Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license
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---
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name: qdrant
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description: Vector search engine for production RAG systems.
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version: 1.0.1
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author: Orchestra Research
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license: MIT
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dependencies: [qdrant-client>=1.14.0]
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platforms: [linux, macos, windows]
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metadata:
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hermes:
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tags: [RAG, Vector Search, Qdrant, Semantic Search, Embeddings, Similarity Search, HNSW, Production, Distributed]
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---
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# Qdrant - Vector Similarity Search Engine
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High-performance vector database written in Rust for production RAG and semantic search.
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## When to use Qdrant
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**Use Qdrant when:**
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- Building production RAG systems requiring low latency
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- Need hybrid search (vectors + metadata filtering)
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- Require horizontal scaling with sharding/replication
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- Want on-premise deployment with full data control
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- Need multi-vector storage per record (dense + sparse)
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- Building real-time recommendation systems
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**Key features:**
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- **Rust-powered**: Memory-safe, high performance
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- **Rich filtering**: Filter by any payload field during search
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- **Multiple vectors**: Dense, sparse, multi-dense per point
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- **Quantization**: Scalar, product, binary for memory efficiency
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- **Distributed**: Raft consensus, sharding, replication
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- **REST + gRPC**: Both APIs with full feature parity
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**Use alternatives instead:**
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- **Chroma**: Simpler setup, embedded use cases
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- **FAISS**: Maximum raw speed, research/batch processing
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- **Pinecone**: Fully managed, zero ops preferred
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- **Weaviate**: GraphQL preference, built-in vectorizers
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## Quick start
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### Installation
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```bash
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# Python client
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pip install qdrant-client
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# Docker (recommended for development)
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docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
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# Docker with persistent storage
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage \
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qdrant/qdrant
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```
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### Basic usage
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```python
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from qdrant_client import QdrantClient
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from qdrant_client.models import Distance, VectorParams, PointStruct
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# Connect to Qdrant
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client = QdrantClient(host="localhost", port=6333)
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# Create collection
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client.create_collection(
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collection_name="documents",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE)
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)
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# Insert vectors with payload
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client.upsert(
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collection_name="documents",
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points=[
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PointStruct(
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id=1,
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vector=[0.1, 0.2, ...], # 384-dim vector
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payload={"title": "Doc 1", "category": "tech"}
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),
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PointStruct(
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id=2,
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vector=[0.3, 0.4, ...],
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payload={"title": "Doc 2", "category": "science"}
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)
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]
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)
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# Search with filtering (query_points is the current API; client.search is removed in qdrant-client 1.14+)
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response = client.query_points(
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collection_name="documents",
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query=[0.15, 0.25, ...],
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query_filter={
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"must": [{"key": "category", "match": {"value": "tech"}}]
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},
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limit=10
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)
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for point in response.points:
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print(f"ID: {point.id}, Score: {point.score}, Payload: {point.payload}")
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```
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## Core concepts
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### Points - Basic data unit
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```python
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from qdrant_client.models import PointStruct
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# Point = ID + Vector(s) + Payload
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point = PointStruct(
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id=123, # Integer or UUID string
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vector=[0.1, 0.2, 0.3, ...], # Dense vector
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payload={ # Arbitrary JSON metadata
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"title": "Document title",
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"category": "tech",
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"timestamp": 1699900000,
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"tags": ["python", "ml"]
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}
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)
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# Batch upsert (recommended)
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client.upsert(
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collection_name="documents",
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points=[point1, point2, point3],
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wait=True # Wait for indexing
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)
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```
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### Collections - Vector containers
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```python
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from qdrant_client.models import VectorParams, Distance, HnswConfigDiff
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# Create with HNSW configuration
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client.create_collection(
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collection_name="documents",
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vectors_config=VectorParams(
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size=384, # Vector dimensions
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distance=Distance.COSINE # COSINE, EUCLID, DOT, MANHATTAN
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),
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hnsw_config=HnswConfigDiff(
