Insta RAG
A modular, plug-and-play Python library for building advanced Retrieval-Augmented Generation pipelines
Core Features
Everything you need to build production-grade RAG pipelines
Semantic Chunking
Splits documents at natural topic boundaries to preserve context and improve retrieval accuracy.
Hybrid Retrieval
Combines semantic vector search with BM25 keyword search for the best of both worlds.
Query Transformation
Uses an LLM to generate hypothetical answers (HyDE), 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 MongoDB integration for cost-effective content storage while keeping Qdrant lean.
Quick Start
1. Installation
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)
Key Capabilities
Semantic Chunking
Intelligent document splitting that preserves context and meaning at natural boundaries.
Hybrid Retrieval
Combine vector and keyword search for comprehensive document retrieval.
Query Transformation
Improve relevance with HyDE (Hypothetical Document Embeddings) support.
Intelligent Reranking
Use state-of-the-art rerankers to refine and order search results.
Extensible Design
Built on a modular architecture that allows easy customization and extension.
Multi-Storage Support
Works with MongoDB, Qdrant, and other vector databases seamlessly.
Documentation
For detailed guides on installation, configuration, and advanced features, please visit the full documentation.
Installation Guide
Get Insta RAG up and running in minutes with our comprehensive setup instructions.
Quickstart Guide
Learn the basics with simple examples to get started immediately.
Document Management
Deep dive into document processing, chunking, and storage strategies.
Advanced Retrieval
Master hybrid search, reranking, and query optimization techniques.
Storage Backends
Configure and optimize your vector database and content storage.
Contributing
Join the community and contribute to the development of Insta RAG.
Contributing
We welcome contributions! Here is how to help:
Set up your development environment
Clone the repository and install dependencies
Code quality tools and pre-commit hooks
Run linters and tests locally before pushing
Follow commit and branch naming conventions
Use clear, descriptive names for your contributions
Version management
Follow semantic versioning guidelines
Submit a pull request
Provide clear description and testing results
Ready to Build Your RAG Pipeline?
Start with Insta RAG today and simplify your document processing workflows