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devin/1745
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311a078ca6 |
@@ -4,6 +4,36 @@ description: View the latest updates and changes to CrewAI
|
||||
icon: timeline
|
||||
---
|
||||
|
||||
<Update label="2025-04-07" description="v0.114.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/v01140.png" />
|
||||
</Frame>
|
||||
|
||||
**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="2025-03-17" description="v0.108.0">
|
||||
**Features**
|
||||
- Converted tabs to spaces in `crew.py` template
|
||||
|
||||
@@ -179,7 +179,78 @@ def crew(self) -> Crew:
|
||||
```
|
||||
</Note>
|
||||
|
||||
### 10. API Keys
|
||||
### 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.
|
||||
- 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.
|
||||
|
||||
|
||||
@@ -42,6 +42,16 @@ CrewAI supports various types of knowledge sources out of the box:
|
||||
| `collection_name` | **str** | No | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided. |
|
||||
| `storage` | **Optional[KnowledgeStorage]** | No | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. |
|
||||
|
||||
|
||||
<Tip>
|
||||
Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
|
||||
Simply add the relevant knowledge sources your agent or crew needs to function.
|
||||
|
||||
Knowledge sources can be added at the agent or crew level.
|
||||
Crew level knowledge sources will be used by **all agents** in the crew.
|
||||
Agent level knowledge sources will be used by the **specific agent** that is preloaded with the knowledge.
|
||||
</Tip>
|
||||
|
||||
## Quickstart Example
|
||||
|
||||
<Tip>
|
||||
@@ -146,6 +156,26 @@ result = crew.kickoff(
|
||||
)
|
||||
```
|
||||
|
||||
## Knowledge Configuration
|
||||
|
||||
You can configure the knowledge configuration for the crew or agent.
|
||||
|
||||
```python Code
|
||||
from crewai.knowledge.knowledge_config import KnowledgeConfig
|
||||
|
||||
knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
|
||||
|
||||
agent = Agent(
|
||||
...
|
||||
knowledge_config=knowledge_config
|
||||
)
|
||||
```
|
||||
|
||||
<Tip>
|
||||
`results_limit`: is the number of relevant documents to return. Default is 3.
|
||||
`score_threshold`: is the minimum score for a document to be considered relevant. Default is 0.35.
|
||||
</Tip>
|
||||
|
||||
## More Examples
|
||||
|
||||
Here are examples of how to use different types of knowledge sources:
|
||||
|
||||
@@ -1,71 +0,0 @@
|
||||
---
|
||||
title: Using LlamaIndex Tools
|
||||
description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
|
||||
icon: toolbox
|
||||
---
|
||||
|
||||
## Using LlamaIndex Tools
|
||||
|
||||
<Info>
|
||||
CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
|
||||
</Info>
|
||||
|
||||
Here are the available built-in tools offered by LlamaIndex.
|
||||
|
||||
```python Code
|
||||
from crewai import Agent
|
||||
from crewai_tools import LlamaIndexTool
|
||||
|
||||
# Example 1: Initialize from FunctionTool
|
||||
from llama_index.core.tools import FunctionTool
|
||||
|
||||
your_python_function = lambda ...: ...
|
||||
og_tool = FunctionTool.from_defaults(
|
||||
your_python_function,
|
||||
name="<name>",
|
||||
description='<description>'
|
||||
)
|
||||
tool = LlamaIndexTool.from_tool(og_tool)
|
||||
|
||||
# Example 2: Initialize from LlamaHub Tools
|
||||
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
|
||||
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
|
||||
wolfram_tools = wolfram_spec.to_tool_list()
|
||||
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
|
||||
|
||||
# Example 3: Initialize Tool from a LlamaIndex Query Engine
|
||||
query_engine = index.as_query_engine()
|
||||
query_tool = LlamaIndexTool.from_query_engine(
|
||||
query_engine,
|
||||
name="Uber 2019 10K Query Tool",
|
||||
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
|
||||
)
|
||||
|
||||
# Create and assign the tools to an agent
|
||||
agent = Agent(
|
||||
role='Research Analyst',
|
||||
goal='Provide up-to-date market analysis',
|
||||
backstory='An expert analyst with a keen eye for market trends.',
|
||||
tools=[tool, *tools, query_tool]
|
||||
)
|
||||
|
||||
# rest of the code ...
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the LlamaIndexTool, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Package Installation">
|
||||
Make sure that `crewai[tools]` package is installed in your Python environment:
|
||||
<CodeGroup>
|
||||
```shell Terminal
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Install and Use LlamaIndex">
|
||||
Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
|
||||
</Step>
|
||||
</Steps>
|
||||
117
docs/docs.json
117
docs/docs.json
@@ -8,25 +8,27 @@
|
||||
"dark": "#C94C3C"
|
||||
},
|
||||
"favicon": "favicon.svg",
|
||||
"contextual": {
|
||||
"options": ["copy", "view", "chatgpt", "claude"]
|
||||
},
|
||||
"navigation": {
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Get Started",
|
||||
"tab": "Documentation",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"installation",
|
||||
"quickstart",
|
||||
"changelog"
|
||||
"quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
{
|
||||
"group": "Concepts",
|
||||
"group": "Strategy",
|
||||
"pages": [
|
||||
"guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
@@ -79,41 +81,6 @@
|
||||
"concepts/event-listener"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "How to Guides",
|
||||
"pages": [
|
||||
"how-to/create-custom-tools",
|
||||
"how-to/sequential-process",
|
||||
"how-to/hierarchical-process",
|
||||
"how-to/custom-manager-agent",
|
||||
"how-to/llm-connections",
|
||||
"how-to/customizing-agents",
|
||||
"how-to/multimodal-agents",
|
||||
"how-to/coding-agents",
|
||||
"how-to/force-tool-output-as-result",
|
||||
"how-to/human-input-on-execution",
|
||||
"how-to/kickoff-async",
|
||||
"how-to/kickoff-for-each",
|
||||
"how-to/replay-tasks-from-latest-crew-kickoff",
|
||||
"how-to/conditional-tasks",
|
||||
"how-to/langchain-tools",
|
||||
"how-to/llamaindex-tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agent Monitoring & Observability",
|
||||
"pages": [
|
||||
"how-to/agentops-observability",
|
||||
"how-to/arize-phoenix-observability",
|
||||
"how-to/langfuse-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/opik-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/weave-integration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Tools",
|
||||
"pages": [
|
||||
@@ -141,6 +108,7 @@
|
||||
"tools/hyperbrowserloadtool",
|
||||
"tools/linkupsearchtool",
|
||||
"tools/llamaindextool",
|
||||
"tools/langchaintool",
|
||||
"tools/serperdevtool",
|
||||
"tools/s3readertool",
|
||||
"tools/s3writertool",
|
||||
@@ -170,6 +138,40 @@
|
||||
"tools/youtubevideosearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agent Monitoring & Observability",
|
||||
"pages": [
|
||||
"how-to/agentops-observability",
|
||||
"how-to/arize-phoenix-observability",
|
||||
"how-to/langfuse-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/opik-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/weave-integration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Learn",
|
||||
"pages": [
|
||||
"how-to/conditional-tasks",
|
||||
"how-to/coding-agents",
|
||||
"how-to/create-custom-tools",
|
||||
"how-to/custom-llm",
|
||||
"how-to/custom-manager-agent",
|
||||
"how-to/customizing-agents",
|
||||
"how-to/force-tool-output-as-result",
|
||||
"how-to/hierarchical-process",
|
||||
"how-to/human-input-on-execution",
|
||||
"how-to/kickoff-async",
|
||||
"how-to/kickoff-for-each",
|
||||
"how-to/llm-connections",
|
||||
"how-to/multimodal-agents",
|
||||
"how-to/replay-tasks-from-latest-crew-kickoff",
|
||||
"how-to/sequential-process"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
@@ -188,19 +190,35 @@
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Releases",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Releases",
|
||||
"pages": [
|
||||
"changelog"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"global": {
|
||||
"anchors": [
|
||||
{
|
||||
"anchor": "Community",
|
||||
"anchor": "Website",
|
||||
"href": "https://crewai.com",
|
||||
"icon": "globe"
|
||||
},
|
||||
{
|
||||
"anchor": "Forum",
|
||||
"href": "https://community.crewai.com",
|
||||
"icon": "discourse"
|
||||
},
|
||||
{
|
||||
"anchor": "Tutorials",
|
||||
"href": "https://www.youtube.com/@crewAIInc",
|
||||
"icon": "youtube"
|
||||
"anchor": "Get Help",
|
||||
"href": "mailto:support@crewai.com",
|
||||
"icon": "headset"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -214,6 +232,12 @@
|
||||
"strict": false
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
{
|
||||
"label": "Start Free Trial",
|
||||
"href": "https://app.crewai.com"
|
||||
}
|
||||
],
|
||||
"primary": {
|
||||
"type": "github",
|
||||
"href": "https://github.com/crewAIInc/crewAI"
|
||||
@@ -223,7 +247,12 @@
|
||||
"prompt": "Search CrewAI docs"
|
||||
},
|
||||
"seo": {
|
||||
"indexing": "navigable"
|
||||
"indexing": "all"
|
||||
},
|
||||
"errors": {
|
||||
"404": {
|
||||
"redirect": true
|
||||
}
|
||||
},
|
||||
"footer": {
|
||||
"socials": {
|
||||
|
||||
@@ -1,9 +1,13 @@
|
||||
# Custom LLM Implementations
|
||||
---
|
||||
title: Custom LLM Implementation
|
||||
description: Learn how to create custom LLM implementations in CrewAI.
