Vector Store Shootout

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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)."}}}

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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 class

scripts/numpy_store.py through scripts/lightrag_store.py — All 8 implementations