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: {"openclaw": {"emoji": "🔍", "requires": {"bins": ["python3"], "env": []}, "primaryEnv": null, "network": {"outbound": false, "reason": "All backends run locally. Network calls depend on your deployment (e.g. managed Qdrant Cloud)."}}}

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

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