qdrant-vector-search

A search engine that matches on meaning rather than on the words themselves, and that you run on your own machines — kept on your premises, under your control, instead of handed to a hosted service.

WHEN TO USE

SCOPE

USE FOR

Retrieve the nearest documents to a query embedding with sub-100ms latency.
Filter search results by fields like category or timestamp alongside the vector search.
Store both dense and sparse vectors per document for hybrid search.
Cut memory use on a large collection with scalar or binary quantization.

NOT FOR

Use Chroma instead for a simpler, embedded setup.
Use FAISS instead for maximum raw speed in research or batch jobs.
Use Pinecone instead when you want a fully managed service with no ops work.
Use Weaviate instead if you prefer GraphQL or built-in vectorizers.

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In this document (14)

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

# 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

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

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

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

# 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

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

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

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

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

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)

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)

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)

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

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

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

# 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",
    optimizer_config={"indexing_threshold": 20000}
)

Best practices

Batch operations - Use batch upsert/search for efficiency
Payload indexing - Index fields used in filters
Quantization - Enable for large collections (>1M vectors)
Sharding - Use for collections >10M vectors
On-disk storage - Enable on_disk_payload for large payloads
Connection pooling - Reuse client instances

Common issues

Slow search with filters:

# Create payload index for filtered fields
client.create_payload_index(
    collection_name="docs",
    field_name="category",
    field_schema=PayloadSchemaType.KEYWORD
)

Out of memory:

# Enable quantization and on-disk storage
client.create_collection(
    collection_name="large_collection",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
    quantization_config=ScalarQuantization(...),
    on_disk_payload=True
)

Connection issues:

# Use timeout and retry
client = QdrantClient(
    host="localhost",
    port=6333,
    timeout=30,
    prefer_grpc=True  # gRPC for better performance
)

References

Advanced Usage - Distributed mode, hybrid search, recommendations
Troubleshooting - Common issues, debugging, performance tuning

Resources

GitHub: https://github.com/qdrant/qdrant (22k+ stars)
Docs: https://qdrant.tech/documentation/
Python Client: https://github.com/qdrant/qdrant-client
Cloud: https://cloud.qdrant.io
Version: 1.12.0+
License: Apache 2.0

Local tool required

1 FOR YOU

01
Start a Qdrant server
Run docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant (add a volume mount for persistent storage) before connecting with the Python client, or point the client at Qdrant Cloud instead.
```
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
```

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