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feat: adopt directory-based docs versioning with Edge channel

Browse Source 
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
2026-06-17 09:33:56 -03:00
parent 7bb9bc7e1a
commit 93dafe2637
15793 changed files with 3237032 additions and 16873 deletions
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---
title: 'Qdrant Vector Search Tool'
description: 'Semantic search capabilities for CrewAI agents using Qdrant vector database'
icon: vector-square
mode: "wide"
---
## Overview
The Qdrant Vector Search Tool enables semantic search capabilities in your CrewAI agents by leveraging [Qdrant](https://qdrant.tech/), a vector similarity search engine. This tool allows your agents to search through documents stored in a Qdrant collection using semantic similarity.
## Installation
Install the required packages:
```bash
uv add qdrant-client
```
## Basic Usage
Here's a minimal example of how to use the tool:
```python
from crewai import Agent
from crewai_tools import QdrantVectorSearchTool, QdrantConfig
# Initialize the tool with QdrantConfig
qdrant_tool = QdrantVectorSearchTool(
qdrant_config=QdrantConfig(
qdrant_url="your_qdrant_url",
qdrant_api_key="your_qdrant_api_key",
collection_name="your_collection"
)
)
# Create an agent that uses the tool
agent = Agent(
role="Research Assistant",
goal="Find relevant information in documents",
tools=[qdrant_tool]
)
# The tool will automatically use OpenAI embeddings
# and return the 3 most relevant results with scores > 0.35
```
## Complete Working Example
Here's a complete example showing how to:
1. Extract text from a PDF
2. Generate embeddings using OpenAI
3. Store in Qdrant
4. Create a CrewAI agentic RAG workflow for semantic search
```python
import os
import uuid
import pdfplumber
from openai import OpenAI
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import QdrantVectorSearchTool
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, Distance, VectorParams
# Load environment variables
load_dotenv()
# Initialize OpenAI client
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
# Extract text from PDF
def extract_text_from_pdf(pdf_path):
text = []
with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
page_text = page.extract_text()
if page_text:
text.append(page_text.strip())
return text
# Generate OpenAI embeddings
def get_openai_embedding(text):
response = client.embeddings.create(
input=text,
model="text-embedding-3-large"
)
return response.data[0].embedding
# Store text and embeddings in Qdrant
def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
# Extract text from PDF
text_chunks = extract_text_from_pdf(pdf_path)
# Create Qdrant collection
if qdrant.collection_exists(collection_name):
qdrant.delete_collection(collection_name)
qdrant.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
)
# Store embeddings
points = []
for chunk in text_chunks:
embedding = get_openai_embedding(chunk)
points.append(PointStruct(
id=str(uuid.uuid4()),
vector=embedding,
payload={"text": chunk}
))
qdrant.upsert(collection_name=collection_name, points=points)
# Initialize Qdrant client and load data
qdrant = QdrantClient(
url=os.getenv("QDRANT_URL"),
api_key=os.getenv("QDRANT_API_KEY")
)
collection_name = "example_collection"
pdf_path = "path/to/your/document.pdf"
load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
# Initialize Qdrant search tool
from crewai_tools import QdrantConfig
qdrant_tool = QdrantVectorSearchTool(
qdrant_config=QdrantConfig(
qdrant_url=os.getenv("QDRANT_URL"),
qdrant_api_key=os.getenv("QDRANT_API_KEY"),
collection_name=collection_name,
limit=3,
score_threshold=0.35
)
)
# Create CrewAI agents
search_agent = Agent(
role="Senior Semantic Search Agent",
goal="Find and analyze documents based on semantic search",
backstory="""You are an expert research assistant who can find relevant
information using semantic search in a Qdrant database.""",
tools=[qdrant_tool],
verbose=True
)
answer_agent = Agent(
role="Senior Answer Assistant",
goal="Generate answers to questions based on the context provided",
backstory="""You are an expert answer assistant who can generate
answers to questions based on the context provided.""",
tools=[qdrant_tool],
verbose=True
)
# Define tasks
search_task = Task(
description="""Search for relevant documents about the {query}.
