mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-01 05:08:12 +00:00
93dafe2637
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>
83 lines
3.4 KiB
Plaintext
83 lines
3.4 KiB
Plaintext
---
|
|
title: PG RAG Search
|
|
description: The `PGSearchTool` is designed to search PostgreSQL databases and return the most relevant results.
|
|
icon: elephant
|
|
mode: "wide"
|
|
---
|
|
|
|
## Overview
|
|
|
|
<Note>
|
|
The PGSearchTool is currently under development. This document outlines the intended functionality and interface.
|
|
As development progresses, please be aware that some features may not be available or could change.
|
|
</Note>
|
|
|
|
## Description
|
|
|
|
The PGSearchTool is envisioned as a powerful tool for facilitating semantic searches within PostgreSQL database tables. By leveraging advanced Retrieve and Generate (RAG) technology,
|
|
it aims to provide an efficient means for querying database table content, specifically tailored for PostgreSQL databases.
|
|
The tool's goal is to simplify the process of finding relevant data through semantic search queries, offering a valuable resource for users needing to conduct advanced queries on
|
|
extensive datasets within a PostgreSQL environment.
|
|
|
|
## Installation
|
|
|
|
The `crewai_tools` package, which will include the PGSearchTool upon its release, can be installed using the following command:
|
|
|
|
```shell
|
|
pip install 'crewai[tools]'
|
|
```
|
|
|
|
<Note>
|
|
The PGSearchTool is not yet available in the current version of the `crewai_tools` package. This installation command will be updated once the tool is released.
|
|
</Note>
|
|
|
|
## Example Usage
|
|
|
|
Below is a proposed example showcasing how to use the PGSearchTool for conducting a semantic search on a table within a PostgreSQL database:
|
|
|
|
```python Code
|
|
from crewai_tools import PGSearchTool
|
|
|
|
# Initialize the tool with the database URI and the target table name
|
|
tool = PGSearchTool(
|
|
db_uri='postgresql://user:password@localhost:5432/mydatabase',
|
|
table_name='employees'
|
|
)
|
|
```
|
|
|
|
## Arguments
|
|
|
|
The PGSearchTool is designed to require the following arguments for its operation:
|
|
|
|
| Argument | Type | Description |
|
|
|:---------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------|
|
|
| **db_uri** | `string` | **Mandatory**. A string representing the URI of the PostgreSQL database to be queried. This argument will be mandatory and must include the necessary authentication details and the location of the database. |
|
|
| **table_name** | `string` | **Mandatory**. A string specifying the name of the table within the database on which the semantic search will be performed. This argument will also be mandatory. |
|
|
|
|
## Custom Model and Embeddings
|
|
|
|
The tool intends to use OpenAI for both embeddings and summarization by default. Users will have the option to customize the model using a config dictionary as follows:
|
|
|
|
```python Code
|
|
tool = PGSearchTool(
|
|
config=dict(
|
|
llm=dict(
|
|
provider="ollama", # or google, openai, anthropic, llama2, ...
|
|
config=dict(
|
|
model="llama2",
|
|
# temperature=0.5,
|
|
# top_p=1,
|
|
# stream=true,
|
|
),
|
|
),
|
|
embedder=dict(
|
|
provider="google-generativeai", # or openai, ollama, ...
|
|
config=dict(
|
|
model_name="gemini-embedding-001",
|
|
task_type="RETRIEVAL_DOCUMENT",
|
|
# title="Embeddings",
|
|
),
|
|
),
|
|
)
|
|
)
|
|
``` |