
||||
name: Bug report
|
||||
description: Create a report to help us improve CrewAI
|
||||
title: "[BUG]"
|
||||
labels: ["bug"]
|
||||
assignees: []
|
||||
body:
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
label: Description
|
||||
description: Provide a clear and concise description of what the bug is.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: steps-to-reproduce
|
||||
attributes:
|
||||
label: Steps to Reproduce
|
||||
description: Provide a step-by-step process to reproduce the behavior.
|
||||
placeholder: |
|
||||
1. Go to '...'
|
||||
2. Click on '....'
|
||||
3. Scroll down to '....'
|
||||
4. See error
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: expected-behavior
|
||||
attributes:
|
||||
label: Expected behavior
|
||||
description: A clear and concise description of what you expected to happen.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: screenshots-code
|
||||
attributes:
|
||||
label: Screenshots/Code snippets
|
||||
description: If applicable, add screenshots or code snippets to help explain your problem.
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
id: os
|
||||
attributes:
|
||||
label: Operating System
|
||||
description: Select the operating system you're using
|
||||
options:
|
||||
- Ubuntu 20.04
|
||||
- Ubuntu 22.04
|
||||
- Ubuntu 24.04
|
||||
- macOS Catalina
|
||||
- macOS Big Sur
|
||||
- macOS Monterey
|
||||
- macOS Ventura
|
||||
- macOS Sonoma
|
||||
- Windows 10
|
||||
- Windows 11
|
||||
- Other (specify in additional context)
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
id: python-version
|
||||
attributes:
|
||||
label: Python Version
|
||||
description: Version of Python your Crew is running on
|
||||
options:
|
||||
- '3.10'
|
||||
- '3.11'
|
||||
- '3.12'
|
||||
validations:
|
||||
required: true
|
||||
- type: input
|
||||
id: crewai-version
|
||||
attributes:
|
||||
label: crewAI Version
|
||||
description: What version of CrewAI are you using
|
||||
validations:
|
||||
required: true
|
||||
- type: input
|
||||
id: crewai-tools-version
|
||||
attributes:
|
||||
label: crewAI Tools Version
|
||||
description: What version of CrewAI Tools are you using
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
id: virtual-environment
|
||||
attributes:
|
||||
label: Virtual Environment
|
||||
description: What Virtual Environment are you running your crew in.
|
||||
options:
|
||||
- Venv
|
||||
- Conda
|
||||
- Poetry
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: evidence
|
||||
attributes:
|
||||
label: Evidence
|
||||
description: Include relevant information, logs or error messages. These can be screenshots.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: possible-solution
|
||||
attributes:
|
||||
label: Possible Solution
|
||||
description: Have a solution in mind? Please suggest it here, or write "None".
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: additional-context
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: Add any other context about the problem here.
|
||||
validations:
|
||||
required: true
|
||||
@@ -1 +0,0 @@
|
||||
blank_issues_enabled: false
|
||||
@@ -1,65 +0,0 @@
|
||||
name: Feature request
|
||||
description: Suggest a new feature for CrewAI
|
||||
title: "[FEATURE]"
|
||||
labels: ["feature-request"]
|
||||
assignees: []
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Thanks for taking the time to fill out this feature request!
|
||||
- type: dropdown
|
||||
id: feature-area
|
||||
attributes:
|
||||
label: Feature Area
|
||||
description: Which area of CrewAI does this feature primarily relate to?
|
||||
options:
|
||||
- Core functionality
|
||||
- Agent capabilities
|
||||
- Task management
|
||||
- Integration with external tools
|
||||
- Performance optimization
|
||||
- Documentation
|
||||
- Other (please specify in additional context)
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: problem
|
||||
attributes:
|
||||
label: Is your feature request related to a an existing bug? Please link it here.
|
||||
description: A link to the bug or NA if not related to an existing bug.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: solution
|
||||
attributes:
|
||||
label: Describe the solution you'd like
|
||||
description: A clear and concise description of what you want to happen.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: alternatives
|
||||
attributes:
|
||||
label: Describe alternatives you've considered
|
||||
description: A clear and concise description of any alternative solutions or features you've considered.
|
||||
validations:
|
||||
required: false
|
||||
- type: textarea
|
||||
id: context
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: Add any other context, screenshots, or examples about the feature request here.
|
||||
validations:
|
||||
required: false
|
||||
- type: dropdown
|
||||
id: willingness-to-contribute
|
||||
attributes:
|
||||
label: Willingness to Contribute
|
||||
description: Would you be willing to contribute to the implementation of this feature?
|
||||
options:
|
||||
- Yes, I'd be happy to submit a pull request
|
||||
- I could provide more detailed specifications
|
||||
- I can test the feature once it's implemented
|
||||
- No, I'm just suggesting the idea
|
||||
validations:
|
||||
required: true
|
||||
@@ -1,33 +0,0 @@
|
||||
name: "CodeQL Config"
|
||||
|
||||
paths-ignore:
|
||||
# Ignore template files - these are boilerplate code that shouldn't be analyzed
|
||||
- "lib/crewai/src/crewai/cli/templates/**"
|
||||
# Ignore test cassettes - these are test fixtures/recordings
|
||||
- "lib/crewai/tests/cassettes/**"
|
||||
- "lib/crewai-tools/tests/cassettes/**"
|
||||
# Ignore cache and build artifacts
|
||||
- ".cache/**"
|
||||
# Ignore documentation build artifacts
|
||||
- "docs/.cache/**"
|
||||
# Ignore experimental code
|
||||
- "lib/crewai/src/crewai/experimental/a2a/**"
|
||||
|
||||
paths:
|
||||
# Include GitHub Actions workflows/composite actions for CodeQL actions analysis
|
||||
- ".github/workflows/**"
|
||||
- ".github/actions/**"
|
||||
# Include all Python source code from workspace packages
|
||||
- "lib/crewai/src/**"
|
||||
- "lib/crewai-tools/src/**"
|
||||
- "lib/crewai-files/src/**"
|
||||
- "lib/devtools/src/**"
|
||||
# Include tests (but exclude cassettes via paths-ignore)
|
||||
- "lib/crewai/tests/**"
|
||||
- "lib/crewai-tools/tests/**"
|
||||
- "lib/crewai-files/tests/**"
|
||||
- "lib/devtools/tests/**"
|
||||
|
||||
# Configure specific queries or packs if needed
|
||||
# queries:
|
||||
# - uses: security-and-quality
|
||||
@@ -1,16 +0,0 @@
|
||||
# To get started with Dependabot version updates, you'll need to specify which
|
||||
# package ecosystems to update and where the package manifests are located.
|
||||
# Please see the documentation for all configuration options:
|
||||
# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
|
||||
|
||||
version: 2
|
||||
updates:
|
||||
- package-ecosystem: uv
|
||||
directory: "/"
|
||||
schedule:
|
||||
interval: "weekly"
|
||||
groups:
|
||||
security-updates:
|
||||
applies-to: security-updates
|
||||
patterns:
|
||||
- "*"
|
||||
@@ -1,12 +0,0 @@
|
||||
## CrewAI Security Policy
|
||||
|
||||
We are committed to protecting the confidentiality, integrity, and availability of the
|
||||
CrewAI ecosystem.
|
||||
|
||||
### How to Report
|
||||
|
||||
Please submit reports to **crewai-vdp-ess@submit.bugcrowd.com**
|
||||
|
||||
- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests,
|
||||
or social media
|
||||
- Reports submitted via channels other than this Bugcrowd submission email will not be reviewed and will be dismissed
|
||||
@@ -1,48 +0,0 @@
|
||||
name: Build uv cache
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "uv.lock"
|
||||
- "pyproject.toml"
|
||||
schedule:
|
||||
- cron: "0 0 */5 * *" # Run every 5 days at midnight UTC to prevent cache expiration
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
build-cache:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version: ["3.10", "3.11", "3.12", "3.13"]
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: ${{ matrix.python-version }}
|
||||
enable-cache: false
|
||||
|
||||
- name: Install dependencies and populate cache
|
||||
run: |
|
||||
echo "Building global UV cache for Python ${{ matrix.python-version }}..."
|
||||
uv sync --all-groups --all-extras --no-install-project
|
||||
echo "Cache populated successfully"
|
||||
|
||||
- name: Save uv caches
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
@@ -1,103 +0,0 @@
|
||||
# For most projects, this workflow file will not need changing; you simply need
|
||||
# to commit it to your repository.
|
||||
#
|
||||
# You may wish to alter this file to override the set of languages analyzed,
|
||||
# or to provide custom queries or build logic.
|
||||
#
|
||||
# ******** NOTE ********
|
||||
# We have attempted to detect the languages in your repository. Please check
|
||||
# the `language` matrix defined below to confirm you have the correct set of
|
||||
# supported CodeQL languages.
|
||||
#
|
||||
name: "CodeQL Advanced"
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ "main" ]
|
||||
paths-ignore:
|
||||
- "lib/crewai/src/crewai/cli/templates/**"
|
||||
pull_request:
|
||||
branches: [ "main" ]
|
||||
paths-ignore:
|
||||
- "lib/crewai/src/crewai/cli/templates/**"
|
||||
|
||||
jobs:
|
||||
analyze:
|
||||
name: Analyze (${{ matrix.language }})
|
||||
# Runner size impacts CodeQL analysis time. To learn more, please see:
|
||||
# - https://gh.io/recommended-hardware-resources-for-running-codeql
|
||||
# - https://gh.io/supported-runners-and-hardware-resources
|
||||
# - https://gh.io/using-larger-runners (GitHub.com only)
|
||||
# Consider using larger runners or machines with greater resources for possible analysis time improvements.
|
||||
runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
|
||||
permissions:
|
||||
# required for all workflows
|
||||
security-events: write
|
||||
|
||||
# required to fetch internal or private CodeQL packs
|
||||
packages: read
|
||||
|
||||
# only required for workflows in private repositories
|
||||
actions: read
|
||||
contents: read
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- language: actions
|
||||
build-mode: none
|
||||
- language: python
|
||||
build-mode: none
|
||||
# CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
|
||||
# Use `c-cpp` to analyze code written in C, C++ or both
|
||||
# Use 'java-kotlin' to analyze code written in Java, Kotlin or both
|
||||
# Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
|
||||
# To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
|
||||
# see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
|
||||
# If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
|
||||
# your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
# Add any setup steps before running the `github/codeql-action/init` action.
|
||||
# This includes steps like installing compilers or runtimes (`actions/setup-node`
|
||||
# or others). This is typically only required for manual builds.
|
||||
# - name: Setup runtime (example)
|
||||
# uses: actions/setup-example@v1
|
||||
|
||||
# Initializes the CodeQL tools for scanning.
|
||||
- name: Initialize CodeQL
|
||||
uses: github/codeql-action/init@v4
|
||||
with:
|
||||
languages: ${{ matrix.language }}
|
||||
build-mode: ${{ matrix.build-mode }}
|
||||
config-file: ./.github/codeql/codeql-config.yml
|
||||
# If you wish to specify custom queries, you can do so here or in a config file.
|
||||
# By default, queries listed here will override any specified in a config file.
|
||||
# Prefix the list here with "+" to use these queries and those in the config file.
|
||||
|
||||
# For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
|
||||
# queries: security-extended,security-and-quality
|
||||
|
||||
# If the analyze step fails for one of the languages you are analyzing with
|
||||
# "We were unable to automatically build your code", modify the matrix above
|
||||
# to set the build mode to "manual" for that language. Then modify this step
|
||||
# to build your code.
|
||||
# ℹ️ Command-line programs to run using the OS shell.
|
||||
# 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
|
||||
- if: matrix.build-mode == 'manual'
|
||||
shell: bash
|
||||
run: |
|
||||
echo 'If you are using a "manual" build mode for one or more of the' \
|
||||
'languages you are analyzing, replace this with the commands to build' \
|
||||
'your code, for example:'
|
||||
echo ' make bootstrap'
|
||||
echo ' make release'
|
||||
exit 1
|
||||
|
||||
- name: Perform CodeQL Analysis
|
||||
uses: github/codeql-action/analyze@v4
|
||||
with:
|
||||
category: "/language:${{matrix.language}}"
|
||||
@@ -1,35 +0,0 @@
|
||||
name: Check Documentation Broken Links
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- "docs/**"
|
||||
- "docs.json"
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "docs/**"
|
||||
- "docs.json"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
check-links:
|
||||
name: Check broken links
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Node
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: "22"
|
||||
|
||||
- name: Install Mintlify CLI
|
||||
run: npm i -g mintlify
|
||||
|
||||
- name: Run broken link checker
|
||||
run: |
|
||||
# Auto-answer the prompt with yes command
|
||||
yes "" | mintlify broken-links || test $? -eq 141
|
||||
working-directory: ./docs
|
||||
@@ -1,64 +0,0 @@
|
||||
name: Generate Tool Specifications
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- 'lib/crewai-tools/src/crewai_tools/**'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
pull-requests: write
|
||||
|
||||
jobs:
|
||||
generate-specs:
|
||||
if: github.event_name == 'workflow_dispatch' || github.event.pull_request.head.repo.full_name == github.repository
|
||||
runs-on: ubuntu-latest
|
||||
env:
|
||||
PYTHONUNBUFFERED: 1
|
||||
|
||||
steps:
|
||||
- name: Generate GitHub App token
|
||||
id: app-token
|
||||
uses: tibdex/github-app-token@v2
|
||||
with:
|
||||
app_id: ${{ secrets.CREWAI_TOOL_SPECS_APP_ID }}
|
||||
private_key: ${{ secrets.CREWAI_TOOL_SPECS_PRIVATE_KEY }}
|
||||
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ github.head_ref }}
|
||||
token: ${{ steps.app-token.outputs.token }}
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: "3.12"
|
||||
enable-cache: true
|
||||
|
||||
- name: Install the project
|
||||
working-directory: lib/crewai-tools
|
||||
run: uv sync --dev --all-extras
|
||||
|
||||
- name: Generate tool specifications
|
||||
working-directory: lib/crewai-tools
|
||||
run: uv run python src/crewai_tools/generate_tool_specs.py
|
||||
|
||||
- name: Check for changes and commit
|
||||
run: |
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
|
||||
|
||||
git add lib/crewai-tools/tool.specs.json
|
||||
|
||||
if git diff --quiet --staged; then
|
||||
echo "No changes detected in tool.specs.json"
|
||||
else
|
||||
echo "Changes detected in tool.specs.json, committing..."
|
||||
git commit -m "chore: update tool specifications"
|
||||
git push
|
||||
fi
|
||||
@@ -1,87 +0,0 @@
|
||||
name: Lint
|
||||
|
||||
on: [pull_request]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
changes:
|
||||
name: Detect changes
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
code: ${{ steps.filter.outputs.code }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dorny/paths-filter@v3
|
||||
id: filter
|
||||
with:
|
||||
filters: |
|
||||
code:
|
||||
- '!docs/**'
|
||||
- '!**/*.md'
|
||||
|
||||
lint-run:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.code == 'true'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Restore global uv cache
|
||||
id: cache-restore
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
|
||||
restore-keys: |
|
||||
uv-main-py3.11-
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: "3.11"
|
||||
enable-cache: false
|
||||
|
||||
- name: Install dependencies
|
||||
run: uv sync --all-groups --all-extras --no-install-project
|
||||
|
||||
- name: Ruff check
|
||||
run: uv run ruff check lib/
|
||||
|
||||
- name: Ruff format
|
||||
run: uv run ruff format --check lib/
|
||||
|
||||
- name: Save uv caches
|
||||
if: steps.cache-restore.outputs.cache-hit != 'true'
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
|
||||
|
||||
# Summary job to provide single status for branch protection
|
||||
lint:
|
||||
name: lint
|
||||
runs-on: ubuntu-latest
|
||||
needs: [changes, lint-run]
|
||||
if: always()
|
||||
steps:
|
||||
- name: Check results
|
||||
run: |
|
||||
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
|
||||
echo "Docs-only change, skipping lint"
|
||||
exit 0
|
||||
fi
|
||||
if [ "${{ needs.lint-run.result }}" == "success" ]; then
|
||||
echo "Lint passed"
|
||||
else
|
||||
echo "Lint failed"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,127 +0,0 @@
|
||||
name: Nightly Canary Release
|
||||
|
||||
on:
|
||||
schedule:
|
||||
- cron: '0 6 * * *' # daily at 6am UTC
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
check:
|
||||
name: Check for new commits
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
outputs:
|
||||
has_changes: ${{ steps.check.outputs.has_changes }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Check for commits in last 24h
|
||||
id: check
|
||||
run: |
|
||||
RECENT=$(git log --since="24 hours ago" --oneline | head -1)
|
||||
if [ -n "$RECENT" ]; then
|
||||
echo "has_changes=true" >> "$GITHUB_OUTPUT"
|
||||
else
|
||||
echo "has_changes=false" >> "$GITHUB_OUTPUT"
|
||||
fi
|
||||
|
||||
build:
|
||||
name: Build nightly packages
|
||||
needs: check
|
||||
if: needs.check.outputs.has_changes == 'true' || github.event_name == 'workflow_dispatch'
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v4
|
||||
|
||||
- name: Stamp nightly versions
|
||||
run: |
|
||||
DATE=$(date +%Y%m%d)
|
||||
for init_file in \
|
||||
lib/crewai/src/crewai/__init__.py \
|
||||
lib/crewai-tools/src/crewai_tools/__init__.py \
|
||||
lib/crewai-files/src/crewai_files/__init__.py; do
|
||||
CURRENT=$(python -c "
|
||||
import re
|
||||
text = open('$init_file').read()
|
||||
print(re.search(r'__version__\s*=\s*\"(.*?)\"\s*$', text, re.MULTILINE).group(1))
|
||||
")
|
||||
NIGHTLY="${CURRENT}.dev${DATE}"
|
||||
sed -i "s/__version__ = .*/__version__ = \"${NIGHTLY}\"/" "$init_file"
|
||||
echo "$init_file: $CURRENT -> $NIGHTLY"
|
||||
done
|
||||
|
||||
# Update cross-package dependency pins to nightly versions
|
||||
sed -i "s/\"crewai-tools==[^\"]*\"/\"crewai-tools==${NIGHTLY}\"/" lib/crewai/pyproject.toml
|
||||
sed -i "s/\"crewai==[^\"]*\"/\"crewai==${NIGHTLY}\"/" lib/crewai-tools/pyproject.toml
|
||||
echo "Updated cross-package dependency pins to ${NIGHTLY}"
|
||||
|
||||
- name: Build packages
|
||||
run: |
|
||||
uv build --all-packages
|
||||
rm dist/.gitignore
|
||||
|
||||
- name: Upload artifacts
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist/
|
||||
|
||||
publish:
|
||||
name: Publish nightly to PyPI
|
||||
needs: build
|
||||
runs-on: ubuntu-latest
|
||||
environment:
|
||||
name: pypi
|
||||
url: https://pypi.org/p/crewai
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: "3.12"
|
||||
enable-cache: false
|
||||
|
||||
- name: Download artifacts
|
||||
uses: actions/download-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
|
||||
- name: Publish to PyPI
|
||||
env:
|
||||
UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
failed=0
|
||||
for package in dist/*; do
|
||||
if [[ "$package" == *"crewai_devtools"* ]]; then
|
||||
echo "Skipping private package: $package"
|
||||
continue
|
||||
fi
|
||||
echo "Publishing $package"
|
||||
if ! uv publish "$package"; then
|
||||
echo "Failed to publish $package"
|
||||
failed=1
|
||||
fi
|
||||
done
|
||||
if [ $failed -eq 1 ]; then
|
||||
echo "Some packages failed to publish"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,32 +0,0 @@
|
||||
name: PR Size Check
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened]
|
||||
|
||||
jobs:
|
||||
pr-size:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
pull-requests: write
|
||||
steps:
|
||||
- uses: codelytv/pr-size-labeler@v1
|
||||
with:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
xs_label: "size/XS"
|
||||
xs_max_size: 25
|
||||
s_label: "size/S"
|
||||
s_max_size: 100
|
||||
m_label: "size/M"
|
||||
m_max_size: 250
|
||||
l_label: "size/L"
|
||||
l_max_size: 500
|
||||
xl_label: "size/XL"
|
||||
fail_if_xl: false
|
||||
files_to_ignore: |
|
||||
uv.lock
|
||||
*.lock
|
||||
lib/crewai/src/crewai/cli/templates/**
|
||||
**/*.json
|
||||
**/test_durations/**
|
||||
**/cassettes/**
|
||||
@@ -1,41 +0,0 @@
|
||||
name: PR Title Check
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types: [opened, edited, synchronize, reopened]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: read
|
||||
|
||||
jobs:
|
||||
pr-title:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: amannn/action-semantic-pull-request@v5
|
||||
continue-on-error: true
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
with:
|
||||
types: |
|
||||
feat
|
||||
fix
|
||||
refactor
|
||||
perf
|
||||
test
|
||||
docs
|
||||
chore
|
||||
ci
|
||||
style
|
||||
revert
|
||||
requireScope: false
|
||||
subjectPattern: ^[a-z].+[^.]$
|
||||
subjectPatternError: >
|
||||
The PR title "{title}" does not follow conventional commit format.
|
||||
|
||||
Expected: <type>(<scope>): <lowercase description without trailing period>
|
||||
|
||||
Examples:
|
||||
feat(memory): add lancedb storage backend
|
||||
fix(agents): resolve deadlock in concurrent execution
|
||||
chore(deps): bump pydantic to 2.11.9
|
||||
@@ -1,166 +0,0 @@
|
||||
name: Publish to PyPI
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
release_tag:
|
||||
description: 'Release tag to publish'
|
||||
required: false
|
||||
type: string
|
||||
|
||||
jobs:
|
||||
build:
|
||||
name: Build packages
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
steps:
|
||||
- name: Determine release tag
|
||||
id: release
|
||||
run: |
|
||||
if [ -n "${{ inputs.release_tag }}" ]; then
|
||||
echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT
|
||||
else
|
||||
echo "tag=" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ steps.release.outputs.tag || github.ref }}
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v4
|
||||
|
||||
- name: Build packages
|
||||
run: |
|
||||
uv build --all-packages
|
||||
rm dist/.gitignore
|
||||
|
||||
- name: Upload artifacts
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist/
|
||||
|
||||
publish:
|
||||
name: Publish to PyPI
|
||||
needs: build
|
||||
runs-on: ubuntu-latest
|
||||
environment:
|
||||
name: pypi
|
||||
url: https://pypi.org/p/crewai
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ inputs.release_tag || github.ref }}
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: "3.12"
|
||||
enable-cache: false
|
||||
|
||||
- name: Download artifacts
|
||||
uses: actions/download-artifact@v4
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
|
||||
- name: Publish to PyPI
|
||||
env:
|
||||
UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
failed=0
|
||||
for package in dist/*; do
|
||||
if [[ "$package" == *"crewai_devtools"* ]]; then
|
||||
echo "Skipping private package: $package"
|
||||
continue
|
||||
fi
|
||||
echo "Publishing $package"
|
||||
if ! uv publish "$package"; then
|
||||
echo "Failed to publish $package"
|
||||
failed=1
|
||||
fi
|
||||
done
|
||||
if [ $failed -eq 1 ]; then
|
||||
echo "Some packages failed to publish"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
- name: Build Slack payload
|
||||
if: success()
|
||||
id: slack
|
||||
env:
|
||||
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
RELEASE_TAG: ${{ inputs.release_tag }}
|
||||
run: |
|
||||
payload=$(uv run python -c "
|
||||
import json, re, subprocess, sys
|
||||
|
||||
with open('lib/crewai/src/crewai/__init__.py') as f:
|
||||
m = re.search(r\"__version__\s*=\s*[\\\"']([^\\\"']+)\", f.read())
|
||||
version = m.group(1) if m else 'unknown'
|
||||
|
||||
import os
|
||||
tag = os.environ.get('RELEASE_TAG') or version
|
||||
|
||||
try:
|
||||
r = subprocess.run(['gh','release','view',tag,'--json','body','-q','.body'],
|
||||
capture_output=True, text=True, check=True)
|
||||
body = r.stdout.strip()
|
||||
except Exception:
|
||||
body = ''
|
||||
|
||||
blocks = [
|
||||
{'type':'section','text':{'type':'mrkdwn',
|
||||
'text':f':rocket: \`crewai v{version}\` published to PyPI'}},
|
||||
{'type':'section','text':{'type':'mrkdwn',
|
||||
'text':f'<https://pypi.org/project/crewai/{version}/|View on PyPI> · <https://github.com/crewAIInc/crewAI/releases/tag/{tag}|Release notes>'}},
|
||||
{'type':'divider'},
|
||||
]
|
||||
|
||||
if body:
|
||||
heading, items = '', []
|
||||
for line in body.split('\n'):
|
||||
line = line.strip()
|
||||
if not line: continue
|
||||
hm = re.match(r'^#{2,3}\s+(.*)', line)
|
||||
if hm:
|
||||
if heading and items:
|
||||
skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
|
||||
if not skip:
|
||||
txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
|
||||
blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
|
||||
heading, items = hm.group(1), []
|
||||
elif line.startswith('- ') or line.startswith('* '):
|
||||
items.append(re.sub(r'\*\*([^*]*)\*\*', r'*\1*', line[2:]))
|
||||
if heading and items:
|
||||
skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
|
||||
if not skip:
|
||||
txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
|
||||
blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
|
||||
|
||||
blocks.append({'type':'divider'})
|
||||
blocks.append({'type':'section','text':{'type':'mrkdwn',
|
||||
'text':f'\`\`\`uv add \"crewai[tools]=={version}\"\`\`\`'}})
|
||||
|
||||
print(json.dumps({'blocks':blocks}))
|
||||
")
|
||||
echo "payload=$payload" >> $GITHUB_OUTPUT
|
||||
|
||||
- name: Notify Slack
|
||||
if: success()
|
||||
uses: slackapi/slack-github-action@v2.1.0
|
||||
with:
|
||||
webhook: ${{ secrets.SLACK_WEBHOOK_URL }}
|
||||
webhook-type: incoming-webhook
|
||||
payload: ${{ steps.slack.outputs.payload }}
|
||||
@@ -1,29 +0,0 @@
|
||||
name: Mark stale issues and pull requests
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
issues: write
|
||||
pull-requests: write
|
||||
|
||||
on:
|
||||
schedule:
|
||||
- cron: '10 12 * * *'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
stale:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/stale@v9
|
||||
with:
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
stale-issue-label: 'no-issue-activity'
|
||||
stale-issue-message: 'This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
|
||||
close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
|
||||
days-before-issue-stale: 30
|
||||
days-before-issue-close: 5
|
||||
stale-pr-label: 'no-pr-activity'
|
||||
stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
|
||||
days-before-pr-stale: 45
|
||||
days-before-pr-close: -1
|
||||
operations-per-run: 1200
|
||||
@@ -1,137 +0,0 @@
|
||||
name: Run Tests
|
||||
|
||||
on: [pull_request]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
changes:
|
||||
name: Detect changes
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
code: ${{ steps.filter.outputs.code }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dorny/paths-filter@v3
|
||||
id: filter
|
||||
with:
|
||||
filters: |
|
||||
code:
|
||||
- '!docs/**'
|
||||
- '!**/*.md'
|
||||
|
||||
tests-matrix:
|
||||
name: tests (${{ matrix.python-version }})
|
||||
needs: changes
|
||||
if: needs.changes.outputs.code == 'true'
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
strategy:
|
||||
fail-fast: true
|
||||
matrix:
|
||||
python-version: ['3.10', '3.11', '3.12', '3.13']
|
||||
group: [1, 2, 3, 4, 5, 6, 7, 8]
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0 # Fetch all history for proper diff
|
||||
|
||||
- name: Restore global uv cache
|
||||
id: cache-restore
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
restore-keys: |
|
||||
uv-main-py${{ matrix.python-version }}-
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: ${{ matrix.python-version }}
|
||||
enable-cache: false
|
||||
|
||||
- name: Install the project
|
||||
run: uv sync --all-groups --all-extras
|
||||
|
||||
- name: Restore test durations
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: .test_durations_py*
|
||||
key: test-durations-py${{ matrix.python-version }}
|
||||
|
||||
- name: Run tests (group ${{ matrix.group }} of 8)
|
||||
run: |
|
||||
PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
|
||||
DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}"
|
||||
|
||||
# Temporarily always skip cached durations to fix test splitting
|
||||
# When durations don't match, pytest-split runs duplicate tests instead of splitting
|
||||
echo "Using even test splitting (duration cache disabled until fix merged)"
|
||||
DURATIONS_ARG=""
|
||||
|
||||
# Original logic (disabled temporarily):
|
||||
# if [ ! -f "$DURATION_FILE" ]; then
|
||||
# echo "No cached durations found, tests will be split evenly"
|
||||
# DURATIONS_ARG=""
|
||||
# elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
|
||||
# echo "Test files have changed, skipping cached durations to avoid mismatches"
|
||||
# DURATIONS_ARG=""
|
||||
# else
|
||||
# echo "No test changes detected, using cached test durations for optimal splitting"
|
||||
# DURATIONS_ARG="--durations-path=${DURATION_FILE}"
|
||||
# fi
|
||||
|
||||
cd lib/crewai && uv run pytest \
|
||||
-vv \
|
||||
--splits 8 \
|
||||
--group ${{ matrix.group }} \
|
||||
$DURATIONS_ARG \
|
||||
--durations=10 \
|
||||
--maxfail=3
|
||||
|
||||
- name: Run tool tests (group ${{ matrix.group }} of 8)
|
||||
run: |
|
||||
cd lib/crewai-tools && uv run pytest \
|
||||
-vv \
|
||||
--splits 8 \
|
||||
--group ${{ matrix.group }} \
|
||||
--durations=10 \
|
||||
--maxfail=3
|
||||
|
||||
|
||||
- name: Save uv caches
|
||||
if: steps.cache-restore.outputs.cache-hit != 'true'
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
|
||||
# Summary job to provide single status for branch protection
|
||||
tests:
|
||||
name: tests
|
||||
runs-on: ubuntu-latest
|
||||
needs: [changes, tests-matrix]
|
||||
if: always()
|
||||
steps:
|
||||
- name: Check results
|
||||
run: |
|
||||
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
|
||||
echo "Docs-only change, skipping tests"
|
||||
exit 0
|
||||
fi
|
||||
if [ "${{ needs.tests-matrix.result }}" == "success" ]; then
|
||||
echo "All tests passed"
|
||||
else
|
||||
echo "Tests failed"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,91 +0,0 @@
|
||||
name: Run Type Checks
|
||||
|
||||
on: [pull_request]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
changes:
|
||||
name: Detect changes
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
code: ${{ steps.filter.outputs.code }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dorny/paths-filter@v3
|
||||
id: filter
|
||||
with:
|
||||
filters: |
|
||||
code:
|
||||
- '!docs/**'
|
||||
- '!**/*.md'
|
||||
|
||||
type-checker-matrix:
|
||||
name: type-checker (${{ matrix.python-version }})
|
||||
needs: changes
|
||||
if: needs.changes.outputs.code == 'true'
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: ["3.10", "3.11", "3.12", "3.13"]
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Restore global uv cache
|
||||
id: cache-restore
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
restore-keys: |
|
||||
uv-main-py${{ matrix.python-version }}-
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: ${{ matrix.python-version }}
|
||||
enable-cache: false
|
||||
|
||||
- name: Install dependencies
|
||||
run: uv sync --all-groups --all-extras
|
||||
|
||||
- name: Run type checks
|
||||
run: uv run mypy lib/
|
||||
|
||||
- name: Save uv caches
|
||||
if: steps.cache-restore.outputs.cache-hit != 'true'
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
|
||||
# Summary job to provide single status for branch protection
|
||||
type-checker:
|
||||
name: type-checker
|
||||
runs-on: ubuntu-latest
|
||||
needs: [changes, type-checker-matrix]
|
||||
if: always()
|
||||
steps:
|
||||
- name: Check results
|
||||
run: |
|
||||
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
|
||||
echo "Docs-only change, skipping type checks"
|
||||
exit 0
|
||||
fi
|
||||
if [ "${{ needs.type-checker-matrix.result }}" == "success" ]; then
|
||||
echo "All type checks passed"
|
||||
else
|
||||
echo "Type checks failed"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,71 +0,0 @@
|
||||
name: Update Test Durations
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- 'tests/**/*.py'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
update-durations:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version: ['3.10', '3.11', '3.12', '3.13']
|
||||
env:
|
||||
OPENAI_API_KEY: fake-api-key
|
||||
PYTHONUNBUFFERED: 1
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Restore global uv cache
|
||||
id: cache-restore
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
restore-keys: |
|
||||
uv-main-py${{ matrix.python-version }}-
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: ${{ matrix.python-version }}
|
||||
enable-cache: false
|
||||
|
||||
- name: Install the project
|
||||
run: uv sync --all-groups --all-extras
|
||||
|
||||
- name: Run all tests and store durations
|
||||
run: |
|
||||
PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
|
||||
uv run pytest --store-durations --durations-path=.test_durations_py${PYTHON_VERSION_SAFE} -n auto
|
||||
continue-on-error: true
|
||||
|
||||
- name: Save durations to cache
|
||||
if: always()
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: .test_durations_py*
|
||||
key: test-durations-py${{ matrix.python-version }}
|
||||
|
||||
- name: Save uv caches
|
||||
if: steps.cache-restore.outputs.cache-hit != 'true'
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
|
||||
@@ -1,97 +0,0 @@
|
||||
name: Vulnerability Scan
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
push:
|
||||
branches: [main]
|
||||
schedule:
|
||||
# Run weekly on Monday at 9:00 UTC
|
||||
- cron: '0 9 * * 1'
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
pip-audit:
|
||||
name: pip-audit
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Restore global uv cache
|
||||
id: cache-restore
|
||||
uses: actions/cache/restore@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
|
||||
restore-keys: |
|
||||
uv-main-py3.11-
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v6
|
||||
with:
|
||||
version: "0.11.3"
|
||||
python-version: "3.11"
|
||||
enable-cache: false
|
||||
|
||||
- name: Install dependencies
|
||||
run: uv sync --all-groups --all-extras --no-install-project
|
||||
|
||||
- name: Install pip-audit
|
||||
run: uv pip install pip-audit
|
||||
|
||||
- name: Run pip-audit
|
||||
run: |
|
||||
uv run pip-audit --desc --aliases --skip-editable --format json --output pip-audit-report.json \
|
||||
--ignore-vuln CVE-2026-3219
|
||||
# Ignored CVEs:
|
||||
# CVE-2026-3219 - pip 26.0.1 (GHSA-58qw-9mgm-455v): no fix available, archive handling issue
|
||||
continue-on-error: true
|
||||
|
||||
- name: Display results
|
||||
if: always()
|
||||
run: |
|
||||
if [ -f pip-audit-report.json ]; then
|
||||
echo "## pip-audit Results" >> $GITHUB_STEP_SUMMARY
|
||||
echo '```json' >> $GITHUB_STEP_SUMMARY
|
||||
cat pip-audit-report.json | python3 -m json.tool >> $GITHUB_STEP_SUMMARY
|
||||
echo '```' >> $GITHUB_STEP_SUMMARY
|
||||
# Fail if vulnerabilities found
|
||||
python3 -c "
|
||||
import json, sys
|
||||
with open('pip-audit-report.json') as f:
|
||||
data = json.load(f)
|
||||
vulns = [d for d in data.get('dependencies', []) if d.get('vulns')]
|
||||
if vulns:
|
||||
print(f'::error::Found vulnerabilities in {len(vulns)} package(s)')
|
||||
for v in vulns:
|
||||
for vuln in v['vulns']:
|
||||
print(f' - {v[\"name\"]}=={v[\"version\"]}: {vuln[\"id\"]}')
|
||||
sys.exit(1)
|
||||
print('No known vulnerabilities found')
|
||||
"
|
||||
else
|
||||
echo "::error::pip-audit failed to produce a report. Check the pip-audit step logs."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
- name: Upload pip-audit report
|
||||
if: always()
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: pip-audit-report
|
||||
path: pip-audit-report.json
|
||||
|
||||
- name: Save uv caches
|
||||
if: steps.cache-restore.outputs.cache-hit != 'true'
|
||||
uses: actions/cache/save@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cache/uv
|
||||
~/.local/share/uv
|
||||
.venv
|
||||
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
|
||||
|
||||
-33
@@ -1,33 +0,0 @@
|
||||
.DS_Store
|
||||
.pytest_cache
|
||||
__pycache__
|
||||
dist/
|
||||
.env
|
||||
assets/*
|
||||
.idea
|
||||
test/
|
||||
docs_crew/
|
||||
chroma.sqlite3
|
||||
old_en.json
|
||||
db/
|
||||
test.py
|
||||
rc-tests/*
|
||||
*.pkl
|
||||
temp/*
|
||||
.vscode/*
|
||||
crew_tasks_output.json
|
||||
.codesight
|
||||
.mypy_cache
|
||||
.ruff_cache
|
||||
.venv
|
||||
test_flow.html
|
||||
crewairules.mdc
|
||||
plan.md
|
||||
conceptual_plan.md
|
||||
build_image
|
||||
chromadb-*.lock
|
||||
.claude
|
||||
.crewai/memory
|
||||
blogs/*
|
||||
secrets/*
|
||||
UNKNOWN.egg-info/
|
||||
File renamed without changes.
@@ -1,41 +0,0 @@
|
||||
repos:
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: ruff
|
||||
name: ruff
|
||||
entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
|
||||
language: system
|
||||
pass_filenames: true
|
||||
types: [python]
|
||||
- id: ruff-format
|
||||
name: ruff-format
|
||||
entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
|
||||
language: system
|
||||
pass_filenames: true
|
||||
types: [python]
|
||||
- id: mypy
|
||||
name: mypy
|
||||
entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
|
||||
language: system
|
||||
pass_filenames: true
|
||||
types: [python]
|
||||
exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/)
|
||||
- repo: https://github.com/astral-sh/uv-pre-commit
|
||||
rev: 0.11.3
|
||||
hooks:
|
||||
- id: uv-lock
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: pip-audit
|
||||
name: pip-audit
|
||||
entry: bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable --ignore-vuln CVE-2026-3219' --
|
||||
language: system
|
||||
pass_filenames: false
|
||||
stages: [pre-push, manual]
|
||||
- repo: https://github.com/commitizen-tools/commitizen
|
||||
rev: v4.10.1
|
||||
hooks:
|
||||
- id: commitizen
|
||||
- id: commitizen-branch
|
||||
stages: [ pre-push ]
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
3.13
|
||||
@@ -1,19 +0,0 @@
|
||||
Copyright (c) 2025 crewAI, Inc.
