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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>
This commit is contained in:
Lucas Gomide
166
docs/edge/en/tools/cloud-storage/bedrockkbretriever.mdx
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166
docs/edge/en/tools/cloud-storage/bedrockkbretriever.mdx
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---
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title: 'Bedrock Knowledge Base Retriever'
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description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
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icon: aws
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mode: "wide"
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---
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# `BedrockKBRetrieverTool`
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The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
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## Installation
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```bash
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uv pip install 'crewai[tools]'
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```
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## Requirements
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- AWS credentials configured (either through environment variables or AWS CLI)
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- `boto3` and `python-dotenv` packages
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- Access to Amazon Bedrock Knowledge Base
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## Usage
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Here's how to use the tool with a CrewAI agent:
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```python {2, 4-17}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
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# Initialize the tool
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kb_tool = BedrockKBRetrieverTool(
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knowledge_base_id="your-kb-id",
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number_of_results=5
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)
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# Create a CrewAI agent that uses the tool
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researcher = Agent(
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role='Knowledge Base Researcher',
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goal='Find information about company policies',
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backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
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tools=[kb_tool],
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verbose=True
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)
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# Create a task for the agent
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research_task = Task(
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description="Find our company's remote work policy and summarize the key points.",
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agent=researcher
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)
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# Create a crew with the agent
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crew = Crew(
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agents=[researcher],
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tasks=[research_task],
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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print(result)
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```
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## Tool Arguments
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| Argument | Type | Required | Default | Description |
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|:---------|:-----|:---------|:---------|:-------------|
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| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
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| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
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| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
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| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
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| **next_token** | `str` | No | None | Token for pagination |
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## Environment Variables
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```bash
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BEDROCK_KB_ID=your-knowledge-base-id # Alternative to passing knowledge_base_id
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AWS_REGION=your-aws-region # Defaults to us-east-1
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AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
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AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
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```
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## Response Format
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The tool returns results in JSON format:
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```json
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{
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"results": [
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{
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"content": "Retrieved text content",
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"content_type": "text",
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"source_type": "S3",
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"source_uri": "s3://bucket/document.pdf",
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"score": 0.95,
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"metadata": {
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"additional": "metadata"
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}
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}
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],
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"nextToken": "pagination-token",
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"guardrailAction": "NONE"
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}
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```
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## Advanced Usage
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### Custom Retrieval Configuration
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```python
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kb_tool = BedrockKBRetrieverTool(
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knowledge_base_id="your-kb-id",
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retrieval_configuration={
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"vectorSearchConfiguration": {
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"numberOfResults": 10,
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"overrideSearchType": "HYBRID"
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}
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}
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)
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policy_expert = Agent(
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role='Policy Expert',
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goal='Analyze company policies in detail',
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backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
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tools=[kb_tool]
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)
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```
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## Supported Data Sources
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- Amazon S3
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- Confluence
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- Salesforce
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- SharePoint
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- Web pages
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- Custom document locations
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- Amazon Kendra
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- SQL databases
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## Use Cases
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### Enterprise Knowledge Integration
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- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
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- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
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- Create agents that can answer questions based on your internal documentation while maintaining data security
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### Specialized Domain Knowledge
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- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
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- Leverage existing knowledge repositories that are already maintained in your AWS environment
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- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
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### Data-Driven Decision Making
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- Ground CrewAI agent responses in your actual company data rather than general knowledge
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- Ensure agents provide recommendations based on your specific business context and documentation
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- Reduce hallucinations by retrieving factual information from your knowledge bases
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### Scalable Information Access
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- Access terabytes of organizational knowledge without embedding it all into your models
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- Dynamically query only the relevant information needed for specific tasks
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- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
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|
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### Compliance and Governance
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- Ensure CrewAI agents provide responses that align with your company's approved documentation
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- Create auditable trails of information sources used by your agents
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- Maintain control over what information sources your agents can access
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51
docs/edge/en/tools/cloud-storage/overview.mdx
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51
docs/edge/en/tools/cloud-storage/overview.mdx
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---
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title: "Overview"
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description: "Interact with cloud services, storage systems, and cloud-based AI platforms"
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icon: "face-smile"
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mode: "wide"
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---
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These tools enable your agents to interact with cloud services, access cloud storage, and leverage cloud-based AI platforms for scalable operations.
