Qdrant — High-Performance Vector Database

What it is

Qdrant is a vector database and similarity search engine designed for AI applications. Written in Rust for performance, it stores high-dimensional vectors alongside arbitrary JSON payloads and supports filtered search, sparse vectors, and multi-tenancy. It runs as a standalone server or embedded in your application.

Qdrant targets developers building RAG pipelines, recommendation engines, image search, anomaly detection, or any system that needs fast nearest-neighbor lookup at scale.

How it saves time or tokens

Vector databases are the backbone of retrieval-augmented generation (RAG). Instead of stuffing entire documents into an LLM prompt, you embed and index them in Qdrant, then retrieve only the relevant chunks. This cuts token usage dramatically -- a well-tuned RAG pipeline might send 2,000 tokens of context instead of 20,000, saving both cost and latency.

How to use

1. Start Qdrant with Docker:

docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

1. Create a collection and insert vectors:

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

client = QdrantClient('localhost', port=6333)

client.create_collection(
    collection_name='docs',
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)

client.upsert(
    collection_name='docs',
    points=[
        PointStruct(id=1, vector=[0.1]*384, payload={'title': 'Getting Started'}),
        PointStruct(id=2, vector=[0.2]*384, payload={'title': 'Advanced Usage'}),
    ]
)

1. Search with filters:

results = client.search(
    collection_name='docs',
    query_vector=[0.15]*384,
    limit=5,
)

Example

Related on TokRepo

• AI tools for RAG -- RAG frameworks and retrieval tools
• AI tools for database -- database tools for AI applications

Common pitfalls

• Vector dimensionality must match your embedding model. A mismatch (e.g., 384 vs 768) causes silent failures or errors at insert time.
• Quantization reduces memory usage significantly but can lower recall. Test with your actual data before enabling in production.
• On-disk storage mode is available for large datasets that do not fit in RAM, but search latency increases. Profile your workload to decide between in-memory and on-disk.