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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> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
119 lines
4.3 KiB
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119 lines
4.3 KiB
Plaintext
---
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title: AI Mind Tool
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description: The `AIMindTool` is designed to query data sources in natural language.
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icon: brain
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mode: "wide"
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---
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# `AIMindTool`
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## Description
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The `AIMindTool` is a wrapper around [AI-Minds](https://mindsdb.com/minds) provided by [MindsDB](https://mindsdb.com/). It allows you to query data sources in natural language by simply configuring their connection parameters. This tool is useful when you need answers to questions from your data stored in various data sources including PostgreSQL, MySQL, MariaDB, ClickHouse, Snowflake, and Google BigQuery.
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Minds are AI systems that work similarly to large language models (LLMs) but go beyond by answering any question from any data. This is accomplished by:
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- Selecting the most relevant data for an answer using parametric search
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- Understanding the meaning and providing responses within the correct context through semantic search
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- Delivering precise answers by analyzing data and using machine learning (ML) models
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## Installation
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To incorporate this tool into your project, you need to install the Minds SDK:
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```shell
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uv add minds-sdk
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```
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## Steps to Get Started
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To effectively use the `AIMindTool`, follow these steps:
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1. **Package Installation**: Confirm that the `crewai[tools]` and `minds-sdk` packages are installed in your Python environment.
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2. **API Key Acquisition**: Sign up for a Minds account [here](https://mdb.ai/register), and obtain an API key.
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3. **Environment Configuration**: Store your obtained API key in an environment variable named `MINDS_API_KEY` to facilitate its use by the tool.
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## Example
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The following example demonstrates how to initialize the tool and execute a query:
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```python Code
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from crewai_tools import AIMindTool
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# Initialize the AIMindTool
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aimind_tool = AIMindTool(
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datasources=[
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{
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"description": "house sales data",
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"engine": "postgres",
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"connection_data": {
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"user": "demo_user",
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"password": "demo_password",
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"host": "samples.mindsdb.com",
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"port": 5432,
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"database": "demo",
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"schema": "demo_data"
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},
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"tables": ["house_sales"]
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}
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]
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)
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# Run a natural language query
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result = aimind_tool.run("How many 3 bedroom houses were sold in 2008?")
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print(result)
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```
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## Parameters
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The `AIMindTool` accepts the following parameters:
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- **api_key**: Optional. Your Minds API key. If not provided, it will be read from the `MINDS_API_KEY` environment variable.
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- **datasources**: A list of dictionaries, each containing the following keys:
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- **description**: A description of the data contained in the datasource.
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- **engine**: The engine (or type) of the datasource.
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- **connection_data**: A dictionary containing the connection parameters for the datasource.
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- **tables**: A list of tables that the data source will use. This is optional and can be omitted if all tables in the data source are to be used.
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A list of supported data sources and their connection parameters can be found [here](https://docs.mdb.ai/docs/data_sources).
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## Agent Integration Example
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Here's how to integrate the `AIMindTool` with a CrewAI agent:
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```python Code
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from crewai import Agent
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from crewai.project import agent
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from crewai_tools import AIMindTool
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# Initialize the tool
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aimind_tool = AIMindTool(
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datasources=[
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{
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"description": "sales data",
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"engine": "postgres",
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"connection_data": {
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"user": "your_user",
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"password": "your_password",
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"host": "your_host",
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"port": 5432,
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"database": "your_db",
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"schema": "your_schema"
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},
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"tables": ["sales"]
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}
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]
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)
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# Define an agent with the AIMindTool
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@agent
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def data_analyst(self) -> Agent:
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return Agent(
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config=self.agents_config["data_analyst"],
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allow_delegation=False,
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tools=[aimind_tool]
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)
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```
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## Conclusion
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The `AIMindTool` provides a powerful way to query your data sources using natural language, making it easier to extract insights without writing complex SQL queries. By connecting to various data sources and leveraging AI-Minds technology, this tool enables agents to access and analyze data efficiently. |