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1,807 +0,0 @@
|
||||
<p align="center">
|
||||
<a href="https://github.com/crewAIInc/crewAI">
|
||||
<img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework">
|
||||
</a>
|
||||
</p>
|
||||
<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
|
||||
<a href="https://trendshift.io/repositories/11239" target="_blank">
|
||||
<img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
|
||||
</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://crewai.com">Homepage</a>
|
||||
·
|
||||
<a href="https://docs.crewai.com">Docs</a>
|
||||
·
|
||||
<a href="https://app.crewai.com">Start Cloud Trial</a>
|
||||
·
|
||||
<a href="https://blog.crewai.com">Blog</a>
|
||||
·
|
||||
<a href="https://community.crewai.com">Forum</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/crewAIInc/crewAI">
|
||||
<img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars">
|
||||
</a>
|
||||
<a href="https://github.com/crewAIInc/crewAI/network/members">
|
||||
<img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks">
|
||||
</a>
|
||||
<a href="https://github.com/crewAIInc/crewAI/issues">
|
||||
<img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues">
|
||||
</a>
|
||||
<a href="https://github.com/crewAIInc/crewAI/pulls">
|
||||
<img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests">
|
||||
</a>
|
||||
<a href="https://opensource.org/licenses/MIT">
|
||||
<img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://pypi.org/project/crewai/">
|
||||
<img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version">
|
||||
</a>
|
||||
<a href="https://pypi.org/project/crewai/">
|
||||
<img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads">
|
||||
</a>
|
||||
<a href="https://twitter.com/crewAIInc">
|
||||
<img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
### Fast and Flexible Multi-Agent Automation Framework
|
||||
|
||||
> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**.
|
||||
> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
|
||||
|
||||
- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
|
||||
- **CrewAI Flows**: The **enterprise and production architecture** for building and deploying multi-agent systems. Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
|
||||
|
||||
With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
|
||||
standard for enterprise-ready AI automation.
|
||||
|
||||
# CrewAI AMP Suite
|
||||
|
||||
CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
|
||||
|
||||
You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
|
||||
|
||||
## Crew Control Plane Key Features:
|
||||
|
||||
- **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
|
||||
- **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
|
||||
- **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
|
||||
- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
|
||||
- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
|
||||
- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
|
||||
- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
|
||||
|
||||
CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
|
||||
intelligent automations.
|
||||
|
||||
## Table of contents
|
||||
|
||||
- [Build with AI](#build-with-ai)
|
||||
- [Why CrewAI?](#why-crewai)
|
||||
- [Getting Started](#getting-started)
|
||||
- [Key Features](#key-features)
|
||||
- [Understanding Flows and Crews](#understanding-flows-and-crews)
|
||||
- [CrewAI vs LangGraph](#how-crewai-compares)
|
||||
- [Examples](#examples)
|
||||
- [Quick Tutorial](#quick-tutorial)
|
||||
- [Write Job Descriptions](#write-job-descriptions)
|
||||
- [Trip Planner](#trip-planner)
|
||||
- [Stock Analysis](#stock-analysis)
|
||||
- [Using Crews and Flows Together](#using-crews-and-flows-together)
|
||||
- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
|
||||
- [How CrewAI Compares](#how-crewai-compares)
|
||||
- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)
|
||||
- [Contribution](#contribution)
|
||||
- [Telemetry](#telemetry)
|
||||
- [License](#license)
|
||||
|
||||
## Build with AI
|
||||
|
||||
Using an AI coding agent? Teach it CrewAI best practices in one command:
|
||||
|
||||
**Claude Code:**
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
Four skills that activate automatically when you ask relevant CrewAI questions:
|
||||
|
||||
| Skill | When it runs |
|
||||
|-------|--------------|
|
||||
| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
|
||||
| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
|
||||
| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
|
||||
| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
|
||||
|
||||
**Cursor, Codex, Windsurf, and others ([skills.sh](https://skills.sh/crewaiinc/skills)):**
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
|
||||
This installs the official [CrewAI Skills](https://github.com/crewAIInc/skills) — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
|
||||
|
||||
## Why CrewAI?
|
||||
|
||||
<div align="center" style="margin-bottom: 30px;">
|
||||
<img src="docs/images/asset.png" alt="CrewAI Logo" width="100%">
|
||||
</div>
|
||||
|
||||
CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
|
||||
|
||||
- **Standalone Framework**: Built from scratch, independent of LangChain or any other agent framework.
|
||||
- **High Performance**: Optimized for speed and minimal resource usage, enabling faster execution.
|
||||
- **Flexible Low Level Customization**: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic.
|
||||
- **Ideal for Every Use Case**: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios.
|
||||
- **Robust Community**: Backed by a rapidly growing community of over **100,000 certified** developers offering comprehensive support and resources.
|
||||
|
||||
CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance.
|
||||
|
||||
## Getting Started
|
||||
|
||||
Setup and run your first CrewAI agents by following this tutorial.
|
||||
|
||||
[](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
|
||||
|
||||
###
|
||||
|
||||
Learning Resources
|
||||
|
||||
Learn CrewAI through our comprehensive courses:
|
||||
|
||||
- [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems
|
||||
- [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations
|
||||
|
||||
### Understanding Flows and Crews
|
||||
|
||||
CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
|
||||
|
||||
1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
|
||||
|
||||
- Natural, autonomous decision-making between agents
|
||||
- Dynamic task delegation and collaboration
|
||||
- Specialized roles with defined goals and expertise
|
||||
- Flexible problem-solving approaches
|
||||
|
||||
2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
|
||||
|
||||
- Fine-grained control over execution paths for real-world scenarios
|
||||
- Secure, consistent state management between tasks
|
||||
- Clean integration of AI agents with production Python code
|
||||
- Conditional branching for complex business logic
|
||||
|
||||
The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
|
||||
|
||||
- Build complex, production-grade applications
|
||||
- Balance autonomy with precise control
|
||||
- Handle sophisticated real-world scenarios
|
||||
- Maintain clean, maintainable code structure
|
||||
|
||||
### Getting Started with Installation
|
||||
|
||||
To get started with CrewAI, follow these simple steps:
|
||||
|
||||
### 1. Installation
|
||||
|
||||
Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
|
||||
|
||||
First, install CrewAI:
|
||||
|
||||
```shell
|
||||
uv pip install crewai
|
||||
```
|
||||
|
||||
If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
|
||||
|
||||
```shell
|
||||
uv pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
The command above installs the basic package and also adds extra components which require more dependencies to function.
|
||||
|
||||
### Troubleshooting Dependencies
|
||||
|
||||
If you encounter issues during installation or usage, here are some common solutions:
|
||||
|
||||
#### Common Issues
|
||||
|
||||
1. **ModuleNotFoundError: No module named 'tiktoken'**
|
||||
|
||||
- Install tiktoken explicitly: `uv pip install 'crewai[embeddings]'`
|
||||
- If using embedchain or other tools: `uv pip install 'crewai[tools]'`
|
||||
|
||||
2. **Failed building wheel for tiktoken**
|
||||
|
||||
- Ensure Rust compiler is installed (see installation steps above)
|
||||
- For Windows: Verify Visual C++ Build Tools are installed
|
||||
- Try upgrading pip: `uv pip install --upgrade pip`
|
||||
- If issues persist, use a pre-built wheel: `uv pip install tiktoken --prefer-binary`
|
||||
|
||||
### 2. Setting Up Your Crew with the YAML Configuration
|
||||
|
||||
To create a new CrewAI project, run the following CLI (Command Line Interface) command:
|
||||
|
||||
```shell
|
||||
crewai create crew <project_name>
|
||||
```
|
||||
|
||||
This command creates a new project folder with the following structure:
|
||||
|
||||
```
|
||||
my_project/
|
||||
├── .gitignore
|
||||
├── pyproject.toml
|
||||
├── README.md
|
||||
├── .env
|
||||
└── src/
|
||||
└── my_project/
|
||||
├── __init__.py
|
||||
├── main.py
|
||||
├── crew.py
|
||||
├── tools/
|
||||
│ ├── custom_tool.py
|
||||
│ └── __init__.py
|
||||
└── config/
|
||||
├── agents.yaml
|
||||
└── tasks.yaml
|
||||
```
|
||||
|
||||
You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks.
|
||||
|
||||
#### To customize your project, you can:
|
||||
|
||||
- Modify `src/my_project/config/agents.yaml` to define your agents.
|
||||
- Modify `src/my_project/config/tasks.yaml` to define your tasks.
|
||||
- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
|
||||
- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
|
||||
- Add your environment variables into the `.env` file.
|
||||
|
||||
#### Example of a simple crew with a sequential process:
|
||||
|
||||
Instantiate your crew:
|
||||
|
||||
```shell
|
||||
crewai create crew latest-ai-development
|
||||
```
|
||||
|
||||
Modify the files as needed to fit your use case:
|
||||
|
||||
**agents.yaml**
|
||||
|
||||
```yaml
|
||||
# src/my_project/config/agents.yaml
|
||||
researcher:
|
||||
role: >
|
||||
{topic} Senior Data Researcher
|
||||
goal: >
|
||||
Uncover cutting-edge developments in {topic}
|
||||
backstory: >
|
||||
You're a seasoned researcher with a knack for uncovering the latest
|
||||
developments in {topic}. Known for your ability to find the most relevant
|
||||
information and present it in a clear and concise manner.
|
||||
|
||||
reporting_analyst:
|
||||
role: >
|
||||
{topic} Reporting Analyst
|
||||
goal: >
|
||||
Create detailed reports based on {topic} data analysis and research findings
|
||||
backstory: >
|
||||
You're a meticulous analyst with a keen eye for detail. You're known for
|
||||
your ability to turn complex data into clear and concise reports, making
|
||||
it easy for others to understand and act on the information you provide.
|
||||
```
|
||||
|
||||
**tasks.yaml**
|
||||
|
||||
````yaml
|
||||
# src/my_project/config/tasks.yaml
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about {topic}
|
||||
Make sure you find any interesting and relevant information given
|
||||
the current year is 2025.
|
||||
expected_output: >
|
||||
A list with 10 bullet points of the most relevant information about {topic}
|
||||
agent: researcher
|
||||
|
||||
reporting_task:
|
||||
description: >
|
||||
Review the context you got and expand each topic into a full section for a report.
|
||||
Make sure the report is detailed and contains any and all relevant information.
|
||||
expected_output: >
|
||||
A fully fledge reports with the mains topics, each with a full section of information.
|
||||
Formatted as markdown without '```'
|
||||
agent: reporting_analyst
|
||||
output_file: report.md
|
||||
````
|
||||
|
||||
**crew.py**
|
||||
|
||||
```python
|
||||
# src/my_project/crew.py
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
from crewai_tools import SerperDevTool
|
||||
from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from typing import List
|
||||
|
||||
@CrewBase
|
||||
class LatestAiDevelopmentCrew():
|
||||
"""LatestAiDevelopment crew"""
|
||||
agents: List[BaseAgent]
|
||||
tasks: List[Task]
|
||||
|
||||
@agent
|
||||
def researcher(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config['researcher'],
|
||||
verbose=True,
|
||||
tools=[SerperDevTool()]
|
||||
)
|
||||
|
||||
@agent
|
||||
def reporting_analyst(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config['reporting_analyst'],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
@task
|
||||
def research_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['research_task'],
|
||||
)
|
||||
|
||||
@task
|
||||
def reporting_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['reporting_task'],
|
||||
output_file='report.md'
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the LatestAiDevelopment crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
```
|
||||
|
||||
**main.py**
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python
|
||||
# src/my_project/main.py
|
||||
import sys
|
||||
from latest_ai_development.crew import LatestAiDevelopmentCrew
|
||||
|
||||
def run():
|
||||
"""
|
||||
Run the crew.
|
||||
"""
|
||||
inputs = {
|
||||
'topic': 'AI Agents'
|
||||
}
|
||||
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
|
||||
```
|
||||
|
||||
### 3. Running Your Crew
|
||||
|
||||
Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
|
||||
|
||||
- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
|
||||
- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
|
||||
|
||||
Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
|
||||
|
||||
```shell
|
||||
cd my_project
|
||||
crewai install (Optional)
|
||||
```
|
||||
|
||||
To run your crew, execute the following command in the root of your project:
|
||||
|
||||
```bash
|
||||
crewai run
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
python src/my_project/main.py
|
||||
```
|
||||
|
||||
If an error happens due to the usage of poetry, please run the following command to update your crewai package:
|
||||
|
||||
```bash
|
||||
crewai update
|
||||
```
|
||||
|
||||
You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.
|
||||
|
||||
In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).
|
||||
|
||||
## Key Features
|
||||
|
||||
CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
|
||||
|
||||
- **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
|
||||
- **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.
|
||||
- **Seamless Integration**: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations.
|
||||
- **Deep Customization**: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors.
|
||||
- **Reliable Performance**: Consistent results across simple tasks and complex, enterprise-level automations.
|
||||
- **Thriving Community**: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance.
|
||||
|
||||
Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations.
|
||||
|
||||
## Examples
|
||||
|
||||
You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
|
||||
|
||||
- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
|
||||
- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
|
||||
- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
|
||||
- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
|
||||
|
||||
### Quick Tutorial
|
||||
|
||||
[](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
|
||||
|
||||
### Write Job Descriptions
|
||||
|
||||
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
|
||||
|
||||
[](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
|
||||
|
||||
### Trip Planner
|
||||
|
||||
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
|
||||
|
||||
[](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
|
||||
|
||||
### Stock Analysis
|
||||
|
||||
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
|
||||
|
||||
[](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
|
||||
|
||||
### Using Crews and Flows Together
|
||||
|
||||
CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
|
||||
CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
|
||||
|
||||
- `or_`: Triggers when any of the specified conditions are met.
|
||||
- `and_`Triggers when all of the specified conditions are met.
|
||||
|
||||
Here's how you can orchestrate multiple Crews within a Flow:
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, listen, start, router, or_
|
||||
from crewai import Crew, Agent, Task, Process
|
||||
from pydantic import BaseModel
|
||||
|
||||
# Define structured state for precise control
|
||||
class MarketState(BaseModel):
|
||||
sentiment: str = "neutral"
|
||||
confidence: float = 0.0
|
||||
recommendations: list = []
|
||||
|
||||
class AdvancedAnalysisFlow(Flow[MarketState]):
|
||||
@start()
|
||||
def fetch_market_data(self):
|
||||
# Demonstrate low-level control with structured state
|
||||
self.state.sentiment = "analyzing"
|
||||
return {"sector": "tech", "timeframe": "1W"} # These parameters match the task description template
|
||||
|
||||
@listen(fetch_market_data)
|
||||
def analyze_with_crew(self, market_data):
|
||||
# Show crew agency through specialized roles
|
||||
analyst = Agent(
|
||||
role="Senior Market Analyst",
|
||||
goal="Conduct deep market analysis with expert insight",
|
||||
backstory="You're a veteran analyst known for identifying subtle market patterns"
|
||||
)
|
||||
researcher = Agent(
|
||||
role="Data Researcher",
|
||||
goal="Gather and validate supporting market data",
|
||||
backstory="You excel at finding and correlating multiple data sources"
|
||||
)
|
||||
|
||||
analysis_task = Task(
|
||||
description="Analyze {sector} sector data for the past {timeframe}",
|
||||
expected_output="Detailed market analysis with confidence score",
|
||||
agent=analyst
|
||||
)
|
||||
research_task = Task(
|
||||
description="Find supporting data to validate the analysis",
|
||||
expected_output="Corroborating evidence and potential contradictions",
|
||||
agent=researcher
|
||||
)
|
||||
|
||||
# Demonstrate crew autonomy
|
||||
analysis_crew = Crew(
|
||||
agents=[analyst, researcher],
|
||||
tasks=[analysis_task, research_task],
|
||||
process=Process.sequential,
|
||||
verbose=True
|
||||
)
|
||||
return analysis_crew.kickoff(inputs=market_data) # Pass market_data as named inputs
|
||||
|
||||
@router(analyze_with_crew)
|
||||
def determine_next_steps(self):
|
||||
# Show flow control with conditional routing
|
||||
if self.state.confidence > 0.8:
|
||||
return "high_confidence"
|
||||
elif self.state.confidence > 0.5:
|
||||
return "medium_confidence"
|
||||
return "low_confidence"
|
||||
|
||||
@listen("high_confidence")
|
||||
def execute_strategy(self):
|
||||
# Demonstrate complex decision making
|
||||
strategy_crew = Crew(
|
||||
agents=[
|
||||
Agent(role="Strategy Expert",
|
||||
goal="Develop optimal market strategy")
|
||||
],
|
||||
tasks=[
|
||||
Task(description="Create detailed strategy based on analysis",
|
||||
expected_output="Step-by-step action plan")
|
||||
]
|
||||
)
|
||||
return strategy_crew.kickoff()
|
||||
|
||||
@listen(or_("medium_confidence", "low_confidence"))
|
||||
def request_additional_analysis(self):
|
||||
self.state.recommendations.append("Gather more data")
|
||||
return "Additional analysis required"
|
||||
```
|
||||
|
||||
This example demonstrates how to:
|
||||
|
||||
1. Use Python code for basic data operations
|
||||
2. Create and execute Crews as steps in your workflow
|
||||
3. Use Flow decorators to manage the sequence of operations
|
||||
4. Implement conditional branching based on Crew results
|
||||
|
||||
## Connecting Your Crew to a Model
|
||||
|
||||
CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
|
||||
|
||||
Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
|
||||
|
||||
## How CrewAI Compares
|
||||
|
||||
**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.
|
||||
|
||||
- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.
|
||||
|
||||
_P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb))._
|
||||
|
||||
- **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
|
||||
- **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
|
||||
|
||||
## Contribution
|
||||
|
||||
CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
|
||||
|
||||
- Fork the repository.
|
||||
- Create a new branch for your feature.
|
||||
- Add your feature or improvement.
|
||||
- Send a pull request.
|
||||
- We appreciate your input!
|
||||
|
||||
### Installing Dependencies
|
||||
|
||||
```bash
|
||||
uv lock
|
||||
uv sync
|
||||
```
|
||||
|
||||
### Virtual Env
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
```
|
||||
|
||||
### Pre-commit hooks
|
||||
|
||||
```bash
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
### Running Tests
|
||||
|
||||
```bash
|
||||
uv run pytest .
|
||||
```
|
||||
|
||||
### Running static type checks
|
||||
|
||||
```bash
|
||||
uvx mypy src
|
||||
```
|
||||
|
||||
### Packaging
|
||||
|
||||
```bash
|
||||
uv build
|
||||
```
|
||||
|
||||
### Installing Locally
|
||||
|
||||
```bash
|
||||
uv pip install dist/*.tar.gz
|
||||
```
|
||||
|
||||
## Telemetry
|
||||
|
||||
CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
|
||||
|
||||
It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
|
||||
|
||||
Data collected includes:
|
||||
|
||||
- Version of CrewAI
|
||||
- So we can understand how many users are using the latest version
|
||||
- Version of Python
|
||||
- So we can decide on what versions to better support
|
||||
- General OS (e.g. number of CPUs, macOS/Windows/Linux)
|
||||
- So we know what OS we should focus on and if we could build specific OS related features
|
||||
- Number of agents and tasks in a crew
|
||||
- So we make sure we are testing internally with similar use cases and educate people on the best practices
|
||||
- Crew Process being used
|
||||
- Understand where we should focus our efforts
|
||||
- If Agents are using memory or allowing delegation
|
||||
- Understand if we improved the features or maybe even drop them
|
||||
- If Tasks are being executed in parallel or sequentially
|
||||
- Understand if we should focus more on parallel execution
|
||||
- Language model being used
|
||||
- Improved support on most used languages
|
||||
- Roles of agents in a crew
|
||||
- Understand high level use cases so we can build better tools, integrations and examples about it
|
||||
- Tools names available
|
||||
- Understand out of the publicly available tools, which ones are being used the most so we can improve them
|
||||
|
||||
Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
|
||||
|
||||
## License
|
||||
|
||||
CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
|
||||
|
||||
## Frequently Asked Questions (FAQ)
|
||||
|
||||
### General
|
||||
|
||||
- [What exactly is CrewAI?](#q-what-exactly-is-crewai)
|
||||
- [How do I install CrewAI?](#q-how-do-i-install-crewai)
|
||||
- [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
|
||||
- [Is CrewAI open-source?](#q-is-crewai-open-source)
|
||||
- [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
|
||||
|
||||
### Features and Capabilities
|
||||
|
||||
- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
|
||||
- [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
|
||||
- [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
|
||||
- [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain)
|
||||
- [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
|
||||
|
||||
### Resources and Community
|
||||
|
||||
- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
|
||||
- [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
|
||||
|
||||
### Enterprise Features
|
||||
|
||||
- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
|
||||
- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
|
||||
- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
|
||||
|
||||
### Q: What exactly is CrewAI?
|
||||
|
||||
A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
|
||||
|
||||
### Q: How do I install CrewAI?
|
||||
|
||||
A: Install CrewAI using pip:
|
||||
|
||||
```shell
|
||||
uv pip install crewai
|
||||
```
|
||||
|
||||
For additional tools, use:
|
||||
|
||||
```shell
|
||||
uv pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
### Q: Does CrewAI depend on LangChain?
|
||||
|
||||
A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
|
||||
|
||||
### Q: Can CrewAI handle complex use cases?
|
||||
|
||||
A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
|
||||
|
||||
### Q: Can I use CrewAI with local AI models?
|
||||
|
||||
A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
|
||||
|
||||
### Q: What makes Crews different from Flows?
|
||||
|
||||
A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
|
||||
|
||||
### Q: How is CrewAI better than LangChain?
|
||||
|
||||
A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
|
||||
|
||||
### Q: Is CrewAI open-source?
|
||||
|
||||
A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
|
||||
|
||||
### Q: Does CrewAI collect data from users?
|
||||
|
||||
A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
|
||||
|
||||
### Q: Where can I find real-world CrewAI examples?
|
||||
|
||||
A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
|
||||
|
||||
### Q: How can I contribute to CrewAI?
|
||||
|
||||
A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
|
||||
|
||||
### Q: What additional features does CrewAI AMP offer?
|
||||
|
||||
A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
|
||||
|
||||
### Q: Is CrewAI AMP available for cloud and on-premise deployments?
|
||||
|
||||
A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
|
||||
|
||||
### Q: Can I try CrewAI AMP for free?
|
||||
|
||||
A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
|
||||
|
||||
### Q: Does CrewAI support fine-tuning or training custom models?
|
||||
|
||||
A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
|
||||
|
||||
### Q: Can CrewAI agents interact with external tools and APIs?
|
||||
|
||||
A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
|
||||
|
||||
### Q: Is CrewAI suitable for production environments?
|
||||
|
||||
A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
|
||||
|
||||
### Q: How scalable is CrewAI?
|
||||
|
||||
A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
|
||||
|
||||
### Q: Does CrewAI offer debugging and monitoring tools?
|
||||
|
||||
A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
|
||||
|
||||
### Q: What programming languages does CrewAI support?
|
||||
|
||||
A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
|
||||
|
||||
### Q: Does CrewAI offer educational resources for beginners?
|
||||
|
||||
A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
|
||||
|
||||
### Q: Can CrewAI automate human-in-the-loop workflows?
|
||||
|
||||
A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.
|
||||
@@ -0,0 +1,119 @@
|
||||
---
|
||||
title: "Introduction"
|
||||
description: "Complete reference for the CrewAI Enterprise REST API"
|
||||
icon: "code"
|
||||
---
|
||||
|
||||
# CrewAI Enterprise API
|
||||
|
||||
Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
|
||||
|
||||
## Quick Start
|
||||
|
||||
<Steps>
|
||||
<Step title="Get Your API Credentials">
|
||||
Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
|
||||
</Step>
|
||||
|
||||
<Step title="Discover Required Inputs">
|
||||
Use the `GET /inputs` endpoint to see what parameters your crew expects.
|
||||
</Step>
|
||||
|
||||
<Step title="Start a Crew Execution">
|
||||
Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
|
||||
</Step>
|
||||
|
||||
<Step title="Monitor Progress">
|
||||
Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Authentication
|
||||
|
||||
All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
|
||||
|
||||
```bash
|
||||
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
https://your-crew-url.crewai.com/inputs
|
||||
```
|
||||
|
||||
### Token Types
|
||||
|
||||
| Token Type | Scope | Use Case |
|
||||
|:-----------|:--------|:----------|
|
||||
| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
|
||||
| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
|
||||
|
||||
<Tip>
|
||||
You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
|
||||
</Tip>
|
||||
|
||||
## Base URL
|
||||
|
||||
Each deployed crew has its own unique API endpoint:
|
||||
|
||||
```
|
||||
https://your-crew-name.crewai.com
|
||||
```
|
||||
|
||||
Replace `your-crew-name` with your actual crew's URL from the dashboard.
|
||||
|
||||
## Typical Workflow
|
||||
|
||||
1. **Discovery**: Call `GET /inputs` to understand what your crew needs
|
||||
2. **Execution**: Submit inputs via `POST /kickoff` to start processing
|
||||
3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
|
||||
4. **Results**: Extract the final output from the completed response
|
||||
|
||||
## Error Handling
|
||||
|
||||
The API uses standard HTTP status codes:
|
||||
|
||||
| Code | Meaning |
|
||||
|------|:--------|
|
||||
| `200` | Success |
|
||||
| `400` | Bad Request - Invalid input format |
|
||||
| `401` | Unauthorized - Invalid bearer token |
|
||||
| `404` | Not Found - Resource doesn't exist |
|
||||
| `422` | Validation Error - Missing required inputs |
|
||||
| `500` | Server Error - Contact support |
|
||||
|
||||
## Interactive Testing
|
||||
|
||||
<Info>
|
||||
**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
|
||||
</Info>
|
||||
|
||||
Each endpoint page shows you:
|
||||
- ✅ **Exact request format** with all parameters
|
||||
- ✅ **Response examples** for success and error cases
|
||||
- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
|
||||
- ✅ **Authentication examples** with proper Bearer token format
|
||||
|
||||
### **To Test Your Actual API:**
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Copy cURL Examples" icon="terminal">
|
||||
Copy the cURL examples and replace the URL + token with your real values
|
||||
</Card>
|
||||
<Card title="Use Postman/Insomnia" icon="play">
|
||||
Import the examples into your preferred API testing tool
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
**Example workflow:**
|
||||
1. **Copy this cURL example** from any endpoint page
|
||||
2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
|
||||
3. **Replace the Bearer token** with your real token from the dashboard
|
||||
4. **Run the request** in your terminal or API client
|
||||
|
||||
## Need Help?
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
|
||||
Get help with API integration and troubleshooting
|
||||
</Card>
|
||||
<Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
|
||||
Manage your crews and view execution logs
|
||||
</Card>
|
||||
</CardGroup>
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 1.8 KiB |
+16
@@ -0,0 +1,16 @@
|
||||
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||||
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|
||||
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|
||||
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/*!
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* Lunr languages, `Danish` language
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* https://github.com/MihaiValentin/lunr-languages
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*
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* Copyright 2014, Mihai Valentin
|
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* http://www.mozilla.org/MPL/
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*/
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/*!
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|
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* Snowball JavaScript Library v0.3
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||||
* http://code.google.com/p/urim/
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* http://snowball.tartarus.org/
|
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*
|
||||
* Copyright 2010, Oleg Mazko
|
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* http://www.mozilla.org/MPL/
|
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*/