Your final answer should include:
- The relevant information found
- The similarity scores of the results
- The metadata of the relevant documents""",
agent=search_agent
)
answer_task = Task(
description="""Given the context and metadata of relevant documents,
generate a final answer based on the context.""",
agent=answer_agent
)
# Run CrewAI workflow
crew = Crew(
agents=[search_agent, answer_agent],
tasks=[search_task, answer_task],
process=Process.sequential,
verbose=True
)
result = crew.kickoff(
inputs={"query": "What is the role of X in the document?"}
)
print(result)
```
## Tool Parameters
### Required Parameters
- `qdrant_config` (QdrantConfig): Configuration object containing all Qdrant settings
### QdrantConfig Parameters
- `qdrant_url` (str): The URL of your Qdrant server
- `qdrant_api_key` (str, optional): API key for authentication with Qdrant
- `collection_name` (str): Name of the Qdrant collection to search
- `limit` (int): Maximum number of results to return (default: 3)
- `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
- `filter` (Any, optional): Qdrant Filter instance for advanced filtering (default: None)
### Optional Tool Parameters
- `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
- `qdrant_package` (str): Base package path for Qdrant (default: "qdrant_client")
- `client` (Any): Pre-initialized Qdrant client (optional)
## Advanced Filtering
The QdrantVectorSearchTool supports powerful filtering capabilities to refine your search results:
### Dynamic Filtering
Use `filter_by` and `filter_value` parameters in your search to filter results on-the-fly:
```python
# Agent will use these parameters when calling the tool
# The tool schema accepts filter_by and filter_value
# Example: search with category filter
# Results will be filtered where category == "technology"
```
### Preset Filters with QdrantConfig
For complex filtering, use Qdrant Filter instances in your configuration:
```python
from qdrant_client.http import models as qmodels
from crewai_tools import QdrantVectorSearchTool, QdrantConfig
# Create a filter for specific conditions
preset_filter = qmodels.Filter(
must=[
qmodels.FieldCondition(
key="category",
match=qmodels.MatchValue(value="research")
),
qmodels.FieldCondition(
key="year",
match=qmodels.MatchValue(value=2024)
)
]
)
# Initialize tool with preset filter
qdrant_tool = QdrantVectorSearchTool(
qdrant_config=QdrantConfig(
qdrant_url="your_url",
qdrant_api_key="your_key",
collection_name="your_collection",
filter=preset_filter # Preset filter applied to all searches
)
)
```
### Combining Filters
The tool automatically combines preset filters from `QdrantConfig` with dynamic filters from `filter_by` and `filter_value`:
```python
# If QdrantConfig has a preset filter for category="research"
# And the search uses filter_by="year", filter_value=2024
# Both filters will be combined (AND logic)
```
## Search Parameters
The tool accepts these parameters in its schema:
- `query` (str): The search query to find similar documents
- `filter_by` (str, optional): Metadata field to filter on
- `filter_value` (Any, optional): Value to filter by
## Return Format
The tool returns results in JSON format:
```json
[
{
"metadata": {
// Any metadata stored with the document
},
"context": "The actual text content of the document",
"distance": 0.95 // Similarity score
}
]
```
## Default Embedding
By default, the tool uses OpenAI's `text-embedding-3-large` model for vectorization. This requires:
- OpenAI API key set in environment: `OPENAI_API_KEY`
## Custom Embeddings
Instead of using the default embedding model, you might want to use your own embedding function in cases where you:
1. Want to use a different embedding model (e.g., Cohere, HuggingFace, Ollama models)
2. Need to reduce costs by using open-source embedding models
3. Have specific requirements for vector dimensions or embedding quality
4. Want to use domain-specific embeddings (e.g., for medical or legal text)
Here's an example using a HuggingFace model:
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
def custom_embeddings(text: str) -> list[float]:
# Tokenize and get model outputs
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
# Use mean pooling to get text embedding
embeddings = outputs.last_hidden_state.mean(dim=1)
# Convert to list of floats and return
return embeddings[0].tolist()
# Use custom embeddings with the tool
from crewai_tools import QdrantConfig
tool = QdrantVectorSearchTool(
qdrant_config=QdrantConfig(
qdrant_url="your_url",
qdrant_api_key="your_key",
collection_name="your_collection"
),
custom_embedding_fn=custom_embeddings # Pass your custom function
)
```
## Error Handling
The tool handles these specific errors:
- Raises ImportError if `qdrant-client` is not installed (with option to auto-install)
- Raises ValueError if `QDRANT_URL` is not set
- Prompts to install `qdrant-client` if missing using `uv add qdrant-client`
## Environment Variables
Required environment variables:
```bash
export QDRANT_URL="your_qdrant_url" # If not provided in constructor
export QDRANT_API_KEY="your_api_key" # If not provided in constructor
export OPENAI_API_KEY="your_openai_key" # If using default embeddings