Eight vector store backends with a common VectorStore interface. Swap backends by changing one line — the rest of your code stays the same.
| 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 |
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): ...
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.
scripts/base.py — Abstract base classscripts/numpy_store.py through scripts/lightrag_store.py — All 8 implementations共 1 个版本