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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
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docs/edge/ar/tools/database-data/qdrantvectorsearchtool.mdx
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docs/edge/ar/tools/database-data/qdrantvectorsearchtool.mdx
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---
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title: 'أداة البحث المتجهي Qdrant'
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description: 'إمكانيات البحث الدلالي لوكلاء CrewAI باستخدام قاعدة بيانات Qdrant المتجهية'
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icon: vector-square
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mode: "wide"
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---
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## نظرة عامة
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تتيح أداة البحث المتجهي Qdrant إمكانيات البحث الدلالي في وكلاء CrewAI من خلال الاستفادة من [Qdrant](https://qdrant.tech/)، محرك بحث التشابه المتجهي. تسمح هذه الأداة لوكلائك بالبحث في المستندات المخزنة في مجموعة Qdrant باستخدام التشابه الدلالي.
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## التثبيت
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قم بتثبيت الحزم المطلوبة:
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```bash
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uv add qdrant-client
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```
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## الاستخدام الأساسي
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إليك مثال بسيط لكيفية استخدام الأداة:
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```python
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from crewai import Agent
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from crewai_tools import QdrantVectorSearchTool, QdrantConfig
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# Initialize the tool with QdrantConfig
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_qdrant_url",
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qdrant_api_key="your_qdrant_api_key",
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collection_name="your_collection"
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)
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)
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# Create an agent that uses the tool
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agent = Agent(
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role="Research Assistant",
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goal="Find relevant information in documents",
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tools=[qdrant_tool]
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)
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# The tool will automatically use OpenAI embeddings
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# and return the 3 most relevant results with scores > 0.35
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```
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## مثال عملي كامل
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إليك مثالاً كاملاً يوضح كيفية:
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1. استخراج النص من ملف PDF
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2. توليد التضمينات باستخدام OpenAI
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3. التخزين في Qdrant
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4. إنشاء سير عمل RAG وكيلي باستخدام CrewAI للبحث الدلالي
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```python
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import os
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import uuid
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import pdfplumber
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from openai import OpenAI
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew, Process, LLM
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from crewai_tools import QdrantVectorSearchTool
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from qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct, Distance, VectorParams
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI client
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# Extract text from PDF
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def extract_text_from_pdf(pdf_path):
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text = []
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with pdfplumber.open(pdf_path) as pdf:
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for page in pdf.pages:
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page_text = page.extract_text()
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if page_text:
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text.append(page_text.strip())
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return text
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# Generate OpenAI embeddings
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def get_openai_embedding(text):
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response = client.embeddings.create(
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input=text,
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model="text-embedding-3-large"
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)
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return response.data[0].embedding
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# Store text and embeddings in Qdrant
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def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
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# Extract text from PDF
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text_chunks = extract_text_from_pdf(pdf_path)
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# Create Qdrant collection
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if qdrant.collection_exists(collection_name):
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qdrant.delete_collection(collection_name)
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qdrant.create_collection(
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collection_name=collection_name,
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vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
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)
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# Store embeddings
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points = []
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for chunk in text_chunks:
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embedding = get_openai_embedding(chunk)
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points.append(PointStruct(
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id=str(uuid.uuid4()),
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vector=embedding,
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payload={"text": chunk}
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))
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qdrant.upsert(collection_name=collection_name, points=points)
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# Initialize Qdrant client and load data
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qdrant = QdrantClient(
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url=os.getenv("QDRANT_URL"),
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api_key=os.getenv("QDRANT_API_KEY")
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)
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collection_name = "example_collection"
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pdf_path = "path/to/your/document.pdf"
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load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
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# Initialize Qdrant search tool
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from crewai_tools import QdrantConfig
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url=os.getenv("QDRANT_URL"),
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qdrant_api_key=os.getenv("QDRANT_API_KEY"),
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collection_name=collection_name,
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limit=3,
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score_threshold=0.35
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)
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)
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# Create CrewAI agents
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search_agent = Agent(
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role="Senior Semantic Search Agent",
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goal="Find and analyze documents based on semantic search",
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backstory="""You are an expert research assistant who can find relevant
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information using semantic search in a Qdrant database.""",
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tools=[qdrant_tool],
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verbose=True
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)
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answer_agent = Agent(
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role="Senior Answer Assistant",
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goal="Generate answers to questions based on the context provided",
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backstory="""You are an expert answer assistant who can generate
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answers to questions based on the context provided.""",
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tools=[qdrant_tool],
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verbose=True
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)
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# Define tasks
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search_task = Task(
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description="""Search for relevant documents about the {query}.
