RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Automate PDF loading for LLM pipelines with RAG helper functions from NirDiamant/RAG_Techniques. Streamline chunking, embedding, and vector storage for efficient document processing.
Agentic RAG Architecture and Implementation: A Complete Technical GuideLearn agentic RAG architecture and implementation. This guide details how autonomous agents dynamically reformulate queries, orchestrate retrieval, and validate outputs.
How to Use RAPTOR for RAG: Implementing Hierarchical Tree-Based RetrievalLearn how to use RAPTOR for RAG. Implement hierarchical tree-based retrieval for precise, context-aware answers. Explore this advanced RAG technique.
Microsoft GraphRAG Implementation: Building Knowledge Graphs for Enhanced RAGImplement Microsoft GraphRAG to build knowledge graphs from text. Enhance RAG with entity extraction and community detection for global answer synthesis. Explore the NirDiamant/RAG_Techniques repo.
Implementing Graph RAG with LangChain: A Complete Technical GuideMaster Graph RAG with LangChain. This technical guide explains multi-hop reasoning using vector search and knowledge graphs, featuring the NirDiamant RAG Techniques repository.
Using Groq Universal Sentence Encoder for RAG Evaluation: Implementation GuideLearn how to evaluate RAG pipelines using Groq Universal Sentence Encoder with the GroqEmbeddings class and the evaluate_rag function. Implement RAG techniques now.
Retrieval with Feedback Loops in RAG: Implementing Continuous Improvement in Vector SearchEnhance RAG retrieval quality with feedback loops. Learn how to implement continuous improvement in vector search by using user ratings and re-indexing for better results.
How to Implement Multi-Modal RAG: Two Production-Ready Architectures ExplainedLearn how to implement multi-modal RAG by exploring two production-ready architectures. Understand combining text and visual data for enhanced document comprehension and retrieval.
Document Augmentation Strategies for RAG: Boost Retrieval with Synthetic QuestionsBoost RAG retrieval with document augmentation. Generate synthetic questions for each text chunk to improve query-to-context alignment. Discover efficient RAG techniques today.
How to Use Contextual Compression in RAG: A Complete Implementation GuideMaster Contextual Compression in RAG with our implementation guide. Filter irrelevant text using an LLM compressor to enhance RAG performance and reduce token usage for better results.
Implementing Semantic Chunking for RAG: A Complete Guide to Meaning-Based Document SplittingLearn semantic chunking for RAG to split documents by meaning. Enhance retrieval relevance in your RAG pipelines with this essential guide.
Context Window Enhancement in RAG: Techniques for Improving Retrieval ContextEnhance RAG context window with neighboring segments for better narrative flow and answer coherence. Discover techniques implemented in NirDiamant/RAG_Techniques.
Have a question about this repo?
These articles cover the highlights, but your codebase questions are specific. Give your agent direct access to the source. Share this with your agent to get started:
curl -s "https://instagit.com/install.md" 
Maintain an open-source project? Get it listed too →