velesdb 0.4.0

A high-performance vector database for AI applications

VelesDB Python

Python bindings for VelesDB - a high-performance vector database for AI applications.

Installation

pip install velesdb

Quick Start

import velesdb

# Open or create a database
db = velesdb.Database("./my_vectors")

# Create a collection for 768-dimensional vectors (e.g., BERT embeddings)
collection = db.create_collection(
    name="documents",
    dimension=768,
    metric="cosine"  # Options: "cosine", "euclidean", "dot"
)

# Insert vectors with metadata
collection.upsert([
    {
        "id": 1,
        "vector": [0.1, 0.2, ...],  # 768-dim vector
        "payload": {"title": "Introduction to AI", "category": "tech"}
    },
    {
        "id": 2,
        "vector": [0.3, 0.4, ...],
        "payload": {"title": "Machine Learning Basics", "category": "tech"}
    }
])

# Search for similar vectors
results = collection.search(
    vector=[0.15, 0.25, ...],  # Query vector
    top_k=5
)

for result in results:
    print(f"ID: {result['id']}, Score: {result['score']:.4f}")
    print(f"  Payload: {result['payload']}")

API Reference

Database

# Create/open database
db = velesdb.Database("./path/to/data")

# List collections
names = db.list_collections()

# Create collection
collection = db.create_collection("name", dimension=768, metric="cosine")

# Get existing collection
collection = db.get_collection("name")

# Delete collection
db.delete_collection("name")

Collection

# Get collection info
info = collection.info()
# {"name": "documents", "dimension": 768, "metric": "cosine", "point_count": 100}

# Insert/update vectors
collection.upsert([
    {"id": 1, "vector": [...], "payload": {"key": "value"}}
])

# Search
results = collection.search(vector=[...], top_k=10)

# Get specific points
points = collection.get([1, 2, 3])

# Delete points
collection.delete([1, 2, 3])

# Check if empty
is_empty = collection.is_empty()

# Flush to disk
collection.flush()

Distance Metrics

Metric	Description	Use Case
cosine	Cosine similarity (default)	Text embeddings, normalized vectors
euclidean	Euclidean (L2) distance	Image features, spatial data
dot	Dot product	When vectors are pre-normalized
hamming	Hamming distance	Binary vectors, fingerprints, hashes
jaccard	Jaccard similarity	Set similarity, tags, recommendations

Performance

VelesDB is built in Rust with explicit SIMD optimizations:

Operation	Time (768d)	Throughput
Cosine	~76 ns	13M ops/sec
Euclidean	~47 ns	21M ops/sec
Hamming	~6 ns	164M ops/sec

• Sub-millisecond search latency
• 4x memory reduction with SQ8 quantization
• Millions of vectors per collection

Requirements

• Python 3.9+
• No external dependencies (pure Rust engine)

License

Elastic License 2.0 (ELv2)

See LICENSE for details.

Links

• GitHub Repository
• Documentation
• Issue Tracker