insta_rag 0.1.0b3

A RAG (Retrieval-Augmented Generation) library for document processing and retrieval.

insta_rag

insta_rag is a modular, plug-and-play Python library for building advanced Retrieval-Augmented Generation (RAG) pipelines. It abstracts the complexity of document processing, embedding, and hybrid retrieval into a simple, configuration-driven client.

Core Features

• Semantic Chunking: Splits documents at natural topic boundaries to preserve context.
• Hybrid Retrieval: Combines semantic vector search with BM25 keyword search for the best of both worlds.
• Query Transformation (HyDE): Uses an LLM to generate hypothetical answers, improving retrieval relevance.
• Reranking: Integrates with state-of-the-art rerankers like Cohere to intelligently re-order results.
• Pluggable Architecture: Easily extend the library by adding new chunkers, embedders, or vector databases.
• Hybrid Storage: Optional integration with MongoDB for cost-effective content storage, keeping Qdrant lean for vector search.

Quick Start

1. Installation

# Recommended: using uv
uv pip install insta-rag

# Or with pip
pip install insta-rag

2. Basic Usage

from insta_rag import RAGClient, RAGConfig, DocumentInput

# Load configuration from environment variables (.env file)
config = RAGConfig.from_env()
client = RAGClient(config)

# 1. Add documents to a collection
documents = [DocumentInput.from_text("Your first document content.")]
client.add_documents(documents, collection_name="my_docs")

# 2. Retrieve relevant information
response = client.retrieve(
    query="What is this document about?", collection_name="my_docs"
)

# Print the most relevant chunk
if response.chunks:
    print(response.chunks[0].content)

Documentation

For detailed guides on installation, configuration, and advanced features, please see the Full Documentation.

Key sections include:

• Installation Guide
• Quickstart Guide
• Guides
  • Document Management
  • Advanced Retrieval
  • Storage Backends

License

This project is licensed under the MIT License.