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## **Available Tools**
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<CardGroup cols={2}>
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<Card title="S3 Reader Tool" icon="cloud" href="/en/tools/cloud-storage/s3readertool">
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Read files and data from Amazon S3 buckets.
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</Card>
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<Card title="S3 Writer Tool" icon="cloud-arrow-up" href="/en/tools/cloud-storage/s3writertool">
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Write and upload files to Amazon S3 storage.
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</Card>
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<Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/integration/bedrockinvokeagenttool">
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Invoke Amazon Bedrock agents for AI-powered tasks.
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</Card>
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<Card title="Bedrock KB Retriever" icon="database" href="/en/tools/cloud-storage/bedrockkbretriever">
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Retrieve information from Amazon Bedrock knowledge bases.
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</Card>
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</CardGroup>
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## **Common Use Cases**
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- **File Storage**: Store and retrieve files from cloud storage systems
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- **Data Backup**: Backup important data to cloud storage
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- **AI Services**: Access cloud-based AI models and services
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- **Knowledge Retrieval**: Query cloud-hosted knowledge bases
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- **Scalable Operations**: Leverage cloud infrastructure for processing
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```python
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from crewai_tools import S3ReaderTool, S3WriterTool, BedrockInvokeAgentTool
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# Create cloud tools
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s3_reader = S3ReaderTool()
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s3_writer = S3WriterTool()
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bedrock_agent = BedrockInvokeAgentTool()
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# Add to your agent
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agent = Agent(
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role="Cloud Operations Specialist",
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tools=[s3_reader, s3_writer, bedrock_agent],
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goal="Manage cloud resources and AI services"
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)
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145
docs/edge/en/tools/cloud-storage/s3readertool.mdx
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145
docs/edge/en/tools/cloud-storage/s3readertool.mdx
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|
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---
|
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title: S3 Reader Tool
|
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description: The `S3ReaderTool` enables CrewAI agents to read files from Amazon S3 buckets.
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icon: aws
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mode: "wide"
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---
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|
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# `S3ReaderTool`
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|
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## Description
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|
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The `S3ReaderTool` is designed to read files from Amazon S3 buckets. This tool allows CrewAI agents to access and retrieve content stored in S3, making it ideal for workflows that require reading data, configuration files, or any other content stored in AWS S3 storage.
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|
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## Installation
|
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|
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To use this tool, you need to install the required dependencies:
|
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|
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```shell
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uv add boto3
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```
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|
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## Steps to Get Started
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|
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To effectively use the `S3ReaderTool`, follow these steps:
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|
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1. **Install Dependencies**: Install the required packages using the command above.
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2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
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3. **Initialize the Tool**: Create an instance of the tool.
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4. **Specify S3 Path**: Provide the S3 path to the file you want to read.
