/rag-qdrant

name: rag-qdrant
description: High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [RAG, Vector Search, Qdrant, Semantic Search, Embeddings, Similarity Search, HNSW, Production, Distributed]
dependencies: [qdrant-client>=1.12.0]

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 qdra