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|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=f.cursor+3;if(d=f.limit,0<=r&&r<=f.limit){for(a=r;;){if(e=f.cursor,f.in_grouping(w,97,248)){f.cursor=e;break}if(f.cursor=e,e>=f.limit)return;f.cursor++}for(;!f.out_grouping(w,97,248);){if(f.cursor>=f.limit)return;f.cursor++}d=f.cursor,d<a&&(d=a)}}function n(){var e,r;if(f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(c,32),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del();break;case 2:f.in_grouping_b(p,97,229)&&f.slice_del()}}function t(){var e,r=f.limit-f.cursor;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.find_among_b(l,4)?(f.bra=f.cursor,f.limit_backward=e,f.cursor=f.limit-r,f.cursor>f.limit_backward&&(f.cursor--,f.bra=f.cursor,f.slice_del())):f.limit_backward=e)}function s(){var e,r,i,n=f.limit-f.cursor;if(f.ket=f.cursor,f.eq_s_b(2,"st")&&(f.bra=f.cursor,f.eq_s_b(2,"ig")&&f.slice_del()),f.cursor=f.limit-n,f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(m,5),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del(),i=f.limit-f.cursor,t(),f.cursor=f.limit-i;break;case 2:f.slice_from("løs")}}function o(){var e;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.out_grouping_b(w,97,248)?(f.bra=f.cursor,u=f.slice_to(u),f.limit_backward=e,f.eq_v_b(u)&&f.slice_del()):f.limit_backward=e)}var a,d,u,c=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],l=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],p=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],f=new i;this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var r=f.cursor;return e(),f.limit_backward=r,f.cursor=f.limit,n(),f.cursor=f.limit,t(),f.cursor=f.limit,s(),f.cursor=f.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `German` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!v.eq_s(1,e)||(v.ket=v.cursor,!v.in_grouping(p,97,252)))&&(v.slice_from(r),v.cursor=n,!0)}function i(){for(var r,n,i,s,t=v.cursor;;)if(r=v.cursor,v.bra=r,v.eq_s(1,"ß"))v.ket=v.cursor,v.slice_from("ss");else{if(r>=v.limit)break;v.cursor=r+1}for(v.cursor=t;;)for(n=v.cursor;;){if(i=v.cursor,v.in_grouping(p,97,252)){if(s=v.cursor,v.bra=s,e("u","U",i))break;if(v.cursor=s,e("y","Y",i))break}if(i>=v.limit)return void(v.cursor=n);v.cursor=i+1}}function s(){for(;!v.in_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function t(){m=v.limit,l=m;var e=v.cursor+3;0<=e&&e<=v.limit&&(d=e,s()||(m=v.cursor,m<d&&(m=d),s()||(l=v.cursor)))}function o(){for(var e,r;;){if(r=v.cursor,v.bra=r,!(e=v.find_among(h,6)))return;switch(v.ket=v.cursor,e){case 1:v.slice_from("y");break;case 2:case 5:v.slice_from("u");break;case 3:v.slice_from("a");break;case 4:v.slice_from("o");break;case 6:if(v.cursor>=v.limit)return;v.cursor++}}}function c(){return m<=v.cursor}function u(){return l<=v.cursor}function a(){var e,r,n,i,s=v.limit-v.cursor;if(v.ket=v.cursor,(e=v.find_among_b(w,7))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:v.slice_del(),v.ket=v.cursor,v.eq_s_b(1,"s")&&(v.bra=v.cursor,v.eq_s_b(3,"nis")&&v.slice_del());break;case 3:v.in_grouping_b(g,98,116)&&v.slice_del()}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(f,4))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:if(v.in_grouping_b(k,98,116)){var t=v.cursor-3;v.limit_backward<=t&&t<=v.limit&&(v.cursor=t,v.slice_del())}}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(_,8))&&(v.bra=v.cursor,u()))switch(e){case 1:v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,r=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-r,u()&&v.slice_del()));break;case 2:n=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-n,v.slice_del());break;case 3:if(v.slice_del(),v.ket=v.cursor,i=v.limit-v.cursor,!v.eq_s_b(2,"er")&&(v.cursor=v.limit-i,!v.eq_s_b(2,"en")))break;v.bra=v.cursor,c()&&v.slice_del();break;case 4:v.slice_del(),v.ket=v.cursor,e=v.find_among_b(b,2),e&&(v.bra=v.cursor,u()&&1==e&&v.slice_del())}}var d,l,m,h=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],w=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],f=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],b=[new r("ig",-1,1),new r("lich",-1,1)],_=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],p=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],g=[117,30,5],k=[117,30,4],v=new n;this.setCurrent=function(e){v.setCurrent(e)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return i(),v.cursor=e,t(),v.limit_backward=e,v.cursor=v.limit,a(),v.cursor=v.limit_backward,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indemLine truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Dutch` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");console.warn('[Lunr Languages] Please use the "nl" instead of the "du". The "nl" code is the standard code for Dutch language, and "du" will be removed in the next major versions.'),e.du=function(){this.pipeline.reset(),this.pipeline.add(e.du.trimmer,e.du.stopWordFilter,e.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.du.stemmer))},e.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.du.trimmer=e.trimmerSupport.generateTrimmer(e.du.wordCharacters),e.Pipeline.registerFunction(e.du.trimmer,"trimmer-du"),e.du.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){for(var e,r,i,o=C.cursor;;){if(C.bra=C.cursor,e=C.find_among(b,11))switch(C.ket=C.cursor,e){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(r=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=r);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=r;else if(n(r))break}else if(n(r))break}function n(e){return C.cursor=e,e>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,f=_,t()||(_=C.cursor,_<3&&(_=3),t()||(f=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var e;;)if(C.bra=C.cursor,e=C.find_among(p,3))switch(C.ket=C.cursor,e){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return f<=C.cursor}function a(){var e=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-e,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var e;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.slice_del(),w=!0,a())))}function m(){var e;u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.eq_s_b(3,"gem")||(C.cursor=C.limit-e,C.slice_del(),a())))}function d(){var e,r,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,e=C.find_among_b(h,5))switch(C.bra=C.cursor,e){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 3:u()&&C.out_grouping_b(z,97,232)&&C.slice_del()}if(C.cursor=C.limit-s,l(),C.cursor=C.limit-s,C.ket=C.cursor,C.eq_s_b(4,"heid")&&(C.bra=C.cursor,c()&&(r=C.limit-C.cursor,C.eq_s_b(1,"c")||(C.cursor=C.limit-r,C.slice_del(),C.ket=C.cursor,C.eq_s_b(2,"en")&&(C.bra=C.cursor,m())))),C.cursor=C.limit-s,C.ket=C.cursor,e=C.find_among_b(k,6))switch(C.bra=C.cursor,e){case 1:if(c()){if(C.slice_del(),i=C.limit-C.cursor,C.ket=C.cursor,C.eq_s_b(2,"ig")&&(C.bra=C.cursor,c()&&(n=C.limit-C.cursor,!C.eq_s_b(1,"e")))){C.cursor=C.limit-n,C.slice_del();break}C.cursor=C.limit-i,a()}break;case 2:c()&&(o=C.limit-C.cursor,C.eq_s_b(1,"e")||(C.cursor=C.limit-o,C.slice_del()));break;case 3:c()&&(C.slice_del(),l());break;case 4:c()&&C.slice_del();break;case 5:c()&&w&&C.slice_del()}C.cursor=C.limit-s,C.out_grouping_b(j,73,232)&&(t=C.limit-C.cursor,C.find_among_b(v,4)&&C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-t,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del())))}var f,_,w,b=[new r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursoLine truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.el=function(){this.pipeline.reset(),void 0===this.searchPipeline&&this.pipeline.add(e.el.trimmer,e.el.normilizer),this.pipeline.add(e.el.stopWordFilter,e.el.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.el.stemmer))},e.el.wordCharacters="A-Za-zΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩωΆάΈέΉήΊίΌόΎύΏώΪΐΫΰΐΰ",e.el.trimmer=e.trimmerSupport.generateTrimmer(e.el.wordCharacters),e.Pipeline.registerFunction(e.el.trimmer,"trimmer-el"),e.el.stemmer=function(){function e(e){return s.test(e)}function t(e){return/[ΑΕΗΙΟΥΩ]$/.test(e)}function r(e){return/[ΑΕΗΙΟΩ]$/.test(e)}function n(n){var s=n;if(n.length<3)return s;if(!e(n))return s;if(i.indexOf(n)>=0)return s;var u=new RegExp("(.*)("+Object.keys(l).join("|")+")$"),o=u.exec(s);return null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤLine truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Spanish` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.es=function(){this.pipeline.reset(),this.pipeline.add(e.es.trimmer,e.es.stopWordFilter,e.es.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.es.stemmer))},e.es.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.es.trimmer=e.trimmerSupport.generateTrimmer(e.es.wordCharacters),e.Pipeline.registerFunction(e.es.trimmer,"trimmer-es"),e.es.stemmer=function(){var s=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,n=new function(){function e(){if(A.out_grouping(x,97,252)){for(;!A.in_grouping(x,97,252);){if(A.cursor>=A.limit)return!0;A.cursor++}return!1}return!0}function n(){if(A.in_grouping(x,97,252)){var s=A.cursor;if(e()){if(A.cursor=s,!A.in_grouping(x,97,252))return!0;for(;!A.out_grouping(x,97,252);){if(A.cursor>=A.limit)return!0;A.cursor++}}return!1}return!0}function i(){var s,r=A.cursor;if(n()){if(A.cursor=r,!A.out_grouping(x,97,252))return;if(s=A.cursor,e()){if(A.cursor=s,!A.in_grouping(x,97,252)||A.cursor>=A.limit)return;A.cursor++}}g=A.cursor}function a(){for(;!A.in_grouping(x,97,252);){if(A.cursor>=A.limit)return!1;A.cursor++}for(;!A.out_grouping(x,97,252);){if(A.cursor>=A.limit)return!1;A.cursor++}return!0}function t(){var e=A.cursor;g=A.limit,p=g,v=g,i(),A.cursor=e,a()&&(p=A.cursor,a()&&(v=A.cursor))}function o(){for(var e;;){if(A.bra=A.cursor,e=A.find_among(k,6))switch(A.ket=A.cursor,e){case 1:A.slice_from("a");continue;case 2:A.slice_from("e");continue;case 3:A.slice_from("i");continue;case 4:A.slice_from("o");continue;case 5:A.slice_from("u");continue;case 6:if(A.cursor>=A.limit)break;A.cursor++;continue}break}}function u(){return g<=A.cursor}function w(){return p<=A.cursor}function c(){return v<=A.cursor}function m(){var e;if(A.ket=A.cursor,A.find_among_b(y,13)&&(A.bra=A.cursor,(e=A.find_among_b(q,11))&&u()))switch(e){case 1:A.bra=A.cursor,A.slice_from("iendo");break;case 2:A.bra=A.cursor,A.slice_from("ando");break;case 3:A.bra=A.cursor,A.slice_from("ar");break;case 4:A.bra=A.cursor,A.slice_from("er");break;case 5:A.bra=A.cursor,A.slice_from("ir");break;case 6:A.slice_del();break;case 7:A.eq_s_b(1,"u")&&A.slice_del()}}function l(e,s){if(!c())return!0;A.slice_del(),A.ket=A.cursor;var r=A.find_among_b(e,s);return r&&(A.bra=A.cursor,1==r&&c()&&A.slice_del()),!1}function d(e){return!c()||(A.slice_del(),A.ket=A.cursor,A.eq_s_b(2,e)&&(A.bra=A.cursor,c()&&A.slice_del()),!1)}function b(){var e;if(A.ket=A.cursor,e=A.find_among_b(S,46)){switch(A.bra=A.cursor,e){case 1:if(!c())return!1;A.slice_del();break;case 2:if(d("ic"))return!1;break;case 3:if(!c())return!1;A.slice_from("log");break;case 4:if(!c())return!1;A.slice_from("u");break;case 5:if(!c())return!1;A.slice_from("ente");break;case 6:if(!w())return!1;A.slice_del(),A.ket=A.cursor,e=A.find_among_b(C,4),e&&(A.bra=A.cursor,c()&&(A.slice_del(),1==e&&(A.ket=A.cursor,A.eq_s_b(2,"at")&&(A.bra=A.cursor,c()&&A.slice_del()))));break;case 7:if(l(P,3))return!1;break;case 8:if(l(F,3))return!1;break;case 9:if(d("at"))return!1}return!0}return!1}function f(){var e,s;if(A.cursor>=g&&(s=A.limit_backward,A.limit_backward=g,A.ket=A.cursor,e=A.find_among_b(W,12),A.limit_backward=s,e)){if(A.bra=A.cursor,1==e){if(!A.eq_s_b(1,"u"))return!1;A.slice_del()}return!0}return!1}function _(){var e,s,r,n;if(A.cursor>=g&&(s=A.limit_backward,A.limit_backward=g,A.ket=A.cursor,e=A.find_among_b(L,96),A.limit_backward=s,e))switch(A.bra=A.cursor,e){case 1:r=A.limit-A.cursor,A.eq_s_b(1,"u")?(n=A.limit-A.cursor,A.eq_s_b(1,"g")?A.cursor=A.limit-n:A.cursor=A.limit-r):A.cursor=A.limit-r,A.bra=A.cursor;case 2:A.slice_del()}}function h(){var e,s;if(A.ket=A.cursor,e=A.find_among_b(z,8))switch(A.bra=A.cursor,e){case 1:u()&&A.slice_del();break;case 2:u()&&(A.slice_del(),A.ket=A.cursor,A.eq_s_b(1,"u")&&(A.bra=A.cursor,s=A.limit-A.cursor,A.eq_s_b(1,"g")&&(A.cursor=A.limit-s,u()&&A.slice_del())))}}var v,p,g,k=[new s("",-1,6),new s("á",0,1),new s("é",0,2),new s("í",0,3),new s("ó",0,4),new s("ú",0,5)],y=[new s("la",-1,-1),new s("sela",0,-1),new s("le",-1,-1),new s("me",-1,-1),new s("se",-1,-1),new s("lo",-1,-1),new s("selo",5,-1),new s("las",-1,-1),new s("selas",7,-1),new s("les",-1,-1),new s("los",-1,-1),new s("selos",10,-1),new s("nos",-1,-1)],q=[new s("ando",-1,6),new s("iendo",-1,6),new s("yendo",-1,7),new s("ándo",-1,2),new s("iéndo",-1,1),new s("ar",-1,6),new s("er",-1,6),new s("ir",-1,6),new s("ár",-1,3),new s("ér",-1,4),new s("ír",-1,5)],C=[new s("ic",-1,-1),new s("ad",-1,-1),new s("os",-1,-1),new s("iLine truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Finnish` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(i,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(i.lunr)}(this,function(){return function(i){if(void 0===i)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===i.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");i.fi=function(){this.pipeline.reset(),this.pipeline.add(i.fi.trimmer,i.fi.stopWordFilter,i.fi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(i.fi.stemmer))},i.fi.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",i.fi.trimmer=i.trimmerSupport.generateTrimmer(i.fi.wordCharacters),i.Pipeline.registerFunction(i.fi.trimmer,"trimmer-fi"),i.fi.stemmer=function(){var e=i.stemmerSupport.Among,r=i.stemmerSupport.SnowballProgram,n=new function(){function i(){f=A.limit,d=f,n()||(f=A.cursor,n()||(d=A.cursor))}function n(){for(var i;;){if(i=A.cursor,A.in_grouping(W,97,246))break;if(A.cursor=i,i>=A.limit)return!0;A.cursor++}for(A.cursor=i;!A.out_grouping(W,97,246);){if(A.cursor>=A.limit)return!0;A.cursor++}return!1}function t(){return d<=A.cursor}function s(){var i,e;if(A.cursor>=f)if(e=A.limit_backward,A.limit_backward=f,A.ket=A.cursor,i=A.find_among_b(h,10)){switch(A.bra=A.cursor,A.limit_backward=e,i){case 1:if(!A.in_grouping_b(x,97,246))return;break;case 2:if(!t())return}A.slice_del()}else A.limit_backward=e}function o(){var i,e,r;if(A.cursor>=f)if(e=A.limit_backward,A.limit_backward=f,A.ket=A.cursor,i=A.find_among_b(v,9))switch(A.bra=A.cursor,A.limit_backward=e,i){case 1:r=A.limit-A.cursor,A.eq_s_b(1,"k")||(A.cursor=A.limit-r,A.slice_del());break;case 2:A.slice_del(),A.ket=A.cursor,A.eq_s_b(3,"kse")&&(A.bra=A.cursor,A.slice_from("ksi"));break;case 3:A.slice_del();break;case 4:A.find_among_b(p,6)&&A.slice_del();break;case 5:A.find_among_b(g,6)&&A.slice_del();break;case 6:A.find_among_b(j,2)&&A.slice_del()}else A.limit_backward=e}function l(){return A.find_among_b(q,7)}function a(){return A.eq_s_b(1,"i")&&A.in_grouping_b(L,97,246)}function u(){var i,e,r;if(A.cursor>=f)if(e=A.limit_backward,A.limit_backward=f,A.ket=A.cursor,i=A.find_among_b(C,30)){switch(A.bra=A.cursor,A.limit_backward=e,i){case 1:if(!A.eq_s_b(1,"a"))return;break;case 2:case 9:if(!A.eq_s_b(1,"e"))return;break;case 3:if(!A.eq_s_b(1,"i"))return;break;case 4:if(!A.eq_s_b(1,"o"))return;break;case 5:if(!A.eq_s_b(1,"ä"))return;break;case 6:if(!A.eq_s_b(1,"ö"))return;break;case 7:if(r=A.limit-A.cursor,!l()&&(A.cursor=A.limit-r,!A.eq_s_b(2,"ie"))){A.cursor=A.limit-r;break}if(A.cursor=A.limit-r,A.cursor<=A.limit_backward){A.cursor=A.limit-r;break}A.cursor--,A.bra=A.cursor;break;case 8:if(!A.in_grouping_b(W,97,246)||!A.out_grouping_b(W,97,246))return}A.slice_del(),k=!0}else A.limit_backward=e}function c(){var i,e,r;if(A.cursor>=d)if(e=A.limit_backward,A.limit_backward=d,A.ket=A.cursor,i=A.find_among_b(P,14)){if(A.bra=A.cursor,A.limit_backward=e,1==i){if(r=A.limit-A.cursor,A.eq_s_b(2,"po"))return;A.cursor=A.limit-r}A.slice_del()}else A.limit_backward=e}function m(){var i;A.cursor>=f&&(i=A.limit_backward,A.limit_backward=f,A.ket=A.cursor,A.find_among_b(F,2)?(A.bra=A.cursor,A.limit_backward=i,A.slice_del()):A.limit_backward=i)}function w(){var i,e,r,n,t,s;if(A.cursor>=f){if(e=A.limit_backward,A.limit_backward=f,A.ket=A.cursor,A.eq_s_b(1,"t")&&(A.bra=A.cursor,r=A.limit-A.cursor,A.in_grouping_b(W,97,246)&&(A.cursor=A.limit-r,A.slice_del(),A.limit_backward=e,n=A.limit-A.cursor,A.cursor>=d&&(A.cursor=d,t=A.limit_backward,A.limit_backward=A.cursor,A.cursor=A.limit-n,A.ket=A.cursor,i=A.find_among_b(S,2))))){if(A.bra=A.cursor,A.limit_backward=t,1==i){if(s=A.limit-A.cursor,A.eq_s_b(2,"po"))return;A.cursor=A.limit-s}return void A.slice_del()}A.limit_backward=e}}function _(){var i,e,r,n;if(A.cursor>=f){for(i=A.limit_backward,A.limit_backward=f,e=A.limit-A.cursor,l()&&(A.cursor=A.limit-e,A.ket=A.cursor,A.cursor>A.limit_backward&&(A.cursor--,A.bra=A.cursor,A.slice_del())),A.cursor=A.limit-e,A.ket=A.cursor,A.in_grouping_b(y,97,228)&&(A.bra=A.cursor,A.out_grouping_b(W,97,246)&&A.slice_del()),A.cursor=A.limit-e,A.ket=A.cursor,A.eq_s_b(1,"j")&&(A.bra=A.cursor,r=A.limit-A.cursor,A.eq_s_b(1,"o")?A.slice_del():(A.cursor=A.limit-r,A.eq_s_b(1,"u")&&A.slice_del())),A.cursor=A.limit-e,A.ket=A.cursor,A.eq_s_b(1,"o")&&(A.bra=A.cursor,A.eq_s_b(1,"j")&&A.slice_del()),A.cursor=A.limit-e,A.limit_backward=i;;){if(n=A.limit-A.cursor,A.out_grouping_b(W,97,246)){A.cursor=A.limit-n;break}if(A.cursor=A.limit-n,A.cursor<=A.limit_backward)return;A.cursor--}A.ket=A.cursor,A.cursor>A.limit_backward&&(A.cursor--,A.bra=A.cursor,b=A.slice_to(),A.eq_v_b(b)&&A.slice_del())}}var k,b,d,f,h=[new e("pa",-1,1),new e("sti",-1,2),new e("kaan",-1,1),new e("han",-1,1),new e("kin",-1,1),new e("hän",-1,1),new e("kään",-1,1),new e("ko",-1,Line truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `French` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.fr=function(){this.pipeline.reset(),this.pipeline.add(e.fr.trimmer,e.fr.stopWordFilter,e.fr.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.fr.stemmer))},e.fr.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.fr.trimmer=e.trimmerSupport.generateTrimmer(e.fr.wordCharacters),e.Pipeline.registerFunction(e.fr.trimmer,"trimmer-fr"),e.fr.stemmer=function(){var r=e.stemmerSupport.Among,s=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,s){return!(!W.eq_s(1,e)||(W.ket=W.cursor,!W.in_grouping(F,97,251)))&&(W.slice_from(r),W.cursor=s,!0)}function i(e,r,s){return!!W.eq_s(1,e)&&(W.ket=W.cursor,W.slice_from(r),W.cursor=s,!0)}function n(){for(var r,s;;){if(r=W.cursor,W.in_grouping(F,97,251)){if(W.bra=W.cursor,s=W.cursor,e("u","U",r))continue;if(W.cursor=s,e("i","I",r))continue;if(W.cursor=s,i("y","Y",r))continue}if(W.cursor=r,W.bra=r,!e("y","Y",r)){if(W.cursor=r,W.eq_s(1,"q")&&(W.bra=W.cursor,i("u","U",r)))continue;if(W.cursor=r,r>=W.limit)return;W.cursor++}}}function t(){for(;!W.in_grouping(F,97,251);){if(W.cursor>=W.limit)return!0;W.cursor++}for(;!W.out_grouping(F,97,251);){if(W.cursor>=W.limit)return!0;W.cursor++}return!1}function u(){var e=W.cursor;if(q=W.limit,g=q,p=q,W.in_grouping(F,97,251)&&W.in_grouping(F,97,251)&&W.cursor<W.limit)W.cursor++;else if(W.cursor=e,!W.find_among(v,3)){W.cursor=e;do{if(W.cursor>=W.limit){W.cursor=q;break}W.cursor++}while(!W.in_grouping(F,97,251))}q=W.cursor,W.cursor=e,t()||(g=W.cursor,t()||(p=W.cursor))}function o(){for(var e,r;;){if(r=W.cursor,W.bra=r,!(e=W.find_among(h,4)))break;switch(W.ket=W.cursor,e){case 1:W.slice_from("i");break;case 2:W.slice_from("u");break;case 3:W.slice_from("y");break;case 4:if(W.cursor>=W.limit)return;W.cursor++}}}function c(){return q<=W.cursor}function a(){return g<=W.cursor}function l(){return p<=W.cursor}function w(){var e,r;if(W.ket=W.cursor,e=W.find_among_b(C,43)){switch(W.bra=W.cursor,e){case 1:if(!l())return!1;W.slice_del();break;case 2:if(!l())return!1;W.slice_del(),W.ket=W.cursor,W.eq_s_b(2,"ic")&&(W.bra=W.cursor,l()?W.slice_del():W.slice_from("iqU"));break;case 3:if(!l())return!1;W.slice_from("log");break;case 4:if(!l())return!1;W.slice_from("u");break;case 5:if(!l())return!1;W.slice_from("ent");break;case 6:if(!c())return!1;if(W.slice_del(),W.ket=W.cursor,e=W.find_among_b(z,6))switch(W.bra=W.cursor,e){case 1:l()&&(W.slice_del(),W.ket=W.cursor,W.eq_s_b(2,"at")&&(W.bra=W.cursor,l()&&W.slice_del()));break;case 2:l()?W.slice_del():a()&&W.slice_from("eux");break;case 3:l()&&W.slice_del();break;case 4:c()&&W.slice_from("i")}break;case 7:if(!l())return!1;if(W.slice_del(),W.ket=W.cursor,e=W.find_among_b(y,3))switch(W.bra=W.cursor,e){case 1:l()?W.slice_del():W.slice_from("abl");break;case 2:l()?W.slice_del():W.slice_from("iqU");break;case 3:l()&&W.slice_del()}break;case 8:if(!l())return!1;if(W.slice_del(),W.ket=W.cursor,W.eq_s_b(2,"at")&&(W.bra=W.cursor,l()&&(W.slice_del(),W.ket=W.cursor,W.eq_s_b(2,"ic")))){W.bra=W.cursor,l()?W.slice_del():W.slice_from("iqU");break}break;case 9:W.slice_from("eau");break;case 10:if(!a())return!1;W.slice_from("al");break;case 11:if(l())W.slice_del();else{if(!a())return!1;W.slice_from("eux")}break;case 12:if(!a()||!W.out_grouping_b(F,97,251))return!1;W.slice_del();break;case 13:return c()&&W.slice_from("ant"),!1;case 14:return c()&&W.slice_from("ent"),!1;case 15:return r=W.limit-W.cursor,W.in_grouping_b(F,97,251)&&c()&&(W.cursor=W.limit-r,W.slice_del()),!1}return!0}return!1}function f(){var e,r;if(W.cursor<q)return!1;if(r=W.limit_backward,W.limit_backward=q,W.ket=W.cursor,!(e=W.find_among_b(x,35)))return W.limit_backward=r,!1;if(W.bra=W.cursor,1==e){if(!W.out_grouping_b(F,97,251))return W.limit_backward=r,!1;W.slice_del()}return W.limit_backward=r,!0}function m(){var e,r,s;if(W.cursor<q)return!1;if(r=W.limit_backward,W.limit_backward=q,W.ket=W.cursor,!(e=W.find_among_b(I,38)))return W.limit_backward=r,!1;switch(W.bra=W.cursor,e){case 1:if(!l())return W.limit_backward=r,!1;W.slice_del();break;case 2:W.slice_del();break;case 3:W.slice_del(),s=W.limit-W.cursor,W.ket=W.cursor,W.eq_s_b(1,"e")?(W.bra=W.cursor,W.slice_del()):W.cursor=W.limit-s}return W.limit_backward=r,!0}function _(){var e,r,s,i,n=W.limit-W.cursor;if(W.ket=W.cursor,W.eq_s_b(1,"s")?(W.bra=W.cursor,r=W.limit-W.cursor,W.out_grouping_b(S,97,232)?(W.cursor=W.limit-r,W.slice_del()):W.cursor=W.limit-n):W.cursor=W.limit-n,W.cursor>=q){if(s=W.limit_backward,W.limit_backwarLine truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.he=function(){this.pipeline.reset(),this.pipeline.add(e.he.trimmer,e.he.stopWordFilter,e.he.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.he.stemmer))},e.he.wordCharacters="֑-״א-תa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.he.trimmer=e.trimmerSupport.generateTrimmer(e.he.wordCharacters),e.Pipeline.registerFunction(e.he.trimmer,"trimmer-he"),e.he.stemmer=function(){var e=this;return e.result=!1,e.preRemoved=!1,e.sufRemoved=!1,e.pre={pre1:"ה ו י ת",pre2:"ב כ ל מ ש כש",pre3:"הב הכ הל המ הש בש לכ",pre4:"וב וכ ול ומ וש",pre5:"מה שה כל",pre6:"מב מכ מל ממ מש",pre7:"בה בו בי בת כה כו כי כת לה לו לי לת",pre8:"ובה ובו ובי ובת וכה וכו וכי וכת ולה ולו ולי ולת"},e.suf={suf1:"ך כ ם ן נ",suf2:"ים ות וך וכ ום ון ונ הם הן יכ יך ינ ים",suf3:"תי תך תכ תם תן תנ",suf4:"ותי ותך ותכ ותם ותן ותנ",suf5:"נו כם כן הם הן",suf6:"ונו וכם וכן והם והן",suf7:"תכם תכן תנו תהם תהן",suf8:"הוא היא הם הן אני אתה את אנו אתם אתן",suf9:"ני נו כי כו כם כן תי תך תכ תם תן",suf10:"י ך כ ם ן נ ת"},e.patterns=JSON.parse('{"hebrewPatterns": [{"pt1": [{"c": "ה", "l": 0}]}, {"pt2": [{"c": "ו", "l": 0}]}, {"pt3": [{"c": "י", "l": 0}]}, {"pt4": [{"c": "ת", "l": 0}]}, {"pt5": [{"c": "מ", "l": 0}]}, {"pt6": [{"c": "ל", "l": 0}]}, {"pt7": [{"c": "ב", "l": 0}]}, {"pt8": [{"c": "כ", "l": 0}]}, {"pt9": [{"c": "ש", "l": 0}]}, {"pt10": [{"c": "כש", "l": 0}]}, {"pt11": [{"c": "בה", "l": 0}]}, {"pt12": [{"c": "וב", "l": 0}]}, {"pt13": [{"c": "וכ", "l": 0}]}, {"pt14": [{"c": "ול", "l": 0}]}, {"pt15": [{"c": "ומ", "l": 0}]}, {"pt16": [{"c": "וש", "l": 0}]}, {"pt17": [{"c": "הב", "l": 0}]}, {"pt18": [{"c": "הכ", "l": 0}]}, {"pt19": [{"c": "הל", "l": 0}]}, {"pt20": [{"c": "המ", "l": 0}]}, {"pt21": [{"c": "הש", "l": 0}]}, {"pt22": [{"c": "מה", "l": 0}]}, {"pt23": [{"c": "שה", "l": 0}]}, {"pt24": [{"c": "כל", "l": 0}]}]}'),e.execArray=["cleanWord","removeDiacritics","removeStopWords","normalizeHebrewCharacters"],e.stem=function(){var r=0;for(e.result=!1,e.preRemoved=!1,e.sufRemoved=!1;r<e.execArray.length&&1!=e.result;)e.result=e[e.execArray[r]](),r++},e.setCurrent=function(r){e.word=r},e.getCurrent=function(){return e.word},e.cleanWord=function(){return!!new RegExp("[^֑-״א-ת]").test("")},e.removeDiacritics=function(){var r=new RegExp("[ְ-ֿ]","g");return e.word=e.word.replace(r,""),!1},e.removeStopWords=function(){if("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ").indexOf(e.word)>=0)return!0},e.normalizeHebrewCharacters=function(){return e.word=e.word.replace("ך","כ"),e.word=e.word.replace("ם","מ"),e.word=e.word.replace("ן","נ"),e.word=e.word.replace("ף","פ"),e.word=e.word.replace("ץ","צ"),!1},function(r){return"function"==typeof r.update?r.update(function(r){return e.setLine truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Hungarian` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hu=function(){this.pipeline.reset(),this.pipeline.add(e.hu.trimmer,e.hu.stopWordFilter,e.hu.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hu.stemmer))},e.hu.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.hu.trimmer=e.trimmerSupport.generateTrimmer(e.hu.wordCharacters),e.Pipeline.registerFunction(e.hu.trimmer,"trimmer-hu"),e.hu.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,n=L.cursor;if(d=L.limit,L.in_grouping(W,97,252))for(;;){if(e=L.cursor,L.out_grouping(W,97,252))return L.cursor=e,L.find_among(g,8)||(L.cursor=e,e<L.limit&&L.cursor++),void(d=L.cursor);if(L.cursor=e,e>=L.limit)return void(d=e);L.cursor++}if(L.cursor=n,L.out_grouping(W,97,252)){for(;!L.in_grouping(W,97,252);){if(L.cursor>=L.limit)return;L.cursor++}d=L.cursor}}function i(){return d<=L.cursor}function a(){var e;if(L.ket=L.cursor,(e=L.find_among_b(h,2))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("a");break;case 2:L.slice_from("e")}}function t(){var e=L.limit-L.cursor;return!!L.find_among_b(p,23)&&(L.cursor=L.limit-e,!0)}function s(){if(L.cursor>L.limit_backward){L.cursor--,L.ket=L.cursor;var e=L.cursor-1;L.limit_backward<=e&&e<=L.limit&&(L.cursor=e,L.bra=e,L.slice_del())}}function c(){var e;if(L.ket=L.cursor,(e=L.find_among_b(_,2))&&(L.bra=L.cursor,i())){if((1==e||2==e)&&!t())return;L.slice_del(),s()}}function o(){L.ket=L.cursor,L.find_among_b(v,44)&&(L.bra=L.cursor,i()&&(L.slice_del(),a()))}function w(){var e;if(L.ket=L.cursor,(e=L.find_among_b(z,3))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("e");break;case 2:case 3:L.slice_from("a")}}function l(){var e;if(L.ket=L.cursor,(e=L.find_among_b(y,6))&&(L.bra=L.cursor,i()))switch(e){case 1:case 2:L.slice_del();break;case 3:L.slice_from("a");break;case 4:L.slice_from("e")}}function u(){var e;if(L.ket=L.cursor,(e=L.find_among_b(j,2))&&(L.bra=L.cursor,i())){if((1==e||2==e)&&!t())return;L.slice_del(),s()}}function m(){var e;if(L.ket=L.cursor,(e=L.find_among_b(C,7))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("a");break;case 2:L.slice_from("e");break;case 3:case 4:case 5:case 6:case 7:L.slice_del()}}function k(){var e;if(L.ket=L.cursor,(e=L.find_among_b(P,12))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 7:case 9:L.slice_del();break;case 2:case 5:case 8:L.slice_from("e");break;case 3:case 6:L.slice_from("a")}}function f(){var e;if(L.ket=L.cursor,(e=L.find_among_b(F,31))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 7:case 8:case 9:case 12:case 13:case 16:case 17:case 18:L.slice_del();break;case 2:case 5:case 10:case 14:case 19:L.slice_from("a");break;case 3:case 6:case 11:case 15:case 20:L.slice_from("e")}}function b(){var e;if(L.ket=L.cursor,(e=L.find_among_b(S,42))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 5:case 6:case 9:case 10:case 11:case 14:case 15:case 16:case 17:case 20:case 21:case 24:case 25:case 26:case 29:L.slice_del();break;case 2:case 7:case 12:case 18:case 22:case 27:L.slice_from("a");break;case 3:case 8:case 13:case 19:case 23:case 28:L.slice_from("e")}}var d,g=[new n("cs",-1,-1),new n("dzs",-1,-1),new n("gy",-1,-1),new n("ly",-1,-1),new n("ny",-1,-1),new n("sz",-1,-1),new n("ty",-1,-1),new n("zs",-1,-1)],h=[new n("á",-1,1),new n("é",-1,2)],p=[new n("bb",-1,-1),new n("cc",-1,-1),new n("dd",-1,-1),new n("ff",-1,-1),new n("gg",-1,-1),new n("jj",-1,-1),new n("kk",-1,-1),new n("ll",-1,-1),new n("mm",-1,-1),new n("nn",-1,-1),new n("pp",-1,-1),new n("rr",-1,-1),new n("ccs",-1,-1),new n("ss",-1,-1),new n("zzs",-1,-1),new n("tt",-1,-1),new n("vv",-1,-1),new n("ggy",-1,-1),new n("lly",-1,-1),new n("nny",-1,-1),new n("tty",-1,-1),new n("ssz",-1,-1),new n("zz",-1,-1)],_=[new n("al",-1,1),new n("el",-1,2)],v=[new n("ba",-1,-1),new n("ra",-1,-1),new n("be",-1,-1),new n("re",-1,-1),new n("ig",-1,-1),new n("nak",-1,-1),new n("nek",-1,-1),new n("val",-1,-1),new n("vel",-1,-1),new n("ul",-1,-1),new n("nál",-1,-1),new n("nél",-1,-1),new n("ból",-1,-1),new n("ról",-1,-1),new n("tól",-1,-1),new n("bõl",-1,-1),new n("rõl",-1,-1),new n("tõl",-1,-1),new n("ül",-1,-1),new n("n",-1,-1),new n("an",19,-1),new n("ban",20,-1),new n("en",19,-1),new n("ben",22,-1),new n("képpen",22,-1),new n("on",19,-1),new n("ön",19,-1),new n("képp",-1,-1),new n("kor",-1,-1),new n("t",-1,-1),new n("at",29,-1),new n("et",29,-1),new n("ként",29,-1),new n("anként",32,-1),new n("enként",32,-1),new n("onként",32,-1),Line truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hy=function(){this.pipeline.reset(),this.pipeline.add(e.hy.trimmer,e.hy.stopWordFilter)},e.hy.wordCharacters="[A-Za-z-֏ff-ﭏ]",e.hy.trimmer=e.trimmerSupport.generateTrimmer(e.hy.wordCharacters),e.Pipeline.registerFunction(e.hy.trimmer,"trimmer-hy"),e.hy.stopWordFilter=e.generateStopWordFilter("դու և եք էիր էիք հետո նաև նրանք որը վրա է որ պիտի են այս մեջ ն իր ու ի այդ որոնք այն կամ էր մի ես համար այլ իսկ էին ենք հետ ին թ էինք մենք նրա նա դուք եմ էի ըստ որպես ում".split(" ")),e.Pipeline.registerFunction(e.hy.stopWordFilter,"stopWordFilter-hy"),e.hy.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.hy.stemmer,"stemmer-hy")}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Italian` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.it=function(){this.pipeline.reset(),this.pipeline.add(e.it.trimmer,e.it.stopWordFilter,e.it.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.it.stemmer))},e.it.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.it.trimmer=e.trimmerSupport.generateTrimmer(e.it.wordCharacters),e.Pipeline.registerFunction(e.it.trimmer,"trimmer-it"),e.it.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!x.eq_s(1,e)||(x.ket=x.cursor,!x.in_grouping(L,97,249)))&&(x.slice_from(r),x.cursor=n,!0)}function i(){for(var r,n,i,o,t=x.cursor;;){if(x.bra=x.cursor,r=x.find_among(h,7))switch(x.ket=x.cursor,r){case 1:x.slice_from("à");continue;case 2:x.slice_from("è");continue;case 3:x.slice_from("ì");continue;case 4:x.slice_from("ò");continue;case 5:x.slice_from("ù");continue;case 6:x.slice_from("qU");continue;case 7:if(x.cursor>=x.limit)break;x.cursor++;continue}break}for(x.cursor=t;;)for(n=x.cursor;;){if(i=x.cursor,x.in_grouping(L,97,249)){if(x.bra=x.cursor,o=x.cursor,e("u","U",i))break;if(x.cursor=o,e("i","I",i))break}if(x.cursor=i,x.cursor>=x.limit)return void(x.cursor=n);x.cursor++}}function o(e){if(x.cursor=e,!x.in_grouping(L,97,249))return!1;for(;!x.out_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}return!0}function t(){if(x.in_grouping(L,97,249)){var e=x.cursor;if(x.out_grouping(L,97,249)){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return o(e);x.cursor++}return!0}return o(e)}return!1}function s(){var e,r=x.cursor;if(!t()){if(x.cursor=r,!x.out_grouping(L,97,249))return;if(e=x.cursor,x.out_grouping(L,97,249)){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return x.cursor=e,void(x.in_grouping(L,97,249)&&x.cursor<x.limit&&x.cursor++);x.cursor++}return void(k=x.cursor)}if(x.cursor=e,!x.in_grouping(L,97,249)||x.cursor>=x.limit)return;x.cursor++}k=x.cursor}function a(){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}for(;!x.out_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}return!0}function u(){var e=x.cursor;k=x.limit,p=k,g=k,s(),x.cursor=e,a()&&(p=x.cursor,a()&&(g=x.cursor))}function c(){for(var e;;){if(x.bra=x.cursor,!(e=x.find_among(q,3)))break;switch(x.ket=x.cursor,e){case 1:x.slice_from("i");break;case 2:x.slice_from("u");break;case 3:if(x.cursor>=x.limit)return;x.cursor++}}}function w(){return k<=x.cursor}function l(){return p<=x.cursor}function m(){return g<=x.cursor}function f(){var e;if(x.ket=x.cursor,x.find_among_b(C,37)&&(x.bra=x.cursor,(e=x.find_among_b(z,5))&&w()))switch(e){case 1:x.slice_del();break;case 2:x.slice_from("e")}}function v(){var e;if(x.ket=x.cursor,!(e=x.find_among_b(S,51)))return!1;switch(x.bra=x.cursor,e){case 1:if(!m())return!1;x.slice_del();break;case 2:if(!m())return!1;x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"ic")&&(x.bra=x.cursor,m()&&x.slice_del());break;case 3:if(!m())return!1;x.slice_from("log");break;case 4:if(!m())return!1;x.slice_from("u");break;case 5:if(!m())return!1;x.slice_from("ente");break;case 6:if(!w())return!1;x.slice_del();break;case 7:if(!l())return!1;x.slice_del(),x.ket=x.cursor,e=x.find_among_b(P,4),e&&(x.bra=x.cursor,m()&&(x.slice_del(),1==e&&(x.ket=x.cursor,x.eq_s_b(2,"at")&&(x.bra=x.cursor,m()&&x.slice_del()))));break;case 8:if(!m())return!1;x.slice_del(),x.ket=x.cursor,e=x.find_among_b(F,3),e&&(x.bra=x.cursor,1==e&&m()&&x.slice_del());break;case 9:if(!m())return!1;x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"at")&&(x.bra=x.cursor,m()&&(x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"ic")&&(x.bra=x.cursor,m()&&x.slice_del())))}return!0}function b(){var e,r;x.cursor>=k&&(r=x.limit_backward,x.limit_backward=k,x.ket=x.cursor,e=x.find_among_b(W,87),e&&(x.bra=x.cursor,1==e&&x.slice_del()),x.limit_backward=r)}function d(){var e=x.limit-x.cursor;if(x.ket=x.cursor,x.in_grouping_b(y,97,242)&&(x.bra=x.cursor,w()&&(x.slice_del(),x.ket=x.cursor,x.eq_s_b(1,"i")&&(x.bra=x.cursor,w()))))return void x.slice_del();x.cursor=x.limit-e}function _(){d(),x.ket=x.cursor,x.eq_s_b(1,"h")&&(x.bra=x.cursor,x.in_grouping_b(U,99,103)&&w()&&x.slice_del())}var g,p,k,h=[new r("",-1,7),new r("qu",0,6),new r("á",0,1),new r("é",0,2),new r("í",0,3),new r("ó",0,4),new r("ú",0,5)],q=[new r("",-1,3),new r("I",0,1),new r("U",0,2)],C=[new r("la",-1,-1),new r("cela",0,-1),new r("gliela",0,-1),new r("mela",0,-1),new r("tela",0,-1),new r("vela",0,-1),new r("le",-1,-1),new r("cele",6,-1),new r("gliele",6Line truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t.toLowerCase()):t.toLowerCase()});for(o=i.toString().toLowerCase().replace(/^\s+/,""),n=o.length-1;n>=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n<p.length;n++)r?a.push(new e.Token(p[n],{position:[f,p[n].length],index:a.length})):a.push(p[n]),f+=p[n].length;l=c+1}return a},e.ja.stemmer=function(){return function(e){return e}}(),e.Pipeline.registerFunction(e.ja.stemmer,"stemmer-ja"),e.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ja.trimmer=e.trimmerSupport.generateTrimmer(e.ja.wordCharacters),e.Pipeline.registerFunction(e.ja.trimmer,"trimmer-ja"),e.ja.stopWordFilter=e.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),e.Pipeline.registerFunction(e.ja.stopWordFilter,"stopWordFilter-ja"),e.jp=e.ja,e.Pipeline.registerFunction(e.jp.stemmer,"stemmer-jp"),e.Pipeline.registerFunction(e.jp.trimmer,"trimmer-jp"),e.Pipeline.registerFunction(e.jp.stopWordFilter,"stopWordFilter-jp")}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
module.exports=require("./lunr.ja");
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ko=function(){this.pipeline.reset(),this.pipeline.add(e.ko.trimmer,e.ko.stopWordFilter)},e.ko.wordCharacters="[A-Za-z가-힣]",e.ko.trimmer=e.trimmerSupport.generateTrimmer(e.ko.wordCharacters),e.Pipeline.registerFunction(e.ko.trimmer,"trimmer-ko"),e.ko.stopWordFilter=e.generateStopWordFilter("아 휴 아이구 아이쿠 아이고 어 나 우리 저희 따라 의해 을 를 에 의 가 으로 로 에게 뿐이다 의거하여 근거하여 입각하여 기준으로 예하면 예를 들면 예를 들자면 저 소인 소생 저희 지말고 하지마 하지마라 다른 물론 또한 그리고 비길수 없다 해서는 안된다 뿐만 아니라 만이 아니다 만은 아니다 막론하고 관계없이 그치지 않다 그러나 그런데 하지만 든간에 논하지 않다 따지지 않다 설사 비록 더라도 아니면 만 못하다 하는 편이 낫다 불문하고 향하여 향해서 향하다 쪽으로 틈타 이용하여 타다 오르다 제외하고 이 외에 이 밖에 하여야 비로소 한다면 몰라도 외에도 이곳 여기 부터 기점으로 따라서 할 생각이다 하려고하다 이리하여 그리하여 그렇게 함으로써 하지만 일때 할때 앞에서 중에서 보는데서 으로써 로써 까지 해야한다 일것이다 반드시 할줄알다 할수있다 할수있어 임에 틀림없다 한다면 등 등등 제 겨우 단지 다만 할뿐 딩동 댕그 대해서 대하여 대하면 훨씬 얼마나 얼마만큼 얼마큼 남짓 여 얼마간 약간 다소 좀 조금 다수 몇 얼마 지만 하물며 또한 그러나 그렇지만 하지만 이외에도 대해 말하자면 뿐이다 다음에 반대로 반대로 말하자면 이와 반대로 바꾸어서 말하면 바꾸어서 한다면 만약 그렇지않으면 까악 툭 딱 삐걱거리다 보드득 비걱거리다 꽈당 응당 해야한다 에 가서 각 각각 여러분 각종 각자 제각기 하도록하다 와 과 그러므로 그래서 고로 한 까닭에 하기 때문에 거니와 이지만 대하여 관하여 관한 과연 실로 아니나다를가 생각한대로 진짜로 한적이있다 하곤하였다 하 하하 허허 아하 거바 와 오 왜 어째서 무엇때문에 어찌 하겠는가 무슨 어디 어느곳 더군다나 하물며 더욱이는 어느때 언제 야 이봐 어이 여보시오 흐흐 흥 휴 헉헉 헐떡헐떡 영차 여차 어기여차 끙끙 아야 앗 아야 콸콸 졸졸 좍좍 뚝뚝 주룩주룩 솨 우르르 그래도 또 그리고 바꾸어말하면 바꾸어말하자면 혹은 혹시 답다 및 그에 따르는 때가 되어 즉 지든지 설령 가령 하더라도 할지라도 일지라도 지든지 몇 거의 하마터면 인젠 이젠 된바에야 된이상 만큼\t어찌됏든 그위에 게다가 점에서 보아 비추어 보아 고려하면 하게될것이다 일것이다 비교적 좀 보다더 비하면 시키다 하게하다 할만하다 의해서 연이서 이어서 잇따라 뒤따라 뒤이어 결국 의지하여 기대여 통하여 자마자 더욱더 불구하고 얼마든지 마음대로 주저하지 않고 곧 즉시 바로 당장 하자마자 밖에 안된다 하면된다 그래 그렇지 요컨대 다시 말하자면 바꿔 말하면 즉 구체적으로 말하자면 시작하여 시초에 이상 허 헉 허걱 바와같이 해도좋다 해도된다 게다가 더구나 하물며 와르르 팍 퍽 펄렁 동안 이래 하고있었다 이었다 에서 로부터 까지 예하면 했어요 해요 함께 같이 더불어 마저 마저도 양자 모두 습니다 가까스로 하려고하다 즈음하여 다른 다른 방면으로 해봐요 습니까 했어요 말할것도 없고 무릎쓰고 개의치않고 하는것만 못하다 하는것이 낫다 매 매번 들 모 어느것 어느 로써 갖고말하자면 어디 어느쪽 어느것 어느해 어느 년도 라 해도 언젠가 어떤것 어느것 저기 저쪽 저것 그때 그럼 그러면 요만한걸 그래 그때 저것만큼 그저 이르기까지 할 줄 안다 할 힘이 있다 너 너희 당신 어찌 설마 차라리 할지언정 할지라도 할망정 할지언정 구토하다 게우다 토하다 메쓰겁다 옆사람 퉤 쳇 의거하여 근거하여 의해 따라 힘입어 그 다음 버금 두번째로 기타 첫번째로 나머지는 그중에서 견지에서 형식으로 쓰여 입장에서 위해서 단지 의해되다 하도록시키다 뿐만아니라 반대로 전후 전자 앞의것 잠시 잠깐 하면서 그렇지만 다음에 그러한즉 그런즉 남들 아무거나 어찌하Line truncated
|
||||
@@ -0,0 +1 @@
|
||||
!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Dutch` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.nl=function(){this.pipeline.reset(),this.pipeline.add(r.nl.trimmer,r.nl.stopWordFilter,r.nl.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.nl.stemmer))},r.nl.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.nl.trimmer=r.trimmerSupport.generateTrimmer(r.nl.wordCharacters),r.Pipeline.registerFunction(r.nl.trimmer,"trimmer-nl"),r.nl.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(){for(var r,e,i,o=C.cursor;;){if(C.bra=C.cursor,r=C.find_among(b,11))switch(C.ket=C.cursor,r){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(e=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=e);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=e;else if(n(e))break}else if(n(e))break}function n(r){return C.cursor=r,r>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,d=_,t()||(_=C.cursor,_<3&&(_=3),t()||(d=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var r;;)if(C.bra=C.cursor,r=C.find_among(p,3))switch(C.ket=C.cursor,r){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return d<=C.cursor}function a(){var r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var r,e,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,r=C.find_among_b(h,5))switch(C.bra=C.cursor,r){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 3:u()&&C.out_grouping_b(j,97,232)&&C.slice_del()}if(C.cursor=C.limit-s,l(),C.cursor=C.limit-s,C.ket=C.cursor,C.eq_s_b(4,"heid")&&(C.bra=C.cursor,c()&&(e=C.limit-C.cursor,C.eq_s_b(1,"c")||(C.cursor=C.limit-e,C.slice_del(),C.ket=C.cursor,C.eq_s_b(2,"en")&&(C.bra=C.cursor,m())))),C.cursor=C.limit-s,C.ket=C.cursor,r=C.find_among_b(k,6))switch(C.bra=C.cursor,r){case 1:if(c()){if(C.slice_del(),i=C.limit-C.cursor,C.ket=C.cursor,C.eq_s_b(2,"ig")&&(C.bra=C.cursor,c()&&(n=C.limit-C.cursor,!C.eq_s_b(1,"e")))){C.cursor=C.limit-n,C.slice_del();break}C.cursor=C.limit-i,a()}break;case 2:c()&&(o=C.limit-C.cursor,C.eq_s_b(1,"e")||(C.cursor=C.limit-o,C.slice_del()));break;case 3:c()&&(C.slice_del(),l());break;case 4:c()&&C.slice_del();break;case 5:c()&&w&&C.slice_del()}C.cursor=C.limit-s,C.out_grouping_b(z,73,232)&&(t=C.limit-C.cursor,C.find_among_b(v,4)&&C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-t,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del())))}var d,_,w,b=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],p=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],g=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],h=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],k=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],v=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrLine truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Norwegian` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