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|
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## Example
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The following example demonstrates how to use the `S3ReaderTool` to read a file from an S3 bucket:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.s3 import S3ReaderTool
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# Initialize the tool
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s3_reader_tool = S3ReaderTool()
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# Define an agent that uses the tool
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file_reader_agent = Agent(
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role="File Reader",
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goal="Read files from S3 buckets",
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backstory="An expert in retrieving and processing files from cloud storage.",
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tools=[s3_reader_tool],
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verbose=True,
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)
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# Example task to read a configuration file
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read_task = Task(
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description="Read the configuration file from {my_bucket} and summarize its contents.",
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expected_output="A summary of the configuration file contents.",
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agent=file_reader_agent,
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)
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# Create and run the crew
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crew = Crew(agents=[file_reader_agent], tasks=[read_task])
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result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
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```
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## Parameters
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The `S3ReaderTool` accepts the following parameter when used by an agent:
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|
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- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
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## AWS Credentials
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The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
|
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|
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- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
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- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
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- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
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|
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## Usage
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|
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When using the `S3ReaderTool` with an agent, the agent will need to provide the S3 file path:
|
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|
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```python Code
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# Example of using the tool with an agent
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file_reader_agent = Agent(
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role="File Reader",
|
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goal="Read files from S3 buckets",
|
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backstory="An expert in retrieving and processing files from cloud storage.",
|
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tools=[s3_reader_tool],
|
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verbose=True,
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)
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|
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# Create a task for the agent to read a specific file
|
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read_config_task = Task(
|
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description="Read the application configuration file from {my_bucket} and extract the database connection settings.",
|
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expected_output="The database connection settings from the configuration file.",
|
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agent=file_reader_agent,
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)
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|
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# Run the task
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crew = Crew(agents=[file_reader_agent], tasks=[read_config_task])
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result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
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```
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|
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## Error Handling
|
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|
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The `S3ReaderTool` includes error handling for common S3 issues:
|
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|
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- Invalid S3 path format
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- Missing or inaccessible files
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- Permission issues
|
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- AWS credential problems
|
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|
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When an error occurs, the tool will return an error message that includes details about the issue.
|
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|
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## Implementation Details
|
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|
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The `S3ReaderTool` uses the AWS SDK for Python (boto3) to interact with S3:
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|
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```python Code
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class S3ReaderTool(BaseTool):
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name: str = "S3 Reader Tool"
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description: str = "Reads a file from Amazon S3 given an S3 file path"
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|
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def _run(self, file_path: str) -> str:
|
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try:
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bucket_name, object_key = self._parse_s3_path(file_path)
|
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|
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s3 = boto3.client(
|
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's3',
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region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
|
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aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
|
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aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
|
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)
|
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|
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# Read file content from S3
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response = s3.get_object(Bucket=bucket_name, Key=object_key)
|
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file_content = response['Body'].read().decode('utf-8')
|
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|
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return file_content
|
||||
except ClientError as e:
|
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return f"Error reading file from S3: {str(e)}"
|
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```
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|
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## Conclusion
|
||||
|
||||
The `S3ReaderTool` provides a straightforward way to read files from Amazon S3 buckets. By enabling agents to access content stored in S3, it facilitates workflows that require cloud-based file access. This tool is particularly useful for data processing, configuration management, and any task that involves retrieving information from AWS S3 storage.
|
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151
docs/edge/en/tools/cloud-storage/s3writertool.mdx
Normal file
151
docs/edge/en/tools/cloud-storage/s3writertool.mdx
Normal file
@@ -0,0 +1,151 @@
|
||||
---
|
||||
title: S3 Writer Tool
|
||||
description: The `S3WriterTool` enables CrewAI agents to write content to files in Amazon S3 buckets.
|
||||
icon: aws
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# `S3WriterTool`
|
||||
|
||||
## Description
|
||||
|
||||
The `S3WriterTool` is designed to write content to files in Amazon S3 buckets. This tool allows CrewAI agents to create or update files in S3, making it ideal for workflows that require storing data, saving configuration files, or persisting any other content to AWS S3 storage.
|
||||
|
||||
## Installation
|
||||
|
||||
To use this tool, you need to install the required dependencies:
|
||||
|
||||
```shell
|
||||
uv add boto3
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the `S3WriterTool`, follow these steps:
|
||||
|
||||
1. **Install Dependencies**: Install the required packages using the command above.
|
||||
2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
|
||||
3. **Initialize the Tool**: Create an instance of the tool.
|
||||
4. **Specify S3 Path and Content**: Provide the S3 path where you want to write the file and the content to be written.
