langchain-cockroachdb 0.1.0

LangChain integration for CockroachDB with native vector support

langchain-cockroachdb

LangChain integration for CockroachDB with native vector support

Quick Start • Features • Documentation • Examples • Contributing

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Overview

Build LLM applications with CockroachDB's distributed SQL database and native vector search capabilities. This integration provides:

• 🎯 Native Vector Support - CockroachDB's VECTOR type
• 🚀 C-SPANN Indexes - Distributed vector indexes optimized for scale
• 🔄 Automatic Retries - Handles serialization errors transparently
• ⚡ Async & Sync APIs - Choose based on your use case
• 🏗️ Distributed by Design - Built for CockroachDB's architecture

Quick Start

Installation

pip install langchain-cockroachdb

Basic Usage

import asyncio
from langchain_cockroachdb import AsyncCockroachDBVectorStore, CockroachDBEngine
from langchain_openai import OpenAIEmbeddings

async def main():
    # Initialize
    engine = CockroachDBEngine.from_connection_string(
        "cockroachdb://user:pass@host:26257/db"
    )
    
    await engine.ainit_vectorstore_table(
        table_name="documents",
        vector_dimension=1536,
    )
    
    vectorstore = AsyncCockroachDBVectorStore(
        engine=engine,
        embeddings=OpenAIEmbeddings(),
        collection_name="documents",
    )
    
    # Add documents
    await vectorstore.aadd_texts([
        "CockroachDB is a distributed SQL database",
        "LangChain makes building LLM apps easy",
    ])
    
    # Search
    results = await vectorstore.asimilarity_search(
        "Tell me about databases",
        k=2
    )
    
    for doc in results:
        print(doc.page_content)
    
    await engine.aclose()

asyncio.run(main())

Features

Vector Store

• Native VECTOR type support with C-SPANN indexes
• Advanced metadata filtering ($and, $or, $gt, $in, etc.)
• Hybrid search (full-text + vector similarity)
• Multi-tenant index support with prefix columns

Reliability

• Automatic retry logic with exponential backoff
• Connection pooling with health checks
• Configurable for different workloads
• Built for SERIALIZABLE isolation

Developer Experience

• Async-first design for high concurrency
• Sync wrapper for simple scripts
• Type-safe with full type hints
• Comprehensive test suite (92 tests)

Documentation

📚 Complete Documentation

Getting Started:

• Installation
• Quick Start
• Configuration

Guides:

• Vector Store
• Vector Indexes
• Hybrid Search
• Chat History
• Async vs Sync

Examples

🔧 Working Examples

• quickstart.py - Get started in 5 minutes
• sync_usage.py - Synchronous API
• vector_indexes.py - Index optimization
• hybrid_search.py - FTS + vector search
• metadata_filtering.py - Advanced queries
• chat_history.py - Persistent conversations
• retry_configuration.py - Configuration patterns

Development

Setup

# Clone repository
git clone https://github.com/viragtripathi/langchain-cockroachdb.git
cd langchain-cockroachdb

# Install dependencies
pip install -e ".[dev]"

# Start CockroachDB
docker-compose up -d

# Run tests
make test

Documentation

# Install docs dependencies
pip install -e ".[docs]"

# Serve documentation locally
mkdocs serve

# Open http://127.0.0.1:8000

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Why CockroachDB?

• Distributed SQL - Scale horizontally across regions
• Native Vector Support - First-class VECTOR type and C-SPANN indexes
• Strong Consistency - SERIALIZABLE isolation by default
• Cloud Native - Deploy anywhere (AWS, GCP, Azure, on-prem)
• PostgreSQL Compatible - Familiar SQL with distributed superpowers

License

Apache License 2.0 - see LICENSE for details.

Acknowledgments

Built for the CockroachDB and LangChain communities.

• CockroachDB - Distributed SQL database
• LangChain - LLM application framework

Links

• GitHub Repository
• PyPI Package
• CockroachDB Documentation
• LangChain Documentation
• Report Issues