qdrant-vector-search

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

Details, code examples and parameter tables: references/rag-integration.md. Read it when this step applies.

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

1. Batch operations - Use batch upsert/search for efficiency
2. Payload indexing - Index fields used in filters
3. Quantization - Enable for large collections (>1M vectors)
4. Sharding - Use for collections >10M vectors
5. On-disk storage - Enable on_disk_payload for large payloads
6. 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
• 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

Agent operating procedure

1. Check the environment. Confirm the corpus, embedding model and vector store versions, and where the index will live.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Index a small subset and test retrieval on known question-passage pairs.
4. Execute the full task using the instructions and references above.
5. Validate the result. Measure recall@k on a labeled set; answers cite retrieved passages only.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Retrieval returns irrelevant chunks	Revisit chunking, add BM25/hybrid search, or re-rank before changing the generator.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Answers must come from retrieved text; say when the corpus does not contain the answer.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• pinecone: Guides use of Pinecone, a managed serverless vector database, through its Python client and the LangChain and LlamaIndex integrations.
• faiss: Facebook's library for efficient similarity search and clustering of dense vectors.
• chroma: Open-source embedding database for AI applications.