|
||||
icon: code
|
||||
---
|
||||
|
||||
## Custom LLM Implementations
|
||||
|
||||
CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
|
||||
|
||||
## Using Custom LLM Implementations
|
||||
|
||||
To create a custom LLM implementation, you need to:
|
||||
|
||||
1. Inherit from the `BaseLLM` abstract base class
|
||||
@@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Create Your Own Manager Agent
|
||||
title: Custom Manager Agent
|
||||
description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
|
||||
icon: user-shield
|
||||
---
|
||||
|
||||
@@ -20,10 +20,8 @@ Here's an example of how to replay from a task:
|
||||
To use the replay feature, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Open your terminal or command prompt.">
|
||||
</Step>
|
||||
<Step title="Navigate to the directory where your CrewAI project is located.">
|
||||
</Step>
|
||||
<Step title="Open your terminal or command prompt."></Step>
|
||||
<Step title="Navigate to the directory where your CrewAI project is located."></Step>
|
||||
<Step title="Run the following commands:">
|
||||
To view the latest kickoff task_ids use:
|
||||
|
||||
|
||||
BIN
docs/images/v01140.png
Normal file
BIN
docs/images/v01140.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 2.4 MiB |
@@ -336,9 +336,22 @@ email_summarizer_task:
|
||||
- research_task
|
||||
```
|
||||
|
||||
## Deploying Your Project
|
||||
## Deploying Your Crew
|
||||
|
||||
The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
|
||||
The easiest way to deploy your crew to production is through [CrewAI Enterprise](http://app.crewai.com).
|
||||
|
||||
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>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
|
||||
@@ -8,11 +8,29 @@ icon: code-simple
|
||||
|
||||
## Description
|
||||
|
||||
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
|
||||
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
|
||||
|
||||
## Requirements
|
||||
There are several ways to use this tool:
|
||||
|
||||
### Docker Container (Recommended)
|
||||
|
||||
This is the primary option. The code runs in a secure, isolated Docker container, ensuring safety regardless of its content.
|
||||
Make sure Docker is installed and running on your system. If you don’t have it, you can install it from [here](https://docs.docker.com/get-docker/).
|
||||
|
||||
### Sandbox environment
|
||||
|
||||
If Docker is unavailable — either not installed or not accessible for any reason — the code will be executed in a restricted Python environment - called sandbox.
|
||||
This environment is very limited, with strict restrictions on many modules and built-in functions.
|
||||
|
||||
### Unsafe Execution
|
||||
|
||||
**NOT RECOMMENDED FOR PRODUCTION**
|
||||
This mode allows execution of any Python code, including dangerous calls to `sys, os..` and similar modules. [Check out](/tools/codeinterpretertool#enabling-unsafe-mode) how to enable this mode
|
||||
|
||||
## Logging
|
||||
|
||||
The `CodeInterpreterTool` logs the selected execution strategy to STDOUT
|
||||
|
||||
- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
|
||||
|
||||
## Installation
|
||||
|
||||
@@ -74,18 +92,32 @@ programmer_agent = Agent(
|
||||
)
|
||||
```
|
||||
|
||||
### Enabling `unsafe_mode`
|
||||
|
||||
```python Code
|
||||
from crewai_tools import CodeInterpreterTool
|
||||
|
||||
code = """
|
||||
import os
|
||||
os.system("ls -la")
|
||||
"""
|
||||
|
||||
CodeInterpreterTool(unsafe_mode=True).run(code=code)
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `CodeInterpreterTool` accepts the following parameters during initialization:
|
||||
|
||||
- **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
|
||||
- **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
|
||||
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
|
||||
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container or sandbox. Default is `False`. Use with caution!
|
||||
- **default_image_tag**: Optional. Default Docker image tag. Default is `code-interpreter:latest`
|
||||
|
||||
When using the tool with an agent, the agent will need to provide:
|
||||
|
||||
- **code**: Required. The Python 3 code to execute.
|
||||
- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
|
||||
- **libraries_used**: Optional. A list of libraries used in the code that need to be installed. Default is `[]`
|
||||
|
||||
## Agent Integration Example
|
||||
|
||||
@@ -152,7 +184,7 @@ class CodeInterpreterTool(BaseTool):
|
||||
if self.unsafe_mode:
|
||||
return self.run_code_unsafe(code, libraries_used)
|
||||
else:
|
||||
return self.run_code_in_docker(code, libraries_used)
|
||||
return self.run_code_safety(code, libraries_used)
|
||||
```
|
||||
|
||||
The tool performs the following steps:
|
||||
@@ -168,8 +200,9 @@ The tool performs the following steps:
|
||||
By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
|
||||
|
||||
1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
|
||||
2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
|
||||
3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
|
||||
2. If the Docker container is unavailable and the code needs to run safely, it will be executed in a sandbox environment. For security reasons, installing arbitrary libraries is not allowed
|
||||
3. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
|
||||
4. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
|
||||
|
||||
## Conclusion
|
||||
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
---
|
||||
title: Using LangChain Tools
|
||||
description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
|
||||
title: LangChain Tool
|
||||
description: The `LangChainTool` is a wrapper for LangChain tools and query engines.