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m=16, # Connections per node (default 16)
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ef_construct=100, # Build-time accuracy (default 100)
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full_scan_threshold=10000 # Switch to brute force below this
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),
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on_disk_payload=True # Store payload on disk
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)
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# Collection info
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info = client.get_collection("documents")
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print(f"Points: {info.points_count}, Vectors: {info.vectors_count}")
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```
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### Distance metrics
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| Metric | Use Case | Range |
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|--------|----------|-------|
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| `COSINE` | Text embeddings, normalized vectors | 0 to 2 |
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| `EUCLID` | Spatial data, image features | 0 to ∞ |
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| `DOT` | Recommendations, unnormalized | -∞ to ∞ |
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| `MANHATTAN` | Sparse features, discrete data | 0 to ∞ |
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## Search operations
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### Basic search
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```python
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# Simple nearest neighbor search (returns a QueryResponse; use .points)
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response = client.query_points(
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collection_name="documents",
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query=[0.1, 0.2, ...],
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limit=10,
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with_payload=True,
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with_vectors=False # Don't return vectors (faster)
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)
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results = response.points
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```
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### Filtered search
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```python
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from qdrant_client.models import Filter, FieldCondition, MatchValue, Range
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# Complex filtering
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response = client.query_points(
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collection_name="documents",
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query=query_embedding,
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query_filter=Filter(
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must=[
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FieldCondition(key="category", match=MatchValue(value="tech")),
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FieldCondition(key="timestamp", range=Range(gte=1699000000))
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],
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must_not=[
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FieldCondition(key="status", match=MatchValue(value="archived"))
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]
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),
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limit=10
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).points
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# Shorthand filter syntax
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response = client.query_points(
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collection_name="documents",
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query=query_embedding,
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query_filter={
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"must": [
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{"key": "category", "match": {"value": "tech"}},
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{"key": "price", "range": {"gte": 10, "lte": 100}}
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]
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},
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limit=10
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).points
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```
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### Batch search
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```python
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from qdrant_client.models import QueryRequest
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# Multiple queries in one request (search_batch is replaced by query_batch_points)
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responses = client.query_batch_points(
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collection_name="documents",
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requests=[
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QueryRequest(query=[0.1, ...], limit=5),
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QueryRequest(query=[0.2, ...], limit=5, filter={"must": [...]}),
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QueryRequest(query=[0.3, ...], limit=10)
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]
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)
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# Each element is a QueryResponse; use .points
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for resp in responses:
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for point in resp.points:
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print(point.id, point.score)
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```
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## RAG integration
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### With sentence-transformers
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```python
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from sentence_transformers import SentenceTransformer
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from qdrant_client import QdrantClient
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from qdrant_client.models import VectorParams, Distance, PointStruct
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# Initialize
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encoder = SentenceTransformer("all-MiniLM-L6-v2")
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client = QdrantClient(host="localhost", port=6333)
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# Create collection
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client.create_collection(
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collection_name="knowledge_base",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE)
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)
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# Index documents
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documents = [
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{"id": 1, "text": "Python is a programming language", "source": "wiki"},
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{"id": 2, "text": "Machine learning uses algorithms", "source": "textbook"},
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]
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points = [
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PointStruct(
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id=doc["id"],
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vector=encoder.encode(doc["text"]).tolist(),
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payload={"text": doc["text"], "source": doc["source"]}
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)
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for doc in documents
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]
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client.upsert(collection_name="knowledge_base", points=points)
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# RAG retrieval
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def retrieve(query: str, top_k: int = 5) -> list[dict]:
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query_vector = encoder.encode(query).tolist()
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response = client.query_points(
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collection_name="knowledge_base",
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query=query_vector,
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limit=top_k
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)
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return [{"text": r.payload["text"], "score": r.score} for r in response.points]
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# Use in RAG pipeline
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context = retrieve("What is Python?")
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prompt = f"Context: {context}\n\nQuestion: What is Python?"