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|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Portuguese` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.pt=function(){this.pipeline.reset(),this.pipeline.add(e.pt.trimmer,e.pt.stopWordFilter,e.pt.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.pt.stemmer))},e.pt.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.pt.trimmer=e.trimmerSupport.generateTrimmer(e.pt.wordCharacters),e.Pipeline.registerFunction(e.pt.trimmer,"trimmer-pt"),e.pt.stemmer=function(){var r=e.stemmerSupport.Among,s=e.stemmerSupport.SnowballProgram,n=new function(){function e(){for(var e;;){if(z.bra=z.cursor,e=z.find_among(k,3))switch(z.ket=z.cursor,e){case 1:z.slice_from("a~");continue;case 2:z.slice_from("o~");continue;case 3:if(z.cursor>=z.limit)break;z.cursor++;continue}break}}function n(){if(z.out_grouping(y,97,250)){for(;!z.in_grouping(y,97,250);){if(z.cursor>=z.limit)return!0;z.cursor++}return!1}return!0}function i(){if(z.in_grouping(y,97,250))for(;!z.out_grouping(y,97,250);){if(z.cursor>=z.limit)return!1;z.cursor++}return g=z.cursor,!0}function o(){var e,r,s=z.cursor;if(z.in_grouping(y,97,250))if(e=z.cursor,n()){if(z.cursor=e,i())return}else g=z.cursor;if(z.cursor=s,z.out_grouping(y,97,250)){if(r=z.cursor,n()){if(z.cursor=r,!z.in_grouping(y,97,250)||z.cursor>=z.limit)return;z.cursor++}g=z.cursor}}function t(){for(;!z.in_grouping(y,97,250);){if(z.cursor>=z.limit)return!1;z.cursor++}for(;!z.out_grouping(y,97,250);){if(z.cursor>=z.limit)return!1;z.cursor++}return!0}function a(){var e=z.cursor;g=z.limit,b=g,h=g,o(),z.cursor=e,t()&&(b=z.cursor,t()&&(h=z.cursor))}function u(){for(var e;;){if(z.bra=z.cursor,e=z.find_among(q,3))switch(z.ket=z.cursor,e){case 1:z.slice_from("ã");continue;case 2:z.slice_from("õ");continue;case 3:if(z.cursor>=z.limit)break;z.cursor++;continue}break}}function w(){return g<=z.cursor}function m(){return b<=z.cursor}function c(){return h<=z.cursor}function l(){var e;if(z.ket=z.cursor,!(e=z.find_among_b(F,45)))return!1;switch(z.bra=z.cursor,e){case 1:if(!c())return!1;z.slice_del();break;case 2:if(!c())return!1;z.slice_from("log");break;case 3:if(!c())return!1;z.slice_from("u");break;case 4:if(!c())return!1;z.slice_from("ente");break;case 5:if(!m())return!1;z.slice_del(),z.ket=z.cursor,e=z.find_among_b(j,4),e&&(z.bra=z.cursor,c()&&(z.slice_del(),1==e&&(z.ket=z.cursor,z.eq_s_b(2,"at")&&(z.bra=z.cursor,c()&&z.slice_del()))));break;case 6:if(!c())return!1;z.slice_del(),z.ket=z.cursor,e=z.find_among_b(C,3),e&&(z.bra=z.cursor,1==e&&c()&&z.slice_del());break;case 7:if(!c())return!1;z.slice_del(),z.ket=z.cursor,e=z.find_among_b(P,3),e&&(z.bra=z.cursor,1==e&&c()&&z.slice_del());break;case 8:if(!c())return!1;z.slice_del(),z.ket=z.cursor,z.eq_s_b(2,"at")&&(z.bra=z.cursor,c()&&z.slice_del());break;case 9:if(!w()||!z.eq_s_b(1,"e"))return!1;z.slice_from("ir")}return!0}function f(){var e,r;if(z.cursor>=g){if(r=z.limit_backward,z.limit_backward=g,z.ket=z.cursor,e=z.find_among_b(S,120))return z.bra=z.cursor,1==e&&z.slice_del(),z.limit_backward=r,!0;z.limit_backward=r}return!1}function d(){var e;z.ket=z.cursor,(e=z.find_among_b(W,7))&&(z.bra=z.cursor,1==e&&w()&&z.slice_del())}function v(e,r){if(z.eq_s_b(1,e)){z.bra=z.cursor;var s=z.limit-z.cursor;if(z.eq_s_b(1,r))return z.cursor=z.limit-s,w()&&z.slice_del(),!1}return!0}function p(){var e;if(z.ket=z.cursor,e=z.find_among_b(L,4))switch(z.bra=z.cursor,e){case 1:w()&&(z.slice_del(),z.ket=z.cursor,z.limit-z.cursor,v("u","g")&&v("i","c"));break;case 2:z.slice_from("c")}}function _(){if(!l()&&(z.cursor=z.limit,!f()))return z.cursor=z.limit,void d();z.cursor=z.limit,z.ket=z.cursor,z.eq_s_b(1,"i")&&(z.bra=z.cursor,z.eq_s_b(1,"c")&&(z.cursor=z.limit,w()&&z.slice_del()))}var h,b,g,k=[new r("",-1,3),new r("ã",0,1),new r("õ",0,2)],q=[new r("",-1,3),new r("a~",0,1),new r("o~",0,2)],j=[new r("ic",-1,-1),new r("ad",-1,-1),new r("os",-1,-1),new r("iv",-1,1)],C=[new r("ante",-1,1),new r("avel",-1,1),new r("ível",-1,1)],P=[new r("ic",-1,1),new r("abil",-1,1),new r("iv",-1,1)],F=[new r("ica",-1,1),new r("ância",-1,1),new r("ência",-1,4),new r("ira",-1,9),new r("adora",-1,1),new r("osa",-1,1),new r("ista",-1,1),new r("iva",-1,8),new r("eza",-1,1),new r("logía",-1,2),new r("idade",-1,7),new r("ante",-1,1),new r("mente",-1,6),new r("amente",12,5),new r("ável",-1,1),new r("ível",-1,1),new r("ución",-1,3),new r("ico",-1,1),new r("ismo",-1,1),new r("oso",-1,1),new r("amento",-1,1),new r("imento",-1,1),new r("ivo",-1,8),new r("aça~o",-1,1),new r("ador",-1,1),new r("icas",-1,1),new r("ências",-1,4),new r("iras",-1,9),Line truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Romanian` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ro=function(){this.pipeline.reset(),this.pipeline.add(e.ro.trimmer,e.ro.stopWordFilter,e.ro.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ro.stemmer))},e.ro.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.ro.trimmer=e.trimmerSupport.generateTrimmer(e.ro.wordCharacters),e.Pipeline.registerFunction(e.ro.trimmer,"trimmer-ro"),e.ro.stemmer=function(){var i=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,n=new function(){function e(e,i){L.eq_s(1,e)&&(L.ket=L.cursor,L.in_grouping(W,97,259)&&L.slice_from(i))}function n(){for(var i,r;;){if(i=L.cursor,L.in_grouping(W,97,259)&&(r=L.cursor,L.bra=r,e("u","U"),L.cursor=r,e("i","I")),L.cursor=i,L.cursor>=L.limit)break;L.cursor++}}function t(){if(L.out_grouping(W,97,259)){for(;!L.in_grouping(W,97,259);){if(L.cursor>=L.limit)return!0;L.cursor++}return!1}return!0}function a(){if(L.in_grouping(W,97,259))for(;!L.out_grouping(W,97,259);){if(L.cursor>=L.limit)return!0;L.cursor++}return!1}function o(){var e,i,r=L.cursor;if(L.in_grouping(W,97,259)){if(e=L.cursor,!t())return void(h=L.cursor);if(L.cursor=e,!a())return void(h=L.cursor)}L.cursor=r,L.out_grouping(W,97,259)&&(i=L.cursor,t()&&(L.cursor=i,L.in_grouping(W,97,259)&&L.cursor<L.limit&&L.cursor++),h=L.cursor)}function u(){for(;!L.in_grouping(W,97,259);){if(L.cursor>=L.limit)return!1;L.cursor++}for(;!L.out_grouping(W,97,259);){if(L.cursor>=L.limit)return!1;L.cursor++}return!0}function c(){var e=L.cursor;h=L.limit,k=h,g=h,o(),L.cursor=e,u()&&(k=L.cursor,u()&&(g=L.cursor))}function s(){for(var e;;){if(L.bra=L.cursor,e=L.find_among(z,3))switch(L.ket=L.cursor,e){case 1:L.slice_from("i");continue;case 2:L.slice_from("u");continue;case 3:if(L.cursor>=L.limit)break;L.cursor++;continue}break}}function w(){return h<=L.cursor}function m(){return k<=L.cursor}function l(){return g<=L.cursor}function f(){var e,i;if(L.ket=L.cursor,(e=L.find_among_b(C,16))&&(L.bra=L.cursor,m()))switch(e){case 1:L.slice_del();break;case 2:L.slice_from("a");break;case 3:L.slice_from("e");break;case 4:L.slice_from("i");break;case 5:i=L.limit-L.cursor,L.eq_s_b(2,"ab")||(L.cursor=L.limit-i,L.slice_from("i"));break;case 6:L.slice_from("at");break;case 7:L.slice_from("aţi")}}function p(){var e,i=L.limit-L.cursor;if(L.ket=L.cursor,(e=L.find_among_b(P,46))&&(L.bra=L.cursor,m())){switch(e){case 1:L.slice_from("abil");break;case 2:L.slice_from("ibil");break;case 3:L.slice_from("iv");break;case 4:L.slice_from("ic");break;case 5:L.slice_from("at");break;case 6:L.slice_from("it")}return _=!0,L.cursor=L.limit-i,!0}return!1}function d(){var e,i;for(_=!1;;)if(i=L.limit-L.cursor,!p()){L.cursor=L.limit-i;break}if(L.ket=L.cursor,(e=L.find_among_b(F,62))&&(L.bra=L.cursor,l())){switch(e){case 1:L.slice_del();break;case 2:L.eq_s_b(1,"ţ")&&(L.bra=L.cursor,L.slice_from("t"));break;case 3:L.slice_from("ist")}_=!0}}function b(){var e,i,r;if(L.cursor>=h){if(i=L.limit_backward,L.limit_backward=h,L.ket=L.cursor,e=L.find_among_b(q,94))switch(L.bra=L.cursor,e){case 1:if(r=L.limit-L.cursor,!L.out_grouping_b(W,97,259)&&(L.cursor=L.limit-r,!L.eq_s_b(1,"u")))break;case 2:L.slice_del()}L.limit_backward=i}}function v(){var e;L.ket=L.cursor,(e=L.find_among_b(S,5))&&(L.bra=L.cursor,w()&&1==e&&L.slice_del())}var _,g,k,h,z=[new i("",-1,3),new i("I",0,1),new i("U",0,2)],C=[new i("ea",-1,3),new i("aţia",-1,7),new i("aua",-1,2),new i("iua",-1,4),new i("aţie",-1,7),new i("ele",-1,3),new i("ile",-1,5),new i("iile",6,4),new i("iei",-1,4),new i("atei",-1,6),new i("ii",-1,4),new i("ului",-1,1),new i("ul",-1,1),new i("elor",-1,3),new i("ilor",-1,4),new i("iilor",14,4)],P=[new i("icala",-1,4),new i("iciva",-1,4),new i("ativa",-1,5),new i("itiva",-1,6),new i("icale",-1,4),new i("aţiune",-1,5),new i("iţiune",-1,6),new i("atoare",-1,5),new i("itoare",-1,6),new i("ătoare",-1,5),new i("icitate",-1,4),new i("abilitate",-1,1),new i("ibilitate",-1,2),new i("ivitate",-1,3),new i("icive",-1,4),new i("ative",-1,5),new i("itive",-1,6),new i("icali",-1,4),new i("atori",-1,5),new i("icatori",18,4),new i("itori",-1,6),new i("ători",-1,5),new i("icitati",-1,4),new i("abilitati",-1,1),new i("ivitati",-1,3),new i("icivi",-1,4),new i("ativi",-1,5),new i("itivi",-1,6),new i("icităi",-1,4),new i("abilităi",-1,1),new i("ivităi",-1,3),new i("icităţi",-1,4),new i("abilităţi",-1,1),new i("ivităţi",-1,3),new i("ical",-1,4),new i("ator",-1,5),new i("icator",35,4),new i("itor",-1,6),new i("ător",-1,5),new i("iciv",-1,4),neLine truncated
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Russian` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ru=function(){this.pipeline.reset(),this.pipeline.add(e.ru.trimmer,e.ru.stopWordFilter,e.ru.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ru.stemmer))},e.ru.wordCharacters="Ѐ-҄҇-ԯᴫᵸⷠ-ⷿꙀ-ꚟ︮︯",e.ru.trimmer=e.trimmerSupport.generateTrimmer(e.ru.wordCharacters),e.Pipeline.registerFunction(e.ru.trimmer,"trimmer-ru"),e.ru.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,t=new function(){function e(){for(;!W.in_grouping(S,1072,1103);){if(W.cursor>=W.limit)return!1;W.cursor++}return!0}function t(){for(;!W.out_grouping(S,1072,1103);){if(W.cursor>=W.limit)return!1;W.cursor++}return!0}function w(){b=W.limit,_=b,e()&&(b=W.cursor,t()&&e()&&t()&&(_=W.cursor))}function i(){return _<=W.cursor}function u(e,n){var r,t;if(W.ket=W.cursor,r=W.find_among_b(e,n)){switch(W.bra=W.cursor,r){case 1:if(t=W.limit-W.cursor,!W.eq_s_b(1,"а")&&(W.cursor=W.limit-t,!W.eq_s_b(1,"я")))return!1;case 2:W.slice_del()}return!0}return!1}function o(){return u(h,9)}function s(e,n){var r;return W.ket=W.cursor,!!(r=W.find_among_b(e,n))&&(W.bra=W.cursor,1==r&&W.slice_del(),!0)}function c(){return s(g,26)}function m(){return!!c()&&(u(C,8),!0)}function f(){return s(k,2)}function l(){return u(P,46)}function a(){s(v,36)}function p(){var e;W.ket=W.cursor,(e=W.find_among_b(F,2))&&(W.bra=W.cursor,i()&&1==e&&W.slice_del())}function d(){var e;if(W.ket=W.cursor,e=W.find_among_b(q,4))switch(W.bra=W.cursor,e){case 1:if(W.slice_del(),W.ket=W.cursor,!W.eq_s_b(1,"н"))break;W.bra=W.cursor;case 2:if(!W.eq_s_b(1,"н"))break;case 3:W.slice_del()}}var _,b,h=[new n("в",-1,1),new n("ив",0,2),new n("ыв",0,2),new n("вши",-1,1),new n("ивши",3,2),new n("ывши",3,2),new n("вшись",-1,1),new n("ившись",6,2),new n("ывшись",6,2)],g=[new n("ее",-1,1),new n("ие",-1,1),new n("ое",-1,1),new n("ые",-1,1),new n("ими",-1,1),new n("ыми",-1,1),new n("ей",-1,1),new n("ий",-1,1),new n("ой",-1,1),new n("ый",-1,1),new n("ем",-1,1),new n("им",-1,1),new n("ом",-1,1),new n("ым",-1,1),new n("его",-1,1),new n("ого",-1,1),new n("ему",-1,1),new n("ому",-1,1),new n("их",-1,1),new n("ых",-1,1),new n("ею",-1,1),new n("ою",-1,1),new n("ую",-1,1),new n("юю",-1,1),new n("ая",-1,1),new n("яя",-1,1)],C=[new n("ем",-1,1),new n("нн",-1,1),new n("вш",-1,1),new n("ивш",2,2),new n("ывш",2,2),new n("щ",-1,1),new n("ющ",5,1),new n("ующ",6,2)],k=[new n("сь",-1,1),new n("ся",-1,1)],P=[new n("ла",-1,1),new n("ила",0,2),new n("ыла",0,2),new n("на",-1,1),new n("ена",3,2),new n("ете",-1,1),new n("ите",-1,2),new n("йте",-1,1),new n("ейте",7,2),new n("уйте",7,2),new n("ли",-1,1),new n("или",10,2),new n("ыли",10,2),new n("й",-1,1),new n("ей",13,2),new n("уй",13,2),new n("л",-1,1),new n("ил",16,2),new n("ыл",16,2),new n("ем",-1,1),new n("им",-1,2),new n("ым",-1,2),new n("н",-1,1),new n("ен",22,2),new n("ло",-1,1),new n("ило",24,2),new n("ыло",24,2),new n("но",-1,1),new n("ено",27,2),new n("нно",27,1),new n("ет",-1,1),new n("ует",30,2),new n("ит",-1,2),new n("ыт",-1,2),new n("ют",-1,1),new n("уют",34,2),new n("ят",-1,2),new n("ны",-1,1),new n("ены",37,2),new n("ть",-1,1),new n("ить",39,2),new n("ыть",39,2),new n("ешь",-1,1),new n("ишь",-1,2),new n("ю",-1,2),new n("ую",44,2)],v=[new n("а",-1,1),new n("ев",-1,1),new n("ов",-1,1),new n("е",-1,1),new n("ие",3,1),new n("ье",3,1),new n("и",-1,1),new n("еи",6,1),new n("ии",6,1),new n("ами",6,1),new n("ями",6,1),new n("иями",10,1),new n("й",-1,1),new n("ей",12,1),new n("ией",13,1),new n("ий",12,1),new n("ой",12,1),new n("ам",-1,1),new n("ем",-1,1),new n("ием",18,1),new n("ом",-1,1),new n("ям",-1,1),new n("иям",21,1),new n("о",-1,1),new n("у",-1,1),new n("ах",-1,1),new n("ях",-1,1),new n("иях",26,1),new n("ы",-1,1),new n("ь",-1,1),new n("ю",-1,1),new n("ию",30,1),new n("ью",30,1),new n("я",-1,1),new n("ия",33,1),new n("ья",33,1)],F=[new n("ост",-1,1),new n("ость",-1,1)],q=[new n("ейше",-1,1),new n("н",-1,2),new n("ейш",-1,1),new n("ь",-1,3)],S=[33,65,8,232],W=new r;this.setCurrent=function(e){W.setCurrent(e)},this.getCurrent=function(){return W.getCurrent()},this.stem=function(){return w(),W.cursor=W.limit,!(W.cursor<b)&&(W.limit_backward=b,o()||(W.cursor=W.limit,f()||(W.cursor=W.limit),m()||(W.cursor=W.limit,l()||(W.cursor=W.limit,a()))),W.cursor=W.limit,W.ket=W.cursor,W.eq_s_b(1,"и"Line truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम् एकम् इत्यस्मिन् तथा तत् वा अयम् इत्यस्य ते आहूत उपरि तेषाम् किन्तु तेषाम् तदा इत्यनेन अधिकः इत्यस्य तत् केचन बहवः द्वि तथा महत्वपूर्णः अयम् अस्य विषये अयं अस्ति तत् प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम् द न्यूनम् कश्चित् वा विशालः द सः अस्ति तदनुसारम् तत्र अस्ति केवलम् अपि अत्र सर्वे विविधाः तत् बहवः यतः इदानीम् द दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम् सर्वे एकैकम् इत्यादि। एते सन्ति उत इत्थम् मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम् विशेषः अग्रिम तेषाम् समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन् कृतवान् धृतः तदा पुनः पूर्वं सः आगतः किम् कुल इतर पुरा मात्रा स विषये उ अतएव अपि नगरस्य उपरि यतः प्रतिशतं कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत् ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै कस्मै यस्मै यस्मै यस्मै यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});
|
||||
@@ -0,0 +1 @@
|
||||
!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s||e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Swedish` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o<a&&(o=a)}}function t(){var e,r=w.limit_backward;if(w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="-உஊ-ஏஐ-ஙச-ட-னப-யர-ஹ-ிீ-ொ-ௐ---௩௪-௯௰-௹௺-a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[-]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
/*!
|
||||
* Lunr languages, `Turkish` language
|
||||
* https://github.com/MihaiValentin/lunr-languages
|
||||
*
|
||||
* Copyright 2014, Mihai Valentin
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
/*!
|
||||
* based on
|
||||
* Snowball JavaScript Library v0.3
|
||||
* http://code.google.com/p/urim/
|
||||
* http://snowball.tartarus.org/
|
||||
*
|
||||
* Copyright 2010, Oleg Mazko
|
||||
* http://www.mozilla.org/MPL/
|
||||
*/
|
||||
|
||||
!function(r,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.tr=function(){this.pipeline.reset(),this.pipeline.add(r.tr.trimmer,r.tr.stopWordFilter,r.tr.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.tr.stemmer))},r.tr.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.tr.trimmer=r.trimmerSupport.generateTrimmer(r.tr.wordCharacters),r.Pipeline.registerFunction(r.tr.trimmer,"trimmer-tr"),r.tr.stemmer=function(){var i=r.stemmerSupport.Among,e=r.stemmerSupport.SnowballProgram,n=new function(){function r(r,i,e){for(;;){var n=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(r,i,e)){Dr.cursor=Dr.limit-n;break}if(Dr.cursor=Dr.limit-n,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function n(){var i,e;i=Dr.limit-Dr.cursor,r(Wr,97,305);for(var n=0;n<Br.length;n++){e=Dr.limit-Dr.cursor;var t=Br[n];if(Dr.eq_s_b(1,t[0])&&r(t[1],t[2],t[3]))return Dr.cursor=Dr.limit-i,!0;Dr.cursor=Dr.limit-e}return Dr.cursor=Dr.limit-e,!(!Dr.eq_s_b(1,"ü")||!r(Zr,246,252))&&(Dr.cursor=Dr.limit-i,!0)}function t(r,i){var e,n=Dr.limit-Dr.cursor;return r()&&(Dr.cursor=Dr.limit-n,Dr.cursor>Dr.limit_backward&&(Dr.cursor--,e=Dr.limit-Dr.cursor,i()))?(Dr.cursor=Dr.limit-e,!0):(Dr.cursor=Dr.limit-n,r()?(Dr.cursor=Dr.limit-n,!1):(Dr.cursor=Dr.limit-n,!(Dr.cursor<=Dr.limit_backward)&&(Dr.cursor--,!!i()&&(Dr.cursor=Dr.limit-n,!0))))}function u(r){return t(r,function(){return Dr.in_grouping_b(Wr,97,305)})}function o(){return u(function(){return Dr.eq_s_b(1,"n")})}function s(){return u(function(){return Dr.eq_s_b(1,"s")})}function c(){return u(function(){return Dr.eq_s_b(1,"y")})}function l(){return t(function(){return Dr.in_grouping_b(Lr,105,305)},function(){return Dr.out_grouping_b(Wr,97,305)})}function a(){return Dr.find_among_b(ur,10)&&l()}function m(){return n()&&Dr.in_grouping_b(Lr,105,305)&&s()}function d(){return Dr.find_among_b(or,2)}function f(){return n()&&Dr.in_grouping_b(Lr,105,305)&&c()}function b(){return n()&&Dr.find_among_b(sr,4)}function w(){return n()&&Dr.find_among_b(cr,4)&&o()}function _(){return n()&&Dr.find_among_b(lr,2)&&c()}function k(){return n()&&Dr.find_among_b(ar,2)}function p(){return n()&&Dr.find_among_b(mr,4)}function g(){return n()&&Dr.find_among_b(dr,2)}function y(){return n()&&Dr.find_among_b(fr,4)}function z(){return n()&&Dr.find_among_b(br,2)}function v(){return n()&&Dr.find_among_b(wr,2)&&c()}function h(){return Dr.eq_s_b(2,"ki")}function q(){return n()&&Dr.find_among_b(_r,2)&&o()}function C(){return n()&&Dr.find_among_b(kr,4)&&c()}function P(){return n()&&Dr.find_among_b(pr,4)}function F(){return n()&&Dr.find_among_b(gr,4)&&c()}function S(){return Dr.find_among_b(yr,4)}function W(){return n()&&Dr.find_among_b(zr,2)}function L(){return n()&&Dr.find_among_b(vr,4)}function x(){return n()&&Dr.find_among_b(hr,8)}function A(){return Dr.find_among_b(qr,2)}function E(){return n()&&Dr.find_among_b(Cr,32)&&c()}function j(){return Dr.find_among_b(Pr,8)&&c()}function T(){return n()&&Dr.find_among_b(Fr,4)&&c()}function Z(){return Dr.eq_s_b(3,"ken")&&c()}function B(){var r=Dr.limit-Dr.cursor;return!(T()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,Z()))))}function D(){if(A()){var r=Dr.limit-Dr.cursor;if(S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T())return!1}return!0}function G(){if(W()){Dr.bra=Dr.cursor,Dr.slice_del();var r=Dr.limit-Dr.cursor;return Dr.ket=Dr.cursor,x()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,T()||(Dr.cursor=Dr.limit-r)))),nr=!1,!1}return!0}function H(){if(!L())return!0;var r=Dr.limit-Dr.cursor;return!E()&&(Dr.cursor=Dr.limit-r,!j())}function I(){var r,i=Dr.limit-Dr.cursor;return!(S()||(Dr.cursor=Dr.limit-i,F()||(Dr.cursor=Dr.limit-i,P()||(Dr.cursor=Dr.limit-i,C()))))||(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,T()||(Dr.cursor=Dr.limit-r),!1)}function J(){var r,i=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,nr=!0,B()&&(Dr.cursor=Dr.limit-i,D()&&(Dr.cursor=Dr.limit-i,G()&&(Dr.cursor=Dr.limit-i,H()&&(Dr.cursor=Dr.limit-i,I()))))){if(Dr.cursor=Dr.limit-i,!x())return;Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T()||(Dr.cursor=Dr.limit-r)}Dr.bra=Dr.cursor,Dr.slice_del()}function K(){var r,i,e,n;if(Dr.ket=Dr.cursor,h()){if(r=Dr.limit-Dr.cursorLine truncated
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}});
|
||||
+1
@@ -0,0 +1 @@
|
||||
!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
|
||||
@@ -0,0 +1,206 @@
|
||||
/**
|
||||
* export the module via AMD, CommonJS or as a browser global
|
||||
* Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
|
||||
*/
|
||||
;(function (root, factory) {
|
||||
if (typeof define === 'function' && define.amd) {
|
||||
// AMD. Register as an anonymous module.
|
||||
define(factory)
|
||||
} else if (typeof exports === 'object') {
|
||||
/**
|
||||
* Node. Does not work with strict CommonJS, but
|
||||
* only CommonJS-like environments that support module.exports,
|
||||
* like Node.
|
||||
*/
|
||||
module.exports = factory()
|
||||
} else {
|
||||
// Browser globals (root is window)
|
||||
factory()(root.lunr);
|
||||
}
|
||||
}(this, function () {
|
||||
/**
|
||||
* Just return a value to define the module export.
|
||||
* This example returns an object, but the module
|
||||
* can return a function as the exported value.
|
||||
*/
|
||||
|
||||
return function(lunr) {
|
||||
// TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
|
||||
// (c) 2008 Taku Kudo <taku@chasen.org>
|
||||
// TinySegmenter is freely distributable under the terms of a new BSD licence.
|
||||
// For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt
|
||||
|
||||
function TinySegmenter() {
|
||||
var patterns = {
|
||||
"[一二三四五六七八九十百千万億兆]":"M",
|
||||
"[一-龠々〆ヵヶ]":"H",
|
||||
"[ぁ-ん]":"I",
|
||||
"[ァ-ヴーｱ-ﾝﾞｰ]":"K",
|
||||
"[a-zA-Zａ-ｚＡ-Ｚ]":"A",
|
||||
"[0-9０-９]":"N"
|
||||
}
|
||||
this.chartype_ = [];
|
||||
for (var i in patterns) {
|
||||
var regexp = new RegExp(i);
|
||||
this.chartype_.push([regexp, patterns[i]]);
|
||||
}
|
||||
|
||||
this.BIAS__ = -332
|
||||
this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378};
|
||||
this.BC2__ = {"AA":-3267,"AI":2744,"AN":-878,"HH":-4070,"HM":-1711,"HN":4012,"HO":3761,"IA":1327,"IH":-1184,"II":-1332,"IK":1721,"IO":5492,"KI":3831,"KK":-8741,"MH":-3132,"MK":3334,"OO":-2920};
|
||||
this.BC3__ = {"HH":996,"HI":626,"HK":-721,"HN":-1307,"HO":-836,"IH":-301,"KK":2762,"MK":1079,"MM":4034,"OA":-1652,"OH":266};
|
||||
this.BP1__ = {"BB":295,"OB":304,"OO":-125,"UB":352};
|
||||
this.BP2__ = {"BO":60,"OO":-1762};
|
||||
this.BQ1__ = {"BHH":1150,"BHM":1521,"BII":-1158,"BIM":886,"BMH":1208,"BNH":449,"BOH":-91,"BOO":-2597,"OHI":451,"OIH":-296,"OKA":1851,"OKH":-1020,"OKK":904,"OOO":2965};
|
||||
this.BQ2__ = {"BHH":118,"BHI":-1159,"BHM":466,"BIH":-919,"BKK":-1720,"BKO":864,"OHH":-1139,"OHM":-181,"OIH":153,"UHI":-1146};
|
||||
this.BQ3__ = {"BHH":-792,"BHI":2664,"BII":-299,"BKI":419,"BMH":937,"BMM":8335,"BNN":998,"BOH":775,"OHH":2174,"OHM":439,"OII":280,"OKH":1798,"OKI":-793,"OKO":-2242,"OMH":-2402,"OOO":11699};
|
||||
this.BQ4__ = {"BHH":-3895,"BIH":3761,"BII":-4654,"BIK":1348,"BKK":-1806,"BMI":-3385,"BOO":-12396,"OAH":926,"OHH":266,"OHK":-2036,"ONN":-973};
|
||||
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return this;
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}
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TinySegmenter.prototype.ctype_ = function(str) {
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for (var i in this.chartype_) {
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if (str.match(this.chartype_[i][0])) {
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return this.chartype_[i][1];
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}
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}
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return "O";
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}
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TinySegmenter.prototype.ts_ = function(v) {
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if (v) { return v; }
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return 0;
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}
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TinySegmenter.prototype.segment = function(input) {
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if (input == null || input == undefined || input == "") {
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return [];
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}
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var result = [];
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var seg = ["B3","B2","B1"];
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var ctype = ["O","O","O"];
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var o = input.split("");
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ctype.push(this.ctype_(o[i]))
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seg.push("E1");
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seg.push("E2");
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seg.push("E3");
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ctype.push("O");
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ctype.push("O");
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||||
ctype.push("O");
|
||||
var word = seg[3];
|
||||
var p1 = "U";
|
||||
var p2 = "U";
|
||||
var p3 = "U";
|
||||
for (var i = 4; i < seg.length - 3; ++i) {
|
||||
var score = this.BIAS__;
|
||||
var w1 = seg[i-3];
|
||||
var w2 = seg[i-2];
|
||||
var w3 = seg[i-1];
|
||||
var w4 = seg[i];
|
||||
var w5 = seg[i+1];
|
||||
var w6 = seg[i+2];
|
||||
var c1 = ctype[i-3];
|
||||
var c2 = ctype[i-2];
|
||||
var c3 = ctype[i-1];
|
||||
var c4 = ctype[i];
|
||||
var c5 = ctype[i+1];
|
||||
var c6 = ctype[i+2];
|
||||
score += this.ts_(this.UP1__[p1]);
|
||||
score += this.ts_(this.UP2__[p2]);
|
||||
score += this.ts_(this.UP3__[p3]);
|
||||
score += this.ts_(this.BP1__[p1 + p2]);
|
||||
score += this.ts_(this.BP2__[p2 + p3]);
|
||||
score += this.ts_(this.UW1__[w1]);
|
||||
score += this.ts_(this.UW2__[w2]);
|
||||
score += this.ts_(this.UW3__[w3]);
|
||||
score += this.ts_(this.UW4__[w4]);
|
||||
score += this.ts_(this.UW5__[w5]);
|
||||
score += this.ts_(this.UW6__[w6]);
|
||||
score += this.ts_(this.BW1__[w2 + w3]);
|
||||
score += this.ts_(this.BW2__[w3 + w4]);
|
||||
score += this.ts_(this.BW3__[w4 + w5]);
|
||||
score += this.ts_(this.TW1__[w1 + w2 + w3]);
|
||||
score += this.ts_(this.TW2__[w2 + w3 + w4]);
|
||||
score += this.ts_(this.TW3__[w3 + w4 + w5]);
|
||||
score += this.ts_(this.TW4__[w4 + w5 + w6]);
|
||||
score += this.ts_(this.UC1__[c1]);
|
||||
score += this.ts_(this.UC2__[c2]);
|
||||
score += this.ts_(this.UC3__[c3]);
|
||||
score += this.ts_(this.UC4__[c4]);
|
||||
score += this.ts_(this.UC5__[c5]);
|
||||
score += this.ts_(this.UC6__[c6]);
|
||||
score += this.ts_(this.BC1__[c2 + c3]);
|
||||
score += this.ts_(this.BC2__[c3 + c4]);
|
||||
score += this.ts_(this.BC3__[c4 + c5]);
|
||||
score += this.ts_(this.TC1__[c1 + c2 + c3]);
|
||||
score += this.ts_(this.TC2__[c2 + c3 + c4]);
|
||||
score += this.ts_(this.TC3__[c3 + c4 + c5]);
|
||||
score += this.ts_(this.TC4__[c4 + c5 + c6]);
|
||||
// score += this.ts_(this.TC5__[c4 + c5 + c6]);
|
||||
score += this.ts_(this.UQ1__[p1 + c1]);
|
||||
score += this.ts_(this.UQ2__[p2 + c2]);
|
||||
score += this.ts_(this.UQ3__[p3 + c3]);
|
||||
score += this.ts_(this.BQ1__[p2 + c2 + c3]);
|
||||
score += this.ts_(this.BQ2__[p2 + c3 + c4]);
|
||||
score += this.ts_(this.BQ3__[p3 + c2 + c3]);
|
||||
score += this.ts_(this.BQ4__[p3 + c3 + c4]);
|
||||
score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]);
|
||||
score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]);
|
||||
score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]);
|
||||
score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]);
|
||||
var p = "O";
|
||||
if (score > 0) {
|
||||
result.push(word);
|
||||
word = "";
|
||||
p = "B";
|
||||
}
|
||||
p1 = p2;
|
||||
p2 = p3;
|
||||
p3 = p;
|
||||
word += seg[i];
|
||||
}
|
||||
result.push(word);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
lunr.TinySegmenter = TinySegmenter;
|
||||
};
|
||||
|
||||
}));
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,42 @@
|
||||
"use strict";(()=>{var xe=Object.create;var U=Object.defineProperty,ve=Object.defineProperties,Se=Object.getOwnPropertyDescriptor,Te=Object.getOwnPropertyDescriptors,Qe=Object.getOwnPropertyNames,J=Object.getOwnPropertySymbols,Ee=Object.getPrototypeOf,Z=Object.prototype.hasOwnProperty,be=Object.prototype.propertyIsEnumerable;var K=Math.pow,X=(t,e,r)=>e in t?U(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,A=(t,e)=>{for(var r in e||(e={}))Z.call(e,r)&&X(t,r,e[r]);if(J)for(var r of J(e))be.call(e,r)&&X(t,r,e[r]);return t},G=(t,e)=>ve(t,Te(e));var Le=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var we=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Qe(e))!Z.call(t,i)&&i!==r&&U(t,i,{get:()=>e[i],enumerable:!(n=Se(e,i))||n.enumerable});return t};var Pe=(t,e,r)=>(r=t!=null?xe(Ee(t)):{},we(e||!t||!t.__esModule?U(r,"default",{value:t,enumerable:!0}):r,t));var B=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=Le((ee,te)=>{/**
|
||||
* lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
* @license MIT
|
||||
*/(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*!
|
||||
* lunr.utils
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i<n.length;i++){var s=n[i],o=e[s];if(Array.isArray(o)){r[s]=o.slice();continue}if(typeof o=="string"||typeof o=="number"||typeof o=="boolean"){r[s]=o;continue}throw new TypeError("clone is not deep and does not support nested objects")}return r},t.FieldRef=function(e,r,n){this.docRef=e,this.fieldName=r,this._stringValue=n},t.FieldRef.joiner="/",t.FieldRef.fromString=function(e){var r=e.indexOf(t.FieldRef.joiner);if(r===-1)throw"malformed field ref string";var n=e.slice(0,r),i=e.slice(r+1);return new t.FieldRef(i,n,e)},t.FieldRef.prototype.toString=function(){return this._stringValue==null&&(this._stringValue=this.fieldName+t.FieldRef.joiner+this.docRef),this._stringValue};/*!
|
||||
* lunr.Set
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.Set=function(e){if(this.elements=Object.create(null),e){this.length=e.length;for(var r=0;r<this.length;r++)this.elements[e[r]]=!0}else this.length=0},t.Set.complete={intersect:function(e){return e},union:function(){return this},contains:function(){return!0}},t.Set.empty={intersect:function(){return this},union:function(e){return e},contains:function(){return!1}},t.Set.prototype.contains=function(e){return!!this.elements[e]},t.Set.prototype.intersect=function(e){var r,n,i,s=[];if(e===t.Set.complete)return this;if(e===t.Set.empty)return e;this.length<e.length?(r=this,n=e):(r=e,n=this),i=Object.keys(r.elements);for(var o=0;o<i.length;o++){var a=i[o];a in n.elements&&s.push(a)}return new t.Set(s)},t.Set.prototype.union=function(e){return e===t.Set.complete?t.Set.complete:e===t.Set.empty?this:new t.Set(Object.keys(this.elements).concat(Object.keys(e.elements)))},t.idf=function(e,r){var n=0;for(var i in e)i!="_index"&&(n+=Object.keys(e[i]).length);var s=(r-n+.5)/(n+.5);return Math.log(1+Math.abs(s))},t.Token=function(e,r){this.str=e||"",this.metadata=r||{}},t.Token.prototype.toString=function(){return this.str},t.Token.prototype.update=function(e){return this.str=e(this.str,this.metadata),this},t.Token.prototype.clone=function(e){return e=e||function(r){return r},new t.Token(e(this.str,this.metadata),this.metadata)};/*!
|
||||
* lunr.tokenizer
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.tokenizer=function(e,r){if(e==null||e==null)return[];if(Array.isArray(e))return e.map(function(g){return new t.Token(t.utils.asString(g).toLowerCase(),t.utils.clone(r))});for(var n=e.toString().toLowerCase(),i=n.length,s=[],o=0,a=0;o<=i;o++){var u=n.charAt(o),c=o-a;if(u.match(t.tokenizer.separator)||o==i){if(c>0){var f=t.utils.clone(r)||{};f.position=[a,c],f.index=s.length,s.push(new t.Token(n.slice(a,o),f))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*!
|
||||
* lunr.Pipeline
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. This may cause problems when serialising the index.
|
||||
`,e)},t.Pipeline.load=function(e){var r=new t.Pipeline;return e.forEach(function(n){var i=t.Pipeline.registeredFunctions[n];if(i)r.add(i);else throw new Error("Cannot load unregistered function: "+n)}),r},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(r){t.Pipeline.warnIfFunctionNotRegistered(r),this._stack.push(r)},this)},t.Pipeline.prototype.after=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");n=n+1,this._stack.splice(n,0,r)},t.Pipeline.prototype.before=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");this._stack.splice(n,0,r)},t.Pipeline.prototype.remove=function(e){var r=this._stack.indexOf(e);r!=-1&&this._stack.splice(r,1)},t.Pipeline.prototype.run=function(e){for(var r=this._stack.length,n=0;n<r;n++){for(var i=this._stack[n],s=[],o=0;o<e.length;o++){var a=i(e[o],o,e);if(!(a==null||a===""))if(Array.isArray(a))for(var u=0;u<a.length;u++)s.push(a[u]);else s.push(a)}e=s}return e},t.Pipeline.prototype.runString=function(e,r){var n=new t.Token(e,r);return this.run([n]).map(function(i){return i.toString()})},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})};/*!
|
||||
* lunr.Vector
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,f=0;c<s&&f<o;)a=n[c],u=i[f],a<u?c+=2:a>u?f+=2:a==u&&(r+=n[c+1]*i[f+1],c+=2,f+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
|
||||
* lunr.stemmer
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
* Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
|
||||
*/t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,f="^("+s+")?"+i,g=new RegExp(a),l=new RegExp(c),m=new RegExp(u),x=new RegExp(f),v=/^(.+?)(ss|i)es$/,d=/^(.+?)([^s])s$/,y=/^(.+?)eed$/,b=/^(.+?)(ed|ing)$/,E=/.$/,w=/(at|bl|iz)$/,R=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),_=/^(.+?[^aeiou])y$/,D=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,V=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,z=/ll$/,$=new RegExp("^"+s+i+"[^aeiouwxy]$"),M=function(h){var S,k,L,p,T,O,F;if(h.length<3)return h;if(L=h.substr(0,1),L=="y"&&(h=L.toUpperCase()+h.substr(1)),p=v,T=d,p.test(h)?h=h.replace(p,"$1$2"):T.test(h)&&(h=h.replace(T,"$1$2")),p=y,T=b,p.test(h)){var Q=p.exec(h);p=g,p.test(Q[1])&&(p=E,h=h.replace(p,""))}else if(T.test(h)){var Q=T.exec(h);S=Q[1],T=x,T.test(S)&&(h=S,T=w,O=R,F=j,T.test(h)?h=h+"e":O.test(h)?(p=E,h=h.replace(p,"")):F.test(h)&&(h=h+"e"))}if(p=_,p.test(h)){var Q=p.exec(h);S=Q[1],h=S+"i"}if(p=D,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+e[k])}if(p=N,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+r[k])}if(p=C,T=V,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,p.test(S)&&(h=S)}else if(T.test(h)){var Q=T.exec(h);S=Q[1]+Q[2],T=l,T.test(S)&&(h=S)}if(p=P,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,T=m,O=$,(p.test(S)||T.test(S)&&!O.test(S))&&(h=S)}return p=z,T=l,p.test(h)&&T.test(h)&&(p=E,h=h.replace(p,"")),L=="y"&&(h=L.toLowerCase()+h.substr(1)),h};return function(I){return I.update(M)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
|
||||
* lunr.stopWordFilter
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");/*!
|
||||
* lunr.trimmer
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");/*!
|
||||
* lunr.TokenSet
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var f=s.str.charAt(0),g=s.str.charAt(1),l;g in s.node.edges?l=s.node.edges[g]:(l=new t.TokenSet,s.node.edges[g]=l),s.str.length==1&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:f+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var f=s[c],g=0;g<u;g++){var l=a[g];if(l==f||f=="*"){var m=n.node.edges[l],x=n.qNode.edges[f],v=m.final&&x.final,d=void 0;l in n.output.edges?(d=n.output.edges[l],d.final=d.final||v):(d=new t.TokenSet,d.final=v,n.output.edges[l]=d),i.push({qNode:x,output:d,node:m})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
|
||||
* lunr.Index
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],f=null,g=t.Set.empty;c.usePipeline?f=this.pipeline.runString(c.term,{fields:c.fields}):f=[c.term];for(var l=0;l<f.length;l++){var m=f[l];c.term=m;var x=t.TokenSet.fromClause(c),v=this.tokenSet.intersect(x).toArray();if(v.length===0&&c.presence===t.Query.presence.REQUIRED){for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=t.Set.empty}break}for(var b=0;b<v.length;b++)for(var E=v[b],w=this.invertedIndex[E],R=w._index,d=0;d<c.fields.length;d++){var y=c.fields[d],j=w[y],_=Object.keys(j),D=E+"/"+y,N=new t.Set(_);if(c.presence==t.Query.presence.REQUIRED&&(g=g.union(N),o[y]===void 0&&(o[y]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[y]===void 0&&(a[y]=t.Set.empty),a[y]=a[y].union(N);continue}if(i[y].upsert(R,c.boost,function(ye,me){return ye+me}),!s[D]){for(var C=0;C<_.length;C++){var V=_[C],P=new t.FieldRef(V,y),z=j[V],$;($=n[P])===void 0?n[P]=new t.MatchData(E,y,z):$.add(E,y,z)}s[D]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=o[y].intersect(g)}}for(var M=t.Set.complete,I=t.Set.empty,u=0;u<this.fields.length;u++){var y=this.fields[u];o[y]&&(M=M.intersect(o[y])),a[y]&&(I=I.union(a[y]))}var h=Object.keys(n),S=[],k=Object.create(null);if(r.isNegated()){h=Object.keys(this.fieldVectors);for(var u=0;u<h.length;u++){var P=h[u],L=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<h.length;u++){var L=t.FieldRef.fromString(h[u]),p=L.docRef;if(M.contains(p)&&!I.contains(p)){var T=this.fieldVectors[L],O=i[L.fieldName].similarity(T),F;if((F=k[p])!==void 0)F.score+=O,F.matchData.combine(n[L]);else{var Q={ref:p,score:O,matchData:n[L]};k[p]=Q,S.push(Q)}}}return S.sort(function(pe,ge){return ge.score-pe.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var f=i[c],g=f[0],l=f[1];n[g]=new t.Vector(l)}for(var c=0;c<o.length;c++){var f=o[c],m=f[0],x=f[1];a.insert(m),s[m]=x}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
|
||||
* lunr.Builder
|
||||
* Copyright (C) 2020 Oliver Nightingale
|
||||
*/t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),f=this.pipeline.run(c),g=new t.FieldRef(n,o),l=Object.create(null);this.fieldTermFrequencies[g]=l,this.fieldLengths[g]=0,this.fieldLengths[g]+=f.length;for(var m=0;m<f.length;m++){var x=f[m];if(l[x]==null&&(l[x]=0),l[x]+=1,this.invertedIndex[x]==null){var v=Object.create(null);v._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)v[i[d]]=Object.create(null);this.invertedIndex[x]=v}this.invertedIndex[x][o][n]==null&&(this.invertedIndex[x][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=x.metadata[b];this.invertedIndex[x][o][n][b]==null&&(this.invertedIndex[x][o][n][b]=[]),this.invertedIndex[x][o][n][b].push(E)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,f=this.fieldTermFrequencies[o],g=Object.keys(f),l=g.length,m=this._fields[a].boost||1,x=this._documents[o.docRef].boost||1,v=0;v<l;v++){var d=g[v],y=f[d],b=this.invertedIndex[d]._index,E,w,R;i[d]===void 0?(E=t.idf(this.invertedIndex[d],this.documentCount),i[d]=E):E=i[d],w=E*((this._k1+1)*y)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+y),w*=m,w*=x,R=Math.round(w*1e3)/1e3,c.insert(b,R)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var f=u[c];this.metadata[i][a][f]==null?this.metadata[i][a][f]=e.metadata[i][a][f]:this.metadata[i][a][f]=this.metadata[i][a][f].concat(e.metadata[i][a][f])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.