|
||||
icon: link
|
||||
---
|
||||
|
||||
## Using LangChain Tools
|
||||
## `LangChainTool`
|
||||
|
||||
<Info>
|
||||
CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
|
||||
@@ -114,6 +114,14 @@ class Agent(BaseAgent):
|
||||
default=None,
|
||||
description="Embedder configuration for the agent.",
|
||||
)
|
||||
agent_knowledge_context: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Knowledge context for the agent.",
|
||||
)
|
||||
crew_knowledge_context: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Knowledge context for the crew.",
|
||||
)
|
||||
|
||||
@model_validator(mode="after")
|
||||
def post_init_setup(self):
|
||||
@@ -234,22 +242,30 @@ class Agent(BaseAgent):
|
||||
memory = contextual_memory.build_context_for_task(task, context)
|
||||
if memory.strip() != "":
|
||||
task_prompt += self.i18n.slice("memory").format(memory=memory)
|
||||
|
||||
knowledge_config = (
|
||||
self.knowledge_config.model_dump() if self.knowledge_config else {}
|
||||
)
|
||||
if self.knowledge:
|
||||
agent_knowledge_snippets = self.knowledge.query([task.prompt()])
|
||||
agent_knowledge_snippets = self.knowledge.query(
|
||||
[task.prompt()], **knowledge_config
|
||||
)
|
||||
if agent_knowledge_snippets:
|
||||
agent_knowledge_context = extract_knowledge_context(
|
||||
self.agent_knowledge_context = extract_knowledge_context(
|
||||
agent_knowledge_snippets
|
||||
)
|
||||
if agent_knowledge_context:
|
||||
task_prompt += agent_knowledge_context
|
||||
if self.agent_knowledge_context:
|
||||
task_prompt += self.agent_knowledge_context
|
||||
|
||||
if self.crew:
|
||||
knowledge_snippets = self.crew.query_knowledge([task.prompt()])
|
||||
knowledge_snippets = self.crew.query_knowledge(
|
||||
[task.prompt()], **knowledge_config
|
||||
)
|
||||
if knowledge_snippets:
|
||||
crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
|
||||
if crew_knowledge_context:
|
||||
task_prompt += crew_knowledge_context
|
||||
self.crew_knowledge_context = extract_knowledge_context(
|
||||
knowledge_snippets
|
||||
)
|
||||
if self.crew_knowledge_context:
|
||||
task_prompt += self.crew_knowledge_context
|
||||
|
||||
tools = tools or self.tools or []
|
||||
self.create_agent_executor(tools=tools, task=task)
|
||||
|
||||
@@ -19,6 +19,7 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
|
||||
from crewai.agents.cache.cache_handler import CacheHandler
|
||||
from crewai.agents.tools_handler import ToolsHandler
|
||||
from crewai.knowledge.knowledge import Knowledge
|
||||
from crewai.knowledge.knowledge_config import KnowledgeConfig
|
||||
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
|
||||
from crewai.security.security_config import SecurityConfig
|
||||
from crewai.tools.base_tool import BaseTool, Tool
|
||||
@@ -155,6 +156,10 @@ class BaseAgent(ABC, BaseModel):
|
||||
adapted_agent: bool = Field(
|
||||
default=False, description="Whether the agent is adapted"
|
||||
)
|
||||
knowledge_config: Optional[KnowledgeConfig] = Field(
|
||||
default=None,
|
||||
description="Knowledge configuration for the agent such as limits and threshold",
|
||||
)
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
|
||||
@@ -122,7 +122,16 @@ PROVIDERS = [
|
||||
]
|
||||
|
||||
MODELS = {
|
||||
"openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
|
||||
"openai": [
|
||||
"gpt-4",
|
||||
"gpt-4.1",
|
||||
"gpt-4.1-mini-2025-04-14",
|
||||
"gpt-4.1-nano-2025-04-14",
|
||||
"gpt-4o",
|
||||
"gpt-4o-mini",
|
||||
"o1-mini",
|
||||
"o1-preview",
|
||||
],
|
||||
"anthropic": [
|
||||
"claude-3-5-sonnet-20240620",
|
||||
"claude-3-sonnet-20240229",
|
||||
@@ -132,8 +141,17 @@ MODELS = {
|
||||
"gemini": [
|
||||
"gemini/gemini-1.5-flash",
|
||||
"gemini/gemini-1.5-pro",
|
||||
"gemini/gemini-2.0-flash-lite-001",
|
||||
"gemini/gemini-2.0-flash-001",
|
||||
"gemini/gemini-2.0-flash-thinking-exp-01-21",
|
||||
"gemini/gemini-2.5-flash-preview-04-17",
|
||||
"gemini/gemini-2.5-pro-exp-03-25",
|
||||
"gemini/gemini-gemma-2-9b-it",
|
||||
"gemini/gemini-gemma-2-27b-it",
|
||||
"gemini/gemma-3-1b-it",
|
||||
"gemini/gemma-3-4b-it",
|
||||
"gemini/gemma-3-12b-it",
|
||||
"gemini/gemma-3-27b-it",
|
||||
],
|
||||
"nvidia_nim": [
|
||||
"nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
|
||||
|
||||
@@ -117,7 +117,9 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
|
||||
published_handle = publish_response.json()["handle"]
|
||||
console.print(
|
||||
f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
|
||||
f"Successfully published `{published_handle}` ({project_version}).\n\n"
|
||||
+ "⚠️ Security checks are running in the background. Your tool will be available once these are complete.\n"
|
||||
+ f"You can monitor the status or access your tool here:\nhttps://app.crewai.com/crewai_plus/tools/{published_handle}",
|
||||
style="bold green",
|
||||
)
|
||||
|
||||
@@ -153,8 +155,12 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
login_response_json = login_response.json()
|
||||
|
||||
settings = Settings()
|
||||
settings.tool_repository_username = login_response_json["credential"]["username"]
|
||||
settings.tool_repository_password = login_response_json["credential"]["password"]
|
||||
settings.tool_repository_username = login_response_json["credential"][
|
||||
"username"
|
||||
]
|
||||
settings.tool_repository_password = login_response_json["credential"][
|
||||
"password"
|
||||
]
|
||||
settings.dump()
|
||||
|
||||
console.print(
|
||||
@@ -179,7 +185,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
capture_output=False,
|
||||
env=self._build_env_with_credentials(repository_handle),
|
||||
text=True,
|
||||
check=True
|
||||
check=True,
|
||||
)
|
||||
|
||||
if add_package_result.stderr:
|
||||
@@ -204,7 +210,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
settings = Settings()
|
||||
|
||||
env = os.environ.copy()
|
||||
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
|
||||
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
|
||||
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
|
||||
settings.tool_repository_username or ""
|
||||
)
|
||||
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
|
||||
settings.tool_repository_password or ""
|
||||
)
|
||||
|
||||
return env
|
||||
|
||||
@@ -304,9 +304,7 @@ class Crew(BaseModel):
|
||||
"""Initialize private memory attributes."""
