====== Qdrant ======

**Qdrant** is a high-performance, open-source vector similarity search engine and database written in Rust. With over **30,000 GitHub stars**, it is designed for production-grade applications requiring fast, filtered vector search with rich payload support and horizontal scalability.

| **Repository** | [[https://github.com/qdrant/qdrant|github.com/qdrant/qdrant]] |
| **License** | Apache 2.0 |
| **Language** | Rust |
| **Stars** | 30K+ |
| **Category** | Vector Database |

===== Key Features =====

  * **Written in Rust** -- Memory-safe, zero-cost abstractions, and exceptional performance without garbage collection pauses
  * **Filterable HNSW** -- Extended HNSW index with filter-aware edges for efficient filtered vector search
  * **Rich Payload Support** -- Attach JSON metadata to vectors with keyword, float, geo, and datetime index types
  * **Quantization** -- Scalar and product quantization for memory reduction while maintaining search quality
  * **Distributed Mode** -- Horizontal scaling with sharding and replication for high availability
  * **Dual API** -- Both REST and gRPC interfaces for flexibility and performance
  * **Multi-Vector Support** -- Store multiple named vectors per point for different embedding models

===== Architecture =====

Qdrant is designed as a standalone service with a focus on performance and operational simplicity:

  * **Storage Engine** -- Segments-based architecture where each collection is divided into segments containing vectors, payloads, and indexes
  * **HNSW Index** -- Multi-layered graph structure enabling sublinear search scaling (logarithmic with dataset size)
  * **Payload Indexes** -- Separate indexes for structured metadata enabling efficient pre-filtering
  * **WAL** -- Write-ahead log for durability and crash recovery
  * **Consensus** -- Raft-based consensus for distributed deployments

<mermaid>
graph TB
    subgraph API["API Layer"]
        REST[REST API]
        GRPC[gRPC API]
    end
    subgraph Engine["Search Engine"]
        Router[Query Router]
        Planner[Query Planner]
        Filter[Filter Engine]
    end
    subgraph Collections["Collection Layer"]
        C1[Collection 1]
        C2[Collection 2]
    end
    subgraph Segments["Segment Storage"]
        HNSW[HNSW Index]
        Payload[Payload Index]
        Vectors[Vector Storage]
        WAL[Write-Ahead Log]
    end
    subgraph Distributed["Distributed Layer"]
        Shard[Sharding]
        Replica[Replication]
        Raft[Raft Consensus]
    end
    API --> Engine
    Engine --> Collections
    Collections --> Segments
    Collections --> Distributed
</mermaid>

===== HNSW Configuration =====

HNSW performance is controlled through three primary parameters:

  * **m** -- Controls graph connectivity (edges per node). Larger values increase accuracy but require more memory. Default: 16
  * **ef_construct** -- Neighbors considered during index building. Larger values improve accuracy but increase build time. Default: 100
  * **hnsw_ef** -- Set at search time to balance speed vs recall. Default: 128; lower (32) for speed, higher (256) for accuracy

For small datasets below the ''full_scan_threshold'' (default 10,000 KB), Qdrant automatically uses brute-force search for better performance.

===== Filterable HNSW =====

Qdrant extends standard HNSW by adding extra edges based on indexed payload values, enabling efficient searches under structured conditions without full-scan operations. Key considerations:

  * Payload fields are **not indexed by default** -- you must explicitly define which fields to index
  * Best practice: create payload indexes **before** uploading data so HNSW builds filter-aware links from the start
  * Supported index types: KEYWORD (categorical), FLOAT (numerical ranges), GEO (spatial), DATETIME (temporal)

===== Code Example =====

<code python>
from qdrant_client import QdrantClient
from qdrant_client.models import (
    Distance, VectorParams, PointStruct,
    Filter, FieldCondition, MatchValue,
    PayloadSchemaType
)

client = QdrantClient(host="localhost", port=6333)

# Create collection with HNSW configuration
client.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(
        size=768, distance=Distance.COSINE,
        hnsw_config={"m": 16, "ef_construct": 100}
    )
)

# Create payload index BEFORE inserting data
client.create_payload_index(
    collection_name="documents",
    field_name="category",
    field_schema=PayloadSchemaType.KEYWORD
)

# Insert vectors with payloads
client.upsert(
    collection_name="documents",
    points=[
        PointStruct(id=1, vector=[0.1] * 768,
                    payload={"title": "RAG Guide", "category": "ai"}),
        PointStruct(id=2, vector=[0.2] * 768,
                    payload={"title": "SQL Basics", "category": "database"}),
    ]
)

# Filtered vector search
results = client.search(
    collection_name="documents",
    query_vector=[0.15] * 768,
    query_filter=Filter(
        must=[FieldCondition(key="category", match=MatchValue(value="ai"))]
    ),
    limit=5
)
for r in results:
    print(f"{r.payload['title']}: {r.score:.4f}")
</code>

===== References =====

  * [[https://github.com/qdrant/qdrant|Qdrant GitHub Repository]]
  * [[https://qdrant.tech|Qdrant Official Website]]
  * [[https://qdrant.tech/documentation/concepts/indexing/|Qdrant Indexing Documentation]]
  * [[https://qdrant.tech/course/essentials/|Qdrant Essentials Course]]

===== See Also =====

  * [[milvus|Milvus]] -- Cloud-native vector database in Go/C++
  * [[chromadb|ChromaDB]] -- AI-native embedding database
  * [[mem0|Mem0]] -- Memory layer using Qdrant as backend
  * [[ragflow|RAGFlow]] -- RAG engine for document understanding
  * [[lightrag|LightRAG]] -- Knowledge graph RAG framework