Line truncated
|
||||
//# sourceMappingURL=search.d50fe291.min.js.map
|
||||
|
||||
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"version": 3,
|
||||
"sources": ["node_modules/lunr/lunr.js", "src/templates/assets/javascripts/integrations/search/worker/main/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/polyfills/index.ts", "src/templates/assets/javascripts/integrations/search/config/index.ts", "src/templates/assets/javascripts/integrations/search/internal/_/index.ts", "src/templates/assets/javascripts/integrations/search/internal/extract/index.ts", "src/templates/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/templates/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/templates/assets/javascripts/integrations/search/query/transform/index.ts", "src/templates/assets/javascripts/integrations/search/query/_/index.ts", "src/templates/assets/javascripts/integrations/search/query/segment/index.ts", "src/templates/assets/javascripts/integrations/search/_/index.ts"],
|
||||
"sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elementLine truncated
|
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truncated
|
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|
||||
+473
@@ -0,0 +1,473 @@
|
||||
---
|
||||
title: Changelog
|
||||
description: View the latest updates and changes to CrewAI
|
||||
icon: timeline
|
||||
---
|
||||
|
||||
<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01210.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed encoding error when creating tools
|
||||
- Fixed failing llama test
|
||||
- Updated logging configuration for consistency
|
||||
- Enhanced telemetry initialization and event handling
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added **markdown attribute** to the Task class
|
||||
- Added **reasoning attribute** to the Agent class
|
||||
- Added **inject_date flag** to Agent for automatic date injection
|
||||
- Implemented **HallucinationGuardrail** (no-op with test coverage)
|
||||
|
||||
**Documentation & Guides**
|
||||
- Added documentation for **StagehandTool** and improved MDX structure
|
||||
- Added documentation for **MCP integration** and updated enterprise docs
|
||||
- Documented knowledge events and updated reasoning docs
|
||||
- Added stop parameter documentation
|
||||
- Fixed import references in doc examples (before_kickoff, after_kickoff)
|
||||
- General docs updates and restructuring for clarity
|
||||
</Update>
|
||||
|
||||
<Update label="2024-05-15" description="v0.120.1">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01201.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed **interpolation with hyphens**
|
||||
</Update>
|
||||
|
||||
<Update label="2024-05-14" description="v0.120.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01200.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Enabled **full Ruff rule set** by default for stricter linting
|
||||
- Addressed race condition in FilteredStream using context managers
|
||||
- Fixed agent knowledge reset issue
|
||||
- Refactored agent fetching logic into utility module
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added support for **loading an Agent directly from a repository**
|
||||
- Enabled setting an empty context for Task
|
||||
- Enhanced Agent repository feedback and fixed Tool auto-import behavior
|
||||
- Introduced direct initialization of knowledge (bypassing knowledge_sources)
|
||||
|
||||
**Documentation & Guides**
|
||||
- Updated security.md for current security practices
|
||||
- Cleaned up Google setup section for clarity
|
||||
- Added link to AI Studio when entering Gemini key
|
||||
- Updated Arize Phoenix observability guide
|
||||
- Refreshed flow documentation
|
||||
</Update>
|
||||
|
||||
<Update label="2024-05-08" description="v0.119.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01190.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Improved test reliability by enhancing pytest handling for flaky tests
|
||||
- Fixed memory reset crash when embedding dimensions mismatch
|
||||
- Enabled parent flow identification for Crew and LiteAgent
|
||||
- Prevented telemetry-related crashes when unavailable
|
||||
- Upgraded **LiteLLM version** for better compatibility
|
||||
- Fixed llama converter tests by removing skip_external_api
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
|
||||
- Made LLM setup and quickstart guides model-agnostic
|
||||
|
||||
**Documentation & Guides**
|
||||
- Added advanced configuration docs for the RAG tool
|
||||
- Updated Windows troubleshooting guide
|
||||
- Refined documentation examples for better clarity
|
||||
- Fixed typos across docs and config files
|
||||
</Update>
|
||||
|
||||
<Update label="2024-04-28" description="v0.118.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01180.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed issues with missing prompt or system templates
|
||||
- Removed global logging configuration to avoid unintended overrides
|
||||
- Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
|
||||
- Downgraded litellm to version 1.167.1 for compatibility
|
||||
- Added missing init.py files to ensure proper module initialization
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added support for **no-code Guardrail creation** to simplify AI behavior controls
|
||||
|
||||
**Documentation & Guides**
|
||||
- Removed CrewStructuredTool from public documentation to reflect internal usage
|
||||
- Updated enterprise documentation and YouTube embed for improved onboarding experience
|
||||
</Update>
|
||||
|
||||
<Update label="2024-04-20" description="v0.117.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01170.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added `result_as_answer` parameter support in `@tool` decorator.
|
||||
- Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
|
||||
- Enhanced knowledge management capabilities.
|
||||
- Added Huggingface provider option in CLI.
|
||||
- Improved compatibility and CI support for Python 3.10+.
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed issues with incorrect template parameters and missing inputs.
|
||||
- Improved asynchronous flow handling with coroutine condition checks.
|
||||
- Enhanced memory management with isolated configuration and correct memory object copying.
|
||||
- Fixed initialization of lite agents with correct references.
|
||||
- Addressed Python type hint issues and removed redundant imports.
|
||||
- Updated event placement for improved tool usage tracking.
|
||||
- Raised explicit exceptions when flows fail.
|
||||
- Removed unused code and redundant comments from various modules.
|
||||
- Updated GitHub App token action to v2.
|
||||
|
||||
**Documentation & Guides**
|
||||
- Enhanced documentation structure, including enterprise deployment instructions.
|
||||
- Automatically create output folders for documentation generation.
|
||||
- Fixed broken link in WeaviateVectorSearchTool documentation.
|
||||
- Fixed guardrail documentation usage and import paths for JSON search tools.
|
||||
- Updated documentation for CodeInterpreterTool.
|
||||
- Improved SEO, contextual navigation, and error handling for documentation pages.
|
||||
</Update>
|
||||
|
||||
<Update label="2024-04-25" description="v0.117.1">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01171.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Upgraded **crewai-tools** to latest version
|
||||
- Upgraded **liteLLM** to latest version
|
||||
- Fixed **Mem0 OSS**
|
||||
</Update>
|
||||
|
||||
<Update label="2024-04-07" description="v0.114.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01140.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Agents as an atomic unit. (`Agent(...).kickoff()`)
|
||||
- Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
|
||||
- Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
|
||||
- Enhanced YAML extraction.
|
||||
- Multimodal agent validation.
|
||||
- Added Secure fingerprints for agents and crews.
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Improved serialization, agent copying, and Python compatibility.
|
||||
- Added wildcard support to `emit()`
|
||||
- Added support for additional router calls and context window adjustments.
|
||||
- Fixed typing issues, validation, and import statements.
|
||||
- Improved method performance.
|
||||
- Enhanced agent task handling, event emissions, and memory management.
|
||||
- Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
|
||||
|
||||
**Documentation & Guides**
|
||||
- Improved documentation structure, theme, and organization.
|
||||
- Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
|
||||
- Updated tool configuration examples, prompts, and observability docs.
|
||||
- Guide on using singular agents within Flows.
|
||||
</Update>
|
||||
|
||||
<Update label="2024-03-17" description="v0.108.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01080.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Converted tabs to spaces in `crew.py` template
|
||||
- Enhanced LLM Streaming Response Handling and Event System
|
||||
- Included `model_name`
|
||||
- Enhanced Event Listener with rich visualization and improved logging
|
||||
- Added fingerprints
|
||||
|
||||
**Bug Fixes**
|
||||
- Fixed Mistral issues
|
||||
- Fixed a bug in documentation
|
||||
- Fixed type check error in fingerprint property
|
||||
|
||||
**Documentation Updates**
|
||||
- Improved tool documentation
|
||||
- Updated installation guide for the `uv` tool package
|
||||
- Added instructions for upgrading crewAI with the `uv` tool
|
||||
- Added documentation for `ApifyActorsTool`
|
||||
</Update>
|
||||
|
||||
<Update label="2024-03-10" description="v0.105.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01050.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed issues with missing template variables and user memory configuration
|
||||
- Improved async flow support and addressed agent response formatting
|
||||
- Enhanced memory reset functionality and fixed CLI memory commands
|
||||
- Fixed type issues, tool calling properties, and telemetry decoupling
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added Flow state export and improved state utilities
|
||||
- Enhanced agent knowledge setup with optional crew embedder
|
||||
- Introduced event emitter for better observability and LLM call tracking
|
||||
- Added support for Python 3.10 and ChatOllama from langchain_ollama
|
||||
- Integrated context window size support for the o3-mini model
|
||||
- Added support for multiple router calls
|
||||
|
||||
**Documentation & Guides**
|
||||
- Improved documentation layout and hierarchical structure
|
||||
- Added QdrantVectorSearchTool guide and clarified event listener usage
|
||||
- Fixed typos in prompts and updated Amazon Bedrock model listings
|
||||
</Update>
|
||||
|
||||
<Update label="2024-02-12" description="v0.102.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01020.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
|
||||
- Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
|
||||
- Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
|
||||
- Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
|
||||
- Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
|
||||
- Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
|
||||
- General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
|
||||
- Adding new tool: `QdrantVectorSearchTool`
|
||||
|
||||
**Documentation & Guides**
|
||||
- Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
|
||||
- Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
|
||||
- Fixed Various Typos & Formatting Issues
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-28" description="v0.100.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v01000.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Features**
|
||||
- Add Composio docs
|
||||
- Add SageMaker as a LLM provider
|
||||
|
||||
**Fixes**
|
||||
- Overall LLM connection issues
|
||||
- Using safe accessors on training
|
||||
- Add version check to crew_chat.py
|
||||
|
||||
**Documentation**
|
||||
- New docs for crewai chat
|
||||
- Improve formatting and clarity in CLI and Composio Tool docs
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-20" description="v0.98.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v0980.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**Features**
|
||||
- Conversation crew v1
|
||||
- Add unique ID to flow states
|
||||
- Add @persist decorator with FlowPersistence interface
|
||||
|
||||
**Integrations**
|
||||
- Add SambaNova integration
|
||||
- Add NVIDIA NIM provider in cli
|
||||
- Introducing VoyageAI
|
||||
|
||||
**Fixes**
|
||||
- Fix API Key Behavior and Entity Handling in Mem0 Integration
|
||||
- Fixed core invoke loop logic and relevant tests
|
||||
- Make tool inputs actual objects and not strings
|
||||
- Add important missing parts to creating tools
|
||||
- Drop litellm version to prevent windows issue
|
||||
- Before kickoff if inputs are none
|
||||
- Fixed typos, nested pydantic model issue, and docling issues
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-04" description="v0.95.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v0950.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
|
||||
</div>
|
||||
|
||||
**New Features**
|
||||
- Adding Multimodal Abilities to Crew
|
||||
- Programatic Guardrails
|
||||
- HITL multiple rounds
|
||||
- Gemini 2.0 Support
|
||||
- CrewAI Flows Improvements
|
||||
- Add Workflow Permissions
|
||||
- Add support for langfuse with litellm
|
||||
- Portkey Integration with CrewAI
|
||||
- Add interpolate_only method and improve error handling
|
||||
- Docling Support
|
||||
- Weviate Support
|
||||
|
||||
**Fixes**
|
||||
- output_file not respecting system path
|
||||
- disk I/O error when resetting short-term memory
|
||||
- CrewJSONEncoder now accepts enums
|
||||
- Python max version
|
||||
- Interpolation for output_file in Task
|
||||
- Handle coworker role name case/whitespace properly
|
||||
- Add tiktoken as explicit dependency and document Rust requirement
|
||||
- Include agent knowledge in planning process
|
||||
- Change storage initialization to None for KnowledgeStorage
|
||||
- Fix optional storage checks
|
||||
- include event emitter in flows
|
||||
- Docstring, Error Handling, and Type Hints Improvements
|
||||
- Suppressed userWarnings from litellm pydantic issues
|
||||
</Update>
|
||||
|
||||
<Update label="2024-12-05" description="v0.86.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v0860.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
|
||||
</div>
|
||||
**Changes**
|
||||
- Remove all references to pipeline and pipeline router
|
||||
- Add Nvidia NIM as provider in Custom LLM
|
||||
- Add knowledge demo + improve knowledge docs
|
||||
- Add HITL multiple rounds of followup
|
||||
- New docs about yaml crew with decorators
|
||||
- Simplify template crew
|
||||
</Update>
|
||||
|
||||
<Update label="2024-12-04" description="v0.85.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/releases/v0850.png" />
|
||||
</Frame>
|
||||
|
||||
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
|
||||
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
|
||||
</div>
|
||||
**Features**
|
||||
- Added knowledge to agent level
|
||||
- Feat/remove langchain
|
||||
- Improve typed task outputs
|
||||
- Log in to Tool Repository on crewai login
|
||||
|
||||
**Fixes**
|
||||
- Fixes issues with result as answer not properly exiting LLM loop
|
||||
- Fix missing key name when running with ollama provider
|
||||
- Fix spelling issue found
|
||||
|
||||
**Documentation**
|
||||
- Update readme for running mypy
|
||||
- Add knowledge to mint.json
|
||||
- Update Github actions
|
||||
- Update Agents docs to include two approaches for creating an agent
|
||||
- Improvements to LLM Configuration and Usage
|
||||
</Update>
|
||||
|
||||
<Update label="2024-11-25" description="v0.83.0">
|
||||
**New Features**
|
||||
- New before_kickoff and after_kickoff crew callbacks
|
||||
- Support to pre-seed agents with Knowledge
|
||||
- Add support for retrieving user preferences and memories using Mem0
|
||||
|
||||
**Fixes**
|
||||
- Fix Async Execution
|
||||
- Upgrade chroma and adjust embedder function generator
|
||||
- Update CLI Watson supported models + docs
|
||||
- Reduce level for Bandit
|
||||
- Fixing all tests
|
||||
|
||||
**Documentation**
|
||||
- Update Docs
|
||||
</Update>
|
||||
|
||||
<Update label="2024-11-13" description="v0.80.0">
|
||||
**Fixes**
|
||||
- Fixing Tokens callback replacement bug
|
||||
- Fixing Step callback issue
|
||||
- Add cached prompt tokens info on usage metrics
|
||||
- Fix crew_train_success test
|
||||
</Update>
|
||||
File renamed without changes.
@@ -2,13 +2,11 @@
|
||||
title: Agents
|
||||
description: Detailed guide on creating and managing agents within the CrewAI framework.
|
||||
icon: robot
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview of an Agent
|
||||
|
||||
In the CrewAI framework, an `Agent` is an autonomous unit that can:
|
||||
|
||||
- Perform specific tasks
|
||||
- Make decisions based on its role and goal
|
||||
- Use tools to accomplish objectives
|
||||
@@ -17,19 +15,15 @@ In the CrewAI framework, an `Agent` is an autonomous unit that can:
|
||||
- Delegate tasks when allowed
|
||||
|
||||
<Tip>
|
||||
Think of an agent as a specialized team member with specific skills,
|
||||
expertise, and responsibilities. For example, a `Researcher` agent might excel
|
||||
at gathering and analyzing information, while a `Writer` agent might be better
|
||||
at creating content.
|
||||
Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
|
||||
</Tip>
|
||||
|
||||
<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
|
||||
CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
|
||||
CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
|
||||
|
||||
|
||||
|
||||
The Visual Agent Builder enables:
|
||||
|
||||
- Intuitive agent configuration with form-based interfaces
|
||||
- Real-time testing and validation
|
||||
- Template library with pre-configured agent types
|
||||
@@ -38,36 +32,36 @@ The Visual Agent Builder enables:
|
||||
|
||||
## Agent Attributes
|
||||
|
||||
| Attribute | Parameter | Type | Description |
|
||||
| :-------------------------------------- | :----------------------- | :------------------------------------ | :------------------------------------------------------------------------------------------------------- |
|
||||
| **Role** | `role` | `str` | Defines the agent's function and expertise within the crew. |
|
||||
| **Goal** | `goal` | `str` | The individual objective that guides the agent's decision-making. |
|
||||
| **Backstory** | `backstory` | `str` | Provides context and personality to the agent, enriching interactions. |
|
||||
| **LLM** _(optional)_ | `llm` | `Union[str, LLM, Any]` | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4". |
|
||||
| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | Capabilities or functions available to the agent. Defaults to an empty list. |
|
||||
| **Function Calling LLM** _(optional)_ | `function_calling_llm` | `Optional[Any]` | Language model for tool calling, overrides crew's LLM if specified. |
|
||||
| **Max Iterations** _(optional)_ | `max_iter` | `int` | Maximum iterations before the agent must provide its best answer. Default is 20. |
|
||||
| **Max RPM** _(optional)_ | `max_rpm` | `Optional[int]` | Maximum requests per minute to avoid rate limits. |
|
||||
| **Max Execution Time** _(optional)_ | `max_execution_time` | `Optional[int]` | Maximum time (in seconds) for task execution. |
|
||||
| **Verbose** _(optional)_ | `verbose` | `bool` | Enable detailed execution logs for debugging. Default is False. |
|
||||
| **Allow Delegation** _(optional)_ | `allow_delegation` | `bool` | Allow the agent to delegate tasks to other agents. Default is False. |
|
||||
| **Step Callback** _(optional)_ | `step_callback` | `Optional[Any]` | Function called after each agent step, overrides crew callback. |
|
||||
| **Cache** _(optional)_ | `cache` | `bool` | Enable caching for tool usage. Default is True. |
|
||||
| **System Template** _(optional)_ | `system_template` | `Optional[str]` | Custom system prompt template for the agent. |
|
||||
| **Prompt Template** _(optional)_ | `prompt_template` | `Optional[str]` | Custom prompt template for the agent. |
|
||||
| **Response Template** _(optional)_ | `response_template` | `Optional[str]` | Custom response template for the agent. |
|
||||
| **Allow Code Execution** _(optional)_ | `allow_code_execution` | `Optional[bool]` | Enable code execution for the agent. Default is False. |
|
||||
| **Max Retry Limit** _(optional)_ | `max_retry_limit` | `int` | Maximum number of retries when an error occurs. Default is 2. |
|
||||
| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool` | Keep messages under context window size by summarizing. Default is True. |
|
||||
| **Code Execution Mode** _(optional)_ | `code_execution_mode` | `Literal["safe", "unsafe"]` | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. |
|
||||
| **Multimodal** _(optional)_ | `multimodal` | `bool` | Whether the agent supports multimodal capabilities. Default is False. |
|
||||
| **Inject Date** _(optional)_ | `inject_date` | `bool` | Whether to automatically inject the current date into tasks. Default is False. |
|
||||
| **Date Format** _(optional)_ | `date_format` | `str` | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format). |
|
||||
| **Reasoning** _(optional)_ | `reasoning` | `bool` | Whether the agent should reflect and create a plan before executing a task. Default is False. |
|
||||
| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]` | Maximum number of reasoning attempts before executing the task. If None, will try until ready. |
|
||||
| **Embedder** _(optional)_ | `embedder` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. |
|
||||
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent. |
|
||||
| **Use System Prompt** _(optional)_ | `use_system_prompt` | `Optional[bool]` | Whether to use system prompt (for o1 model support). Default is True. |
|
||||
| Attribute | Parameter | Type | Description |
|
||||
| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Role** | `role` | `str` | Defines the agent's function and expertise within the crew. |
|
||||
| **Goal** | `goal` | `str` | The individual objective that guides the agent's decision-making. |
|
||||
| **Backstory** | `backstory` | `str` | Provides context and personality to the agent, enriching interactions. |
|
||||
| **LLM** _(optional)_ | `llm` | `Union[str, LLM, Any]` | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4". |
|
||||
| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | Capabilities or functions available to the agent. Defaults to an empty list. |
|
||||
| **Function Calling LLM** _(optional)_ | `function_calling_llm` | `Optional[Any]` | Language model for tool calling, overrides crew's LLM if specified. |
|
||||
| **Max Iterations** _(optional)_ | `max_iter` | `int` | Maximum iterations before the agent must provide its best answer. Default is 20. |
|
||||
| **Max RPM** _(optional)_ | `max_rpm` | `Optional[int]` | Maximum requests per minute to avoid rate limits. |
|
||||
| **Max Execution Time** _(optional)_ | `max_execution_time` | `Optional[int]` | Maximum time (in seconds) for task execution. |
|
||||
| **Verbose** _(optional)_ | `verbose` | `bool` | Enable detailed execution logs for debugging. Default is False. |
|
||||
| **Allow Delegation** _(optional)_ | `allow_delegation` | `bool` | Allow the agent to delegate tasks to other agents. Default is False. |
|
||||
| **Step Callback** _(optional)_ | `step_callback` | `Optional[Any]` | Function called after each agent step, overrides crew callback. |
|
||||
| **Cache** _(optional)_ | `cache` | `bool` | Enable caching for tool usage. Default is True. |
|
||||
| **System Template** _(optional)_ | `system_template` | `Optional[str]` | Custom system prompt template for the agent. |
|
||||
| **Prompt Template** _(optional)_ | `prompt_template` | `Optional[str]` | Custom prompt template for the agent. |
|
||||
| **Response Template** _(optional)_ | `response_template` | `Optional[str]` | Custom response template for the agent. |
|
||||
| **Allow Code Execution** _(optional)_ | `allow_code_execution` | `Optional[bool]` | Enable code execution for the agent. Default is False. |
|
||||
| **Max Retry Limit** _(optional)_ | `max_retry_limit` | `int` | Maximum number of retries when an error occurs. Default is 2. |
|
||||
| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool` | Keep messages under context window size by summarizing. Default is True. |
|
||||
| **Code Execution Mode** _(optional)_ | `code_execution_mode` | `Literal["safe", "unsafe"]` | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. |
|
||||
| **Multimodal** _(optional)_ | `multimodal` | `bool` | Whether the agent supports multimodal capabilities. Default is False. |
|
||||
| **Inject Date** _(optional)_ | `inject_date` | `bool` | Whether to automatically inject the current date into tasks. Default is False. |
|
||||
| **Date Format** _(optional)_ | `date_format` | `str` | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format). |
|
||||
| **Reasoning** _(optional)_ | `reasoning` | `bool` | Whether the agent should reflect and create a plan before executing a task. Default is False. |
|
||||
| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]` | Maximum number of reasoning attempts before executing the task. If None, will try until ready. |
|
||||
| **Embedder** _(optional)_ | `embedder` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. |
|
||||
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent. |
|
||||
| **Use System Prompt** _(optional)_ | `use_system_prompt` | `Optional[bool]` | Whether to use system prompt (for o1 model support). Default is True. |
|
||||
|
||||
## Creating Agents
|
||||
|
||||
@@ -77,7 +71,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom
|
||||
|
||||
Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
|
||||
|
||||
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
|
||||
After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
|
||||
|
||||
<Note>
|
||||
Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
|
||||
@@ -142,8 +136,7 @@ class LatestAiDevelopmentCrew():
|
||||
```
|
||||
|
||||
<Note>
|
||||
The names you use in your YAML files (`agents.yaml`) should match the method
|
||||
names in your Python code.
|
||||
The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
|
||||
</Note>
|
||||
|
||||
### Direct Code Definition
|
||||
@@ -190,7 +183,6 @@ agent = Agent(
|
||||
Let's break down some key parameter combinations for common use cases:
|
||||
|
||||
#### Basic Research Agent
|
||||
|
||||
```python Code
|
||||
research_agent = Agent(
|
||||
role="Research Analyst",
|
||||
@@ -202,7 +194,6 @@ research_agent = Agent(
|
||||
```
|
||||
|
||||
#### Code Development Agent
|
||||
|
||||
```python Code
|
||||
dev_agent = Agent(
|
||||
role="Senior Python Developer",
|
||||
@@ -216,7 +207,6 @@ dev_agent = Agent(
|
||||
```
|
||||
|
||||
#### Long-Running Analysis Agent
|
||||
|
||||
```python Code
|
||||
analysis_agent = Agent(
|
||||
role="Data Analyst",
|
||||
@@ -230,7 +220,6 @@ analysis_agent = Agent(
|
||||
```
|
||||
|
||||
#### Custom Template Agent
|
||||
|
||||
```python Code
|
||||
custom_agent = Agent(
|
||||
role="Customer Service Representative",
|
||||
@@ -246,7 +235,6 @@ custom_agent = Agent(
|
||||
```
|
||||
|
||||
#### Date-Aware Agent with Reasoning
|
||||
|
||||
```python Code
|
||||
strategic_agent = Agent(
|
||||
role="Market Analyst",
|
||||
@@ -261,7 +249,6 @@ strategic_agent = Agent(
|
||||
```
|
||||
|
||||
#### Reasoning Agent
|
||||
|
||||
```python Code
|
||||
reasoning_agent = Agent(
|
||||
role="Strategic Planner",
|
||||
@@ -275,7 +262,6 @@ reasoning_agent = Agent(
|
||||
```
|
||||
|
||||
#### Multimodal Agent
|
||||
|
||||
```python Code
|
||||
multimodal_agent = Agent(
|
||||
role="Visual Content Analyst",
|
||||
@@ -289,60 +275,47 @@ multimodal_agent = Agent(
|
||||
### Parameter Details
|
||||
|
||||
#### Critical Parameters
|
||||
|
||||
- `role`, `goal`, and `backstory` are required and shape the agent's behavior
|
||||
- `llm` determines the language model used (default: OpenAI's GPT-4)
|
||||
|
||||
#### Memory and Context
|
||||
|
||||
- `memory`: Enable to maintain conversation history
|
||||
- `respect_context_window`: Prevents token limit issues
|
||||
- `knowledge_sources`: Add domain-specific knowledge bases
|
||||
|
||||
#### Execution Control
|
||||
|
||||
- `max_iter`: Maximum attempts before giving best answer
|
||||
- `max_execution_time`: Timeout in seconds
|
||||
- `max_rpm`: Rate limiting for API calls
|
||||
- `max_retry_limit`: Retries on error
|
||||
|
||||
#### Code Execution
|
||||
|
||||
<Warning>
|
||||
`allow_code_execution` and `code_execution_mode` are deprecated. `CodeInterpreterTool` has been removed from `crewai-tools`. Use a dedicated sandbox service such as [E2B](https://e2b.dev) or [Modal](https://modal.com) for secure code execution.
|
||||
</Warning>
|
||||
|
||||
- `allow_code_execution` _(deprecated)_: Previously enabled built-in code execution via `CodeInterpreterTool`.
|
||||
- `code_execution_mode` _(deprecated)_: Previously controlled execution mode (`"safe"` for Docker, `"unsafe"` for direct execution).
|
||||
- `allow_code_execution`: Must be True to run code
|
||||
- `code_execution_mode`:
|
||||
- `"safe"`: Uses Docker (recommended for production)
|
||||
- `"unsafe"`: Direct execution (use only in trusted environments)
|
||||
|
||||
#### Advanced Features
|
||||
|
||||
- `multimodal`: Enable multimodal capabilities for processing text and visual content
|
||||
- `reasoning`: Enable agent to reflect and create plans before executing tasks
|
||||
- `inject_date`: Automatically inject current date into task descriptions
|
||||
|
||||
#### Templates
|
||||
|
||||
- `system_template`: Defines agent's core behavior
|
||||
- `prompt_template`: Structures input format
|
||||
- `response_template`: Formats agent responses
|
||||
|
||||
<Note>
|
||||
When using custom templates, ensure that both `system_template` and
|
||||
`prompt_template` are defined. The `response_template` is optional but
|
||||
recommended for consistent output formatting.
|
||||
</Note>
|
||||
When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
|
||||
</Note>
|
||||
|
||||
<Note>
|
||||
When using custom templates, you can use variables like `{role}`, `{goal}`,
|
||||
and `{backstory}` in your templates. These will be automatically populated
|
||||
during execution.
|
||||
When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
|
||||
</Note>
|
||||
|
||||
## Agent Tools
|
||||
|
||||
Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
|
||||
|
||||
- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
|
||||
- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
|
||||
|
||||
@@ -381,8 +354,7 @@ analyst = Agent(
|
||||
```
|
||||
|
||||
<Note>
|
||||
When `memory` is enabled, the agent will maintain context across multiple
|
||||
interactions, improving its ability to handle complex, multi-step tasks.
|
||||
When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
|
||||
</Note>
|
||||
|
||||
## Context Window Management
|
||||
@@ -412,7 +384,6 @@ smart_agent = Agent(
|
||||
```
|
||||
|
||||
**What happens when context limits are exceeded:**
|
||||
|
||||
- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
|
||||
- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
|
||||
- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
|
||||
@@ -434,7 +405,6 @@ strict_agent = Agent(
|
||||
```
|
||||
|
||||
**What happens when context limits are exceeded:**
|
||||
|
||||
- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
|
||||
- 🛑 **Execution stops**: Task execution halts immediately
|
||||
- 🔧 **Manual intervention required**: You need to modify your approach
|
||||
@@ -442,7 +412,6 @@ strict_agent = Agent(
|
||||
### Choosing the Right Setting
|
||||
|
||||
#### Use `respect_context_window=True` (Default) when:
|
||||
|
||||
- **Processing large documents** that might exceed context limits
|
||||
- **Long-running conversations** where some summarization is acceptable
|
||||
- **Research tasks** where general context is more important than exact details
|
||||
@@ -451,7 +420,7 @@ strict_agent = Agent(
|
||||
```python Code
|
||||
# Perfect for document processing
|
||||
document_processor = Agent(
|
||||
role="Document Analyst",
|
||||
role="Document Analyst",
|
||||
goal="Extract insights from large research papers",
|
||||
backstory="Expert at analyzing extensive documentation",
|
||||
respect_context_window=True, # Handle large documents gracefully
|
||||
@@ -461,7 +430,6 @@ document_processor = Agent(
|
||||
```
|
||||
|
||||
#### Use `respect_context_window=False` when:
|
||||
|
||||
- **Precision is critical** and information loss is unacceptable
|
||||
- **Legal or medical tasks** requiring complete context
|
||||
- **Code review** where missing details could introduce bugs
|
||||
@@ -484,7 +452,6 @@ precision_agent = Agent(
|
||||
When dealing with very large datasets, consider these strategies:
|
||||
|
||||
#### 1. Use RAG Tools
|
||||
|
||||
```python Code
|
||||
from crewai_tools import RagTool
|
||||
|
||||
@@ -502,7 +469,6 @@ rag_agent = Agent(
|
||||
```
|
||||
|
||||
#### 2. Use Knowledge Sources
|
||||
|
||||
```python Code
|
||||
# Use knowledge sources instead of large prompts
|
||||
knowledge_agent = Agent(
|
||||
@@ -526,7 +492,6 @@ knowledge_agent = Agent(
|
||||
### Troubleshooting Context Issues
|
||||
|
||||
**If you're getting context limit errors:**
|
||||
|
||||
```python Code
|
||||
# Quick fix: Enable automatic handling
|
||||
agent.respect_context_window = True
|
||||
@@ -540,7 +505,6 @@ agent.tools = [RagTool()]
|
||||
```
|
||||
|
||||
**If automatic summarization loses important information:**
|
||||
|
||||
```python Code
|
||||
# Disable auto-summarization and use RAG instead
|
||||
agent = Agent(
|
||||
@@ -554,134 +518,28 @@ agent = Agent(
|
||||
```
|
||||
|
||||
<Note>
|
||||
The context window management feature works automatically in the background.
|
||||
You don't need to call any special functions - just set
|
||||
`respect_context_window` to your preferred behavior and CrewAI handles the
|
||||
rest!
|
||||
</Note>
|
||||
|
||||
## Direct Agent Interaction with `kickoff()`
|
||||
|
||||
Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
|
||||
|
||||
### How `kickoff()` Works
|
||||
|
||||
The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
|
||||
|
||||
```python Code
|
||||
from crewai import Agent
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
# Create an agent
|
||||
researcher = Agent(
|
||||
role="AI Technology Researcher",
|
||||
goal="Research the latest AI developments",
|
||||
tools=[SerperDevTool()],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Use kickoff() to interact directly with the agent
|
||||
result = researcher.kickoff("What are the latest developments in language models?")
|
||||
|
||||
# Access the raw response
|
||||
print(result.raw)
|
||||
```
|
||||
|
||||
### Parameters and Return Values
|
||||
|
||||
| Parameter | Type | Description |
|
||||
| :---------------- | :--------------------------------- | :------------------------------------------------------------------------ |
|
||||
| `messages` | `Union[str, List[Dict[str, str]]]` | Either a string query or a list of message dictionaries with role/content |
|
||||
| `response_format` | `Optional[Type[Any]]` | Optional Pydantic model for structured output |
|
||||
|
||||
The method returns a `LiteAgentOutput` object with the following properties:
|
||||
|
||||
- `raw`: String containing the raw output text
|
||||
- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
|
||||
- `agent_role`: Role of the agent that produced the output
|
||||
- `usage_metrics`: Token usage metrics for the execution
|
||||
|
||||
### Structured Output
|
||||
|
||||
You can get structured output by providing a Pydantic model as the `response_format`:
|
||||
|
||||
```python Code
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
|
||||
class ResearchFindings(BaseModel):
|
||||
main_points: List[str]
|
||||
key_technologies: List[str]
|
||||
future_predictions: str
|
||||
|
||||
# Get structured output
|
||||
result = researcher.kickoff(
|
||||
"Summarize the latest developments in AI for 2025",
|
||||
response_format=ResearchFindings
|
||||
)
|
||||
|
||||
# Access structured data
|
||||
print(result.pydantic.main_points)
|
||||
print(result.pydantic.future_predictions)
|
||||
```
|
||||
|
||||
### Multiple Messages
|
||||
|
||||
You can also provide a conversation history as a list of message dictionaries:
|
||||
|
||||
```python Code
|
||||
messages = [
|
||||
{"role": "user", "content": "I need information about large language models"},
|
||||
{"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
|
||||
{"role": "user", "content": "What are the latest developments in 2025?"}
|
||||
]
|
||||
|
||||
result = researcher.kickoff(messages)
|
||||
```
|
||||
|
||||
### Async Support
|
||||
|
||||
An asynchronous version is available via `kickoff_async()` with the same parameters:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def main():
|
||||
result = await researcher.kickoff_async("What are the latest developments in AI?")
|
||||
print(result.raw)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
<Note>
|
||||
The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler
|
||||
execution flow while preserving all of the agent's configuration (role, goal,
|
||||
backstory, tools, etc.).
|
||||
The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
|
||||
</Note>
|
||||
|
||||
## Important Considerations and Best Practices
|
||||
|
||||
### Security and Code Execution
|
||||
|
||||
<Warning>
|
||||
`allow_code_execution` and `code_execution_mode` are deprecated and `CodeInterpreterTool` has been removed. Use a dedicated sandbox service such as [E2B](https://e2b.dev) or [Modal](https://modal.com) for secure code execution.
|
||||
</Warning>
|
||||
- When using `allow_code_execution`, be cautious with user input and always validate it
|
||||
- Use `code_execution_mode: "safe"` (Docker) in production environments
|
||||
- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
|
||||
|
||||
### Performance Optimization
|
||||
|
||||
- Use `respect_context_window: true` to prevent token limit issues
|
||||
- Set appropriate `max_rpm` to avoid rate limiting
|
||||
- Enable `cache: true` to improve performance for repetitive tasks
|
||||
- Adjust `max_iter` and `max_retry_limit` based on task complexity
|
||||
|
||||
### Memory and Context Management
|
||||
|
||||
- Leverage `knowledge_sources` for domain-specific information
|
||||
- Configure `embedder` when using custom embedding models
|
||||
- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
|
||||
|
||||
### Advanced Features
|
||||
|
||||
- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
|
||||
- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
|
||||
- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
|
||||
@@ -689,7 +547,6 @@ asyncio.run(main())
|
||||
- Enable `multimodal: true` for agents that need to process both text and visual content
|
||||
|
||||
### Agent Collaboration
|
||||
|
||||
- Enable `allow_delegation: true` when agents need to work together
|
||||
- Use `step_callback` to monitor and log agent interactions
|
||||
- Consider using different LLMs for different purposes:
|
||||
@@ -697,7 +554,6 @@ asyncio.run(main())
|
||||
- `function_calling_llm` for efficient tool usage
|
||||
|
||||
### Date Awareness and Reasoning
|
||||
|
||||
- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
|
||||
- Customize the date format with `date_format` using standard Python datetime format codes
|
||||
- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
|
||||
@@ -705,26 +561,22 @@ asyncio.run(main())
|
||||
- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
|
||||
|
||||
### Model Compatibility
|
||||
|
||||
- Set `use_system_prompt: false` for older models that don't support system messages
|
||||
- Ensure your chosen `llm` supports the features you need (like function calling)
|
||||
|
||||
## Troubleshooting Common Issues
|
||||
|
||||
1. **Rate Limiting**: If you're hitting API rate limits:
|
||||
|
||||
- Implement appropriate `max_rpm`
|
||||
- Use caching for repetitive operations
|
||||
- Consider batching requests
|
||||
|
||||
2. **Context Window Errors**: If you're exceeding context limits:
|
||||
|
||||
- Enable `respect_context_window`
|
||||
- Use more efficient prompts
|
||||
- Clear agent memory periodically
|
||||
|
||||
3. **Code Execution Issues**: If code execution fails:
|
||||
|
||||
- Verify Docker is installed for safe mode
|
||||
- Check execution permissions
|
||||
- Review code sandbox settings
|
||||
@@ -0,0 +1,315 @@
|
||||
---
|
||||
title: CLI
|
||||
description: Learn how to use the CrewAI CLI to interact with CrewAI.
|
||||
icon: terminal
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
|
||||
|
||||
## Installation
|
||||
|
||||
To use the CrewAI CLI, make sure you have CrewAI installed:
|
||||
|
||||
```shell Terminal
|
||||
pip install crewai
|
||||
```
|
||||
|
||||
## Basic Usage
|
||||
|
||||
The basic structure of a CrewAI CLI command is:
|
||||
|
||||
```shell Terminal
|
||||
crewai [COMMAND] [OPTIONS] [ARGUMENTS]
|
||||
```
|
||||
|
||||
## Available Commands
|
||||
|
||||
### 1. Create
|
||||
|
||||
Create a new crew or flow.
|
||||
|
||||
```shell Terminal
|
||||
crewai create [OPTIONS] TYPE NAME
|
||||
```
|
||||
|
||||
- `TYPE`: Choose between "crew" or "flow"
|
||||
- `NAME`: Name of the crew or flow
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai create crew my_new_crew
|
||||
crewai create flow my_new_flow
|
||||
```
|
||||
|
||||
### 2. Version
|
||||
|
||||
Show the installed version of CrewAI.
|
||||
|
||||
```shell Terminal
|
||||
crewai version [OPTIONS]
|
||||
```
|
||||
|
||||
- `--tools`: (Optional) Show the installed version of CrewAI tools
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai version
|
||||
crewai version --tools
|
||||
```
|
||||
|
||||
### 3. Train
|
||||
|
||||
Train the crew for a specified number of iterations.
|
||||
|
||||
```shell Terminal
|
||||
crewai train [OPTIONS]
|
||||
```
|
||||
|
||||
- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
|
||||
- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai train -n 10 -f my_training_data.pkl
|
||||
```
|
||||
|
||||
### 4. Replay
|
||||
|
||||
Replay the crew execution from a specific task.
|
||||
|
||||
```shell Terminal
|
||||
crewai replay [OPTIONS]
|
||||
```
|
||||
|
||||
- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai replay -t task_123456
|
||||
```
|
||||
|
||||
### 5. Log-tasks-outputs
|
||||
|
||||
Retrieve your latest crew.kickoff() task outputs.
|
||||
|
||||
```shell Terminal
|
||||
crewai log-tasks-outputs
|
||||
```
|
||||
|
||||
### 6. Reset-memories
|
||||
|
||||
Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
|
||||
|
||||
```shell Terminal
|
||||
crewai reset-memories [OPTIONS]
|
||||
```
|
||||
|
||||
- `-l, --long`: Reset LONG TERM memory
|
||||
- `-s, --short`: Reset SHORT TERM memory
|
||||
- `-e, --entities`: Reset ENTITIES memory
|
||||
- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
|
||||
- `-kn, --knowledge`: Reset KNOWLEDGE storage
|
||||
- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
|
||||
- `-a, --all`: Reset ALL memories
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai reset-memories --long --short
|
||||
crewai reset-memories --all
|
||||
```
|
||||
|
||||
### 7. Test
|
||||
|
||||
Test the crew and evaluate the results.
|
||||
|
||||
```shell Terminal
|
||||
crewai test [OPTIONS]
|
||||
```
|
||||
|
||||
- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
|
||||
- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
|
||||
|
||||
Example:
|
||||
```shell Terminal
|
||||
crewai test -n 5 -m gpt-3.5-turbo
|
||||
```
|
||||
|
||||
### 8. Run
|
||||
|
||||
Run the crew or flow.
|
||||
|
||||
```shell Terminal
|
||||
crewai run
|
||||
```
|
||||
|
||||
<Note>
|
||||
Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
|
||||
</Note>
|
||||
|
||||
<Note>
|
||||
Make sure to run these commands from the directory where your CrewAI project is set up.
|
||||
Some commands may require additional configuration or setup within your project structure.
|
||||
</Note>
|
||||
|
||||
### 9. Chat
|
||||
|
||||
Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
|
||||
|
||||
After receiving the results, you can continue interacting with the assistant for further instructions or questions.
|
||||
|
||||
```shell Terminal
|
||||
crewai chat
|
||||
```
|
||||
<Note>
|
||||
Ensure you execute these commands from your CrewAI project's root directory.
|
||||
</Note>
|
||||
<Note>
|
||||
IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
|
||||
|
||||
```python
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
return Crew(
|
||||
agents=self.agents,
|
||||
tasks=self.tasks,
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
chat_llm="gpt-4o", # LLM for chat orchestration
|
||||
)
|
||||
```
|
||||
</Note>
|
||||
|
||||
### 10. Deploy
|
||||
|
||||
Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
|
||||
|
||||
- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
|
||||
```shell Terminal
|
||||
crewai signup
|
||||
```
|
||||
If you already have an account, you can login with:
|
||||
```shell Terminal
|
||||
crewai login
|
||||
```
|
||||
|
||||
- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
|
||||
```shell Terminal
|
||||
crewai deploy create
|
||||
```
|
||||
- Reads your local project configuration.
