mistralvdb 0.1.0

A vector database optimized for Mistral AI embeddings with HNSW indexing

MistralVDB

A vector database optimized for Mistral AI embeddings with HNSW indexing. Features include:

• Efficient similarity search using HNSW indexing
• Vector compression for reduced memory usage
• Built-in REST API server
• Secure authentication
• Multiple collection support
• Automatic persistence

Installation

pip install mistralvdb

Quick Start

As a Python Library

from mistralvdb import MistralVDB

# Initialize database
db = MistralVDB(
    api_key="your-mistral-api-key",
    collection_name="my_collection"
)

# Add documents
texts = [
    "The quick brown fox jumps over the lazy dog",
    "Python is a versatile programming language"
]
metadata = [
    {"type": "example"},
    {"type": "programming"}
]
ids = db.add_texts(texts, metadata=metadata)

# Search
results = db.search("programming language", k=4)
for doc_id, score in results:
    print(f"Score: {score}")
    print(f"Text: {db.get_text(doc_id)}")
    print(f"Metadata: {db.get_metadata(doc_id)}\n")

As an API Server

1. Start the server:

# Set your API key
export MISTRAL_API_KEY=your-mistral-api-key

# Start server
mistralvdb-server --host 127.0.0.1 --port 8000

2. Use the client:

from mistralvdb.client import MistralVDBClient

# Create client
client = MistralVDBClient()

# Login (default credentials)
client.login(username="admin", password="admin-password")

# Add documents
docs = ["Document 1", "Document 2"]
client.add_documents(docs, collection_name="my_collection")

# Search
results = client.search("my query", collection_name="my_collection")

Features

HNSW Indexing

• Efficient approximate nearest neighbor search
• Configurable index parameters (M, ef_construction, ef)
• Thread-safe operations

Vector Compression

• Product Quantization for reduced memory usage
• Configurable compression parameters
• Minimal accuracy loss

API Server

• RESTful API with FastAPI
• JWT authentication
• Collection management
• Swagger UI documentation

Storage Management

• Automatic persistence
• Multiple collections
• Custom storage location
• Collection metadata

Configuration

Environment Variables

• MISTRAL_API_KEY: Your Mistral AI API key (required)

Server Options

mistralvdb-server --help

Development

1. Clone the repository:

git clone https://github.com/yourusername/mistralvdb.git
cd mistralvdb

2. Install development dependencies:

pip install -e ".[dev]"

3. Run tests:

pytest tests/

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.