|
||||
self._external_memory = (
|
||||
# External memory doesn’t support a default value since it was designed to be managed entirely externally
|
||||
self.external_memory.set_crew(self)
|
||||
if self.external_memory
|
||||
else None
|
||||
self.external_memory.set_crew(self) if self.external_memory else None
|
||||
)
|
||||
|
||||
self._long_term_memory = self.long_term_memory
|
||||
@@ -1136,9 +1134,13 @@ class Crew(BaseModel):
|
||||
result = self._execute_tasks(self.tasks, start_index, True)
|
||||
return result
|
||||
|
||||
def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
|
||||
def query_knowledge(
|
||||
self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
|
||||
) -> Union[List[Dict[str, Any]], None]:
|
||||
if self.knowledge:
|
||||
return self.knowledge.query(query)
|
||||
return self.knowledge.query(
|
||||
query, results_limit=results_limit, score_threshold=score_threshold
|
||||
)
|
||||
return None
|
||||
|
||||
def fetch_inputs(self) -> Set[str]:
|
||||
@@ -1220,9 +1222,13 @@ class Crew(BaseModel):
|
||||
copied_data = self.model_dump(exclude=exclude)
|
||||
copied_data = {k: v for k, v in copied_data.items() if v is not None}
|
||||
if self.short_term_memory:
|
||||
copied_data["short_term_memory"] = self.short_term_memory.model_copy(deep=True)
|
||||
copied_data["short_term_memory"] = self.short_term_memory.model_copy(
|
||||
deep=True
|
||||
)
|
||||
if self.long_term_memory:
|
||||
copied_data["long_term_memory"] = self.long_term_memory.model_copy(deep=True)
|
||||
copied_data["long_term_memory"] = self.long_term_memory.model_copy(
|
||||
deep=True
|
||||
)
|
||||
if self.entity_memory:
|
||||
copied_data["entity_memory"] = self.entity_memory.model_copy(deep=True)
|
||||
if self.external_memory:
|
||||
@@ -1230,7 +1236,6 @@ class Crew(BaseModel):
|
||||
if self.user_memory:
|
||||
copied_data["user_memory"] = self.user_memory.model_copy(deep=True)
|
||||
|
||||
|
||||
copied_data.pop("agents", None)
|
||||
copied_data.pop("tasks", None)
|
||||
|
||||
@@ -1403,7 +1408,10 @@ class Crew(BaseModel):
|
||||
"short": (getattr(self, "_short_term_memory", None), "short term"),
|
||||
"entity": (getattr(self, "_entity_memory", None), "entity"),
|
||||
"knowledge": (getattr(self, "knowledge", None), "knowledge"),
|
||||
"kickoff_outputs": (getattr(self, "_task_output_handler", None), "task output"),
|
||||
"kickoff_outputs": (
|
||||
getattr(self, "_task_output_handler", None),
|
||||
"task output",
|
||||
),
|
||||
"external": (getattr(self, "_external_memory", None), "external"),
|
||||
}
|
||||
|
||||
|
||||
@@ -43,7 +43,9 @@ class Knowledge(BaseModel):
|
||||
self.storage.initialize_knowledge_storage()
|
||||
self._add_sources()
|
||||
|
||||
def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
|
||||
def query(
|
||||
self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Query across all knowledge sources to find the most relevant information.
|
||||
Returns the top_k most relevant chunks.
|
||||
@@ -56,7 +58,8 @@ class Knowledge(BaseModel):
|
||||
|
||||
results = self.storage.search(
|
||||
query,
|
||||
limit,
|
||||
limit=results_limit,
|
||||
score_threshold=score_threshold,
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
16
src/crewai/knowledge/knowledge_config.py
Normal file
16
src/crewai/knowledge/knowledge_config.py
Normal file
@@ -0,0 +1,16 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class KnowledgeConfig(BaseModel):
|
||||
"""Configuration for knowledge retrieval.
|
||||
|
||||
Args:
|
||||
results_limit (int): The number of relevant documents to return.
|
||||
score_threshold (float): The minimum score for a document to be considered relevant.
|
||||
"""
|
||||
|
||||
results_limit: int = Field(default=3, description="The number of results to return")
|
||||
score_threshold: float = Field(
|
||||
default=0.35,
|
||||
description="The minimum score for a result to be considered relevant",
|
||||
)
|
||||
@@ -4,13 +4,15 @@ import io
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
from typing import Any, Dict, List, Optional, Union, cast
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
|
||||
|
||||
import chromadb
|
||||
import chromadb.errors
|
||||
from chromadb.api import ClientAPI
|
||||
from chromadb.api.types import OneOrMany
|
||||
from chromadb.config import Settings
|
||||
# Type checking imports that don't cause runtime imports
|
||||
if TYPE_CHECKING:
|
||||
import chromadb
|
||||
import chromadb.errors
|
||||
from chromadb.api import ClientAPI
|
||||
from chromadb.api.types import OneOrMany
|
||||
from chromadb.config import Settings
|
||||
|
||||
from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
|
||||
from crewai.utilities import EmbeddingConfigurator
|
||||
@@ -43,9 +45,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
|
||||
search efficiency.
|
||||
"""
|
||||
|
||||
collection: Optional[chromadb.Collection] = None
|
||||
collection: Optional[Any] = None
|
||||
collection_name: Optional[str] = "knowledge"
|
||||
app: Optional[ClientAPI] = None
|
||||
app: Optional[Any] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -84,6 +86,10 @@ class KnowledgeStorage(BaseKnowledgeStorage):
|
||||
raise Exception("Collection not initialized")
|
||||
|
||||
def initialize_knowledge_storage(self):
|
||||
# Import chromadb here to avoid importing at module level
|
||||
import chromadb
|
||||
from chromadb.config import Settings
|
||||
|
||||
base_path = os.path.join(db_storage_path(), "knowledge")
|
||||
chroma_client = chromadb.PersistentClient(
|
||||
path=base_path,
|
||||
@@ -109,6 +115,10 @@ class KnowledgeStorage(BaseKnowledgeStorage):
|
||||
raise Exception("Failed to create or get collection")
|
||||
|
||||
def reset(self):
|
||||
# Import chromadb here to avoid importing at module level
|
||||
import chromadb
|
||||
from chromadb.config import Settings
|
||||
|
||||
base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
|
||||
if not self.app:
|
||||
self.app = chromadb.PersistentClient(
|
||||
@@ -126,6 +136,11 @@ class KnowledgeStorage(BaseKnowledgeStorage):
|
||||
documents: List[str],
|
||||
metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
|
||||
):
|
||||
# Import chromadb here to avoid importing at module level
|
||||
import chromadb
|
||||
import chromadb.errors
|
||||
from chromadb.api.types import OneOrMany
|
||||
|
||||
if not self.collection:
|
||||
raise Exception("Collection not initialized")
|
||||
|
||||
@@ -156,7 +171,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
|
||||
filtered_ids.append(doc_id)
|
||||
|
||||
# If we have no metadata at all, set it to None
|
||||
final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
|
||||
final_metadata: Optional[OneOrMany['chromadb.Metadata']] = (
|
||||
None if all(m is None for m in filtered_metadata) else filtered_metadata
|
||||
)
|
||||
|
||||
|
||||
@@ -81,14 +81,26 @@ LLM_CONTEXT_WINDOW_SIZES = {
|
||||
"gpt-4o": 128000,
|
||||
"gpt-4o-mini": 128000,
|
||||
"gpt-4-turbo": 128000,
|
||||
"gpt-4.1": 1047576, # Based on official docs
|
||||
"gpt-4.1-mini-2025-04-14": 1047576,
|
||||
"gpt-4.1-nano-2025-04-14": 1047576,
|
||||
"o1-preview": 128000,
|
||||
"o1-mini": 128000,
|
||||
"o3-mini": 200000, # Based on official o3-mini specifications
|
||||
# gemini
|
||||
"gemini-2.0-flash": 1048576,
|
||||
"gemini-2.0-flash-thinking-exp-01-21": 32768,
|
||||
"gemini-2.0-flash-lite-001": 1048576,
|
||||
"gemini-2.0-flash-001": 1048576,
|
||||
"gemini-2.5-flash-preview-04-17": 1048576,
|
||||
"gemini-2.5-pro-exp-03-25": 1048576,
|
||||
"gemini-1.5-pro": 2097152,
|
||||
"gemini-1.5-flash": 1048576,
|
||||
"gemini-1.5-flash-8b": 1048576,
|
||||
"gemini/gemma-3-1b-it": 32000,
|
||||
"gemini/gemma-3-4b-it": 128000,
|
||||
"gemini/gemma-3-12b-it": 128000,
|
||||
"gemini/gemma-3-27b-it": 128000,
|
||||
# deepseek
|
||||
"deepseek-chat": 128000,
|
||||
# groq
|
||||
|
||||
@@ -4,13 +4,18 @@ import logging
|
||||
import os
|
||||
import shutil
|
||||
import uuid
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from chromadb.api import ClientAPI
|
||||
# Type checking imports that don't cause runtime imports
|
||||
if TYPE_CHECKING:
|
||||
import chromadb
|
||||
from chromadb.api import ClientAPI
|
||||
from chromadb.config import Settings
|
||||
|
||||
from crewai.memory.storage.base_rag_storage import BaseRAGStorage
|
||||
from crewai.utilities import EmbeddingConfigurator
|
||||
from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
|
||||
from crewai.utilities.chromadb import sanitize_collection_name
|
||||
from crewai.utilities.logger import Logger
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
|
||||
|
||||
@@ -37,76 +42,45 @@ class RAGStorage(BaseRAGStorage):
|
||||
search efficiency.
