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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>
98 lines
3.4 KiB
Plaintext
98 lines
3.4 KiB
Plaintext
---
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title: Brave Search
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description: BraveSearchTool은 Brave Search API를 사용하여 인터넷을 검색하도록 설계되었습니다.
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icon: searchengin
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mode: "wide"
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---
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# `BraveSearchTool`
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## 설명
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이 도구는 Brave Search API를 사용하여 웹 검색을 수행하도록 설계되었습니다. 지정한 쿼리를 사용하여 인터넷을 검색하고 관련 결과를 가져올 수 있습니다. 이 도구는 결과 개수와 국가별 검색을 사용자 지정할 수 있는 기능을 지원합니다.
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## 설치
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이 도구를 프로젝트에 통합하려면 아래의 설치 지침을 따르세요:
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```shell
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pip install 'crewai[tools]'
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```
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## 시작 단계
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`BraveSearchTool`을(를) 효과적으로 사용하려면 다음 단계를 따르세요:
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1. **패키지 설치**: Python 환경에 `crewai[tools]` 패키지가 설치되어 있는지 확인합니다.
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2. **API 키 획득**: https://api.search.brave.com/app/keys 에서 Brave Search API 키를 획득합니다(로그인하여 키를 생성).
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3. **환경 설정**: 획득한 API 키를 `BRAVE_API_KEY`라는 환경 변수에 저장하여 도구에서 사용할 수 있도록 합니다.
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## 예시
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다음 예시는 도구를 초기화하고 주어진 쿼리로 검색을 실행하는 방법을 보여줍니다:
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```python Code
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from crewai_tools import BraveSearchTool
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# 인터넷 검색 기능을 위한 도구 초기화
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tool = BraveSearchTool()
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# 검색 실행
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results = tool.run(search_query="CrewAI agent framework")
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print(results)
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```
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## 매개변수
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`BraveSearchTool`은 다음과 같은 매개변수를 받습니다:
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- **search_query**: 필수. 인터넷 검색에 사용할 검색 쿼리입니다.
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- **country**: 선택. 검색 결과의 국가를 지정합니다. 기본값은 빈 문자열입니다.
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- **n_results**: 선택. 반환할 검색 결과의 개수입니다. 기본값은 `10`입니다.
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- **save_file**: 선택. 검색 결과를 파일로 저장할지 여부입니다. 기본값은 `False`입니다.
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## 매개변수와 함께 사용하는 예시
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다음은 추가 매개변수를 사용하여 도구를 활용하는 방법을 보여주는 예시입니다:
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```python Code
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from crewai_tools import BraveSearchTool
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# Initialize the tool with custom parameters
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tool = BraveSearchTool(
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country="US",
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n_results=5,
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save_file=True
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)
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# Execute a search
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results = tool.run(search_query="Latest AI developments")
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print(results)
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```
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## 에이전트 통합 예시
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다음은 `BraveSearchTool`을 CrewAI 에이전트와 통합하는 방법입니다:
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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 BraveSearchTool
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# Initialize the tool
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brave_search_tool = BraveSearchTool()
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# Define an agent with the BraveSearchTool
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config["researcher"],
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allow_delegation=False,
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tools=[brave_search_tool]
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)
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```
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## 결론
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`BraveSearchTool`을 Python 프로젝트에 통합함으로써, 사용자는 애플리케이션 내에서 직접 실시간으로 관련성 높은 인터넷 검색을 수행할 수 있습니다. 이 도구는 강력한 Brave Search API에 대한 간단한 인터페이스를 제공하여, 검색 결과를 프로그래밍적으로 손쉽게 가져오고 처리할 수 있게 해줍니다. 제공된 설정 및 사용 지침을 따르면, 이 도구를 프로젝트에 통합하는 과정이 간편하고 직관적입니다.
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