|
||||
- Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
|
||||
|
||||
### 11. Organization Management
|
||||
|
||||
Manage your CrewAI Enterprise organizations.
|
||||
|
||||
```shell Terminal
|
||||
crewai org [COMMAND] [OPTIONS]
|
||||
```
|
||||
|
||||
#### Commands:
|
||||
|
||||
- `list`: List all organizations you belong to
|
||||
```shell Terminal
|
||||
crewai org list
|
||||
```
|
||||
|
||||
- `current`: Display your currently active organization
|
||||
```shell Terminal
|
||||
crewai org current
|
||||
```
|
||||
|
||||
- `switch`: Switch to a specific organization
|
||||
```shell Terminal
|
||||
crewai org switch <organization_id>
|
||||
```
|
||||
|
||||
<Note>
|
||||
You must be authenticated to CrewAI Enterprise to use these organization management commands.
|
||||
</Note>
|
||||
|
||||
- **Create a deployment** (continued):
|
||||
- Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
|
||||
|
||||
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
|
||||
```shell Terminal
|
||||
crewai deploy push
|
||||
```
|
||||
- Initiates the deployment process on the CrewAI Enterprise platform.
|
||||
- Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
|
||||
|
||||
- **Deployment Status**: You can check the status of your deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy status
|
||||
```
|
||||
This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
|
||||
|
||||
- **Deployment Logs**: You can check the logs of your deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy logs
|
||||
```
|
||||
This streams the deployment logs to your terminal.
|
||||
|
||||
- **List deployments**: You can list all your deployments with:
|
||||
```shell Terminal
|
||||
crewai deploy list
|
||||
```
|
||||
This lists all your deployments.
|
||||
|
||||
- **Delete a deployment**: You can delete a deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy remove
|
||||
```
|
||||
This deletes the deployment from the CrewAI Enterprise platform.
|
||||
|
||||
- **Help Command**: You can get help with the CLI with:
|
||||
```shell Terminal
|
||||
crewai deploy --help
|
||||
```
|
||||
This shows the help message for the CrewAI Deploy CLI.
|
||||
|
||||
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
|
||||
|
||||
<iframe
|
||||
width="100%"
|
||||
height="400"
|
||||
src="https://www.youtube.com/embed/3EqSV-CYDZA"
|
||||
title="CrewAI Deployment Guide"
|
||||
frameborder="0"
|
||||
style={{ borderRadius: '10px' }}
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
### 11. API Keys
|
||||
|
||||
When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
|
||||
|
||||
Once you've selected an LLM provider, you will be prompted for API keys.
|
||||
|
||||
#### Initial API key providers
|
||||
|
||||
The CLI will initially prompt for API keys for the following services:
|
||||
|
||||
* OpenAI
|
||||
* Groq
|
||||
* Anthropic
|
||||
* Google Gemini
|
||||
* SambaNova
|
||||
|
||||
When you select a provider, the CLI will prompt you to enter your API key.
|
||||
|
||||
#### Other Options
|
||||
|
||||
If you select option 6, you will be able to select from a list of LiteLLM supported providers.
|
||||
|
||||
When you select a provider, the CLI will prompt you to enter the Key name and the API key.
|
||||
|
||||
See the following link for each provider's key name:
|
||||
|
||||
* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
|
||||
|
||||
|
||||
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Collaboration
|
||||
description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
|
||||
icon: screen-users
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Crews
|
||||
description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
|
||||
icon: people-group
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -21,7 +20,8 @@ A crew in crewAI represents a collaborative group of agents working together to
|
||||
| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
|
||||
| **Config** _(optional)_ | `config` | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
|
||||
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
|
||||
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). | |
|
||||
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
|
||||
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
|
||||
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
|
||||
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
|
||||
| **Step Callback** _(optional)_ | `step_callback` | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
|
||||
@@ -32,15 +32,6 @@ A crew in crewAI represents a collaborative group of agents working together to
|
||||
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
|
||||
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. |
|
||||
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
|
||||
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | Knowledge sources available at the crew level, accessible to all the agents. |
|
||||
| **Stream** _(optional)_ | `stream` | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`. |
|
||||
| **Chat LLM** _(optional)_ | `chat_llm` | The language model used to orchestrate `crewai chat` CLI interactions with the crew. Accepts a model name string or `LLM` instance. Defaults to `None`. |
|
||||
| **Before Kickoff Callbacks** _(optional)_ | `before_kickoff_callbacks` | A list of callable functions executed **before** the crew starts. Each callback receives and can modify the inputs dict. Distinct from the `@before_kickoff` decorator. Defaults to `[]`. |
|
||||
| **After Kickoff Callbacks** _(optional)_ | `after_kickoff_callbacks` | A list of callable functions executed **after** the crew finishes. Each callback receives and can modify the `CrewOutput`. Distinct from the `@after_kickoff` decorator. Defaults to `[]`. |
|
||||
| **Tracing** _(optional)_ | `tracing` | Controls OpenTelemetry tracing for the crew. `True` = always enable, `False` = always disable, `None` = inherit from environment / user settings. Defaults to `None`. |
|
||||
| **Skills** _(optional)_ | `skills` | A list of `Path` objects (skill search directories) or pre-loaded `Skill` objects applied to all agents in the crew. Defaults to `None`. |
|
||||
| **Security Config** _(optional)_ | `security_config` | A `SecurityConfig` instance managing crew fingerprinting and identity. Defaults to `SecurityConfig()`. |
|
||||
| **Checkpoint** _(optional)_ | `checkpoint` | Enables automatic checkpointing. Pass `True` for sensible defaults, a `CheckpointConfig` for full control, `False` to opt out, or `None` to inherit. See the [Checkpointing](#checkpointing) section below. Defaults to `None`. |
|
||||
|
||||
<Tip>
|
||||
**Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
|
||||
@@ -54,7 +45,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
|
||||
|
||||
Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
|
||||
|
||||
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
|
||||
After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
|
||||
|
||||
#### Example Crew Class with Decorators
|
||||
|
||||
@@ -75,8 +66,8 @@ class YourCrewName:
|
||||
# To see an example agent and task defined in YAML, checkout the following:
|
||||
# - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
|
||||
# - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
|
||||
agents_config = 'config/agents.yaml'
|
||||
tasks_config = 'config/tasks.yaml'
|
||||
agents_config = 'config/agents.yaml'
|
||||
tasks_config = 'config/tasks.yaml'
|
||||
|
||||
@before_kickoff
|
||||
def prepare_inputs(self, inputs):
|
||||
@@ -120,7 +111,7 @@ class YourCrewName:
|
||||
def crew(self) -> Crew:
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically collected by the @agent decorator
|
||||
tasks=self.tasks, # Automatically collected by the @task decorator.
|
||||
tasks=self.tasks, # Automatically collected by the @task decorator.
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
@@ -278,72 +269,6 @@ crew = Crew(output_log_file = file_name.json) # Logs will be saved as file_name
|
||||
|
||||
|
||||
|
||||
## Checkpointing
|
||||
|
||||
Checkpointing lets a crew automatically save its state after key events (e.g. task completion) so that long-running or interrupted runs can be resumed exactly where they left off without re-executing completed tasks.
|
||||
|
||||
### Quick Start
|
||||
|
||||
Pass `checkpoint=True` to enable checkpointing with sensible defaults (saves to `.checkpoints/` after every task):
|
||||
|
||||
```python Code
|
||||
from crewai import Crew, Process
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[research_task, write_task],
|
||||
process=Process.sequential,
|
||||
checkpoint=True, # saves to .checkpoints/ after every task
|
||||
)
|
||||
|
||||
crew.kickoff(inputs={"topic": "AI trends"})
|
||||
```
|
||||
|
||||
### Full Control with `CheckpointConfig`
|
||||
|
||||
Use `CheckpointConfig` for fine-grained control over location, trigger events, storage backend, and retention:
|
||||
|
||||
```python Code
|
||||
from crewai import Crew, Process
|
||||
from crewai.state.checkpoint_config import CheckpointConfig
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[research_task, write_task],
|
||||
process=Process.sequential,
|
||||
checkpoint=CheckpointConfig(
|
||||
location="./.checkpoints", # directory for JSON files (default)
|
||||
on_events=["task_completed"], # trigger after each task (default)
|
||||
max_checkpoints=5, # keep only the 5 most recent checkpoints
|
||||
),
|
||||
)
|
||||
|
||||
crew.kickoff(inputs={"topic": "AI trends"})
|
||||
```
|
||||
|
||||
### Resuming from a Checkpoint
|
||||
|
||||
Use `Crew.from_checkpoint()` to restore a crew from a saved checkpoint file, then call `kickoff()` to resume:
|
||||
|
||||
```python Code
|
||||
# Resume from the most recent checkpoint
|
||||
crew = Crew.from_checkpoint(".checkpoints/latest.json")
|
||||
crew.kickoff()
|
||||
```
|
||||
|
||||
<Note>
|
||||
When restoring from a checkpoint, `checkpoint_inputs`, `checkpoint_train`, and `checkpoint_kickoff_event_id` are automatically reconstructed — you do not need to set these manually.
|
||||
</Note>
|
||||
|
||||
### `CheckpointConfig` Attributes
|
||||
|
||||
| Attribute | Type | Default | Description |
|
||||
| :----------------- | :------------------------------------- | :------------------- | :-------------------------------------------------------------------------------------------- |
|
||||
| `location` | `str` | `"./.checkpoints"` | Storage destination. For `JsonProvider` this is a directory path; for `SqliteProvider` a database file path. |
|
||||
| `on_events` | `list[str]` | `["task_completed"]` | Event types that trigger a checkpoint write. Use `["*"]` to checkpoint on every event. |
|
||||
| `provider` | `JsonProvider \| SqliteProvider` | `JsonProvider()` | Storage backend. Defaults to `JsonProvider` (plain JSON files). |
|
||||
| `max_checkpoints` | `int \| None` | `None` | Maximum checkpoints to keep. Oldest are pruned after each write. `None` keeps all. |
|
||||
|
||||
## Memory Utilization
|
||||
|
||||
Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
|
||||
@@ -380,27 +305,12 @@ print(result)
|
||||
|
||||
### Different Ways to Kick Off a Crew
|
||||
|
||||
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process.
|
||||
|
||||
#### Synchronous Methods
|
||||
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
|
||||
|
||||
- `kickoff()`: Starts the execution process according to the defined process flow.
|
||||
- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
|
||||
|
||||
#### Asynchronous Methods
|
||||
|
||||
CrewAI offers two approaches for async execution:
|
||||
|
||||
| Method | Type | Description |
|
||||
|--------|------|-------------|
|
||||
| `akickoff()` | Native async | True async/await throughout the entire execution chain |
|
||||
| `akickoff_for_each()` | Native async | Native async execution for each input in a list |
|
||||
| `kickoff_async()` | Thread-based | Wraps synchronous execution in `asyncio.to_thread` |
|
||||
| `kickoff_for_each_async()` | Thread-based | Thread-based async for each input in a list |
|
||||
|
||||
<Note>
|
||||
For high-concurrency workloads, `akickoff()` and `akickoff_for_each()` are recommended as they use native async for task execution, memory operations, and knowledge retrieval.
|
||||
</Note>
|
||||
- `kickoff_async()`: Initiates the workflow asynchronously.
|
||||
- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
|
||||
|
||||
```python Code
|
||||
# Start the crew's task execution
|
||||
@@ -413,53 +323,19 @@ results = my_crew.kickoff_for_each(inputs=inputs_array)
|
||||
for result in results:
|
||||
print(result)
|
||||
|
||||
# Example of using native async with akickoff
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.akickoff(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Example of using native async with akickoff_for_each
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.akickoff_for_each(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
|
||||
# Example of using thread-based kickoff_async
|
||||
# Example of using kickoff_async
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.kickoff_async(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Example of using thread-based kickoff_for_each_async
|
||||
# Example of using kickoff_for_each_async
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
```
|
||||
|
||||
These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs. For detailed async examples, see the [Kickoff Crew Asynchronously](/en/learn/kickoff-async) guide.
|
||||
|
||||
### Streaming Crew Execution
|
||||
|
||||
For real-time visibility into crew execution, you can enable streaming to receive output as it's generated:
|
||||
|
||||
```python Code
|
||||
# Enable streaming
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# Iterate over streaming output
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Access final result
|
||||
result = streaming.result
|
||||
```
|
||||
|
||||
Learn more about streaming in the [Streaming Crew Execution](/en/learn/streaming-crew-execution) guide.
|
||||
These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
|
||||
|
||||
### Replaying from a Specific Task
|
||||
|
||||
@@ -0,0 +1,374 @@
|
||||
---
|
||||
title: 'Event Listeners'
|
||||
description: 'Tap into CrewAI events to build custom integrations and monitoring'
|
||||
icon: spinner
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
|
||||
|
||||
## How It Works
|
||||
|
||||
CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
|
||||
|
||||
1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
|
||||
2. **BaseEvent**: Base class for all events in the system
|
||||
3. **BaseEventListener**: Abstract base class for creating custom event listeners
|
||||
|
||||
When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
|
||||
|
||||
<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
|
||||
CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
|
||||
|
||||
|
||||
|
||||
With Prompt Tracing you can:
|
||||
- View the complete history of all prompts sent to your LLM
|
||||
- Track token usage and costs
|
||||
- Debug agent reasoning failures
|
||||
- Share prompt sequences with your team
|
||||
- Compare different prompt strategies
|
||||
- Export traces for compliance and auditing
|
||||
</Note>
|
||||
|
||||
## Creating a Custom Event Listener
|
||||
|
||||
To create a custom event listener, you need to:
|
||||
|
||||
1. Create a class that inherits from `BaseEventListener`
|
||||
2. Implement the `setup_listeners` method
|
||||
3. Register handlers for the events you're interested in
|
||||
4. Create an instance of your listener in the appropriate file
|
||||
|
||||
Here's a simple example of a custom event listener class:
|
||||
|
||||
```python
|
||||
from crewai.utilities.events import (
|
||||
CrewKickoffStartedEvent,
|
||||
CrewKickoffCompletedEvent,
|
||||
AgentExecutionCompletedEvent,
|
||||
)
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
|
||||
class MyCustomListener(BaseEventListener):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def setup_listeners(self, crewai_event_bus):
|
||||
@crewai_event_bus.on(CrewKickoffStartedEvent)
|
||||
def on_crew_started(source, event):
|
||||
print(f"Crew '{event.crew_name}' has started execution!")
|
||||
|
||||
@crewai_event_bus.on(CrewKickoffCompletedEvent)
|
||||
def on_crew_completed(source, event):
|
||||
print(f"Crew '{event.crew_name}' has completed execution!")
|
||||
print(f"Output: {event.output}")
|
||||
|
||||
@crewai_event_bus.on(AgentExecutionCompletedEvent)
|
||||
def on_agent_execution_completed(source, event):
|
||||
print(f"Agent '{event.agent.role}' completed task")
|
||||
print(f"Output: {event.output}")
|
||||
```
|
||||
|
||||
## Properly Registering Your Listener
|
||||
|
||||
Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
|
||||
|
||||
1. The event handlers are registered with the event bus
|
||||
2. The listener instance remains in memory (not garbage collected)
|
||||
3. The listener is active when events are emitted
|
||||
|
||||
### Option 1: Import and Instantiate in Your Crew or Flow Implementation
|
||||
|
||||
The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
|
||||
|
||||
#### For Crew-based Applications
|
||||
|
||||
Create and import your listener at the top of your Crew implementation file:
|
||||
|
||||
```python
|
||||
# In your crew.py file
|
||||
from crewai import Agent, Crew, Task
|
||||
from my_listeners import MyCustomListener
|
||||
|
||||
# Create an instance of your listener
|
||||
my_listener = MyCustomListener()
|
||||
|
||||
class MyCustomCrew:
|
||||
# Your crew implementation...
|
||||
|
||||
def crew(self):
|
||||
return Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
# ...
|
||||
)
|
||||
```
|
||||
|
||||
#### For Flow-based Applications
|
||||
|
||||
Create and import your listener at the top of your Flow implementation file:
|
||||
|
||||
```python
|
||||
# In your main.py or flow.py file
|
||||
from crewai.flow import Flow, listen, start
|
||||
from my_listeners import MyCustomListener
|
||||
|
||||
# Create an instance of your listener
|
||||
my_listener = MyCustomListener()
|
||||
|
||||
class MyCustomFlow(Flow):
|
||||
# Your flow implementation...
|
||||
|
||||
@start()
|
||||
def first_step(self):
|
||||
# ...
|
||||
```
|
||||
|
||||
This ensures that your listener is loaded and active when your Crew or Flow is executed.
|
||||
|
||||
### Option 2: Create a Package for Your Listeners
|
||||
|
||||
For a more structured approach, especially if you have multiple listeners:
|
||||
|
||||
1. Create a package for your listeners:
|
||||
|
||||
```
|
||||
my_project/
|
||||
├── listeners/
|
||||
│ ├── __init__.py
|
||||
│ ├── my_custom_listener.py
|
||||
│ └── another_listener.py
|
||||
```
|
||||
|
||||
2. In `my_custom_listener.py`, define your listener class and create an instance:
|
||||
|
||||
```python
|
||||
# my_custom_listener.py
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
# ... import events ...
|
||||
|
||||
class MyCustomListener(BaseEventListener):
|
||||
# ... implementation ...
|
||||
|
||||
# Create an instance of your listener
|
||||
my_custom_listener = MyCustomListener()
|
||||
```
|
||||
|
||||
3. In `__init__.py`, import the listener instances to ensure they're loaded:
|
||||
|
||||
```python
|
||||
# __init__.py
|
||||
from .my_custom_listener import my_custom_listener
|
||||
from .another_listener import another_listener
|
||||
|
||||
# Optionally export them if you need to access them elsewhere
|
||||
__all__ = ['my_custom_listener', 'another_listener']
|
||||
```
|
||||
|
||||
4. Import your listeners package in your Crew or Flow file:
|
||||
|
||||
```python
|
||||
# In your crew.py or flow.py file
|
||||
import my_project.listeners # This loads all your listeners
|
||||
|
||||
class MyCustomCrew:
|
||||
# Your crew implementation...
|
||||
```
|
||||
|
||||
This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
|
||||
|
||||
```python
|
||||
# src/crewai/utilities/events/third_party/__init__.py
|
||||
from .agentops_listener import agentops_listener
|
||||
```
|
||||
|
||||
This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
|
||||
|
||||
## Available Event Types
|
||||
|
||||
CrewAI provides a wide range of events that you can listen for:
|
||||
|
||||
### Crew Events
|
||||
|
||||
- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
|
||||
- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
|
||||
- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
|
||||
- **CrewTestStartedEvent**: Emitted when a Crew starts testing
|
||||
- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
|
||||
- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
|
||||
- **CrewTrainStartedEvent**: Emitted when a Crew starts training
|
||||
- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
|
||||
- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
|
||||
|
||||
### Agent Events
|
||||
|
||||
- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
|
||||
- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
|
||||
- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
|
||||
|
||||
### Task Events
|
||||
|
||||
- **TaskStartedEvent**: Emitted when a Task starts execution
|
||||
- **TaskCompletedEvent**: Emitted when a Task completes execution
|
||||
- **TaskFailedEvent**: Emitted when a Task fails to complete execution
|
||||
- **TaskEvaluationEvent**: Emitted when a Task is evaluated
|
||||
|
||||
### Tool Usage Events
|
||||
|
||||
- **ToolUsageStartedEvent**: Emitted when a tool execution is started
|
||||
- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
|
||||
- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
|
||||
- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
|
||||
- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
|
||||
- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
|
||||
|
||||
### Knowledge Events
|
||||
|
||||
- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
|
||||
- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
|
||||
- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
|
||||
- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
|
||||
- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
|
||||
- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
|
||||
|
||||
### Flow Events
|
||||
|
||||
- **FlowCreatedEvent**: Emitted when a Flow is created
|
||||
- **FlowStartedEvent**: Emitted when a Flow starts execution
|
||||
- **FlowFinishedEvent**: Emitted when a Flow completes execution
|
||||
- **FlowPlotEvent**: Emitted when a Flow is plotted
|
||||
- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
|
||||
- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
|
||||
- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
|
||||
|
||||
### LLM Events
|
||||
|
||||
- **LLMCallStartedEvent**: Emitted when an LLM call starts
|
||||
- **LLMCallCompletedEvent**: Emitted when an LLM call completes
|
||||
- **LLMCallFailedEvent**: Emitted when an LLM call fails
|
||||
- **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
|
||||
|
||||
## Event Handler Structure
|
||||
|
||||
Each event handler receives two parameters:
|
||||
|
||||
1. **source**: The object that emitted the event
|
||||
2. **event**: The event instance, containing event-specific data
|
||||
|
||||
The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
|
||||
|
||||
- **timestamp**: The time when the event was emitted
|
||||
- **type**: A string identifier for the event type
|
||||
|
||||
Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
|
||||
|
||||
## Real-World Example: Integration with AgentOps
|
||||
|
||||
CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
|
||||
|
||||
```python
|
||||
from typing import Optional
|
||||
|
||||
from crewai.utilities.events import (
|
||||
CrewKickoffCompletedEvent,
|
||||
ToolUsageErrorEvent,
|
||||
ToolUsageStartedEvent,
|
||||
)
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
|
||||
from crewai.utilities.events.task_events import TaskEvaluationEvent
|
||||
|
||||
try:
|
||||
import agentops
|
||||
AGENTOPS_INSTALLED = True
|
||||
except ImportError:
|
||||
AGENTOPS_INSTALLED = False
|
||||
|
||||
class AgentOpsListener(BaseEventListener):
|
||||
tool_event: Optional["agentops.ToolEvent"] = None
|
||||
session: Optional["agentops.Session"] = None
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def setup_listeners(self, crewai_event_bus):
|
||||
if not AGENTOPS_INSTALLED:
|
||||
return
|
||||
|
||||
@crewai_event_bus.on(CrewKickoffStartedEvent)
|
||||
def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
|
||||
self.session = agentops.init()
|
||||
for agent in source.agents:
|
||||
if self.session:
|
||||
self.session.create_agent(
|
||||
name=agent.role,
|
||||
agent_id=str(agent.id),
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(CrewKickoffCompletedEvent)
|
||||
def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
|
||||
if self.session:
|
||||
self.session.end_session(
|
||||
end_state="Success",
|
||||
end_state_reason="Finished Execution",
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(ToolUsageStartedEvent)
|
||||
def on_tool_usage_started(source, event: ToolUsageStartedEvent):
|
||||
self.tool_event = agentops.ToolEvent(name=event.tool_name)
|
||||
if self.session:
|
||||
self.session.record(self.tool_event)
|
||||
|
||||
@crewai_event_bus.on(ToolUsageErrorEvent)
|
||||
def on_tool_usage_error(source, event: ToolUsageErrorEvent):
|
||||
agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
|
||||
```
|
||||
|
||||
This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
|
||||
|
||||
The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
|
||||
|
||||
```python
|
||||
from .agentops_listener import agentops_listener
|
||||
```
|
||||
|
||||
This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
|
||||
|
||||
## Advanced Usage: Scoped Handlers
|
||||
|
||||
For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
|
||||
|
||||
```python
|
||||
from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
|
||||
|
||||
with crewai_event_bus.scoped_handlers():
|
||||
@crewai_event_bus.on(CrewKickoffStartedEvent)
|
||||
def temp_handler(source, event):
|
||||
print("This handler only exists within this context")
|
||||
|
||||
# Do something that emits events
|
||||
|
||||
# Outside the context, the temporary handler is removed
|
||||
```
|
||||
|
||||
## Use Cases
|
||||
|
||||
Event listeners can be used for a variety of purposes:
|
||||
|
||||
1. **Logging and Monitoring**: Track the execution of your Crew and log important events
|
||||
2. **Analytics**: Collect data about your Crew's performance and behavior
|
||||
3. **Debugging**: Set up temporary listeners to debug specific issues
|
||||
4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
|
||||
5. **Custom Behavior**: Trigger custom actions based on specific events
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
|
||||
2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
|
||||
3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
|
||||
4. **Selective Listening**: Only listen for events you actually need to handle
|
||||
5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
|
||||
|
||||
By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Flows
|
||||
description: Learn how to create and manage AI workflows using CrewAI Flows.
|
||||
icon: arrow-progress
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -98,13 +97,7 @@ The state's unique ID and stored data can be useful for tracking flow executions
|
||||
|
||||
### @start()
|
||||
|
||||
The `@start()` decorator marks entry points for a Flow. You can:
|
||||
|
||||
- Declare multiple unconditional starts: `@start()`
|
||||
- Gate a start on a prior method or router label: `@start("method_or_label")`
|
||||
- Provide a callable condition to control when a start should fire
|
||||
|
||||
All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
|
||||
The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
|
||||
|
||||
### @listen()
|
||||
|
||||
@@ -572,59 +565,6 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
|
||||
|
||||
When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
|
||||
|
||||
### Human in the Loop (human feedback)
|
||||
|
||||
<Note>
|
||||
The `@human_feedback` decorator requires **CrewAI version 1.8.0 or higher**.
|
||||
</Note>
|
||||
|
||||
The `@human_feedback` decorator enables human-in-the-loop workflows by pausing flow execution to collect feedback from a human. This is useful for approval gates, quality review, and decision points that require human judgment.
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Do you approve this content?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Content to be reviewed..."
|
||||
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"Approved! Feedback: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def on_rejection(self, result: HumanFeedbackResult):
|
||||
print(f"Rejected. Reason: {result.feedback}")
|
||||
```
|
||||
|
||||
When `emit` is specified, the human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes, which then triggers the corresponding `@listen` decorator.
|
||||
|
||||
You can also use `@human_feedback` without routing to simply collect feedback:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="Any comments on this output?")
|
||||
def my_method(self):
|
||||
return "Output for review"
|
||||
|
||||
@listen(my_method)
|
||||
def next_step(self, result: HumanFeedbackResult):
|
||||
# Access feedback via result.feedback
|
||||
# Access original output via result.output
|
||||
pass
|
||||
```
|
||||
|
||||
Access all feedback collected during a flow via `self.last_human_feedback` (most recent) or `self.human_feedback_history` (all feedback as a list).
|
||||
|
||||
For a complete guide on human feedback in flows, including **async/non-blocking feedback** with custom providers (Slack, webhooks, etc.), see [Human Feedback in Flows](/en/learn/human-feedback-in-flows).
|
||||
|
||||
## Adding Agents to Flows
|
||||
|
||||
Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
|
||||
@@ -928,13 +868,14 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F
|
||||
Also, check out our YouTube video on how to use flows in CrewAI below!
|
||||
|
||||
<iframe
|
||||
className="w-full aspect-video rounded-xl"
|
||||
width="560"
|
||||
height="315"
|
||||
src="https://www.youtube.com/embed/MTb5my6VOT8"
|
||||
title="CrewAI Flows overview"
|
||||
frameBorder="0"
|
||||
title="YouTube video player"
|
||||
frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
referrerPolicy="strict-origin-when-cross-origin"
|
||||
allowFullScreen
|
||||
referrerpolicy="strict-origin-when-cross-origin"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
## Running Flows
|
||||
@@ -950,104 +891,6 @@ flow = ExampleFlow()
|
||||
result = flow.kickoff()
|
||||
```
|
||||
|
||||
### Streaming Flow Execution
|
||||
|
||||
For real-time visibility into flow execution, you can enable streaming to receive output as it's generated:
|
||||
|
||||
```python
|
||||
class StreamingFlow(Flow):
|
||||
stream = True # Enable streaming
|
||||
|
||||
@start()
|
||||
def research(self):
|
||||
# Your flow implementation
|
||||
pass
|
||||
|
||||
# Iterate over streaming output
|
||||
flow = StreamingFlow()
|
||||
streaming = flow.kickoff()
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Access final result
|
||||
result = streaming.result
|
||||
```
|
||||
|
||||
Learn more about streaming in the [Streaming Flow Execution](/en/learn/streaming-flow-execution) guide.
|
||||
|
||||
## Memory in Flows
|
||||
|
||||
Every Flow automatically has access to CrewAI's unified [Memory](/concepts/memory) system. You can store, recall, and extract memories directly inside any flow method using three built-in convenience methods.
|
||||
|
||||
### Built-in Methods
|
||||
|
||||
| Method | Description |
|
||||
| :--- | :--- |
|
||||
| `self.remember(content, **kwargs)` | Store content in memory. Accepts optional `scope`, `categories`, `metadata`, `importance`. |
|
||||
| `self.recall(query, **kwargs)` | Retrieve relevant memories. Accepts optional `scope`, `categories`, `limit`, `depth`. |
|
||||
| `self.extract_memories(content)` | Break raw text into discrete, self-contained memory statements. |
|
||||
|
||||
A default `Memory()` instance is created automatically when the Flow initializes. You can also pass a custom one:
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow
|
||||
from crewai import Memory
|
||||
|
||||
custom_memory = Memory(
|
||||
recency_weight=0.5,
|
||||
recency_half_life_days=7,
|
||||
embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}},
|
||||
)
|
||||
|
||||
flow = MyFlow(memory=custom_memory)
|
||||
```
|
||||
|
||||
### Example: Research and Analyze Flow
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
|
||||
|
||||
class ResearchAnalysisFlow(Flow):
|
||||
@start()
|
||||
def gather_data(self):
|
||||
# Simulate research findings
|
||||
findings = (
|
||||
"PostgreSQL handles 10k concurrent connections with connection pooling. "
|
||||
"MySQL caps at around 5k. MongoDB scales horizontally but adds complexity."
|
||||
)
|
||||
|
||||
# Extract atomic facts and remember each one
|
||||
memories = self.extract_memories(findings)
|
||||
for mem in memories:
|
||||
self.remember(mem, scope="/research/databases")
|
||||
|
||||
return findings
|
||||
|
||||
@listen(gather_data)
|
||||
def analyze(self, raw_findings):
|
||||
# Recall relevant past research (from this run or previous runs)
|
||||
past = self.recall("database performance and scaling", limit=10, depth="shallow")
|
||||
|
||||
context_lines = [f"- {m.record.content}" for m in past]
|
||||
context = "\n".join(context_lines) if context_lines else "No prior context."
|
||||
|
||||
return {
|
||||
"new_findings": raw_findings,
|
||||
"prior_context": context,
|
||||
"total_memories": len(past),
|
||||
}
|
||||
|
||||
|
||||
flow = ResearchAnalysisFlow()
|
||||
result = flow.kickoff()
|
||||
print(result)
|
||||
```
|
||||
|
||||
Because memory persists across runs (backed by LanceDB on disk), the `analyze` step will recall findings from previous executions too -- enabling flows that learn and accumulate knowledge over time.
|
||||
|
||||
See the [Memory documentation](/concepts/memory) for details on scopes, slices, composite scoring, embedder configuration, and more.
|
||||
|
||||
### Using the CLI
|
||||
|
||||
Starting from version 0.103.0, you can run flows using the `crewai run` command:
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Knowledge
|
||||
description: What is knowledge in CrewAI and how to use it.
|
||||
icon: book
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -25,41 +24,6 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
|
||||
Also, use relative paths from the `knowledge` directory when creating the source.
|
||||
</Tip>
|
||||
|
||||
### Vector store (RAG) client configuration
|
||||
|
||||
CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
|
||||
|
||||
Supported today:
|
||||
- ChromaDB (default)
|
||||
- Qdrant
|
||||
|
||||
```python Code
|
||||
from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
|
||||
|
||||
# ChromaDB (default)
|
||||
from crewai.rag.chromadb.config import ChromaDBConfig
|
||||
set_rag_config(ChromaDBConfig())
|
||||
chromadb_client = get_rag_client()
|
||||
|
||||
# Qdrant
|
||||
from crewai.rag.qdrant.config import QdrantConfig
|
||||
set_rag_config(QdrantConfig())
|
||||
qdrant_client = get_rag_client()
|
||||
|
||||
# Example operations (same API for any provider)
|
||||
client = qdrant_client # or chromadb_client
|
||||
client.create_collection(collection_name="docs")
|
||||
client.add_documents(
|
||||
collection_name="docs",
|
||||
documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
|
||||
)
|
||||
results = client.search(collection_name="docs", query="collaborative agents", limit=3)
|
||||
|
||||
clear_rag_config() # optional reset
|
||||
```
|
||||
|
||||
This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
|
||||
|
||||
### Basic String Knowledge Example
|
||||
|
||||
```python Code
|
||||
@@ -388,8 +352,8 @@ crew = Crew(
|
||||
agents=[sales_agent, tech_agent, support_agent],
|
||||
tasks=[...],
|
||||
embedder={ # Fallback embedder for agents without their own
|
||||
"provider": "google-generativeai",
|
||||
"config": {"model_name": "gemini-embedding-001"}
|
||||
"provider": "google",
|
||||
"config": {"model": "text-embedding-004"}
|
||||
}
|
||||
)
|
||||
|
||||
@@ -629,9 +593,9 @@ agent = Agent(
|
||||
backstory="Expert researcher",
|
||||
knowledge_sources=[knowledge_source],
|
||||
embedder={
|
||||
"provider": "google-generativeai",
|
||||
"provider": "google",
|
||||
"config": {
|
||||
"model_name": "gemini-embedding-001",
|
||||
"model": "models/text-embedding-004",
|
||||
"api_key": "your-google-key"
|
||||
}
|
||||
}
|
||||
@@ -717,11 +681,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
|
||||
#### Example: Monitoring Knowledge Retrieval
|
||||
|
||||
```python
|
||||
from crewai.events import (
|
||||
from crewai.utilities.events import (
|
||||
KnowledgeRetrievalStartedEvent,
|
||||
KnowledgeRetrievalCompletedEvent,
|
||||
BaseEventListener,
|
||||
)
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
|
||||
class KnowledgeMonitorListener(BaseEventListener):
|
||||
def setup_listeners(self, crewai_event_bus):
|
||||
@@ -739,7 +703,7 @@ class KnowledgeMonitorListener(BaseEventListener):
|
||||
knowledge_monitor = KnowledgeMonitorListener()
|
||||
```
|
||||
|
||||
For more information on using events, see the [Event Listeners](/en/concepts/event-listener) documentation.
|
||||
For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
|
||||
|
||||
### Custom Knowledge Sources
|
||||
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,992 @@
|
||||
---
|
||||
title: Memory
|
||||
description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
|
||||
icon: database
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
|
||||
|
||||
1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
|
||||
2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)
|
||||
3. **External Memory** - Standalone external memory providers (new approach)
|
||||
|
||||
## Memory System Components
|
||||
|
||||
| Component | Description |
|
||||
| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
|
||||
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
|
||||
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
|
||||
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
|
||||
|
||||
## 1. Basic Memory System (Recommended)
|
||||
|
||||
The simplest and most commonly used approach. Enable memory for your crew with a single parameter:
|
||||
|
||||
### Quick Start
|
||||
```python
|
||||
from crewai import Crew, Agent, Task, Process
|
||||
|
||||
# Enable basic memory system
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
process=Process.sequential,
|
||||
memory=True, # Enables short-term, long-term, and entity memory
|
||||
verbose=True
|
||||
)
|
||||
```
|
||||
|
||||
### How It Works
|
||||
- **Short-Term Memory**: Uses ChromaDB with RAG for current context
|
||||
- **Long-Term Memory**: Uses SQLite3 to store task results across sessions
|
||||
- **Entity Memory**: Uses RAG to track entities (people, places, concepts)
|
||||
- **Storage Location**: Platform-specific location via `appdirs` package
|
||||
- **Custom Storage Directory**: Set `CREWAI_STORAGE_DIR` environment variable
|
||||
|
||||
## Storage Location Transparency
|
||||
|
||||
<Info>
|
||||
**Understanding Storage Locations**: CrewAI uses platform-specific directories to store memory and knowledge files following OS conventions. Understanding these locations helps with production deployments, backups, and debugging.
|
||||
</Info>
|
||||
|
||||
### Where CrewAI Stores Files
|
||||
|
||||
By default, CrewAI uses the `appdirs` library to determine storage locations following platform conventions. Here's exactly where your files are stored:
|
||||
|
||||
#### Default Storage Locations by Platform
|
||||
|
||||
**macOS:**
|
||||
```
|
||||
~/Library/Application Support/CrewAI/{project_name}/
|
||||
├── knowledge/ # Knowledge base ChromaDB files
|
||||
├── short_term_memory/ # Short-term memory ChromaDB files
|
||||
├── long_term_memory/ # Long-term memory ChromaDB files
|
||||
├── entities/ # Entity memory ChromaDB files
|
||||
└── long_term_memory_storage.db # SQLite database
|
||||
```
|
||||
|
||||
**Linux:**
|
||||
```
|
||||
~/.local/share/CrewAI/{project_name}/
|
||||
├── knowledge/
|
||||
├── short_term_memory/
|
||||
├── long_term_memory/
|
||||
├── entities/
|
||||
└── long_term_memory_storage.db
|
||||
```
|
||||
|
||||
**Windows:**
|
||||
```
|
||||
C:\Users\{username}\AppData\Local\CrewAI\{project_name}\
|
||||
├── knowledge\
|
||||
├── short_term_memory\
|
||||
├── long_term_memory\
|
||||
├── entities\
|
||||
└── long_term_memory_storage.db
|
||||
```
|
||||
|
||||
### Finding Your Storage Location
|
||||
|
||||
To see exactly where CrewAI is storing files on your system:
|
||||
|
||||
```python
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
import os
|
||||
|
||||
# Get the base storage path
|
||||
storage_path = db_storage_path()
|
||||
print(f"CrewAI storage location: {storage_path}")
|
||||
|
||||
# List all CrewAI storage directories
|
||||
if os.path.exists(storage_path):
|
||||
print("\nStored files and directories:")
|
||||
for item in os.listdir(storage_path):
|
||||
item_path = os.path.join(storage_path, item)
|
||||
if os.path.isdir(item_path):
|
||||
print(f"📁 {item}/")
|
||||
# Show ChromaDB collections
|
||||
if os.path.exists(item_path):
|
||||
for subitem in os.listdir(item_path):
|
||||
print(f" └── {subitem}")
|
||||
else:
|
||||
print(f"📄 {item}")
|
||||
else:
|
||||
print("No CrewAI storage directory found yet.")
|
||||
```
|
||||
|
||||
### Controlling Storage Locations
|
||||
|
||||
#### Option 1: Environment Variable (Recommended)
|
||||
```python
|
||||
import os
|
||||
from crewai import Crew
|
||||
|
||||
# Set custom storage location
|
||||
os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage"
|
||||
|
||||
# All memory and knowledge will now be stored in ./my_project_storage/
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True
|
||||
)
|
||||
```
|
||||
|
||||
#### Option 2: Custom Storage Paths
|
||||
```python
|
||||
import os
|
||||
from crewai import Crew
|
||||
from crewai.memory import LongTermMemory
|
||||
from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
|
||||
|
||||
# Configure custom storage location
|
||||
custom_storage_path = "./storage"
|
||||
os.makedirs(custom_storage_path, exist_ok=True)
|
||||
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
long_term_memory=LongTermMemory(
|
||||
storage=LTMSQLiteStorage(
|
||||
db_path=f"{custom_storage_path}/memory.db"
|
||||
)
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
#### Option 3: Project-Specific Storage
|
||||
```python
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# Store in project directory
|
||||
project_root = Path(__file__).parent
|
||||
storage_dir = project_root / "crewai_storage"
|
||||
|
||||
os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
|
||||
|
||||
# Now all storage will be in your project directory
|
||||
```
|
||||
|
||||
### Embedding Provider Defaults
|
||||
|
||||
<Info>
|
||||
**Default Embedding Provider**: CrewAI defaults to OpenAI embeddings for consistency and reliability. You can easily customize this to match your LLM provider or use local embeddings.
