vector-store-client 1.1.3

Client library for Vector Store service

Vector Store Client

A Python client for interacting with Vector Store API services. This client provides a convenient interface for working with vector embeddings, semantic search, and metadata filtering.

Features

• Full-featured API for Vector Store operations
• Asynchronous interface based on httpx
• Automatic parameter handling and validation
• Comprehensive error management
• Support for all vector store operations:
  • Creating records from text or vectors
  • Vector similarity search
  • Metadata filtering
  • Record management

Installation

pip install vector-store-client

Or install from source:

git clone https://github.com/yourusername/vector-store-client.git
cd vector-store-client
pip install -e .

Quick Start

import asyncio
from vector_store_client import create_client

async def main():
    # Create client with connection to the service
    client = await create_client(base_url="http://localhost:8007")
    
    # Create a record from text
    record_id = await client.create_text_record(
        text="Example text for vectorization",
        metadata={"type": "example", "tags": ["test", "vector"]}
    )
    
    # Search for similar records
    results = await client.search_text_records(
        text="vectorization example",
        limit=5
    )
    
    for result in results:
        print(f"ID: {result.id}, Similarity: {result.score:.4f}")

    # Close the client session
    await client._client.aclose()

if __name__ == "__main__":
    asyncio.run(main())

Documentation

Detailed documentation is available in both English and Russian:

• English Documentation
• Русская документация

Examples

Check out the example scripts to get started:

• Basic Usage - Essential operations
• Advanced Usage - Complex scenarios and filtering

Development

Requirements

• Python 3.7+
• httpx
• pydantic

Running Tests

# Install test dependencies
pip install pytest pytest-asyncio pytest-cov

# Run tests
python run_tests.py

License

MIT

Пример использования

# ...
record = await client.create_record(vector=vector, metadata=metadata)
print("Record ID:", record["record_id"])

text_record = await client.create_text_record(text="Some text", metadata=metadata)
print("Text Record ID:", text_record["record_id"])
# ... existing code ...

Metadata Validation

All metadata passed to create_record or create_text_record must pass strict validation using the chunk_metadata_adapter package. This ensures compatibility with the server and prevents runtime errors.

Example:

from chunk_metadata_adapter.models import SemanticChunk

metadata = {"type": "example", "body": "Some text"}
obj, err = SemanticChunk.validate_and_fill(metadata)
if not obj:
    raise Exception(f"Strict metadata validation failed: {err}")
record = await client.create_record(vector=vector, metadata=metadata)

If validation fails, fix your metadata according to the requirements of the server schema.