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Your final answer should include:
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- The relevant information found
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- The similarity scores of the results
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- The metadata of the relevant documents""",
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agent=search_agent
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)
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answer_task = Task(
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description="""Given the context and metadata of relevant documents,
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generate a final answer based on the context.""",
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agent=answer_agent
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)
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# Run CrewAI workflow
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crew = Crew(
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agents=[search_agent, answer_agent],
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tasks=[search_task, answer_task],
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process=Process.sequential,
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verbose=True
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)
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result = crew.kickoff(
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inputs={"query": "What is the role of X in the document?"}
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)
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print(result)
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```
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## معاملات الأداة
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### المعاملات المطلوبة
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- `qdrant_config` (QdrantConfig): كائن التكوين الذي يحتوي على جميع إعدادات Qdrant
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### معاملات QdrantConfig
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- `qdrant_url` (str): عنوان URL لخادم Qdrant الخاص بك
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- `qdrant_api_key` (str, اختياري): مفتاح API للمصادقة مع Qdrant
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- `collection_name` (str): اسم مجموعة Qdrant المراد البحث فيها
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- `limit` (int): الحد الأقصى لعدد النتائج المُرجعة (الافتراضي: 3)
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- `score_threshold` (float): الحد الأدنى لدرجة التشابه (الافتراضي: 0.35)
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- `filter` (Any, اختياري): نسخة Filter من Qdrant للتصفية المتقدمة (الافتراضي: None)
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### المعاملات الاختيارية للأداة
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- `custom_embedding_fn` (Callable[[str], list[float]]): دالة مخصصة لتحويل النص إلى متجهات
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- `qdrant_package` (str): مسار الحزمة الأساسية لـ Qdrant (الافتراضي: "qdrant_client")
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- `client` (Any): عميل Qdrant مُهيأ مسبقاً (اختياري)
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## التصفية المتقدمة
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تدعم أداة QdrantVectorSearchTool إمكانيات تصفية قوية لتحسين نتائج البحث:
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### التصفية الديناميكية
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استخدم معاملات `filter_by` و `filter_value` في بحثك لتصفية النتائج أثناء التنفيذ:
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```python
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# Agent will use these parameters when calling the tool
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# The tool schema accepts filter_by and filter_value
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# Example: search with category filter
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# Results will be filtered where category == "technology"
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```
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### المرشحات المسبقة مع QdrantConfig
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للتصفية المعقدة، استخدم نسخ Filter من Qdrant في تكوينك:
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```python
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from qdrant_client.http import models as qmodels
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from crewai_tools import QdrantVectorSearchTool, QdrantConfig
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# Create a filter for specific conditions
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preset_filter = qmodels.Filter(
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must=[
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qmodels.FieldCondition(
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key="category",
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match=qmodels.MatchValue(value="research")
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),
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qmodels.FieldCondition(
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key="year",
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match=qmodels.MatchValue(value=2024)
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)
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]
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)
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# Initialize tool with preset filter
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_url",
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qdrant_api_key="your_key",
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collection_name="your_collection",
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filter=preset_filter # Preset filter applied to all searches
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)
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)
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```
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### دمج المرشحات
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تقوم الأداة تلقائياً بدمج المرشحات المسبقة من `QdrantConfig` مع المرشحات الديناميكية من `filter_by` و `filter_value`:
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```python
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# If QdrantConfig has a preset filter for category="research"
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# And the search uses filter_by="year", filter_value=2024
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# Both filters will be combined (AND logic)
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```
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## معاملات البحث
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تقبل الأداة هذه المعاملات في مخططها:
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- `query` (str): استعلام البحث للعثور على مستندات مشابهة
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- `filter_by` (str, اختياري): حقل البيانات الوصفية للتصفية عليه
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- `filter_value` (Any, اختياري): القيمة المراد التصفية بها
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## صيغة الإرجاع
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تُرجع الأداة النتائج بصيغة JSON:
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```json
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[
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{
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"metadata": {
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// Any metadata stored with the document
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},
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"context": "The actual text content of the document",
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"distance": 0.95 // Similarity score
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}
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]
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```
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## التضمين الافتراضي
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بشكل افتراضي، تستخدم الأداة نموذج `text-embedding-3-large` من OpenAI للتحويل إلى متجهات. يتطلب ذلك:
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- تعيين مفتاح OpenAI API في البيئة: `OPENAI_API_KEY`
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## التضمينات المخصصة
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بدلاً من استخدام نموذج التضمين الافتراضي، قد ترغب في استخدام دالة تضمين خاصة بك في الحالات التالية:
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1. تريد استخدام نموذج تضمين مختلف (مثل Cohere أو HuggingFace أو نماذج Ollama)
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2. تحتاج إلى تقليل التكاليف باستخدام نماذج تضمين مفتوحة المصدر
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3. لديك متطلبات محددة لأبعاد المتجهات أو جودة التضمين
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4. تريد استخدام تضمينات خاصة بمجال معين (مثل النصوص الطبية أو القانونية)
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إليك مثال باستخدام نموذج HuggingFace:
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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def custom_embeddings(text: str) -> list[float]:
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# Tokenize and get model outputs
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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# Use mean pooling to get text embedding
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embeddings = outputs.last_hidden_state.mean(dim=1)
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# Convert to list of floats and return
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return embeddings[0].tolist()
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# Use custom embeddings with the tool
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from crewai_tools import QdrantConfig
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tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_url",
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qdrant_api_key="your_key",
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collection_name="your_collection"
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),
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custom_embedding_fn=custom_embeddings # Pass your custom function
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)
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```
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## معالجة الأخطاء
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تتعامل الأداة مع هذه الأخطاء المحددة:
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- تُثير ImportError إذا لم يكن `qdrant-client` مثبتاً (مع خيار التثبيت التلقائي)
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- تُثير ValueError إذا لم يتم تعيين `QDRANT_URL`
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- تطلب تثبيت `qdrant-client` إذا كان مفقوداً باستخدام `uv add qdrant-client`
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## متغيرات البيئة
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متغيرات البيئة المطلوبة:
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```bash
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export QDRANT_URL="your_qdrant_url" # If not provided in constructor
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export QDRANT_API_KEY="your_api_key" # If not provided in constructor
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export OPENAI_API_KEY="your_openai_key" # If using default embeddings
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```
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