SQLite for Embeddings — A simple, embedded vector database that stores everything in a single file.

pip install vectorlitedb
db = VectorLiteDB("my.db", 1536)  # Start building in 30 seconds

No server. No setup. Just vectors in a file.

Every vector database is either a cloud service (Pinecone), needs a server (Chroma), or doesn't persist (FAISS).

Sometimes you just want to store embeddings in a file and search them. Like SQLite does for relational data.

Start small, swap later:

# Local dev
db = VectorLiteDB("local.db", 1536)

# Swap to Pinecone in prod  
db = pinecone.Index("my-index")

# Same interface works for both
results = db.search(query, top_k=5)

Build your entire AI app locally, then swap to a cloud service when you actually need scale.

pip install vectorlitedb

from vectorlitedb import VectorLiteDB

# Create a database (just a file)
db = VectorLiteDB("my_vectors.db", dimension=384)

# Store vectors with metadata
db.insert(id="doc1", vector=embedding, metadata={"text": "Hello world"})
db.insert(id="doc2", vector=embedding2, metadata={"text": "Goodbye world"})

# Search (returns most similar vectors)
results = db.search(query=query_embedding, top_k=5)

# Results format
for result in results:
    print(f"ID: {result['id']}")
    print(f"Similarity: {result['similarity']}")
    print(f"Metadata: {result['metadata']}")

Your vectors are saved in my_vectors.db.

• Stores vectors (embeddings) with metadata in a single file
• Searches for similar vectors using cosine/L2/dot distance
• Persists everything to disk automatically
• Filters results by metadata
• Works offline - no internet required

• ❌ Generate embeddings (use OpenAI, etc.)
• ❌ Scale beyond ~100K vectors (uses brute force search)
• ❌ Handle concurrent writes
• ❌ Optimize for speed

• Local RAG: Store document embeddings for offline search
• Personal AI: Give your assistant persistent memory
• Prototyping: Test vector search ideas without infrastructure
• Edge devices: Semantic search on Raspberry Pi
• Privacy: Keep embeddings on your device

db = VectorLiteDB("path/to/file.db", dimension=384, distance_metric="cosine")
# distance_metric: "cosine" (default), "l2", or "dot"

db.insert(id="unique_id", vector=[0.1, 0.2, ...], metadata={"key": "value"})

results = db.search(
    query=[0.1, 0.2, ...], 
    top_k=5,
    filter=lambda meta: meta.get("type") == "document"  # optional
)

vector, metadata = db.get("unique_id")
db.delete("unique_id")

print(len(db))  # number of vectors
print(repr(db))  # database summary

This is v0.1.0 - a working alpha focused on simplicity over performance.

What works:

• ✅ Basic CRUD operations
• ✅ File persistence
• ✅ Metadata filtering
• ✅ ~100ms search for 10K vectors

This is development mode - the codebase is small and readable.

• First-time contributors
• Performance optimizations
• Documentation improvements
• Feature requests

See issues labeled good first issue.

• SQLite: Single-file, serverless, embedded
• FAISS: Fast similarity search

VectorLiteDB combines the best parts: embedded like SQLite, simple like a Python dict, persistent like a real database.

VectorLiteDB is licensed under the Apache 2.0 License.

Built with ❤️ for AI developers