- name
- vector-store-shootout
- version
- 1.0.0
- description
- 8 vector store implementations behind a common interface — numpy, lancedb, qdrant, pgvector, weaviate, weaviate_hybrid, milvus, lightrag. Use when evaluating RAG backends, building vector search, or comparing embedding stores. Each backend is a drop-in replacement via the base class.
- metadata
Vector Store Shootout
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 classscripts/numpy_store.pythroughscripts/lightrag_store.py— All 8 implementations