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```
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### With LangChain
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```python
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from langchain_community.vectorstores import Qdrant
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from langchain_community.embeddings import HuggingFaceEmbeddings
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embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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vectorstore = Qdrant.from_documents(documents, embeddings, url="http://localhost:6333", collection_name="docs")
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retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
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```
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### With LlamaIndex
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```python
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from llama_index.vector_stores.qdrant import QdrantVectorStore
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from llama_index.core import VectorStoreIndex, StorageContext
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vector_store = QdrantVectorStore(client=client, collection_name="llama_docs")
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storage_context = StorageContext.from_defaults(vector_store=vector_store)
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index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
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query_engine = index.as_query_engine()
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```
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## Multi-vector support
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### Named vectors (different embedding models)
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```python
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from qdrant_client.models import VectorParams, Distance
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# Collection with multiple vector types
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client.create_collection(
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collection_name="hybrid_search",
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vectors_config={
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"dense": VectorParams(size=384, distance=Distance.COSINE),
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"sparse": VectorParams(size=30000, distance=Distance.DOT)
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}
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)
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# Insert with named vectors
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client.upsert(
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collection_name="hybrid_search",
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points=[
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PointStruct(
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id=1,
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vector={
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"dense": dense_embedding,
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"sparse": sparse_embedding
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},
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payload={"text": "document text"}
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)
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]
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)
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# Search specific named vector (pass the vector name via `using`)
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response = client.query_points(
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collection_name="hybrid_search",
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query=query_dense,
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using="dense", # Specify which named vector to search
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limit=10
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)
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results = response.points
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```
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### Sparse vectors (BM25, SPLADE)
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```python
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from qdrant_client.models import SparseVectorParams, SparseIndexParams, SparseVector
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# Collection with sparse vectors
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client.create_collection(
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collection_name="sparse_search",
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vectors_config={},
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sparse_vectors_config={"text": SparseVectorParams(index=SparseIndexParams(on_disk=False))}
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)
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# Insert sparse vector
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client.upsert(
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collection_name="sparse_search",
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points=[PointStruct(id=1, vector={"text": SparseVector(indices=[1, 5, 100], values=[0.5, 0.8, 0.2])}, payload={"text": "document"})]
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)
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```
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## Quantization (memory optimization)
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```python
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from qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType
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# Scalar quantization (4x memory reduction)
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client.create_collection(
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collection_name="quantized",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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quantization_config=ScalarQuantization(
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scalar=ScalarQuantizationConfig(
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type=ScalarType.INT8,
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quantile=0.99, # Clip outliers
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always_ram=True # Keep quantized in RAM
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)
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)
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)
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# Search with rescoring
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response = client.query_points(
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collection_name="quantized",
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query=query,
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search_params={"quantization": {"rescore": True}}, # Rescore top results
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limit=10
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)
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results = response.points
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```
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## Payload indexing
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```python
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from qdrant_client.models import PayloadSchemaType
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# Create payload index for faster filtering
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client.create_payload_index(
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collection_name="documents",
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field_name="category",
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field_schema=PayloadSchemaType.KEYWORD
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)
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client.create_payload_index(
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collection_name="documents",
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field_name="timestamp",
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field_schema=PayloadSchemaType.INTEGER
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)
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# Index types: KEYWORD, INTEGER, FLOAT, GEO, TEXT (full-text), BOOL
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```
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## Production deployment
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### Qdrant Cloud
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```python
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from qdrant_client import QdrantClient
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# Connect to Qdrant Cloud
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client = QdrantClient(
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url="https://your-cluster.cloud.qdrant.io",
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api_key="your-api-key"
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)
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```
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### Performance tuning
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```python
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# Optimize for search speed (higher recall)
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client.update_collection(
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collection_name="documents",
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hnsw_config=HnswConfigDiff(ef_construct=200, m=32)
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)
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# Optimize for indexing speed (bulk loads)
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client.update_collection(
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collection_name="documents",
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optimizer_config={"indexing_threshold": 20000}
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)
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```
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## Best practices
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1. **Batch operations** - Use batch upsert/search for efficiency
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2. **Payload indexing** - Index fields used in filters
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3. **Quantization** - Enable for large collections (>1M vectors)
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4. **Sharding** - Use for collections >10M vectors
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5. **On-disk storage** - Enable `on_disk_payload` for large payloads
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6. **Connection pooling** - Reuse client instances
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## Common issues
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**Slow search with filters:**
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```python
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# Create payload index for filtered fields
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client.create_payload_index(
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collection_name="docs",
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field_name="category",
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field_schema=PayloadSchemaType.KEYWORD
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)
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```
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**Out of memory:**
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```python
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# Enable quantization and on-disk storage
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client.create_collection(
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collection_name="large_collection",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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quantization_config=ScalarQuantization(...),
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on_disk_payload=True
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)
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```
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**Connection issues:**
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```python
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# Use timeout and retry
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client = QdrantClient(
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host="localhost",
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port=6333,
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timeout=30,
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prefer_grpc=True # gRPC for better performance
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)
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```
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## References
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- **[Advanced Usage](references/advanced-usage.md)** - Distributed mode, hybrid search, recommendations
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- **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging, performance tuning
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## Resources
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- **GitHub**: https://github.com/qdrant/qdrant (22k+ stars)
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- **Docs**: https://qdrant.tech/documentation/
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- **Python Client**: https://github.com/qdrant/qdrant-client
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- **Cloud**: https://cloud.qdrant.io
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- **Version**: 1.14.0+
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- **License**: Apache 2.0
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