|
||||
"""
|
||||
|
||||
app: ClientAPI | None = None
|
||||
collection: Optional[Any] = None
|
||||
collection_name: Optional[str] = "memory"
|
||||
app: Optional[Any] = None
|
||||
|
||||
def __init__(
|
||||
self, type, allow_reset=True, embedder_config=None, crew=None, path=None
|
||||
self,
|
||||
type: str = "memory",
|
||||
allow_reset: bool = True,
|
||||
embedder_config: Optional[Dict[str, Any]] = None,
|
||||
crew: Any = None,
|
||||
collection_name: Optional[str] = None,
|
||||
):
|
||||
super().__init__(type, allow_reset, embedder_config, crew)
|
||||
agents = crew.agents if crew else []
|
||||
agents = [self._sanitize_role(agent.role) for agent in agents]
|
||||
agents = "_".join(agents)
|
||||
self.agents = agents
|
||||
self.storage_file_name = self._build_storage_file_name(type, agents)
|
||||
self.collection_name = collection_name or type
|
||||
self._set_embedder_config(embedder_config)
|
||||
|
||||
self.type = type
|
||||
def save(
|
||||
self,
|
||||
value: Any,
|
||||
metadata: Dict[str, Any],
|
||||
) -> None:
|
||||
with suppress_logging():
|
||||
if not self.collection:
|
||||
self._initialize_app()
|
||||
|
||||
self.allow_reset = allow_reset
|
||||
self.path = path
|
||||
self._initialize_app()
|
||||
if isinstance(value, list):
|
||||
documents = value
|
||||
metadatas = [metadata] * len(value) if metadata else None
|
||||
ids = [str(uuid.uuid4()) for _ in range(len(documents))]
|
||||
else:
|
||||
documents = [value]
|
||||
metadatas = [metadata] if metadata else None
|
||||
ids = [str(uuid.uuid4())]
|
||||
|
||||
def _set_embedder_config(self):
|
||||
configurator = EmbeddingConfigurator()
|
||||
self.embedder_config = configurator.configure_embedder(self.embedder_config)
|
||||
|
||||
def _initialize_app(self):
|
||||
import chromadb
|
||||
from chromadb.config import Settings
|
||||
|
||||
self._set_embedder_config()
|
||||
chroma_client = chromadb.PersistentClient(
|
||||
path=self.path if self.path else self.storage_file_name,
|
||||
settings=Settings(allow_reset=self.allow_reset),
|
||||
)
|
||||
|
||||
self.app = chroma_client
|
||||
|
||||
try:
|
||||
self.collection = self.app.get_collection(
|
||||
name=self.type, embedding_function=self.embedder_config
|
||||
self.collection.add(
|
||||
documents=documents,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
)
|
||||
except Exception:
|
||||
self.collection = self.app.create_collection(
|
||||
name=self.type, embedding_function=self.embedder_config
|
||||
)
|
||||
|
||||
def _sanitize_role(self, role: str) -> str:
|
||||
"""
|
||||
Sanitizes agent roles to ensure valid directory names.
|
||||
"""
|
||||
return role.replace("\n", "").replace(" ", "_").replace("/", "_")
|
||||
|
||||
def _build_storage_file_name(self, type: str, file_name: str) -> str:
|
||||
"""
|
||||
Ensures file name does not exceed max allowed by OS
|
||||
"""
|
||||
base_path = f"{db_storage_path()}/{type}"
|
||||
|
||||
if len(file_name) > MAX_FILE_NAME_LENGTH:
|
||||
logging.warning(
|
||||
f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
|
||||
)
|
||||
file_name = file_name[:MAX_FILE_NAME_LENGTH]
|
||||
|
||||
return f"{base_path}/{file_name}"
|
||||
|
||||
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
|
||||
if not hasattr(self, "app") or not hasattr(self, "collection"):
|
||||
self._initialize_app()
|
||||
try:
|
||||
self._generate_embedding(value, metadata)
|
||||
except Exception as e:
|
||||
logging.error(f"Error during {self.type} save: {str(e)}")
|
||||
|
||||
def search(
|
||||
self,
|
||||
@@ -115,54 +89,96 @@ class RAGStorage(BaseRAGStorage):
|
||||
filter: Optional[dict] = None,
|
||||
score_threshold: float = 0.35,
|
||||
) -> List[Any]:
|
||||
if not hasattr(self, "app"):
|
||||
self._initialize_app()
|
||||
with suppress_logging():
|
||||
if not hasattr(self, "collection") or not self.collection:
|
||||
self._initialize_app()
|
||||
|
||||
try:
|
||||
with suppress_logging():
|
||||
response = self.collection.query(query_texts=query, n_results=limit)
|
||||
if isinstance(query, str):
|
||||
query = [query]
|
||||
|
||||
fetched = self.collection.query(
|
||||
query_texts=query,
|
||||
n_results=limit,
|
||||
where=filter,
|
||||
)
|
||||
results = []
|
||||
for i in range(len(response["ids"][0])):
|
||||
for i in range(len(fetched["ids"][0])): # type: ignore
|
||||
result = {
|
||||
"id": response["ids"][0][i],
|
||||
"metadata": response["metadatas"][0][i],
|
||||
"context": response["documents"][0][i],
|
||||
"score": response["distances"][0][i],
|
||||
"id": fetched["ids"][0][i], # type: ignore
|
||||
"metadata": fetched["metadatas"][0][i], # type: ignore
|
||||
"context": fetched["documents"][0][i], # type: ignore
|
||||
"score": fetched["distances"][0][i], # type: ignore
|
||||
}
|
||||
if result["score"] >= score_threshold:
|
||||
results.append(result)
|
||||
|
||||
return results
|
||||
except Exception as e:
|
||||
logging.error(f"Error during {self.type} search: {str(e)}")
|
||||
return []
|
||||
|
||||
def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None: # type: ignore
|
||||
if not hasattr(self, "app") or not hasattr(self, "collection"):
|
||||
def _initialize_app(self):
|
||||
# Import chromadb here to avoid importing at module level
|
||||
import chromadb
|
||||
from chromadb.config import Settings
|
||||
|
||||
base_path = os.path.join(db_storage_path(), "memory")
|
||||
chroma_client = chromadb.PersistentClient(
|
||||
path=base_path,
|
||||
settings=Settings(allow_reset=self.allow_reset),
|
||||
)
|
||||
|
||||
self.app = chroma_client
|
||||
|
||||
try:
|
||||
collection_name = (
|
||||
f"memory_{self.collection_name}"
|
||||
if self.collection_name
|
||||
else "memory"
|
||||
)
|
||||
if self.app:
|
||||
self.collection = self.app.get_or_create_collection(
|
||||
name=sanitize_collection_name(collection_name),
|
||||
embedding_function=self.embedder,
|
||||
)
|
||||
else:
|
||||
raise Exception("Vector Database Client not initialized")
|
||||