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to use the `S3WriterTool` to write content to a file in an S3 bucket:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools.aws.s3 import S3WriterTool
|
||||
|
||||
# Initialize the tool
|
||||
s3_writer_tool = S3WriterTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
file_writer_agent = Agent(
|
||||
role="File Writer",
|
||||
goal="Write content to files in S3 buckets",
|
||||
backstory="An expert in storing and managing files in cloud storage.",
|
||||
tools=[s3_writer_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Example task to write a report
|
||||
write_task = Task(
|
||||
description="Generate a summary report of the quarterly sales data and save it to {my_bucket}.",
|
||||
expected_output="Confirmation that the report was successfully saved to S3.",
|
||||
agent=file_writer_agent,
|
||||
)
|
||||
|
||||
# Create and run the crew
|
||||
crew = Crew(agents=[file_writer_agent], tasks=[write_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/reports/quarterly-summary.txt"})
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `S3WriterTool` accepts the following parameters when used by an agent:
|
||||
|
||||
- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
|
||||
- **content**: Required. The content to write to the file.
|
||||
|
||||
## AWS Credentials
|
||||
|
||||
The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
|
||||
|
||||
- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
|
||||
- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
|
||||
- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
|
||||
|
||||
## Usage
|
||||
|
||||
When using the `S3WriterTool` with an agent, the agent will need to provide both the S3 file path and the content to write:
|
||||
|
||||
```python Code
|
||||
# Example of using the tool with an agent
|
||||
file_writer_agent = Agent(
|
||||
role="File Writer",
|
||||
goal="Write content to files in S3 buckets",
|
||||
backstory="An expert in storing and managing files in cloud storage.",
|
||||
tools=[s3_writer_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create a task for the agent to write a specific file
|
||||
write_config_task = Task(
|
||||
description="""
|
||||
Create a configuration file with the following database settings:
|
||||
- host: db.example.com
|
||||
- port: 5432
|
||||
- username: app_user
|
||||
- password: secure_password
|
||||
|
||||
Save this configuration as JSON to {my_bucket}.
|
||||
""",
|
||||
expected_output="Confirmation that the configuration file was successfully saved to S3.",
|
||||
agent=file_writer_agent,
|
||||
)
|
||||
|
||||
# Run the task
|
||||
crew = Crew(agents=[file_writer_agent], tasks=[write_config_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/db-config.json"})
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The `S3WriterTool` includes error handling for common S3 issues:
|
||||
|
||||
- Invalid S3 path format
|
||||
- Permission issues (e.g., no write access to the bucket)
|
||||
- AWS credential problems
|
||||
- Bucket does not exist
|
||||
|
||||
When an error occurs, the tool will return an error message that includes details about the issue.
|
||||
|
||||
## Implementation Details
|
||||
|
||||
The `S3WriterTool` uses the AWS SDK for Python (boto3) to interact with S3:
|
||||
|
||||
```python Code
|
||||
class S3WriterTool(BaseTool):
|
||||
name: str = "S3 Writer Tool"
|
||||
description: str = "Writes content to a file in Amazon S3 given an S3 file path"
|
||||
|
||||
def _run(self, file_path: str, content: str) -> str:
|
||||
try:
|
||||
bucket_name, object_key = self._parse_s3_path(file_path)
|
||||
|
||||
s3 = boto3.client(
|
||||
's3',
|
||||
region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
|
||||
aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
|
||||
aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
|
||||
)
|
||||
|
||||
s3.put_object(Bucket=bucket_name, Key=object_key, Body=content.encode('utf-8'))
|
||||
return f"Successfully wrote content to {file_path}"
|
||||
except ClientError as e:
|
||||
return f"Error writing file to S3: {str(e)}"
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `S3WriterTool` provides a straightforward way to write content to files in Amazon S3 buckets. By enabling agents to create and update files in S3, it facilitates workflows that require cloud-based file storage. This tool is particularly useful for data persistence, configuration management, report generation, and any task that involves storing information in AWS S3 storage.
|
||||
Reference in New Issue
Block a user