|
||||
</Info>
|
||||
|
||||
#### Understanding Default Behavior
|
||||
```python
|
||||
# When using Claude as your LLM...
|
||||
from crewai import Agent, LLM
|
||||
|
||||
agent = Agent(
|
||||
role="Analyst",
|
||||
goal="Analyze data",
|
||||
backstory="Expert analyst",
|
||||
llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude
|
||||
)
|
||||
|
||||
# CrewAI will use OpenAI embeddings by default for consistency
|
||||
# You can easily customize this to match your preferred provider
|
||||
```
|
||||
|
||||
#### Customizing Embedding Providers
|
||||
```python
|
||||
from crewai import Crew
|
||||
|
||||
# Option 1: Match your LLM provider
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "anthropic", # Match your LLM provider
|
||||
"config": {
|
||||
"api_key": "your-anthropic-key",
|
||||
"model": "text-embedding-3-small"
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
# Option 2: Use local embeddings (no external API calls)
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "ollama",
|
||||
"config": {"model": "mxbai-embed-large"}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Debugging Storage Issues
|
||||
|
||||
#### Check Storage Permissions
|
||||
```python
|
||||
import os
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
|
||||
storage_path = db_storage_path()
|
||||
print(f"Storage path: {storage_path}")
|
||||
print(f"Path exists: {os.path.exists(storage_path)}")
|
||||
print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}")
|
||||
|
||||
# Create with proper permissions
|
||||
if not os.path.exists(storage_path):
|
||||
os.makedirs(storage_path, mode=0o755, exist_ok=True)
|
||||
print(f"Created storage directory: {storage_path}")
|
||||
```
|
||||
|
||||
#### Inspect ChromaDB Collections
|
||||
```python
|
||||
import chromadb
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
|
||||
# Connect to CrewAI's ChromaDB
|
||||
storage_path = db_storage_path()
|
||||
chroma_path = os.path.join(storage_path, "knowledge")
|
||||
|
||||
if os.path.exists(chroma_path):
|
||||
client = chromadb.PersistentClient(path=chroma_path)
|
||||
collections = client.list_collections()
|
||||
|
||||
print("ChromaDB Collections:")
|
||||
for collection in collections:
|
||||
print(f" - {collection.name}: {collection.count()} documents")
|
||||
else:
|
||||
print("No ChromaDB storage found")
|
||||
```
|
||||
|
||||
#### Reset Storage (Debugging)
|
||||
```python
|
||||
from crewai import Crew
|
||||
|
||||
# Reset all memory storage
|
||||
crew = Crew(agents=[...], tasks=[...], memory=True)
|
||||
|
||||
# Reset specific memory types
|
||||
crew.reset_memories(command_type='short') # Short-term memory
|
||||
crew.reset_memories(command_type='long') # Long-term memory
|
||||
crew.reset_memories(command_type='entity') # Entity memory
|
||||
crew.reset_memories(command_type='knowledge') # Knowledge storage
|
||||
```
|
||||
|
||||
### Production Best Practices
|
||||
|
||||
1. **Set `CREWAI_STORAGE_DIR`** to a known location in production for better control
|
||||
2. **Choose explicit embedding providers** to match your LLM setup
|
||||
3. **Monitor storage directory size** for large-scale deployments
|
||||
4. **Include storage directories** in your backup strategy
|
||||
5. **Set appropriate file permissions** (0o755 for directories, 0o644 for files)
|
||||
6. **Use project-relative paths** for containerized deployments
|
||||
|
||||
### Common Storage Issues
|
||||
|
||||
**"ChromaDB permission denied" errors:**
|
||||
```bash
|
||||
# Fix permissions
|
||||
chmod -R 755 ~/.local/share/CrewAI/
|
||||
```
|
||||
|
||||
**"Database is locked" errors:**
|
||||
```python
|
||||
# Ensure only one CrewAI instance accesses storage
|
||||
import fcntl
|
||||
import os
|
||||
|
||||
storage_path = db_storage_path()
|
||||
lock_file = os.path.join(storage_path, ".crewai.lock")
|
||||
|
||||
with open(lock_file, 'w') as f:
|
||||
fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
|
||||
# Your CrewAI code here
|
||||
```
|
||||
|
||||
**Storage not persisting between runs:**
|
||||
```python
|
||||
# Verify storage location is consistent
|
||||
import os
|
||||
print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR"))
|
||||
print("Current working directory:", os.getcwd())
|
||||
print("Computed storage path:", db_storage_path())
|
||||
```
|
||||
|
||||
## Custom Embedder Configuration
|
||||
|
||||
CrewAI supports multiple embedding providers to give you flexibility in choosing the best option for your use case. Here's a comprehensive guide to configuring different embedding providers for your memory system.
|
||||
|
||||
### Why Choose Different Embedding Providers?
|
||||
|
||||
- **Cost Optimization**: Local embeddings (Ollama) are free after initial setup
|
||||
- **Privacy**: Keep your data local with Ollama or use your preferred cloud provider
|
||||
- **Performance**: Some models work better for specific domains or languages
|
||||
- **Consistency**: Match your embedding provider with your LLM provider
|
||||
- **Compliance**: Meet specific regulatory or organizational requirements
|
||||
|
||||
### OpenAI Embeddings (Default)
|
||||
|
||||
OpenAI provides reliable, high-quality embeddings that work well for most use cases.
|
||||
|
||||
```python
|
||||
from crewai import Crew
|
||||
|
||||
# Basic OpenAI configuration (uses environment OPENAI_API_KEY)
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "text-embedding-3-small" # or "text-embedding-3-large"
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
# Advanced OpenAI configuration
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"api_key": "your-openai-api-key", # Optional: override env var
|
||||
"model": "text-embedding-3-large",
|
||||
"dimensions": 1536, # Optional: reduce dimensions for smaller storage
|
||||
"organization_id": "your-org-id" # Optional: for organization accounts
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Azure OpenAI Embeddings
|
||||
|
||||
For enterprise users with Azure OpenAI deployments.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai", # Use openai provider for Azure
|
||||
"config": {
|
||||
"api_key": "your-azure-api-key",
|
||||
"api_base": "https://your-resource.openai.azure.com/",
|
||||
"api_type": "azure",
|
||||
"api_version": "2023-05-15",
|
||||
"model": "text-embedding-3-small",
|
||||
"deployment_id": "your-deployment-name" # Azure deployment name
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Google AI Embeddings
|
||||
|
||||
Use Google's text embedding models for integration with Google Cloud services.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "google",
|
||||
"config": {
|
||||
"api_key": "your-google-api-key",
|
||||
"model": "text-embedding-004" # or "text-embedding-preview-0409"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Vertex AI Embeddings
|
||||
|
||||
For Google Cloud users with Vertex AI access.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "vertexai",
|
||||
"config": {
|
||||
"project_id": "your-gcp-project-id",
|
||||
"region": "us-central1", # or your preferred region
|
||||
"api_key": "your-service-account-key",
|
||||
"model_name": "textembedding-gecko"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Ollama Embeddings (Local)
|
||||
|
||||
Run embeddings locally for privacy and cost savings.
|
||||
|
||||
```python
|
||||
# First, install and run Ollama locally, then pull an embedding model:
|
||||
# ollama pull mxbai-embed-large
|
||||
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "ollama",
|
||||
"config": {
|
||||
"model": "mxbai-embed-large", # or "nomic-embed-text"
|
||||
"url": "http://localhost:11434/api/embeddings" # Default Ollama URL
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
# For custom Ollama installations
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "ollama",
|
||||
"config": {
|
||||
"model": "mxbai-embed-large",
|
||||
"url": "http://your-ollama-server:11434/api/embeddings"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Cohere Embeddings
|
||||
|
||||
Use Cohere's embedding models for multilingual support.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "cohere",
|
||||
"config": {
|
||||
"api_key": "your-cohere-api-key",
|
||||
"model": "embed-english-v3.0" # or "embed-multilingual-v3.0"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### VoyageAI Embeddings
|
||||
|
||||
High-performance embeddings optimized for retrieval tasks.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "voyageai",
|
||||
"config": {
|
||||
"api_key": "your-voyage-api-key",
|
||||
"model": "voyage-large-2", # or "voyage-code-2" for code
|
||||
"input_type": "document" # or "query"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### AWS Bedrock Embeddings
|
||||
|
||||
For AWS users with Bedrock access.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "bedrock",
|
||||
"config": {
|
||||
"aws_access_key_id": "your-access-key",
|
||||
"aws_secret_access_key": "your-secret-key",
|
||||
"region_name": "us-east-1",
|
||||
"model": "amazon.titan-embed-text-v1"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Hugging Face Embeddings
|
||||
|
||||
Use open-source models from Hugging Face.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "huggingface",
|
||||
"config": {
|
||||
"api_key": "your-hf-token", # Optional for public models
|
||||
"model": "sentence-transformers/all-MiniLM-L6-v2",
|
||||
"api_url": "https://api-inference.huggingface.co" # or your custom endpoint
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### IBM Watson Embeddings
|
||||
|
||||
For IBM Cloud users.
|
||||
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "watson",
|
||||
"config": {
|
||||
"api_key": "your-watson-api-key",
|
||||
"url": "your-watson-instance-url",
|
||||
"model": "ibm/slate-125m-english-rtrvr"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Choosing the Right Embedding Provider
|
||||
|
||||
| Provider | Best For | Pros | Cons |
|
||||
|:---------|:----------|:------|:------|
|
||||
| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
|
||||
| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
|
||||
| **Google AI** | Google ecosystem | Good performance | Requires Google account |
|
||||
| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
|
||||
| **Cohere** | Multilingual content | Great language support | Specialized use case |
|
||||
| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
|
||||
|
||||
### Environment Variable Configuration
|
||||
|
||||
For security, store API keys in environment variables:
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
# Set environment variables
|
||||
os.environ["OPENAI_API_KEY"] = "your-openai-key"
|
||||
os.environ["GOOGLE_API_KEY"] = "your-google-key"
|
||||
os.environ["COHERE_API_KEY"] = "your-cohere-key"
|
||||
|
||||
# Use without exposing keys in code
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "text-embedding-3-small"
|
||||
# API key automatically loaded from environment
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Testing Different Embedding Providers
|
||||
|
||||
Compare embedding providers for your specific use case:
|
||||
|
||||
```python
|
||||
from crewai import Crew
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
|
||||
# Test different providers with the same data
|
||||
providers_to_test = [
|
||||
{
|
||||
"name": "OpenAI",
|
||||
"config": {
|
||||
"provider": "openai",
|
||||
"config": {"model": "text-embedding-3-small"}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "Ollama",
|
||||
"config": {
|
||||
"provider": "ollama",
|
||||
"config": {"model": "mxbai-embed-large"}
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
for provider in providers_to_test:
|
||||
print(f"\nTesting {provider['name']} embeddings...")
|
||||
|
||||
# Create crew with specific embedder
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True,
|
||||
embedder=provider['config']
|
||||
)
|
||||
|
||||
# Run your test and measure performance
|
||||
result = crew.kickoff()
|
||||
print(f"{provider['name']} completed successfully")
|
||||
```
|
||||
|
||||
### Troubleshooting Embedding Issues
|
||||
|
||||
**Model not found errors:**
|
||||
```python
|
||||
# Verify model availability
|
||||
from crewai.utilities.embedding_configurator import EmbeddingConfigurator
|
||||
|
||||
configurator = EmbeddingConfigurator()
|
||||
try:
|
||||
embedder = configurator.configure_embedder({
|
||||
"provider": "ollama",
|
||||
"config": {"model": "mxbai-embed-large"}
|
||||
})
|
||||
print("Embedder configured successfully")
|
||||
except Exception as e:
|
||||
print(f"Configuration error: {e}")
|
||||
```
|
||||
|
||||
**API key issues:**
|
||||
```python
|
||||
import os
|
||||
|
||||
# Check if API keys are set
|
||||
required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"]
|
||||
for key in required_keys:
|
||||
if os.getenv(key):
|
||||
print(f"✅ {key} is set")
|
||||
else:
|
||||
print(f"❌ {key} is not set")
|
||||
```
|
||||
|
||||
**Performance comparison:**
|
||||
```python
|
||||
import time
|
||||
|
||||
def test_embedding_performance(embedder_config, test_text="This is a test document"):
|
||||
start_time = time.time()
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True,
|
||||
embedder=embedder_config
|
||||
)
|
||||
|
||||
# Simulate memory operation
|
||||
crew.kickoff()
|
||||
|
||||
end_time = time.time()
|
||||
return end_time - start_time
|
||||
|
||||
# Compare performance
|
||||
openai_time = test_embedding_performance({
|
||||
"provider": "openai",
|
||||
"config": {"model": "text-embedding-3-small"}
|
||||
})
|
||||
|
||||
ollama_time = test_embedding_performance({
|
||||
"provider": "ollama",
|
||||
"config": {"model": "mxbai-embed-large"}
|
||||
})
|
||||
|
||||
print(f"OpenAI: {openai_time:.2f}s")
|
||||
print(f"Ollama: {ollama_time:.2f}s")
|
||||
```
|
||||
|
||||
## 2. User Memory with Mem0 (Legacy)
|
||||
|
||||
<Warning>
|
||||
**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
|
||||
</Warning>
|
||||
|
||||
User Memory integrates with [Mem0](https://mem0.ai/) to provide user-specific memory that persists across sessions and integrates with the crew's contextual memory system.
|
||||
|
||||
### Prerequisites
|
||||
```bash
|
||||
pip install mem0ai
|
||||
```
|
||||
|
||||
### Mem0 Cloud Configuration
|
||||
```python
|
||||
import os
|
||||
from crewai import Crew, Process
|
||||
|
||||
# Set your Mem0 API key
|
||||
os.environ["MEM0_API_KEY"] = "m0-your-api-key"
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True, # Required for contextual memory integration
|
||||
memory_config={
|
||||
"provider": "mem0",
|
||||
"config": {"user_id": "john"},
|
||||
"user_memory": {} # Required - triggers user memory initialization
|
||||
},
|
||||
process=Process.sequential,
|
||||
verbose=True
|
||||
)
|
||||
```
|
||||
|
||||
### Advanced Mem0 Configuration
|
||||
```python
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True,
|
||||
memory_config={
|
||||
"provider": "mem0",
|
||||
"config": {
|
||||
"user_id": "john",
|
||||
"org_id": "my_org_id", # Optional
|
||||
"project_id": "my_project_id", # Optional
|
||||
"api_key": "custom-api-key" # Optional - overrides env var
|
||||
},
|
||||
"user_memory": {}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Local Mem0 Configuration
|
||||
```python
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True,
|
||||
memory_config={
|
||||
"provider": "mem0",
|
||||
"config": {
|
||||
"user_id": "john",
|
||||
"local_mem0_config": {
|
||||
"vector_store": {
|
||||
"provider": "qdrant",
|
||||
"config": {"host": "localhost", "port": 6333}
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {"api_key": "your-api-key", "model": "gpt-4"}
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "openai",
|
||||
"config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
|
||||
}
|
||||
}
|
||||
},
|
||||
"user_memory": {}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
## 3. External Memory (New Approach)
|
||||
|
||||
External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
|
||||
|
||||
### Basic External Memory with Mem0
|
||||
```python
|
||||
import os
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.memory.external.external_memory import ExternalMemory
|
||||
|
||||
os.environ["MEM0_API_KEY"] = "your-api-key"
|
||||
|
||||
# Create external memory instance
|
||||
external_memory = ExternalMemory(
|
||||
embedder_config={
|
||||
"provider": "mem0",
|
||||
"config": {"user_id": "U-123"}
|
||||
}
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
external_memory=external_memory, # Separate from basic memory
|
||||
process=Process.sequential,
|
||||
verbose=True
|
||||
)
|
||||
```
|
||||
|
||||
### Custom Storage Implementation
|
||||
```python
|
||||
from crewai.memory.external.external_memory import ExternalMemory
|
||||
from crewai.memory.storage.interface import Storage
|
||||
|
||||
class CustomStorage(Storage):
|
||||
def __init__(self):
|
||||
self.memories = []
|
||||
|
||||
def save(self, value, metadata=None, agent=None):
|
||||
self.memories.append({
|
||||
"value": value,
|
||||
"metadata": metadata,
|
||||
"agent": agent
|
||||
})
|
||||
|
||||
def search(self, query, limit=10, score_threshold=0.5):
|
||||
# Implement your search logic here
|
||||
return [m for m in self.memories if query.lower() in str(m["value"]).lower()]
|
||||
|
||||
def reset(self):
|
||||
self.memories = []
|
||||
|
||||
# Use custom storage
|
||||
external_memory = ExternalMemory(storage=CustomStorage())
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
external_memory=external_memory
|
||||
)
|
||||
```
|
||||
|
||||
## Memory System Comparison
|
||||
|
||||
| Feature | Basic Memory | User Memory (Legacy) | External Memory |
|
||||
|---------|-------------|---------------------|----------------|
|
||||
| **Setup Complexity** | Simple | Medium | Medium |
|
||||
| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
|
||||
| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
|
||||
| **Cross-session** | ✅ | ✅ | ✅ |
|
||||
| **User-specific** | ❌ | ✅ | ✅ |
|
||||
| **Custom providers** | Limited | Mem0 only | Any provider |
|
||||
| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
|
||||
|
||||
## Supported Embedding Providers
|
||||
|
||||
### OpenAI (Default)
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {"model": "text-embedding-3-small"}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Ollama
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "ollama",
|
||||
"config": {"model": "mxbai-embed-large"}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Google AI
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "google",
|
||||
"config": {
|
||||
"api_key": "your-api-key",
|
||||
"model": "text-embedding-004"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Azure OpenAI
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"api_key": "your-api-key",
|
||||
"api_base": "https://your-resource.openai.azure.com/",
|
||||
"api_version": "2023-05-15",
|
||||
"model_name": "text-embedding-3-small"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Vertex AI
|
||||
```python
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "vertexai",
|
||||
"config": {
|
||||
"project_id": "your-project-id",
|
||||
"region": "your-region",
|
||||
"api_key": "your-api-key",
|
||||
"model_name": "textembedding-gecko"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
## Security Best Practices
|
||||
|
||||
### Environment Variables
|
||||
```python
|
||||
import os
|
||||
from crewai import Crew
|
||||
|
||||
# Store sensitive data in environment variables
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
embedder={
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"api_key": os.getenv("OPENAI_API_KEY"),
|
||||
"model": "text-embedding-3-small"
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Storage Security
|
||||
```python
|
||||
import os
|
||||
from crewai import Crew
|
||||
from crewai.memory import LongTermMemory
|
||||
from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
|
||||
|
||||
# Use secure storage paths
|
||||
storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
|
||||
os.makedirs(storage_path, mode=0o700, exist_ok=True) # Restricted permissions
|
||||
|
||||
crew = Crew(
|
||||
memory=True,
|
||||
long_term_memory=LongTermMemory(
|
||||
storage=LTMSQLiteStorage(
|
||||
db_path=f"{storage_path}/memory.db"
|
||||
)
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
**Memory not persisting between sessions?**
|
||||
- Check `CREWAI_STORAGE_DIR` environment variable
|
||||
- Ensure write permissions to storage directory
|
||||
- Verify memory is enabled with `memory=True`
|
||||
|
||||
**Mem0 authentication errors?**
|
||||
- Verify `MEM0_API_KEY` environment variable is set
|
||||
- Check API key permissions on Mem0 dashboard
|
||||
- Ensure `mem0ai` package is installed
|
||||
|
||||
**High memory usage with large datasets?**
|
||||
- Consider using External Memory with custom storage
|
||||
- Implement pagination in custom storage search methods
|
||||
- Use smaller embedding models for reduced memory footprint
|
||||
|
||||
### Performance Tips
|
||||
|
||||
- Use `memory=True` for most use cases (simplest and fastest)
|
||||
- Only use User Memory if you need user-specific persistence
|
||||
- Consider External Memory for high-scale or specialized requirements
|
||||
- Choose smaller embedding models for faster processing
|
||||
- Set appropriate search limits to control memory retrieval size
|
||||
|
||||
## Benefits of Using CrewAI's Memory System
|
||||
|
||||
- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
|
||||
- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
|
||||
- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
|
||||
|
||||
## Conclusion
|
||||
|
||||
Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations,
|
||||
you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Planning
|
||||
description: Learn how to add planning to your CrewAI Crew and improve their performance.
|
||||
icon: ruler-combined
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Processes
|
||||
description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
|
||||
icon: bars-staggered
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Reasoning
|
||||
description: "Learn how to enable and use agent reasoning to improve task execution."
|
||||
icon: brain
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Tasks
|
||||
description: Detailed guide on managing and creating tasks within the CrewAI framework.
|
||||
icon: list-check
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -14,12 +13,11 @@ Tasks provide all necessary details for execution, such as a description, the ag
|
||||
Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
|
||||
|
||||
<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
|
||||
CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
|
||||
CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
|
||||
|
||||
|
||||
|
||||
The Visual Task Builder enables:
|
||||
|
||||
- Drag-and-drop task creation
|
||||
- Visual task dependencies and flow
|
||||
- Real-time testing and validation
|
||||
@@ -29,12 +27,10 @@ The Visual Task Builder enables:
|
||||
### Task Execution Flow
|
||||
|
||||
Tasks can be executed in two ways:
|
||||
|
||||
- **Sequential**: Tasks are executed in the order they are defined
|
||||
- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise
|
||||
|
||||
The execution flow is defined when creating the crew:
|
||||
|
||||
```python Code
|
||||
crew = Crew(
|
||||
agents=[agent1, agent2],
|
||||
@@ -45,32 +41,22 @@ crew = Crew(
|
||||
|
||||
## Task Attributes
|
||||
|
||||
| Attribute | Parameters | Type | Description |
|
||||
| :------------------------------------- | :---------------------- | :-------------------------- | :-------------------------------------------------------------------------------------------------------------- |
|
||||
| **Description** | `description` | `str` | A clear, concise statement of what the task entails. |
|
||||
| **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. |
|
||||
| **Name** _(optional)_ | `name` | `Optional[str]` | A name identifier for the task. |
|
||||
| **Agent** _(optional)_ | `agent` | `Optional[BaseAgent]` | The agent responsible for executing the task. |
|
||||
| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | The tools/resources the agent is limited to use for this task. |
|
||||
| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Other tasks whose outputs will be used as context for this task. |
|
||||
| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | Whether the task should be executed asynchronously. Defaults to False. |
|
||||
| **Human Input** _(optional)_ | `human_input` | `Optional[bool]` | Whether the task should have a human review the final answer of the agent. Defaults to False. |
|
||||
| **Markdown** _(optional)_ | `markdown` | `Optional[bool]` | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False. |
|
||||
| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Task-specific configuration parameters. |
|
||||
| **Output File** _(optional)_ | `output_file` | `Optional[str]` | File path for storing the task output. |
|
||||
| **Create Directory** _(optional)_ | `create_directory` | `Optional[bool]` | Whether to create the directory for output_file if it doesn't exist. Defaults to True. |
|
||||
| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | A Pydantic model to structure the JSON output. |
|
||||
| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | A Pydantic model for task output. |
|
||||
| **Callback** _(optional)_ | `callback` | `Optional[Any]` | Function/object to be executed after task completion. |
|
||||
| **Guardrail** _(optional)_ | `guardrail` | `Optional[Callable]` | Function to validate task output before proceeding to next task. |
|
||||
| **Guardrails** _(optional)_ | `guardrails` | `Optional[List[Callable]]` | List of guardrails to validate task output before proceeding to next task. |
|
||||
| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]` | Maximum number of retries when guardrail validation fails. Defaults to 3. |
|
||||
|
||||
<Note type="warning" title="Deprecated: max_retries">
|
||||
The task attribute `max_retries` is deprecated and will be removed in v1.0.0.
|
||||
Use `guardrail_max_retries` instead to control retry attempts when a guardrail
|
||||
fails.
|
||||
</Note>
|
||||
| Attribute | Parameters | Type | Description |
|
||||
| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Description** | `description` | `str` | A clear, concise statement of what the task entails. |
|
||||
| **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. |
|
||||
| **Name** _(optional)_ | `name` | `Optional[str]` | A name identifier for the task. |
|
||||
| **Agent** _(optional)_ | `agent` | `Optional[BaseAgent]` | The agent responsible for executing the task. |
|
||||
| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | The tools/resources the agent is limited to use for this task. |
|
||||
| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Other tasks whose outputs will be used as context for this task. |
|
||||
| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | Whether the task should be executed asynchronously. Defaults to False. |
|
||||
| **Human Input** _(optional)_ | `human_input` | `Optional[bool]` | Whether the task should have a human review the final answer of the agent. Defaults to False. |
|
||||
| **Markdown** _(optional)_ | `markdown` | `Optional[bool]` | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False. |
|
||||
| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Task-specific configuration parameters. |
|
||||
| **Output File** _(optional)_ | `output_file` | `Optional[str]` | File path for storing the task output. |
|
||||
| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | A Pydantic model to structure the JSON output. |
|
||||
| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | A Pydantic model for task output. |
|
||||
| **Callback** _(optional)_ | `callback` | `Optional[Any]` | Function/object to be executed after task completion. |
|
||||
|
||||
## Creating Tasks
|
||||
|
||||
@@ -80,7 +66,7 @@ There are two ways to create tasks in CrewAI: using **YAML configuration (recomm
|
||||
|
||||
Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
|
||||
|
||||
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
|
||||
After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
|
||||
|
||||
<Note>
|
||||
Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
|
||||
@@ -91,7 +77,7 @@ crew.kickoff(inputs={'topic': 'AI Agents'})
|
||||
|
||||
Here's an example of how to configure tasks using YAML:
|
||||
|
||||
````yaml tasks.yaml
|
||||
```yaml tasks.yaml
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about {topic}
|
||||
@@ -111,7 +97,7 @@ reporting_task:
|
||||
agent: reporting_analyst
|
||||
markdown: true
|
||||
output_file: report.md
|
||||
````
|
||||
```
|
||||
|
||||
To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
|
||||
|
||||
@@ -169,8 +155,7 @@ class LatestAiDevelopmentCrew():
|
||||
```
|
||||
|
||||
<Note>
|
||||
The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should
|
||||
match the method names in your Python code.
|
||||
The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
|
||||
</Note>
|
||||
|
||||
### Direct Code Definition (Alternative)
|
||||
@@ -207,8 +192,7 @@ reporting_task = Task(
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's
|
||||
process decide based on roles, availability, etc.
|
||||
Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
|
||||
</Tip>
|
||||
|
||||
## Task Output
|
||||
@@ -230,7 +214,6 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
|
||||
| **JSON Dict** | `json_dict` | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task. |
|
||||
| **Agent** | `agent` | `str` | The agent that executed the task. |
|
||||
| **Output Format** | `output_format` | `OutputFormat` | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
|
||||
| **Messages** | `messages` | `list[LLMMessage]` | The messages from the last task execution. |
|
||||
|
||||
### Task Methods and Properties
|
||||
|
||||
@@ -293,13 +276,12 @@ formatted_task = Task(
|
||||
```
|
||||
|
||||
When `markdown=True`, the agent will receive additional instructions to format the output using:
|
||||
|
||||
- `#` for headers
|
||||
- `**text**` for bold text
|
||||
- `**text**` for bold text
|
||||
- `*text*` for italic text
|
||||
- `-` or `*` for bullet points
|
||||
- `` `code` `` for inline code
|
||||
- ` `language ``` for code blocks
|
||||
- ``` ```language ``` for code blocks
|
||||
|
||||
### YAML Configuration with Markdown
|
||||
|
||||
@@ -310,7 +292,7 @@ analysis_task:
|
||||
expected_output: >
|
||||
A comprehensive analysis with charts and key findings
|
||||
agent: analyst
|
||||
markdown: true # Enable markdown formatting
|
||||
markdown: true # Enable markdown formatting
|
||||
output_file: analysis.md
|
||||
```
|
||||
|
||||
@@ -322,9 +304,7 @@ analysis_task:
|
||||
- **Cross-Platform Compatibility**: Markdown is universally supported
|
||||
|
||||
<Note>
|
||||
The markdown formatting instructions are automatically added to the task
|
||||
prompt when `markdown=True`, so you don't need to specify formatting
|
||||
requirements in your task description.
|
||||
The markdown formatting instructions are automatically added to the task prompt when `markdown=True`, so you don't need to specify formatting requirements in your task description.
|
||||
</Note>
|
||||
|
||||
## Task Dependencies and Context
|
||||
@@ -352,15 +332,9 @@ Task guardrails provide a way to validate and transform task outputs before they
|
||||
are passed to the next task. This feature helps ensure data quality and provides
|
||||
feedback to agents when their output doesn't meet specific criteria.
|
||||
|
||||
CrewAI supports two types of guardrails:
|
||||
### Using Task Guardrails
|
||||
|
||||
1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
|
||||
|
||||
2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
|
||||
|
||||
### Function-Based Guardrails
|
||||
|
||||
To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
|
||||
To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
|
||||
|
||||
```python Code
|
||||
from typing import Tuple, Union, Dict, Any
|
||||
@@ -370,12 +344,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""Validate blog content meets requirements."""
|
||||
try:
|
||||
# Check word count
|
||||
word_count = len(result.raw.split())
|
||||
word_count = len(result.split())
|
||||
if word_count > 200:
|
||||
return (False, "Blog content exceeds 200 words")
|
||||
|
||||
# Additional validation logic here
|
||||
return (True, result.raw.strip())
|
||||
return (True, result.strip())
|
||||
except Exception as e:
|
||||
return (False, "Unexpected error during validation")
|
||||
|
||||
@@ -387,156 +361,9 @@ blog_task = Task(
|
||||
)
|
||||
```
|
||||
|
||||
### LLM-Based Guardrails (String Descriptions)
|
||||
|
||||
Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
|
||||
|
||||
**Requirements**:
|
||||
|
||||
- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
|
||||
- Provide a clear, descriptive string explaining the validation criteria
|
||||
|
||||
```python Code
|
||||
from crewai import Task
|
||||
|
||||
# Single LLM-based guardrail
|
||||
blog_task = Task(
|
||||
description="Write a blog post about AI",
|
||||
expected_output="A blog post under 200 words",
|
||||
agent=blog_agent,
|
||||
guardrail="The blog post must be under 200 words and contain no technical jargon"
|
||||
)
|
||||
```
|
||||
|
||||
LLM-based guardrails are particularly useful for:
|
||||
|
||||
- **Complex validation logic** that's difficult to express programmatically
|
||||
- **Subjective criteria** like tone, style, or quality assessments
|
||||
- **Natural language requirements** that are easier to describe than code
|
||||
|
||||
The LLM guardrail will:
|
||||
|
||||
1. Analyze the task output against your description
|
||||
2. Return `(True, output)` if the output complies with the criteria
|
||||
3. Return `(False, feedback)` with specific feedback if validation fails
|
||||
|
||||
**Example with detailed validation criteria**:
|
||||
|
||||
```python Code
|
||||
research_task = Task(
|
||||
description="Research the latest developments in quantum computing",
|
||||
expected_output="A comprehensive research report",
|
||||
agent=researcher_agent,
|
||||
guardrail="""
|
||||
The research report must:
|
||||
- Be at least 1000 words long
|
||||
- Include at least 5 credible sources
|
||||
- Cover both technical and practical applications
|
||||
- Be written in a professional, academic tone
|
||||
- Avoid speculation or unverified claims
|
||||
"""
|
||||
)
|
||||
```
|
||||
|
||||
### Multiple Guardrails
|
||||
|
||||
You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
|
||||
|
||||
The `guardrails` parameter accepts:
|
||||
|
||||
- A list of guardrail functions or string descriptions
|
||||
- A single guardrail function or string (same as `guardrail`)
|
||||
|
||||
**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
|
||||
|
||||
```python Code
|
||||
from typing import Tuple, Any
|
||||
from crewai import TaskOutput, Task
|
||||
|
||||
def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""Validate word count is within limits."""
|
||||
word_count = len(result.raw.split())
|
||||
if word_count < 100:
|
||||
return (False, f"Content too short: {word_count} words. Need at least 100 words.")
|
||||
if word_count > 500:
|
||||
return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
|
||||
return (True, result.raw)
|
||||
|
||||
def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""Check for inappropriate language."""
|
||||
profanity_words = ["badword1", "badword2"] # Example list
|
||||
content_lower = result.raw.lower()
|
||||
for word in profanity_words:
|
||||
if word in content_lower:
|
||||
return (False, f"Inappropriate language detected: {word}")
|
||||
return (True, result.raw)
|
||||
|
||||
def format_output(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""Format and clean the output."""
|
||||
formatted = result.raw.strip()
|
||||
# Capitalize first letter
|
||||
formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
|
||||
return (True, formatted)
|
||||
|
||||
# Apply multiple guardrails sequentially
|
||||
blog_task = Task(
|
||||
description="Write a blog post about AI",
|
||||
expected_output="A well-formatted blog post between 100-500 words",
|
||||
agent=blog_agent,
|
||||
guardrails=[
|
||||
validate_word_count, # First: validate length
|
||||
validate_no_profanity, # Second: check content
|
||||
format_output # Third: format the result
|
||||
],
|
||||
guardrail_max_retries=3
|
||||
)
|
||||
```
|
||||
|
||||
In this example, the guardrails execute in order:
|
||||
|
||||
1. `validate_word_count` checks the word count
|
||||
2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
|
||||
3. `format_output` formats the final result (using the output from step 2)
|
||||
|
||||
If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
|
||||
|
||||
**Mixing function-based and LLM-based guardrails**:
|
||||
|
||||
You can combine both function-based and string-based guardrails in the same list:
|
||||
|
||||
```python Code
|
||||
from typing import Tuple, Any
|
||||
from crewai import TaskOutput, Task
|
||||
|
||||
def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
"""Validate word count is within limits."""
|
||||
word_count = len(result.raw.split())
|
||||
if word_count < 100:
|
||||
return (False, f"Content too short: {word_count} words. Need at least 100 words.")
|
||||
if word_count > 500:
|
||||
return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
|
||||
return (True, result.raw)
|
||||
|
||||
# Mix function-based and LLM-based guardrails
|
||||
blog_task = Task(
|
||||
description="Write a blog post about AI",
|
||||
expected_output="A well-formatted blog post between 100-500 words",
|
||||
agent=blog_agent,
|
||||
guardrails=[
|
||||
validate_word_count, # Function-based: precise word count check
|
||||
"The content must be engaging and suitable for a general audience", # LLM-based: subjective quality check
|
||||
"The writing style should be clear, concise, and free of technical jargon" # LLM-based: style validation
|
||||
],
|
||||
guardrail_max_retries=3
|
||||
)
|
||||
```
|
||||
|
||||
This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
|
||||
|
||||
### Guardrail Function Requirements
|
||||
|
||||
1. **Function Signature**:
|
||||
|
||||
- Must accept exactly one parameter (the task output)
|
||||
- Should return a tuple of `(bool, Any)`
|
||||
- Type hints are recommended but optional
|
||||
@@ -545,10 +372,13 @@ This approach combines the precision of programmatic validation with the flexibi
|
||||
- On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
|
||||
- On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
|
||||
|
||||
### LLMGuardrail
|
||||
|
||||
The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
|
||||
|
||||
### Error Handling Best Practices
|
||||
|
||||
1. **Structured Error Responses**:
|
||||
|
||||
```python Code
|
||||
from crewai import TaskOutput, LLMGuardrail
|
||||
|
||||
@@ -564,13 +394,11 @@ def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
```
|
||||
|
||||
2. **Error Categories**:
|
||||
|
||||
- Use specific error codes
|
||||
- Include relevant context
|
||||
- Provide actionable feedback
|
||||
|
||||
3. **Validation Chain**:
|
||||
|
||||
```python Code
|
||||
from typing import Any, Dict, List, Tuple, Union
|
||||
from crewai import TaskOutput
|
||||
@@ -597,15 +425,13 @@ def complex_validation(result: TaskOutput) -> Tuple[bool, Any]:
|
||||
### Handling Guardrail Results
|
||||
|
||||
When a guardrail returns `(False, error)`:
|
||||
|
||||
1. The error is sent back to the agent
|
||||
2. The agent attempts to fix the issue
|
||||
3. The process repeats until:
|
||||
- The guardrail returns `(True, result)`
|
||||
- Maximum retries are reached (`guardrail_max_retries`)
|
||||
- Maximum retries are reached
|
||||
|
||||
Example with retry handling:
|
||||
|
||||
```python Code
|
||||
from typing import Optional, Tuple, Union
|
||||
from crewai import TaskOutput, Task
|
||||
@@ -624,19 +450,17 @@ task = Task(
|
||||
expected_output="A valid JSON object",
|
||||
agent=analyst,
|
||||
guardrail=validate_json_output,
|
||||
guardrail_max_retries=3 # Limit retry attempts
|
||||
max_retries=3 # Limit retry attempts
|
||||
)
|
||||
```
|
||||
|
||||
## Getting Structured Consistent Outputs from Tasks
|
||||
|
||||
<Note>
|
||||
It's also important to note that the output of the final task of a crew
|
||||
becomes the final output of the actual crew itself.
|
||||
It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
|
||||
</Note>
|
||||
|
||||
### Using `output_pydantic`
|
||||
|
||||
The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
|
||||
|
||||
Here's an example demonstrating how to use output_pydantic:
|
||||
@@ -706,22 +530,18 @@ print("Accessing Properties - Option 5")
|
||||
print("Blog:", result)
|
||||
|
||||
```
|
||||
|
||||
In this example:
|
||||
|
||||
- A Pydantic model Blog is defined with title and content fields.
|
||||
- The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model.
|
||||
- After executing the crew, you can access the structured output in multiple ways as shown.
|
||||
* A Pydantic model Blog is defined with title and content fields.
|
||||
* The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model.
|
||||
* After executing the crew, you can access the structured output in multiple ways as shown.
|
||||
|
||||
#### Explanation of Accessing the Output
|
||||
|
||||
1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the **getitem** method.
|
||||
1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the __getitem__ method.
|
||||
2. Directly from Pydantic Model: Access the attributes directly from the result.pydantic object.
|
||||
3. Using to_dict() Method: Convert the output to a dictionary and access the fields.
|
||||
4. Printing the Entire Object: Simply print the result object to see the structured output.
|
||||
|
||||
### Using `output_json`
|
||||
|
||||
The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
|
||||
|
||||
Here's an example demonstrating how to use `output_json`:
|
||||
@@ -781,15 +601,14 @@ print("Blog:", result)
|
||||
```
|
||||
|
||||
In this example:
|
||||
|
||||
- A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output.
|
||||
- The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model.
|
||||
- After executing the crew, you can access the structured JSON output in two ways as shown.
|
||||
* A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output.
|
||||
* The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model.
|
||||
* After executing the crew, you can access the structured JSON output in two ways as shown.
|
||||
|
||||
#### Explanation of Accessing the Output
|
||||
|
||||
1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the **getitem** method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result.
|
||||
2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the **str** method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object.
|
||||
1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the __getitem__ method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result.
|
||||
2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the __str__ method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object.
|
||||
|
||||
---
|
||||
|
||||
@@ -979,103 +798,197 @@ While creating and executing tasks, certain validation mechanisms are in place t
|
||||
|
||||
These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
|
||||
|
||||
## Task Guardrails
|
||||
|
||||
Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
|
||||
|
||||
### Basic Usage
|
||||
|
||||
#### Define your own logic to validate
|
||||
|
||||
```python Code
|
||||
from typing import Tuple, Union
|
||||
from crewai import Task
|
||||
|
||||
def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
|
||||
"""Validate that the output is valid JSON."""
|
||||
try:
|
||||
json_data = json.loads(result)
|
||||
return (True, json_data)
|
||||
except json.JSONDecodeError:
|
||||
return (False, "Output must be valid JSON")
|
||||
|
||||
task = Task(
|
||||
description="Generate JSON data",
|
||||
expected_output="Valid JSON object",
|
||||
guardrail=validate_json_output
|
||||
)
|
||||
```
|
||||
|
||||
#### Leverage a no-code approach for validation
|
||||
|
||||
```python Code
|
||||
from crewai import Task
|
||||
|
||||
task = Task(
|
||||
description="Generate JSON data",
|
||||
expected_output="Valid JSON object",
|
||||
guardrail="Ensure the response is a valid JSON object"
|
||||
)
|
||||
```
|
||||
|
||||
#### Using YAML
|
||||
|
||||
```yaml
|
||||
research_task:
|
||||
...
|
||||
guardrail: make sure each bullet contains a minimum of 100 words
|
||||
...
|
||||
```
|
||||
|
||||
```python Code
|
||||
@CrewBase
|
||||
class InternalCrew:
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
...
|
||||
@task
|
||||
def research_task(self):
|
||||
return Task(config=self.tasks_config["research_task"]) # type: ignore[index]
|
||||
...
|
||||
```
|
||||
|
||||
|
||||
#### Use custom models for code generation
|
||||
|
||||
```python Code
|
||||
from crewai import Task
|
||||
from crewai.llm import LLM
|
||||
|
||||
task = Task(
|
||||
description="Generate JSON data",
|
||||
expected_output="Valid JSON object",
|
||||
guardrail=LLMGuardrail(
|
||||
description="Ensure the response is a valid JSON object",
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
### How Guardrails Work
|
||||
|
||||
1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
|
||||
2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
|
||||
3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
|
||||
- If `success` is `True`, `data` is the validated/transformed result
|
||||
- If `success` is `False`, `data` is the error message
|
||||
4. **Result Routing**:
|
||||
- On success (`True`), the result is automatically passed to the next task
|
||||
- On failure (`False`), the error is sent back to the agent to generate a new answer
|
||||
|
||||
### Common Use Cases
|
||||
|
||||
#### Data Format Validation
|
||||
```python Code
|
||||
def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
|
||||
"""Ensure the output contains a valid email address."""
|
||||
import re
|
||||
email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
|
||||
if re.match(email_pattern, result.strip()):
|
||||
return (True, result.strip())
|
||||
return (False, "Output must be a valid email address")
|
||||
```
|
||||
|
||||
#### Content Filtering
|
||||
```python Code
|
||||
def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
|
||||
"""Remove or validate sensitive information."""
|
||||
sensitive_patterns = ['SSN:', 'password:', 'secret:']
|
||||
for pattern in sensitive_patterns:
|
||||
if pattern.lower() in result.lower():
|
||||
return (False, f"Output contains sensitive information ({pattern})")
|
||||
return (True, result)
|
||||
```
|
||||
|
||||
#### Data Transformation
|
||||
```python Code
|
||||
def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
|
||||
"""Ensure phone numbers are in a consistent format."""
|
||||
import re
|
||||
digits = re.sub(r'\D', '', result)
|
||||
if len(digits) == 10:
|
||||
formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
|
||||
return (True, formatted)
|
||||
return (False, "Output must be a 10-digit phone number")
|
||||
```
|
||||
|
||||
### Advanced Features
|
||||
|
||||
#### Chaining Multiple Validations
|
||||
```python Code
|
||||
def chain_validations(*validators):
|
||||
"""Chain multiple validators together."""
