research-qdrant

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