mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-22 15:25:09 +00:00
a237ebabba
* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
140 lines
4.8 KiB
Plaintext
140 lines
4.8 KiB
Plaintext
---
|
|
title: "Tavily Extractor Tool"
|
|
description: "Extract structured content from web pages using the Tavily API"
|
|
icon: square-poll-horizontal
|
|
mode: "wide"
|
|
---
|
|
|
|
The `TavilyExtractorTool` allows CrewAI agents to extract structured content from web pages using the Tavily API. It can process single URLs or lists of URLs and provides options for controlling the extraction depth and including images.
|
|
|
|
## Installation
|
|
|
|
To use the `TavilyExtractorTool`, you need to install the `tavily-python` library:
|
|
|
|
```shell
|
|
uv add 'crewai[tools]' tavily-python
|
|
```
|
|
|
|
You also need to set your Tavily API key as an environment variable:
|
|
|
|
```bash
|
|
export TAVILY_API_KEY='your-tavily-api-key'
|
|
```
|
|
|
|
## Example Usage
|
|
|
|
Here's how to initialize and use the `TavilyExtractorTool` within a CrewAI agent:
|
|
|
|
```python
|
|
import os
|
|
from crewai import Agent, Task, Crew
|
|
from crewai_tools import TavilyExtractorTool
|
|
|
|
# Ensure TAVILY_API_KEY is set in your environment
|
|
# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"
|
|
|
|
# Initialize the tool
|
|
tavily_tool = TavilyExtractorTool()
|
|
|
|
# Create an agent that uses the tool
|
|
extractor_agent = Agent(
|
|
role='Web Content Extractor',
|
|
goal='Extract key information from specified web pages',
|
|
backstory='You are an expert at extracting relevant content from websites using the Tavily API.',
|
|
tools=[tavily_tool],
|
|
verbose=True
|
|
)
|
|
|
|
# Define a task for the agent
|
|
extract_task = Task(
|
|
description='Extract the main content from the URL https://example.com using basic extraction depth.',
|
|
expected_output='A JSON string containing the extracted content from the URL.',
|
|
agent=extractor_agent
|
|
)
|
|
|
|
# Create and run the crew
|
|
crew = Crew(
|
|
agents=[extractor_agent],
|
|
tasks=[extract_task],
|
|
verbose=2
|
|
)
|
|
|
|
result = crew.kickoff()
|
|
print(result)
|
|
```
|
|
|
|
## Configuration Options
|
|
|
|
The `TavilyExtractorTool` accepts the following arguments:
|
|
|
|
- `urls` (Union[List[str], str]): **Required**. A single URL string or a list of URL strings to extract data from.
|
|
- `include_images` (Optional[bool]): Whether to include images in the extraction results. Defaults to `False`.
|
|
- `extract_depth` (Literal["basic", "advanced"]): The depth of extraction. Use `"basic"` for faster, surface-level extraction or `"advanced"` for more comprehensive extraction. Defaults to `"basic"`.
|
|
- `timeout` (int): The maximum time in seconds to wait for the extraction request to complete. Defaults to `60`.
|
|
|
|
## Advanced Usage
|
|
|
|
### Multiple URLs with Advanced Extraction
|
|
|
|
```python
|
|
# Example with multiple URLs and advanced extraction
|
|
multi_extract_task = Task(
|
|
description='Extract content from https://example.com and https://anotherexample.org using advanced extraction.',
|
|
expected_output='A JSON string containing the extracted content from both URLs.',
|
|
agent=extractor_agent
|
|
)
|
|
|
|
# Configure the tool with custom parameters
|
|
custom_extractor = TavilyExtractorTool(
|
|
extract_depth='advanced',
|
|
include_images=True,
|
|
timeout=120
|
|
)
|
|
|
|
agent_with_custom_tool = Agent(
|
|
role="Advanced Content Extractor",
|
|
goal="Extract comprehensive content with images",
|
|
tools=[custom_extractor]
|
|
)
|
|
```
|
|
|
|
### Tool Parameters
|
|
|
|
You can customize the tool's behavior by setting parameters during initialization:
|
|
|
|
```python
|
|
# Initialize with custom configuration
|
|
extractor_tool = TavilyExtractorTool(
|
|
extract_depth='advanced', # More comprehensive extraction
|
|
include_images=True, # Include image results
|
|
timeout=90 # Custom timeout
|
|
)
|
|
```
|
|
|
|
## Features
|
|
|
|
- **Single or Multiple URLs**: Extract content from one URL or process multiple URLs in a single request
|
|
- **Configurable Depth**: Choose between basic (fast) and advanced (comprehensive) extraction modes
|
|
- **Image Support**: Optionally include images in the extraction results
|
|
- **Structured Output**: Returns well-formatted JSON containing the extracted content
|
|
- **Error Handling**: Robust handling of network timeouts and extraction errors
|
|
|
|
## Response Format
|
|
|
|
The tool returns a JSON string representing the structured data extracted from the provided URL(s). The exact structure depends on the content of the pages and the `extract_depth` used.
|
|
|
|
Common response elements include:
|
|
- **Title**: The page title
|
|
- **Content**: Main text content of the page
|
|
- **Images**: Image URLs and metadata (when `include_images=True`)
|
|
- **Metadata**: Additional page information like author, description, etc.
|
|
|
|
## Use Cases
|
|
|
|
- **Content Analysis**: Extract and analyze content from competitor websites
|
|
- **Research**: Gather structured data from multiple sources for analysis
|
|
- **Content Migration**: Extract content from existing websites for migration
|
|
- **Monitoring**: Regular extraction of content for change detection
|
|
- **Data Collection**: Systematic extraction of information from web sources
|
|
|
|
Refer to the [Tavily API documentation](https://docs.tavily.com/docs/tavily-api/python-sdk#extract) for detailed information about the response structure and available options. |