philiprehberger-embedding-store 0.2.2

In-memory vector store with cosine similarity search

philiprehberger-embedding-store

In-memory vector store with cosine similarity search.

Installation

pip install philiprehberger-embedding-store

Usage

from philiprehberger_embedding_store import VectorStore

store = VectorStore(dimensions=1536)

# Add vectors with metadata
store.add("doc1", embedding=[0.1, 0.2, ...], metadata={"title": "First doc"})
store.add("doc2", embedding=[0.3, 0.1, ...], metadata={"title": "Second doc"})

# Search by similarity
results = store.search(query_embedding=[0.15, 0.18, ...], top_k=5)
for result in results:
    print(f"{result.id}: score={result.score:.3f}, {result.metadata}")

# Filter by metadata
results = store.search(query, top_k=10, filter=lambda m: m["category"] == "tech")

# Minimum score threshold
results = store.search(query, min_score=0.7)

# Persistence
store.save("vectors.json")
loaded = VectorStore.load("vectors.json")

# Batch operations
store.add_many([("id1", emb1, meta1), ("id2", emb2, meta2)])

Distance metrics

• "cosine" (default) — cosine similarity
• "dot" — dot product

API

Function / Class	Description
VectorStore(dimensions)	Create a new vector store with the given dimensionality
add(id, embedding, metadata?)	Add a vector
add_many(items)	Batch add
search(query, top_k?, metric?, filter?, min_score?)	Similarity search
get(id)	Get entry by ID
delete(id)	Delete entry
update_metadata(id, metadata)	Update metadata
save(path)	Save to JSON
VectorStore.load(path)	Load from JSON
clear()	Remove all entries
ids()	List all IDs
len(store)	Number of entries
id in store	Check if ID exists
store.size	Number of entries (property)

Development

pip install -e .
python -m pytest tests/ -v

License

MIT