except Exception:
|
||||
raise Exception("Failed to create or get collection")
|
||||
|
||||
def initialize_rag_storage(self):
|
||||
self._initialize_app()
|
||||
|
||||
def reset(self) -> None:
|
||||
# Import chromadb here to avoid importing at module level
|
||||
import chromadb
|
||||
from chromadb.config import Settings
|
||||
|
||||
base_path = os.path.join(db_storage_path(), "memory")
|
||||
if not self.app:
|
||||
self.app = chromadb.PersistentClient(
|
||||
path=base_path,
|
||||
settings=Settings(allow_reset=True),
|
||||
)
|
||||
|
||||
self.app.reset()
|
||||
shutil.rmtree(base_path)
|
||||
self.app = None
|
||||
self.collection = None
|
||||
|
||||
def _generate_embedding(
|
||||
self, text: str, metadata: Optional[Dict[str, Any]] = None
|
||||
) -> Any:
|
||||
if not hasattr(self, "collection") or not self.collection:
|
||||
self._initialize_app()
|
||||
|
||||
id = str(uuid.uuid4())
|
||||
self.collection.add(
|
||||
documents=[text],
|
||||
metadatas=[metadata or {}],
|
||||
ids=[str(uuid.uuid4())],
|
||||
ids=[id],
|
||||
)
|
||||
return id
|
||||
|
||||
def reset(self) -> None:
|
||||
try:
|
||||
if self.app:
|
||||
self.app.reset()
|
||||
shutil.rmtree(f"{db_storage_path()}/{self.type}")
|
||||
self.app = None
|
||||
self.collection = None
|
||||
except Exception as e:
|
||||
if "attempt to write a readonly database" in str(e):
|
||||
# Ignore this specific error
|
||||
pass
|
||||
else:
|
||||
raise Exception(
|
||||
f"An error occurred while resetting the {self.type} memory: {e}"
|
||||
)
|
||||
def _sanitize_role(self, role: str) -> str:
|
||||
"""Sanitize role name for use in file names."""
|
||||
return role.lower().replace(" ", "_").replace("\n", "").replace("/", "_")
|
||||
|
||||
def _create_default_embedding_function(self):
|
||||
from chromadb.utils.embedding_functions.openai_embedding_function import (
|
||||
@@ -172,3 +188,20 @@ class RAGStorage(BaseRAGStorage):
|
||||
return OpenAIEmbeddingFunction(
|
||||
api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
|
||||
)
|
||||
|
||||
def _set_embedder_config(self, embedder_config: Optional[Dict[str, Any]] = None) -> None:
|
||||
"""Set the embedding configuration for the RAG storage.
|
||||
|
||||
Args:
|
||||
embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
|
||||
If None or empty, defaults to the default embedding function.
|
||||
"""
|
||||
self.embedder = (
|
||||
EmbeddingConfigurator().configure_embedder(embedder_config)
|
||||
if embedder_config
|
||||
else self._create_default_embedding_function()
|
||||
)
|
||||
|
||||
def _build_storage_file_name(self, role_name: str) -> str:
|
||||
"""Build storage file name from role name."""
|
||||
return f"{self._sanitize_role(role_name)}_memory"
|
||||
|
||||
@@ -1,8 +1,10 @@
|
||||
import os
|
||||
from typing import Any, Dict, Optional, cast
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, cast
|
||||
|
||||
from chromadb import Documents, EmbeddingFunction, Embeddings
|
||||
from chromadb.api.types import validate_embedding_function
|
||||
# Type checking imports that don't cause runtime imports
|
||||
if TYPE_CHECKING:
|
||||
from chromadb import Documents, EmbeddingFunction, Embeddings
|
||||
from chromadb.api.types import EmbeddingFunction
|
||||
|
||||
|
||||
class EmbeddingConfigurator:
|
||||
@@ -24,8 +26,12 @@ class EmbeddingConfigurator:
|
||||
def configure_embedder(
|
||||
self,
|
||||
embedder_config: Optional[Dict[str, Any]] = None,
|
||||
) -> EmbeddingFunction:
|
||||
) -> 'EmbeddingFunction':
|
||||
"""Configures and returns an embedding function based on the provided config."""
|
||||
# Import here to avoid importing chromadb at module level
|
||||
from chromadb import EmbeddingFunction
|
||||
from chromadb.api.types import validate_embedding_function
|
||||
|
||||
if embedder_config is None:
|
||||
return self._create_default_embedding_function()
|
||||
|
||||
@@ -182,6 +188,9 @@ class EmbeddingConfigurator:
|
||||
"IBM Watson dependencies are not installed. Please install them to use Watson embedding."
|
||||
) from e
|
||||
|
||||
# Import chromadb types here to avoid importing at module level
|
||||
from chromadb import Documents, EmbeddingFunction, Embeddings
|
||||
|
||||
class WatsonEmbeddingFunction(EmbeddingFunction):
|
||||
def __call__(self, input: Documents) -> Embeddings:
|
||||
if isinstance(input, str):
|
||||
@@ -212,6 +221,10 @@ class EmbeddingConfigurator:
|
||||
|
||||
@staticmethod
|
||||
def _configure_custom(config):
|
||||
# Import here to avoid importing chromadb at module level
|
||||
from chromadb import EmbeddingFunction
|
||||
from chromadb.api.types import validate_embedding_function
|
||||
|
||||
custom_embedder = config.get("embedder")
|
||||
if isinstance(custom_embedder, EmbeddingFunction):
|
||||
try:
|
||||
|
||||
@@ -10,6 +10,8 @@ from crewai import Agent, Crew, Task
|
||||
from crewai.agents.cache import CacheHandler
|
||||
from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
|
||||
from crewai.agents.parser import CrewAgentParser, OutputParserException
|
||||
from crewai.knowledge.knowledge import Knowledge
|
||||
from crewai.knowledge.knowledge_config import KnowledgeConfig
|
||||
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
|
||||
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
|
||||
from crewai.llm import LLM
|
||||
@@ -259,7 +261,9 @@ def test_cache_hitting():
|
||||
def handle_tool_end(source, event):
|
||||
received_events.append(event)
|
||||
|
||||
with (patch.object(CacheHandler, "read") as read,):
|
||||
with (
|
||||
patch.object(CacheHandler, "read") as read,
|
||||
):
|
||||
read.return_value = "0"
|
||||
task = Task(
|
||||
description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
|
||||
@@ -1611,6 +1615,78 @@ def test_agent_with_knowledge_sources():
|
||||
assert "red" in result.raw.lower()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold():
|
||||
content = "Brandon's favorite color is red and he likes Mexican food."
