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>
67 lines
2.8 KiB
Plaintext
67 lines
2.8 KiB
Plaintext
---
|
|
title: "개요"
|
|
description: "포괄적인 데이터 액세스를 위해 데이터베이스, 벡터 스토어, 데이터 웨어하우스에 연결하세요"
|
|
icon: "face-smile"
|
|
mode: "wide"
|
|
---
|
|
|
|
이러한 툴을 통해 에이전트는 전통적인 SQL 데이터베이스부터 최신 벡터 저장소 및 데이터 웨어하우스에 이르기까지 다양한 데이터베이스 시스템과 상호 작용할 수 있습니다.
|
|
|
|
## **사용 가능한 도구**
|
|
|
|
<CardGroup cols={2}>
|
|
<Card title="MySQL 도구" icon="database" href="/ko/tools/database-data/mysqltool">
|
|
SQL 연산을 사용하여 MySQL 데이터베이스에 연결하고 쿼리할 수 있습니다.
|
|
</Card>
|
|
|
|
<Card title="PostgreSQL 검색" icon="elephant" href="/ko/tools/database-data/pgsearchtool">
|
|
PostgreSQL 데이터베이스를 효율적으로 검색하고 쿼리할 수 있습니다.
|
|
</Card>
|
|
|
|
<Card title="Snowflake 검색" icon="snowflake" href="/ko/tools/database-data/snowflakesearchtool">
|
|
분석 및 리포팅을 위해 Snowflake 데이터 웨어하우스에 접근합니다.
|
|
</Card>
|
|
|
|
<Card title="NL2SQL 도구" icon="language" href="/ko/tools/database-data/nl2sqltool">
|
|
자연어 쿼리를 자동으로 SQL 구문으로 변환합니다.
|
|
</Card>
|
|
|
|
<Card title="Qdrant 벡터 검색" icon="vector-square" href="/ko/tools/database-data/qdrantvectorsearchtool">
|
|
Qdrant 벡터 데이터베이스를 사용하여 벡터 임베딩을 검색합니다.
|
|
</Card>
|
|
|
|
<Card title="Weaviate 벡터 검색" icon="network-wired" href="/ko/tools/database-data/weaviatevectorsearchtool">
|
|
Weaviate 벡터 데이터베이스로 의미론적 검색을 수행합니다.
|
|
</Card>
|
|
|
|
<Card title="MongoDB 벡터 검색" icon="leaf" href="/ko/tools/database-data/mongodbvectorsearchtool">
|
|
인덱싱 도우미를 사용하여 MongoDB Atlas에서 벡터 유사도 검색을 실행합니다.
|
|
</Card>
|
|
|
|
<Card title="SingleStore 검색" icon="database" href="/ko/tools/database-data/singlestoresearchtool">
|
|
풀링과 검증을 통해 SingleStore에서 안전한 SELECT/SHOW 쿼리를 실행할 수 있습니다.
|
|
</Card>
|
|
</CardGroup>
|
|
|
|
## **일반적인 사용 사례**
|
|
|
|
- **데이터 분석**: 비즈니스 인텔리전스와 보고를 위해 데이터베이스 쿼리
|
|
- **벡터 검색**: 시맨틱 임베딩을 사용하여 유사한 콘텐츠 찾기
|
|
- **ETL 작업**: 시스템 간 데이터 추출, 변환 및 적재
|
|
- **실시간 분석**: 의사 결정에 필요한 실시간 데이터 접근
|
|
|
|
```python
|
|
from crewai_tools import MySQLTool, QdrantVectorSearchTool, NL2SQLTool
|
|
|
|
# Create database tools
|
|
mysql_db = MySQLTool()
|
|
vector_search = QdrantVectorSearchTool()
|
|
nl_to_sql = NL2SQLTool()
|
|
|
|
# Add to your agent
|
|
agent = Agent(
|
|
role="Data Analyst",
|
|
tools=[mysql_db, vector_search, nl_to_sql],
|
|
goal="Extract insights from various data sources"
|
|
)
|
|
``` |