Vector Store Shootout

8 vector store implementations behind a common interface — numpy, lancedb, qdrant, pgvector, weaviate, weaviate_hybrid, milvus, lightrag. Use when evaluating...

Eight vector store backends with a common VectorStore interface. Swap backends by changing one line — the rest of your code stays the same.

Backends

Backend	Type	Dependencies	Best For
numpy	In-memory	numpy only	Prototyping, small datasets
lancedb	File-based	lancedb	Local persistence, Arrow-native
qdrant	Client-server	qdrant-client	Production, filtering
pgvector	Postgres extension	psycopg2	Existing Postgres deployments
weaviate	Client-server	weaviate-client	Hybrid search (BM25 + vector)
weaviate_hybrid	Client-server	weaviate-client	BM25-heavy hybrid (alpha=0.1)
milvus	Client-server	pymilvus	Large-scale, GPU-accelerated
lightrag	Graph-enhanced	lightrag	Graph + vector RAG

Common Interface

from base import VectorStore

class MyStore(VectorStore):
    async def add(self, texts, embeddings, metadatas): ...
    async def search(self, query_embedding, k=5): ...
    async def delete(self, ids): ...

Key Finding

Weaviate hybrid search at alpha=0.1 (BM25-heavy) scored avg 0.9940 vs 0.9700 at default 0.5. For technical content with specific terminology, keyword matching matters more than semantic similarity.

Files

• scripts/base.py — Abstract base class
• scripts/numpy_store.py through scripts/lightrag_store.py — All 8 implementations