|
||||
string_source = StringKnowledgeSource(content=content)
|
||||
knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
|
||||
with patch(
|
||||
"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
|
||||
) as MockKnowledge:
|
||||
mock_knowledge_instance = MockKnowledge.return_value
|
||||
mock_knowledge_instance.sources = [string_source]
|
||||
mock_knowledge_instance.query.return_value = [{"content": content}]
|
||||
with patch.object(Knowledge, "query") as mock_knowledge_query:
|
||||
agent = Agent(
|
||||
role="Information Agent",
|
||||
goal="Provide information based on knowledge sources",
|
||||
backstory="You have access to specific knowledge sources.",
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
knowledge_sources=[string_source],
|
||||
knowledge_config=knowledge_config,
|
||||
)
|
||||
task = Task(
|
||||
description="What is Brandon's favorite color?",
|
||||
expected_output="Brandon's favorite color.",
|
||||
agent=agent,
|
||||
)
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
crew.kickoff()
|
||||
|
||||
assert agent.knowledge is not None
|
||||
mock_knowledge_query.assert_called_once_with(
|
||||
[task.prompt()],
|
||||
**knowledge_config.model_dump(),
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default():
|
||||
content = "Brandon's favorite color is red and he likes Mexican food."
|
||||
string_source = StringKnowledgeSource(content=content)
|
||||
knowledge_config = KnowledgeConfig()
|
||||
with patch(
|
||||
"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
|
||||
) as MockKnowledge:
|
||||
mock_knowledge_instance = MockKnowledge.return_value
|
||||
mock_knowledge_instance.sources = [string_source]
|
||||
mock_knowledge_instance.query.return_value = [{"content": content}]
|
||||
with patch.object(Knowledge, "query") as mock_knowledge_query:
|
||||
string_source = StringKnowledgeSource(content=content)
|
||||
knowledge_config = KnowledgeConfig()
|
||||
agent = Agent(
|
||||
role="Information Agent",
|
||||
goal="Provide information based on knowledge sources",
|
||||
backstory="You have access to specific knowledge sources.",
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
knowledge_sources=[string_source],
|
||||
knowledge_config=knowledge_config,
|
||||
)
|
||||
task = Task(
|
||||
description="What is Brandon's favorite color?",
|
||||
expected_output="Brandon's favorite color.",
|
||||
agent=agent,
|
||||
)
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
crew.kickoff()
|
||||
|
||||
assert agent.knowledge is not None
|
||||
mock_knowledge_query.assert_called_once_with(
|
||||
[task.prompt()],
|
||||
**knowledge_config.model_dump(),
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_agent_with_knowledge_sources_extensive_role():
|
||||
content = "Brandon's favorite color is red and he likes Mexican food."
|
||||
|
||||
@@ -0,0 +1,330 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"input": ["Brandon''s favorite color is red and he likes Mexican food."],
|
||||
"model": "text-embedding-3-small", "encoding_format": "base64"}'
|
||||
headers:
|
||||
accept:
|
||||
- application/json
|
||||
accept-encoding:
|
||||
- gzip, deflate, zstd
|
||||
connection:
|
||||
- keep-alive
|
||||
content-length:
|
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- '137'
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content-type:
|
||||
- application/json
|
||||
host:
|
||||
- api.openai.com
|
||||
user-agent:
|
||||
- OpenAI/Python 1.68.2
|
||||
x-stainless-arch:
|
||||
- arm64
|
||||
x-stainless-async:
|
||||
- 'false'
|
||||
x-stainless-lang:
|
||||
- python
|
||||
x-stainless-os:
|
||||
- MacOS
|
||||
x-stainless-package-version:
|
||||
- 1.68.2
|
||||
x-stainless-read-timeout:
|
||||
- '600'
|
||||
x-stainless-retry-count:
|
||||
- '0'
|
||||
x-stainless-runtime:
|
||||
- CPython
|
||||
x-stainless-runtime-version:
|
||||
- 3.12.9
|
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method: POST
|
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uri: https://api.openai.com/v1/embeddings
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response:
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body:
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60
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60
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|
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@pytest.mark.parametrize(
|
||||
"model",
|
||||
[
|
||||
"gemini/gemini-2.0-flash-thinking-exp-01-21",
|
||||
"gemini/gemini-2.0-flash-001",
|
||||
"gemini/gemini-2.0-flash-lite-001",
|
||||
"gemini/gemini-2.5-flash-preview-04-17",
|
||||
"gemini/gemini-2.5-pro-exp-03-25",
|
||||
],
|
||||
)
|
||||
def test_gemini_models(model):
|
||||
llm = LLM(model=model)
|
||||
result = llm.call("What is the capital of France?")
|
||||
assert isinstance(result, str)
|
||||
assert "Paris" in result
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
|
||||
@pytest.mark.parametrize(
|
||||
"model",
|
||||
[
|
||||
"gemini/gemma-3-1b-it",
|
||||
"gemini/gemma-3-4b-it",
|
||||
"gemini/gemma-3-12b-it",
|
||||
"gemini/gemma-3-27b-it",
|
||||
],
|
||||
)
|
||||
def test_gemma3(model):
|
||||
llm = LLM(model=model)
|
||||
result = llm.call("What is the capital of France?")
|
||||
assert isinstance(result, str)
|
||||
assert "Paris" in result
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.mark.parametrize(
|
||||
"model", ["gpt-4.1", "gpt-4.1-mini-2025-04-14", "gpt-4.1-nano-2025-04-14"]
|
||||
)
|
||||
def test_gpt_4_1(model):
|
||||
llm = LLM(model=model)
|
||||
result = llm.call("What is the capital of France?")
|
||||
assert isinstance(result, str)
|
||||
assert "Paris" in result
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_o3_mini_reasoning_effort_high():
|
||||
llm = LLM(
|
||||
|
||||
49
tests/utilities/test_embedding_configurator.py
Normal file
49
tests/utilities/test_embedding_configurator.py
Normal file
@@ -0,0 +1,49 @@
|
||||
import importlib
|
||||
import sys
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
class TestEmbeddingConfiguratorImports:
|
||||
"""Test that ChromaDB is not imported at module level."""
|
||||
|
||||
def test_no_chromadb_import_at_module_level(self):
|
||||
"""Test that chromadb is not imported when the module is imported."""
|
||||
for module_name in list(sys.modules.keys()):
|
||||
if module_name.startswith('crewai.utilities.embedding_configurator'):
|
||||
del sys.modules[module_name]
|
||||
|
||||
mock_chromadb = MagicMock()
|
||||
|
||||
chromadb_imported = [False]
|
||||
|
||||
def mock_import(name, *args, **kwargs):
|
||||
if name == 'chromadb':
|
||||
chromadb_imported[0] = True
|
||||
return mock_chromadb
|
||||
return importlib.__import__(name, *args, **kwargs)
|
||||
|
||||
with patch('builtins.__import__', side_effect=mock_import):
|
||||
from crewai.utilities import embedding_configurator
|
||||
|
||||
assert not chromadb_imported[0], "chromadb was imported at module level"
|
||||
|
||||
def test_chromadb_import_in_configure_embedder(self):
|
||||
"""Test that chromadb is imported when configure_embedder is called."""