|
||||
def combined_validator(result):
|
||||
for validator in validators:
|
||||
success, data = validator(result)
|
||||
if not success:
|
||||
return (False, data)
|
||||
result = data
|
||||
return (True, result)
|
||||
return combined_validator
|
||||
|
||||
# Usage
|
||||
task = Task(
|
||||
description="Get user contact info",
|
||||
expected_output="Email and phone",
|
||||
guardrail=chain_validations(
|
||||
validate_email_format,
|
||||
filter_sensitive_info
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
#### Custom Retry Logic
|
||||
```python Code
|
||||
task = Task(
|
||||
description="Generate data",
|
||||
expected_output="Valid data",
|
||||
guardrail=validate_data,
|
||||
max_retries=5 # Override default retry limit
|
||||
)
|
||||
```
|
||||
|
||||
## Creating Directories when Saving Files
|
||||
|
||||
The `create_directory` parameter controls whether CrewAI should automatically create directories when saving task outputs to files. This feature is particularly useful for organizing outputs and ensuring that file paths are correctly structured, especially when working with complex project hierarchies.
|
||||
|
||||
### Default Behavior
|
||||
|
||||
By default, `create_directory=True`, which means CrewAI will automatically create any missing directories in the output file path:
|
||||
You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
|
||||
|
||||
```python Code
|
||||
# Default behavior - directories are created automatically
|
||||
report_task = Task(
|
||||
description='Generate a comprehensive market analysis report',
|
||||
expected_output='A detailed market analysis with charts and insights',
|
||||
agent=analyst_agent,
|
||||
output_file='reports/2025/market_analysis.md', # Creates 'reports/2025/' if it doesn't exist
|
||||
markdown=True
|
||||
# ...
|
||||
|
||||
save_output_task = Task(
|
||||
description='Save the summarized AI news to a file',
|
||||
expected_output='File saved successfully',
|
||||
agent=research_agent,
|
||||
tools=[file_save_tool],
|
||||
output_file='outputs/ai_news_summary.txt',
|
||||
create_directory=True
|
||||
)
|
||||
```
|
||||
|
||||
### Disabling Directory Creation
|
||||
|
||||
If you want to prevent automatic directory creation and ensure that the directory already exists, set `create_directory=False`:
|
||||
|
||||
```python Code
|
||||
# Strict mode - directory must already exist
|
||||
strict_output_task = Task(
|
||||
description='Save critical data that requires existing infrastructure',
|
||||
expected_output='Data saved to pre-configured location',
|
||||
agent=data_agent,
|
||||
output_file='secure/vault/critical_data.json',
|
||||
create_directory=False # Will raise RuntimeError if 'secure/vault/' doesn't exist
|
||||
)
|
||||
```
|
||||
|
||||
### YAML Configuration
|
||||
|
||||
You can also configure this behavior in your YAML task definitions:
|
||||
|
||||
```yaml tasks.yaml
|
||||
analysis_task:
|
||||
description: >
|
||||
Generate quarterly financial analysis
|
||||
expected_output: >
|
||||
A comprehensive financial report with quarterly insights
|
||||
agent: financial_analyst
|
||||
output_file: reports/quarterly/q4_2024_analysis.pdf
|
||||
create_directory: true # Automatically create 'reports/quarterly/' directory
|
||||
|
||||
audit_task:
|
||||
description: >
|
||||
Perform compliance audit and save to existing audit directory
|
||||
expected_output: >
|
||||
A compliance audit report
|
||||
agent: auditor
|
||||
output_file: audit/compliance_report.md
|
||||
create_directory: false # Directory must already exist
|
||||
```
|
||||
|
||||
### Use Cases
|
||||
|
||||
**Automatic Directory Creation (`create_directory=True`):**
|
||||
|
||||
- Development and prototyping environments
|
||||
- Dynamic report generation with date-based folders
|
||||
- Automated workflows where directory structure may vary
|
||||
- Multi-tenant applications with user-specific folders
|
||||
|
||||
**Manual Directory Management (`create_directory=False`):**
|
||||
|
||||
- Production environments with strict file system controls
|
||||
- Security-sensitive applications where directories must be pre-configured
|
||||
- Systems with specific permission requirements
|
||||
- Compliance environments where directory creation is audited
|
||||
|
||||
### Error Handling
|
||||
|
||||
When `create_directory=False` and the directory doesn't exist, CrewAI will raise a `RuntimeError`:
|
||||
|
||||
```python Code
|
||||
try:
|
||||
result = crew.kickoff()
|
||||
except RuntimeError as e:
|
||||
# Handle missing directory error
|
||||
print(f"Directory creation failed: {e}")
|
||||
# Create directory manually or use fallback location
|
||||
#...
|
||||
```
|
||||
|
||||
Check out the video below to see how to use structured outputs in CrewAI:
|
||||
|
||||
<iframe
|
||||
className="w-full aspect-video rounded-xl"
|
||||
width="560"
|
||||
height="315"
|
||||
src="https://www.youtube.com/embed/dNpKQk5uxHw"
|
||||
title="Structured outputs in CrewAI"
|
||||
frameBorder="0"
|
||||
title="YouTube video player"
|
||||
frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
referrerPolicy="strict-origin-when-cross-origin"
|
||||
allowFullScreen
|
||||
referrerpolicy="strict-origin-when-cross-origin"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
## Conclusion
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Testing
|
||||
description: Learn how to test your CrewAI Crew and evaluate their performance.
|
||||
icon: vial
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -2,7 +2,6 @@
|
||||
title: Tools
|
||||
description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
|
||||
icon: screwdriver-wrench
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -10,10 +9,6 @@ mode: "wide"
|
||||
CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
|
||||
This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
|
||||
|
||||
<Note type="info" title="Tools are one of five agent capability types">
|
||||
Tools give agents **callable functions** to take action. They work alongside [MCPs](/en/mcp/overview) (remote tool servers), [Apps](/en/concepts/agent-capabilities) (platform integrations), [Skills](/en/concepts/skills) (domain expertise), and [Knowledge](/en/concepts/knowledge) (retrieved facts). See the [Agent Capabilities](/en/concepts/agent-capabilities) overview to understand when to use each.
|
||||
</Note>
|
||||
|
||||
## What is a Tool?
|
||||
|
||||
A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
|
||||
@@ -21,10 +16,9 @@ This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/cre
|
||||
enabling everything from simple searches to complex interactions and effective teamwork among agents.
|
||||
|
||||
<Note type="info" title="Enterprise Enhancement: Tools Repository">
|
||||
CrewAI AMP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
|
||||
CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
|
||||
|
||||
The Enterprise Tools Repository includes:
|
||||
|
||||
- Pre-built connectors for popular enterprise systems
|
||||
- Custom tool creation interface
|
||||
- Version control and sharing capabilities
|
||||
@@ -213,7 +207,7 @@ from crewai.tools import BaseTool
|
||||
class AsyncCustomTool(BaseTool):
|
||||
name: str = "async_custom_tool"
|
||||
description: str = "An asynchronous custom tool"
|
||||
|
||||
|
||||
async def _run(self, query: str = "") -> str:
|
||||
"""Asynchronously run the tool"""
|
||||
# Your async implementation here
|
||||
@@ -0,0 +1,67 @@
|
||||
---
|
||||
title: Training
|
||||
description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
|
||||
icon: dumbbell
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
|
||||
By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
|
||||
|
||||
During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
|
||||
This helps the agents improve their understanding, decision-making, and problem-solving abilities.
|
||||
|
||||
### Training Your Crew Using the CLI
|
||||
|
||||
To use the training feature, follow these steps:
|
||||
|
||||
1. Open your terminal or command prompt.
|
||||
2. Navigate to the directory where your CrewAI project is located.
|
||||
3. Run the following command:
|
||||
|
||||
```shell
|
||||
crewai train -n <n_iterations> <filename> (optional)
|
||||
```
|
||||
<Tip>
|
||||
Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
|
||||
</Tip>
|
||||
|
||||
### Training Your Crew Programmatically
|
||||
|
||||
To train your crew programmatically, use the following steps:
|
||||
|
||||
1. Define the number of iterations for training.
|
||||
2. Specify the input parameters for the training process.
|
||||
3. Execute the training command within a try-except block to handle potential errors.
|
||||
|
||||
```python Code
|
||||
n_iterations = 2
|
||||
inputs = {"topic": "CrewAI Training"}
|
||||
filename = "your_model.pkl"
|
||||
|
||||
try:
|
||||
YourCrewName_Crew().crew().train(
|
||||
n_iterations=n_iterations,
|
||||
inputs=inputs,
|
||||
filename=filename
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"An error occurred while training the crew: {e}")
|
||||
```
|
||||
|
||||
### Key Points to Note
|
||||
|
||||
- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
|
||||
- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
|
||||
- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
|
||||
|
||||
It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
|
||||
|
||||
Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
|
||||
|
||||
Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
|
||||
|
||||
Happy training with CrewAI! 🚀
|
||||
|
||||
-296
@@ -1,296 +0,0 @@
|
||||
"""Pytest configuration for crewAI workspace."""
|
||||
|
||||
import base64
|
||||
from collections.abc import Generator
|
||||
import gzip
|
||||
import os
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
from typing import Any
|
||||
|
||||
from dotenv import load_dotenv
|
||||
import pytest
|
||||
from vcr.request import Request # type: ignore[import-untyped]
|
||||
|
||||
|
||||
try:
|
||||
import vcr.stubs.httpx_stubs as httpx_stubs # type: ignore[import-untyped]
|
||||
except ModuleNotFoundError:
|
||||
import vcr.stubs.httpcore_stubs as httpx_stubs # type: ignore[import-untyped]
|
||||
|
||||
|
||||
env_test_path = Path(__file__).parent / ".env.test"
|
||||
load_dotenv(env_test_path, override=True)
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
def _patched_make_vcr_request(httpx_request: Any, **kwargs: Any) -> Any:
|
||||
"""Patched version of VCR's _make_vcr_request that handles binary content.
|
||||
|
||||
The original implementation fails on binary request bodies (like file uploads)
|
||||
because it assumes all content can be decoded as UTF-8.
|
||||
"""
|
||||
raw_body = httpx_request.read()
|
||||
try:
|
||||
body = raw_body.decode("utf-8")
|
||||
except UnicodeDecodeError:
|
||||
body = base64.b64encode(raw_body).decode("ascii")
|
||||
uri = str(httpx_request.url)
|
||||
headers = dict(httpx_request.headers)
|
||||
return Request(httpx_request.method, uri, body, headers)
|
||||
|
||||
|
||||
httpx_stubs._make_vcr_request = _patched_make_vcr_request
|
||||
|
||||
|
||||
# Patch the response-side of VCR to fix httpx.ResponseNotRead errors.
|
||||
# VCR's _from_serialized_response mocks httpx.Response.read(), which prevents
|
||||
# the response's internal _content attribute from being properly initialized.
|
||||
# When OpenAI's client (using with_raw_response) accesses response.content,
|
||||
# httpx raises ResponseNotRead because read() was never actually called.
|
||||
# This patch ensures _content is explicitly set after response creation.
|
||||
_original_from_serialized_response = getattr(
|
||||
httpx_stubs, "_from_serialized_response", None
|
||||
)
|
||||
|
||||
if _original_from_serialized_response is not None:
|
||||
|
||||
def _patched_from_serialized_response(
|
||||
request: Any, serialized_response: Any, history: Any = None
|
||||
) -> Any:
|
||||
"""Patched version that ensures response._content is properly set."""
|
||||
response = _original_from_serialized_response(request, serialized_response, history)
|
||||
# Explicitly set _content to avoid ResponseNotRead errors
|
||||
# The content was passed to the constructor but the mocked read() prevents
|
||||
# proper initialization of the internal state
|
||||
body_content = serialized_response.get("body", {}).get("string", b"")
|
||||
if isinstance(body_content, str):
|
||||
body_content = body_content.encode("utf-8")
|
||||
response._content = body_content
|
||||
return response
|
||||
|
||||
httpx_stubs._from_serialized_response = _patched_from_serialized_response
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True, scope="function")
|
||||
def cleanup_event_handlers() -> Generator[None, Any, None]:
|
||||
"""Clean up event bus handlers after each test to prevent test pollution."""
|
||||
yield
|
||||
|
||||
try:
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
|
||||
with crewai_event_bus._rwlock.w_locked():
|
||||
crewai_event_bus._sync_handlers.clear()
|
||||
crewai_event_bus._async_handlers.clear()
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True, scope="function")
|
||||
def reset_event_state() -> None:
|
||||
"""Reset event system state before each test for isolation."""
|
||||
from crewai.events.base_events import reset_emission_counter
|
||||
from crewai.events.event_context import (
|
||||
EventContextConfig,
|
||||
_event_context_config,
|
||||
_event_id_stack,
|
||||
)
|
||||
|
||||
reset_emission_counter()
|
||||
_event_id_stack.set(())
|
||||
_event_context_config.set(EventContextConfig())
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True, scope="function")
|
||||
def setup_test_environment() -> Generator[None, Any, None]:
|
||||
"""Setup test environment for crewAI workspace."""
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
storage_dir = Path(temp_dir) / "crewai_test_storage"
|
||||
storage_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if not storage_dir.exists() or not storage_dir.is_dir():
|
||||
raise RuntimeError(
|
||||
f"Failed to create test storage directory: {storage_dir}"
|
||||
)
|
||||
|
||||
try:
|
||||
test_file = storage_dir / ".permissions_test"
|
||||
test_file.touch()
|
||||
test_file.unlink()
|
||||
except (OSError, IOError) as e:
|
||||
raise RuntimeError(
|
||||
f"Test storage directory {storage_dir} is not writable: {e}"
|
||||
) from e
|
||||
|
||||
os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
|
||||
os.environ["CREWAI_TESTING"] = "true"
|
||||
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
os.environ.pop("CREWAI_TESTING", "true")
|
||||
os.environ.pop("CREWAI_STORAGE_DIR", None)
|
||||
os.environ.pop("CREWAI_DISABLE_TELEMETRY", "true")
|
||||
os.environ.pop("OTEL_SDK_DISABLED", "true")
|
||||
os.environ.pop("OPENAI_BASE_URL", "https://api.openai.com/v1")
|
||||
os.environ.pop("OPENAI_API_BASE", "https://api.openai.com/v1")
|
||||
|
||||
|
||||
HEADERS_TO_FILTER = {
|
||||
"authorization": "AUTHORIZATION-XXX",
|
||||
"content-security-policy": "CSP-FILTERED",
|
||||
"cookie": "COOKIE-XXX",
|
||||
"set-cookie": "SET-COOKIE-XXX",
|
||||
"permissions-policy": "PERMISSIONS-POLICY-XXX",
|
||||
"referrer-policy": "REFERRER-POLICY-XXX",
|
||||
"strict-transport-security": "STS-XXX",
|
||||
"x-content-type-options": "X-CONTENT-TYPE-XXX",
|
||||
"x-frame-options": "X-FRAME-OPTIONS-XXX",
|
||||
"x-permitted-cross-domain-policies": "X-PERMITTED-XXX",
|
||||
"x-request-id": "X-REQUEST-ID-XXX",
|
||||
"x-runtime": "X-RUNTIME-XXX",
|
||||
"x-xss-protection": "X-XSS-PROTECTION-XXX",
|
||||
"x-stainless-arch": "X-STAINLESS-ARCH-XXX",
|
||||
"x-stainless-os": "X-STAINLESS-OS-XXX",
|
||||
"x-stainless-read-timeout": "X-STAINLESS-READ-TIMEOUT-XXX",
|
||||
"cf-ray": "CF-RAY-XXX",
|
||||
"etag": "ETAG-XXX",
|
||||
"Strict-Transport-Security": "STS-XXX",
|
||||
"access-control-expose-headers": "ACCESS-CONTROL-XXX",
|
||||
"openai-organization": "OPENAI-ORG-XXX",
|
||||
"openai-project": "OPENAI-PROJECT-XXX",
|
||||
"x-ratelimit-limit-requests": "X-RATELIMIT-LIMIT-REQUESTS-XXX",
|
||||
"x-ratelimit-limit-tokens": "X-RATELIMIT-LIMIT-TOKENS-XXX",
|
||||
"x-ratelimit-remaining-requests": "X-RATELIMIT-REMAINING-REQUESTS-XXX",
|
||||
"x-ratelimit-remaining-tokens": "X-RATELIMIT-REMAINING-TOKENS-XXX",
|
||||
"x-ratelimit-reset-requests": "X-RATELIMIT-RESET-REQUESTS-XXX",
|
||||
"x-ratelimit-reset-tokens": "X-RATELIMIT-RESET-TOKENS-XXX",
|
||||
"x-goog-api-key": "X-GOOG-API-KEY-XXX",
|
||||
"api-key": "X-API-KEY-XXX",
|
||||
"User-Agent": "X-USER-AGENT-XXX",
|
||||
"apim-request-id:": "X-API-CLIENT-REQUEST-ID-XXX",
|
||||
"azureml-model-session": "AZUREML-MODEL-SESSION-XXX",
|
||||
"x-ms-client-request-id": "X-MS-CLIENT-REQUEST-ID-XXX",
|
||||
"x-ms-region": "X-MS-REGION-XXX",
|
||||
"apim-request-id": "APIM-REQUEST-ID-XXX",
|
||||
"x-api-key": "X-API-KEY-XXX",
|
||||
"anthropic-organization-id": "ANTHROPIC-ORGANIZATION-ID-XXX",
|
||||
"request-id": "REQUEST-ID-XXX",
|
||||
"anthropic-ratelimit-input-tokens-limit": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX",
|
||||
"anthropic-ratelimit-input-tokens-remaining": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX",
|
||||
"anthropic-ratelimit-input-tokens-reset": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX",
|
||||
"anthropic-ratelimit-output-tokens-limit": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX",
|
||||
"anthropic-ratelimit-output-tokens-remaining": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX",
|
||||
"anthropic-ratelimit-output-tokens-reset": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX",
|
||||
"anthropic-ratelimit-tokens-limit": "ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX",
|
||||
"anthropic-ratelimit-tokens-remaining": "ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX",
|
||||
"anthropic-ratelimit-tokens-reset": "ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX",
|
||||
"x-amz-date": "X-AMZ-DATE-XXX",
|
||||
"amz-sdk-invocation-id": "AMZ-SDK-INVOCATION-ID-XXX",
|
||||
"accept-encoding": "ACCEPT-ENCODING-XXX",
|
||||
"x-amzn-requestid": "X-AMZN-REQUESTID-XXX",
|
||||
"x-amzn-RequestId": "X-AMZN-REQUESTID-XXX",
|
||||
"x-a2a-notification-token": "X-A2A-NOTIFICATION-TOKEN-XXX",
|
||||
"x-a2a-version": "X-A2A-VERSION-XXX",
|
||||
}
|
||||
|
||||
|
||||
def _filter_request_headers(request: Request) -> Request: # type: ignore[no-any-unimported]
|
||||
"""Filter sensitive headers from request before recording."""
|
||||
for header_name, replacement in HEADERS_TO_FILTER.items():
|
||||
for variant in [header_name, header_name.upper(), header_name.title()]:
|
||||
if variant in request.headers:
|
||||
request.headers[variant] = [replacement]
|
||||
|
||||
request.method = request.method.upper()
|
||||
|
||||
# Normalize Azure OpenAI endpoints to a consistent placeholder for cassette matching.
|
||||
if request.host and request.host.endswith(".openai.azure.com"):
|
||||
original_host = request.host
|
||||
placeholder_host = "fake-azure-endpoint.openai.azure.com"
|
||||
request.uri = request.uri.replace(original_host, placeholder_host)
|
||||
|
||||
return request
|
||||
|
||||
|
||||
def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any] | None:
|
||||
"""Filter sensitive headers from response before recording.
|
||||
|
||||
Returns None to skip recording responses with empty bodies. This handles
|
||||
duplicate recordings caused by OpenAI's stainless client using
|
||||
with_raw_response which triggers httpx to re-read the consumed stream.
|
||||
"""
|
||||
body = response.get("body", {}).get("string", "")
|
||||
headers = response.get("headers", {})
|
||||
content_length = headers.get("content-length", headers.get("Content-Length", []))
|
||||
|
||||
if body == "" or body == b"" or content_length == ["0"]:
|
||||
return None
|
||||
|
||||
for encoding_header in ["Content-Encoding", "content-encoding"]:
|
||||
if encoding_header in headers:
|
||||
encoding = headers.pop(encoding_header)
|
||||
if encoding and encoding[0] == "gzip":
|
||||
body = response.get("body", {}).get("string", b"")
|
||||
if isinstance(body, bytes) and body.startswith(b"\x1f\x8b"):
|
||||
response["body"]["string"] = gzip.decompress(body).decode("utf-8")
|
||||
|
||||
for header_name, replacement in HEADERS_TO_FILTER.items():
|
||||
for variant in [header_name, header_name.upper(), header_name.title()]:
|
||||
if variant in headers:
|
||||
headers[variant] = [replacement]
|
||||
return response
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_cassette_dir(request: Any) -> str:
|
||||
"""Generate cassette directory path based on test module location.
|
||||
|
||||
Organizes cassettes to mirror test directory structure within each package:
|
||||
lib/crewai/tests/llms/google/test_google.py -> lib/crewai/tests/cassettes/llms/google/
|
||||
lib/crewai-tools/tests/tools/test_search.py -> lib/crewai-tools/tests/cassettes/tools/
|
||||
"""
|
||||
test_file = Path(request.fspath)
|
||||
|
||||
for parent in test_file.parents:
|
||||
if (
|
||||
parent.name in ("crewai", "crewai-tools", "crewai-files")
|
||||
and parent.parent.name == "lib"
|
||||
):
|
||||
package_root = parent
|
||||
break
|
||||
else:
|
||||
package_root = test_file.parent
|
||||
|
||||
tests_root = package_root / "tests"
|
||||
test_dir = test_file.parent
|
||||
|
||||
if test_dir != tests_root:
|
||||
relative_path = test_dir.relative_to(tests_root)
|
||||
cassette_dir = tests_root / "cassettes" / relative_path
|
||||
else:
|
||||
cassette_dir = tests_root / "cassettes"
|
||||
|
||||
cassette_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
return str(cassette_dir)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_config(vcr_cassette_dir: str) -> dict[str, Any]:
|
||||
"""Configure VCR with organized cassette storage."""
|
||||
config = {
|
||||
"cassette_library_dir": vcr_cassette_dir,
|
||||
"record_mode": os.getenv("PYTEST_VCR_RECORD_MODE", "once"),
|
||||
"filter_headers": [(k, v) for k, v in HEADERS_TO_FILTER.items()],
|
||||
"before_record_request": _filter_request_headers,
|
||||
"before_record_response": _filter_response_headers,
|
||||
"filter_query_parameters": ["key"],
|
||||
"match_on": ["method", "scheme", "host", "port", "path"],
|
||||
}
|
||||
|
||||
if os.getenv("GITHUB_ACTIONS") == "true":
|
||||
config["record_mode"] = "none"
|
||||
|
||||
return config
|
||||
@@ -0,0 +1,410 @@
|
||||
{
|
||||
"$schema": "https://mintlify.com/docs.json",
|
||||
"theme": "mint",
|
||||
"name": "CrewAI",
|
||||
"colors": {
|
||||
"primary": "#EB6658",
|
||||
"light": "#F3A78B",
|
||||
"dark": "#C94C3C"
|
||||
},
|
||||
"favicon": "images/favicon.svg",
|
||||
"contextual": {
|
||||
"options": [
|
||||
"copy",
|
||||
"view",
|
||||
"chatgpt",
|
||||
"claude"
|
||||
]
|
||||
},
|
||||
"navigation": {
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Documentation",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"installation",
|
||||
"quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
{
|
||||
"group": "Strategy",
|
||||
"pages": [
|
||||
"guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agents",
|
||||
"pages": [
|
||||
"guides/agents/crafting-effective-agents"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Crews",
|
||||
"pages": [
|
||||
"guides/crews/first-crew"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Flows",
|
||||
"pages": [
|
||||
"guides/flows/first-flow",
|
||||
"guides/flows/mastering-flow-state"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Advanced",
|
||||
"pages": [
|
||||
"guides/advanced/customizing-prompts",
|
||||
"guides/advanced/fingerprinting"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Core Concepts",
|
||||
"pages": [
|
||||
"concepts/agents",
|
||||
"concepts/tasks",
|
||||
"concepts/crews",
|
||||
"concepts/flows",
|
||||
"concepts/knowledge",
|
||||
"concepts/llms",
|
||||
"concepts/processes",
|
||||
"concepts/collaboration",
|
||||
"concepts/training",
|
||||
"concepts/memory",
|
||||
"concepts/reasoning",
|
||||
"concepts/planning",
|
||||
"concepts/testing",
|
||||
"concepts/cli",
|
||||
"concepts/tools",
|
||||
"concepts/event-listener"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "MCP Integration",
|
||||
"pages": [
|
||||
"mcp/overview",
|
||||
"mcp/stdio",
|
||||
"mcp/sse",
|
||||
"mcp/streamable-http",
|
||||
"mcp/multiple-servers",
|
||||
"mcp/security"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Tools",
|
||||
"pages": [
|
||||
"tools/overview",
|
||||
{
|
||||
"group": "File & Document",
|
||||
"pages": [
|
||||
"tools/file-document/overview",
|
||||
"tools/file-document/filereadtool",
|
||||
"tools/file-document/filewritetool",
|
||||
"tools/file-document/pdfsearchtool",
|
||||
"tools/file-document/docxsearchtool",
|
||||
"tools/file-document/mdxsearchtool",
|
||||
"tools/file-document/xmlsearchtool",
|
||||
"tools/file-document/txtsearchtool",
|
||||
"tools/file-document/jsonsearchtool",
|
||||
"tools/file-document/csvsearchtool",
|
||||
"tools/file-document/directorysearchtool",
|
||||
"tools/file-document/directoryreadtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Web Scraping & Browsing",
|
||||
"pages": [
|
||||
"tools/web-scraping/overview",
|
||||
"tools/web-scraping/scrapewebsitetool",
|
||||
"tools/web-scraping/scrapeelementfromwebsitetool",
|
||||
"tools/web-scraping/scrapflyscrapetool",
|
||||
"tools/web-scraping/seleniumscrapingtool",
|
||||
"tools/web-scraping/scrapegraphscrapetool",
|
||||
"tools/web-scraping/spidertool",
|
||||
"tools/web-scraping/browserbaseloadtool",
|
||||
"tools/web-scraping/hyperbrowserloadtool",
|
||||
"tools/web-scraping/stagehandtool",
|
||||
"tools/web-scraping/firecrawlcrawlwebsitetool",
|
||||
"tools/web-scraping/firecrawlscrapewebsitetool",
|
||||
"tools/web-scraping/firecrawlsearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Search & Research",
|
||||
"pages": [
|
||||
"tools/search-research/overview",
|
||||
"tools/search-research/serperdevtool",
|
||||
"tools/search-research/bravesearchtool",
|
||||
"tools/search-research/exasearchtool",
|
||||
"tools/search-research/linkupsearchtool",
|
||||
"tools/search-research/githubsearchtool",
|
||||
"tools/search-research/websitesearchtool",
|
||||
"tools/search-research/codedocssearchtool",
|
||||
"tools/search-research/youtubechannelsearchtool",
|
||||
"tools/search-research/youtubevideosearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Database & Data",
|
||||
"pages": [
|
||||
"tools/database-data/overview",
|
||||
"tools/database-data/mysqltool",
|
||||
"tools/database-data/pgsearchtool",
|
||||
"tools/database-data/snowflakesearchtool",
|
||||
"tools/database-data/nl2sqltool",
|
||||
"tools/database-data/qdrantvectorsearchtool",
|
||||
"tools/database-data/weaviatevectorsearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "AI & Machine Learning",
|
||||
"pages": [
|
||||
"tools/ai-ml/overview",
|
||||
"tools/ai-ml/dalletool",
|
||||
"tools/ai-ml/visiontool",
|
||||
"tools/ai-ml/aimindtool",
|
||||
"tools/ai-ml/llamaindextool",
|
||||
"tools/ai-ml/langchaintool",
|
||||
"tools/ai-ml/ragtool",
|
||||
"tools/ai-ml/codeinterpretertool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Cloud & Storage",
|
||||
"pages": [
|
||||
"tools/cloud-storage/overview",
|
||||
"tools/cloud-storage/s3readertool",
|
||||
"tools/cloud-storage/s3writertool",
|
||||
"tools/cloud-storage/bedrockinvokeagenttool",
|
||||
"tools/cloud-storage/bedrockkbretriever"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Automation & Integration",
|
||||
"pages": [
|
||||
"tools/automation/overview",
|
||||
"tools/automation/apifyactorstool",
|
||||
"tools/automation/composiotool",
|
||||
"tools/automation/multiontool"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Observability",
|
||||
"pages": [
|
||||
"observability/overview",
|
||||
"observability/agentops",
|
||||
"observability/arize-phoenix",
|
||||
"observability/langfuse",
|
||||
"observability/langtrace",
|
||||
"observability/maxim",
|
||||
"observability/mlflow",
|
||||
"observability/openlit",
|
||||
"observability/opik",
|
||||
"observability/patronus-evaluation",
|
||||
"observability/portkey",
|
||||
"observability/weave"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Learn",
|
||||
"pages": [
|
||||
"learn/overview",
|
||||
"learn/llm-selection-guide",
|
||||
"learn/conditional-tasks",
|
||||
"learn/coding-agents",
|
||||
"learn/create-custom-tools",
|
||||
"learn/custom-llm",
|
||||
"learn/custom-manager-agent",
|
||||
"learn/customizing-agents",
|
||||
"learn/dalle-image-generation",
|
||||
"learn/force-tool-output-as-result",
|
||||
"learn/hierarchical-process",
|
||||
"learn/human-input-on-execution",
|
||||
"learn/kickoff-async",
|
||||
"learn/kickoff-for-each",
|
||||
"learn/llm-connections",
|
||||
"learn/multimodal-agents",
|
||||
"learn/replay-tasks-from-latest-crew-kickoff",
|
||||
"learn/sequential-process",
|
||||
"learn/using-annotations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
"telemetry"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Enterprise",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Getting Started",
|
||||
"pages": [
|
||||
"enterprise/introduction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Features",
|
||||
"pages": [
|
||||
"enterprise/features/tool-repository",
|
||||
"enterprise/features/webhook-streaming",
|
||||
"enterprise/features/traces",
|
||||
"enterprise/features/hallucination-guardrail",
|
||||
"enterprise/features/integrations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "How-To Guides",
|
||||
"pages": [
|
||||
"enterprise/guides/build-crew",
|
||||
"enterprise/guides/deploy-crew",
|
||||
"enterprise/guides/kickoff-crew",
|
||||
"enterprise/guides/update-crew",
|
||||
"enterprise/guides/enable-crew-studio",
|
||||
"enterprise/guides/azure-openai-setup",
|
||||
"enterprise/guides/hubspot-trigger",
|
||||
"enterprise/guides/react-component-export",
|
||||
"enterprise/guides/salesforce-trigger",
|
||||
"enterprise/guides/slack-trigger",
|
||||
"enterprise/guides/team-management",
|
||||
"enterprise/guides/webhook-automation",
|
||||
"enterprise/guides/human-in-the-loop",
|
||||
"enterprise/guides/zapier-trigger"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Resources",
|
||||
"pages": [
|
||||
"enterprise/resources/frequently-asked-questions"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "API Reference",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Getting Started",
|
||||
"pages": [
|
||||
"api-reference/introduction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Endpoints",
|
||||
"openapi": "enterprise-api.yaml"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Examples",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Examples",
|
||||
"pages": [
|
||||
"examples/example"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Releases",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Releases",
|
||||
"pages": [
|
||||
"changelog"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"global": {
|
||||
"anchors": [
|
||||
{
|
||||
"anchor": "Website",
|
||||
"href": "https://crewai.com",
|
||||
"icon": "globe"
|
||||
},
|
||||
{
|
||||
"anchor": "Forum",
|
||||
"href": "https://community.crewai.com",
|
||||
"icon": "discourse"
|
||||
},
|
||||
{
|
||||
"anchor": "Crew GPT",
|
||||
"href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
|
||||
"icon": "robot"
|
||||
},
|
||||
{
|
||||
"anchor": "Get Help",
|
||||
"href": "mailto:support@crewai.com",
|
||||
"icon": "headset"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"logo": {
|
||||
"light": "images/crew_only_logo.png",
|
||||
"dark": "images/crew_only_logo.png"
|
||||
},
|
||||
"appearance": {
|
||||
"default": "dark",
|
||||
"strict": false
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
{
|
||||
"label": "Start Cloud Trial",
|
||||
"href": "https://app.crewai.com"
|
||||
}
|
||||
],
|
||||
"primary": {
|
||||
"type": "github",
|
||||
"href": "https://github.com/crewAIInc/crewAI"
|
||||
}
|
||||
},
|
||||
"search": {
|
||||
"prompt": "Search CrewAI docs"
|
||||
},
|
||||
"api": {
|
||||
"baseUrl": "https://your-actual-crew-name.crewai.com",
|
||||
"auth": {
|
||||
"method": "bearer",
|
||||
"name": "Authorization"
|
||||
},
|
||||
"playground": {
|
||||
"mode": "simple"
|
||||
}
|
||||
},
|
||||
"seo": {
|
||||
"indexing": "all"
|
||||
},
|
||||
"errors": {
|
||||
"404": {
|
||||
"redirect": true
|
||||
}
|
||||
},
|
||||
"footer": {
|
||||
"socials": {
|
||||
"website": "https://crewai.com",
|
||||
"x": "https://x.com/crewAIInc",
|
||||
"github": "https://github.com/crewAIInc/crewAI",
|
||||
"linkedin": "https://www.linkedin.com/company/crewai-inc",
|
||||
"youtube": "https://youtube.com/@crewAIInc",
|
||||
"reddit": "https://www.reddit.com/r/crewAIInc/"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
---
|
||||
title: "GET /inputs"
|
||||
description: "الحصول على المدخلات المطلوبة لطاقمك"
|
||||
openapi: "/enterprise-api.en.yaml GET /inputs"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
|
||||
@@ -1,135 +0,0 @@
|
||||
---
|
||||
title: "مقدمة"
|
||||
description: "المرجع الكامل لواجهة برمجة تطبيقات CrewAI AMP REST"
|
||||
icon: "code"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# واجهة برمجة تطبيقات CrewAI AMP
|
||||
|
||||
مرحبًا بك في مرجع واجهة برمجة تطبيقات CrewAI AMP. تتيح لك هذه الواجهة التفاعل برمجيًا مع الأطقم المنشورة، مما يمكّنك من دمجها مع تطبيقاتك وسير عملك وخدماتك.
|
||||
|
||||
## البدء السريع
|
||||
|
||||
<Steps>
|
||||
<Step title="الحصول على بيانات اعتماد API">
|
||||
انتقل إلى صفحة تفاصيل طاقمك في لوحة تحكم CrewAI AMP وانسخ رمز Bearer من علامة تبويب الحالة.
|
||||
</Step>
|
||||
|
||||
<Step title="اكتشاف المدخلات المطلوبة">
|
||||
استخدم نقطة النهاية `GET /inputs` لمعرفة المعاملات التي يتوقعها طاقمك.
|
||||
</Step>
|
||||
|
||||
<Step title="بدء تنفيذ الطاقم">
|
||||
استدعِ `POST /kickoff` مع مدخلاتك لبدء تنفيذ الطاقم واستلام
|
||||
`kickoff_id`.
|
||||
</Step>
|
||||
|
||||
<Step title="مراقبة التقدم">
|
||||
استخدم `GET /{kickoff_id}/status` للتحقق من حالة التنفيذ واسترجاع النتائج.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## المصادقة
|
||||
|
||||
تتطلب جميع طلبات API المصادقة باستخدام رمز Bearer. أدرج رمزك في ترويسة `Authorization`:
|
||||
|
||||
```bash
|
||||
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
https://your-crew-url.crewai.com/inputs
|
||||
```
|
||||
|
||||
### أنواع الرموز
|
||||
|
||||
| نوع الرمز | النطاق | حالة الاستخدام |
|
||||
| :-------------------- | :------------------------ | :----------------------------------------------------------- |
|
||||
| **Bearer Token** | وصول على مستوى المؤسسة | عمليات الطاقم الكاملة، مثالي للتكامل بين الخوادم |
|
||||
| **User Bearer Token** | وصول محدد بالمستخدم | صلاحيات محدودة، مناسب للعمليات الخاصة بالمستخدم |
|
||||
|
||||
<Tip>
|
||||
يمكنك العثور على كلا نوعي الرموز في علامة تبويب الحالة من صفحة تفاصيل طاقمك في
|
||||
لوحة تحكم CrewAI AMP.
|
||||
</Tip>
|
||||
|
||||
## عنوان URL الأساسي
|
||||
|
||||
لكل طاقم منشور نقطة نهاية API فريدة خاصة به:
|
||||
|
||||
```
|
||||
https://your-crew-name.crewai.com
|
||||
```
|
||||
|
||||
استبدل `your-crew-name` بعنوان URL الفعلي لطاقمك من لوحة التحكم.
|
||||
|
||||
## سير العمل النموذجي
|
||||
|
||||
1. **الاكتشاف**: استدعِ `GET /inputs` لفهم ما يحتاجه طاقمك
|
||||
2. **التنفيذ**: أرسل المدخلات عبر `POST /kickoff` لبدء المعالجة
|
||||
3. **المراقبة**: استعلم عن `GET /{kickoff_id}/status` حتى الاكتمال
|
||||
4. **النتائج**: استخرج المخرجات النهائية من الاستجابة المكتملة
|
||||
|
||||
## معالجة الأخطاء
|
||||
|
||||
تستخدم الواجهة أكواد حالة HTTP القياسية:
|
||||
|
||||
| الكود | المعنى |
|
||||
| ----- | :----------------------------------------- |
|
||||
| `200` | نجاح |
|
||||
| `400` | طلب غير صالح - تنسيق مدخلات غير صحيح |
|
||||
| `401` | غير مصرّح - رمز bearer غير صالح |
|
||||
| `404` | غير موجود - المورد غير موجود |
|
||||
| `422` | خطأ في التحقق - مدخلات مطلوبة مفقودة |
|
||||
| `500` | خطأ في الخادم - تواصل مع الدعم |
|
||||
|
||||
## الاختبار التفاعلي
|
||||
|
||||
<Info>
|
||||
**لماذا لا يوجد زر "إرسال"؟** نظرًا لأن كل مستخدم CrewAI AMP لديه عنوان URL
|
||||
فريد للطاقم، نستخدم **وضع المرجع** بدلاً من بيئة تفاعلية لتجنب
|
||||
الالتباس. يوضح لك هذا بالضبط كيف يجب أن تبدو الطلبات بدون
|
||||
أزرار إرسال غير فعالة.
|
||||
</Info>
|
||||
|
||||
تعرض لك كل صفحة نقطة نهاية:
|
||||
|
||||
- **تنسيق الطلب الدقيق** مع جميع المعاملات
|
||||
- **أمثلة الاستجابة** لحالات النجاح والخطأ
|
||||
- **عينات الكود** بلغات متعددة (cURL، Python، JavaScript، إلخ)
|
||||
- **أمثلة المصادقة** بتنسيق رمز Bearer الصحيح
|
||||
|
||||
### **لاختبار واجهتك الفعلية:**
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="نسخ أمثلة cURL" icon="terminal">
|
||||
انسخ أمثلة cURL واستبدل العنوان URL + الرمز بقيمك الحقيقية
|
||||
</Card>
|
||||
<Card title="استخدام Postman/Insomnia" icon="play">
|
||||
استورد الأمثلة في أداة اختبار API المفضلة لديك
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
**مثال على سير العمل:**
|
||||
|
||||
1. **انسخ مثال cURL هذا** من أي صفحة نقطة نهاية
|
||||
2. **استبدل `your-actual-crew-name.crewai.com`** بعنوان URL الحقيقي لطاقمك
|
||||
3. **استبدل رمز Bearer** برمزك الحقيقي من لوحة التحكم
|
||||
4. **نفّذ الطلب** في طرفيتك أو عميل API
|
||||
|
||||
## هل تحتاج مساعدة؟
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="دعم المؤسسات"
|
||||
icon="headset"
|
||||
href="mailto:support@crewai.com"
|
||||
>
|
||||
احصل على مساعدة في تكامل API واستكشاف الأخطاء وإصلاحها
|
||||
</Card>
|
||||
<Card
|
||||
title="لوحة تحكم المؤسسات"
|
||||
icon="chart-line"
|
||||
href="https://app.crewai.com"
|
||||
>
|
||||
إدارة أطقمك وعرض سجلات التنفيذ
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -1,8 +0,0 @@
|
||||
---
|
||||
title: "POST /kickoff"
|
||||
description: "بدء تنفيذ الطاقم"
|
||||
openapi: "/enterprise-api.en.yaml POST /kickoff"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
---
|
||||
title: "POST /resume"
|
||||
description: "استئناف تنفيذ الطاقم مع التغذية الراجعة البشرية"
|
||||
openapi: "/enterprise-api.en.yaml POST /resume"
|
||||
mode: "wide"
|
||||
---
|
||||
@@ -1,6 +0,0 @@
|
||||
---
|
||||
title: "GET /{kickoff_id}/status"
|
||||
description: "الحصول على حالة التنفيذ"
|
||||
openapi: "/enterprise-api.en.yaml GET /{kickoff_id}/status"
|
||||
mode: "wide"
|
||||
---
|
||||
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