|
||||
for module_name in list(sys.modules.keys()):
|
||||
if module_name.startswith('crewai.utilities.embedding_configurator'):
|
||||
del sys.modules[module_name]
|
||||
|
||||
from crewai.utilities.embedding_configurator import EmbeddingConfigurator
|
||||
|
||||
mock_chromadb = MagicMock()
|
||||
|
||||
def mock_import(name, *args, **kwargs):
|
||||
if name == 'chromadb':
|
||||
raise ImportError("Mock import error for chromadb")
|
||||
return importlib.__import__(name, *args, **kwargs)
|
||||
|
||||
with patch('builtins.__import__', side_effect=mock_import):
|
||||
with pytest.raises(ImportError, match="Mock import error for chromadb"):
|
||||
EmbeddingConfigurator().configure_embedder()
|
||||
@@ -29,7 +29,7 @@ def mock_knowledge_source():
|
||||
"""
|
||||
return StringKnowledgeSource(content=content)
|
||||
|
||||
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
|
||||
@patch('chromadb.PersistentClient')
|
||||
def test_knowledge_included_in_planning(mock_chroma):
|
||||
"""Test that verifies knowledge sources are properly included in planning."""
|
||||
# Mock ChromaDB collection
|
||||
|
||||
@@ -100,7 +100,7 @@ class InternalCrewPlanner:
|
||||
# Knowledge field should not be present when empty
|
||||
assert '"agent_knowledge"' not in tasks_summary
|
||||
|
||||
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
|
||||
@patch('chromadb.PersistentClient')
|
||||
def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
|
||||
"""Test task summary generation with both knowledge and tools present."""
|
||||
# Mock ChromaDB collection
|
||||
|
||||
438
uv.lock
generated
438
uv.lock
generated
@@ -1,32 +1,18 @@
|
||||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.10, <3.13"
|
||||
resolution-markers = [
|
||||
"python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_version < '0'",
|
||||
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
|
||||
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version < '3.11' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12.4' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -128,18 +114,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17", size = 7617 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aisuite"
|
||||
version = "0.1.10"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6a/9d/c7a8a76abb9011dd2bc9a5cb8ffa8231640e20bbdae177ce9ab6cb67c66c/aisuite-0.1.10.tar.gz", hash = "sha256:170e62d4c91fecb22e82a04e058154a111cef473681171e5df7346272e77f414", size = 29052 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/58/c2/9a34a01516de107e5f9406dbfd319b6004340708101d67fa107373da4058/aisuite-0.1.10-py3-none-any.whl", hash = "sha256:c8510ebe38d6546b6a06819171e201fcaf0bf9ae020ffcfe19b6bd90430781ad", size = 43984 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "alembic"
|
||||
version = "1.13.3"
|
||||
@@ -566,14 +540,14 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.1.8"
|
||||
version = "8.1.7"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
|
||||
{ url = "https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28", size = 97941 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -620,7 +594,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "crewai"
|
||||
version = "0.114.0"
|
||||
version = "0.86.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "appdirs" },
|
||||
@@ -630,7 +604,6 @@ dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "instructor" },
|
||||
{ name = "json-repair" },
|
||||
{ name = "json5" },
|
||||
{ name = "jsonref" },
|
||||
{ name = "litellm" },
|
||||
{ name = "openai" },
|
||||
@@ -652,15 +625,9 @@ dependencies = [
|
||||
agentops = [
|
||||
{ name = "agentops" },
|
||||
]
|
||||
aisuite = [
|
||||
{ name = "aisuite" },
|
||||
]
|
||||
docling = [
|
||||
{ name = "docling" },
|
||||
]
|
||||
embeddings = [
|
||||
{ name = "tiktoken" },
|
||||
]
|
||||
fastembed = [
|
||||
{ name = "fastembed" },
|
||||
]
|
||||
@@ -693,8 +660,8 @@ dev = [
|
||||
{ name = "pre-commit" },
|
||||
{ name = "pytest" },
|
||||
{ name = "pytest-asyncio" },
|
||||
{ name = "pytest-recording" },
|
||||
{ name = "pytest-subprocess" },
|
||||
{ name = "pytest-vcr" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "ruff" },
|
||||
]
|
||||
@@ -702,27 +669,25 @@ dev = [
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "agentops", marker = "extra == 'agentops'", specifier = ">=0.3.0" },
|
||||
{ name = "aisuite", marker = "extra == 'aisuite'", specifier = ">=0.1.10" },
|
||||
{ name = "appdirs", specifier = ">=1.4.4" },
|
||||
{ name = "auth0-python", specifier = ">=4.7.1" },
|
||||
{ name = "blinker", specifier = ">=1.9.0" },
|
||||
{ name = "chromadb", specifier = ">=0.5.23" },
|
||||
{ name = "click", specifier = ">=8.1.7" },
|
||||
{ name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.40.1" },
|
||||
{ name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.17.0" },
|
||||
{ name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
|
||||
{ name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
|
||||
{ name = "instructor", specifier = ">=1.3.3" },
|
||||
{ name = "json-repair", specifier = ">=0.25.2" },
|
||||
{ name = "json5", specifier = ">=0.10.0" },
|
||||
{ name = "jsonref", specifier = ">=1.1.0" },
|
||||
{ name = "litellm", specifier = "==1.60.2" },
|
||||
{ name = "litellm", specifier = ">=1.44.22" },
|
||||
{ name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
|
||||
{ name = "openai", specifier = ">=1.13.3" },
|
||||
{ name = "openpyxl", specifier = ">=3.1.5" },
|
||||
{ name = "openpyxl", marker = "extra == 'openpyxl'", specifier = ">=3.1.5" },
|
||||
{ name = "opentelemetry-api", specifier = ">=1.30.0" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
|
||||
{ name = "opentelemetry-sdk", specifier = ">=1.30.0" },
|
||||
{ name = "opentelemetry-api", specifier = ">=1.22.0" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.22.0" },
|
||||
{ name = "opentelemetry-sdk", specifier = ">=1.22.0" },
|
||||
{ name = "pandas", marker = "extra == 'pandas'", specifier = ">=2.2.3" },
|
||||
{ name = "pdfplumber", specifier = ">=0.11.4" },
|
||||
{ name = "pdfplumber", marker = "extra == 'pdfplumber'", specifier = ">=0.11.4" },
|
||||
@@ -730,12 +695,10 @@ requires-dist = [
|
||||
{ name = "python-dotenv", specifier = ">=1.0.0" },
|
||||
{ name = "pyvis", specifier = ">=0.3.2" },
|
||||
{ name = "regex", specifier = ">=2024.9.11" },
|
||||
{ name = "tiktoken", marker = "extra == 'embeddings'", specifier = "~=0.7.0" },
|
||||
{ name = "tomli", specifier = ">=2.0.2" },
|
||||
{ name = "tomli-w", specifier = ">=1.1.0" },
|
||||
{ name = "uv", specifier = ">=0.4.25" },
|
||||
]
|
||||
provides-extras = ["tools", "embeddings", "agentops", "fastembed", "pdfplumber", "pandas", "openpyxl", "mem0", "docling", "aisuite"]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
dev = [
|
||||
@@ -750,32 +713,34 @@ dev = [
|
||||
{ name = "pre-commit", specifier = ">=3.6.0" },
|
||||
{ name = "pytest", specifier = ">=8.0.0" },
|
||||
{ name = "pytest-asyncio", specifier = ">=0.23.7" },
|
||||
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Reference in New Issue
Block a user