# RAG_Techniques | NirDiamant | Knowledge Base | Instagit

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.

GitHub Stars: 25.4k

Repository: https://github.com/nirdiamant/rag_techniques

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## Articles

### [RAG Helper Functions for PDF Loading: Automating Document Processing for LLM Pipelines](/nirdiamant/rag_techniques/rag-helper-functions-for-pdf-loading)

Automate PDF loading for LLM pipelines with RAG helper functions from NirDiamant/RAG_Techniques. Streamline chunking, embedding, and vector storage for efficient document processing.

- Tags: how-to-guide
- Published: 2026-02-19

### [Agentic RAG Architecture and Implementation: A Complete Technical Guide](/nirdiamant/rag_techniques/agentic-rag-architecture-and-implementation)

Learn agentic RAG architecture and implementation. This guide details how autonomous agents dynamically reformulate queries, orchestrate retrieval, and validate outputs.

- Tags: architecture
- Published: 2026-02-19

### [How to Use RAPTOR for RAG: Implementing Hierarchical Tree-Based Retrieval](/nirdiamant/rag_techniques/how-to-use-raptor-for-rag)

Learn how to use RAPTOR for RAG. Implement hierarchical tree-based retrieval for precise, context-aware answers. Explore this advanced RAG technique.

- Tags: tutorial
- Published: 2026-02-19

### [Microsoft GraphRAG Implementation: Building Knowledge Graphs for Enhanced RAG](/nirdiamant/rag_techniques/microsoft-graphrag-implementation)

Implement 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.

- Tags: tutorial
- Published: 2026-02-19

### [Implementing Graph RAG with LangChain: A Complete Technical Guide](/nirdiamant/rag_techniques/implementing-graph-rag-with-langchain)

Master Graph RAG with LangChain. This technical guide explains multi-hop reasoning using vector search and knowledge graphs, featuring the NirDiamant RAG Techniques repository.

- Tags: how-to-guide
- Published: 2026-02-19

### [Using Groq Universal Sentence Encoder for RAG Evaluation: Implementation Guide](/nirdiamant/rag_techniques/using-grouse-for-rag-evaluation)

Learn how to evaluate RAG pipelines using Groq Universal Sentence Encoder with the GroqEmbeddings class and the evaluate_rag function. Implement RAG techniques now.

- Tags: how-to-guide
- Published: 2026-02-19

### [Retrieval with Feedback Loops in RAG: Implementing Continuous Improvement in Vector Search](/nirdiamant/rag_techniques/retrieval-with-feedback-loops-in-rag)

Enhance 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.

- Tags: deep-dive
- Published: 2026-02-19

### [How to Implement Multi-Modal RAG: Two Production-Ready Architectures Explained](/nirdiamant/rag_techniques/how-to-implement-multi-modal-rag)

Learn how to implement multi-modal RAG by exploring two production-ready architectures. Understand combining text and visual data for enhanced document comprehension and retrieval.

- Tags: architecture
- Published: 2026-02-19

### [Document Augmentation Strategies for RAG: Boost Retrieval with Synthetic Questions](/nirdiamant/rag_techniques/document-augmentation-strategies-for-rag)

Boost RAG retrieval with document augmentation. Generate synthetic questions for each text chunk to improve query-to-context alignment. Discover efficient RAG techniques today.

- Tags: deep-dive
- Published: 2026-02-19

### [How to Use Contextual Compression in RAG: A Complete Implementation Guide](/nirdiamant/rag_techniques/how-to-use-contextual-compression-in-rag)

Master 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.

- Tags: how-to-guide
- Published: 2026-02-19

### [Implementing Semantic Chunking for RAG: A Complete Guide to Meaning-Based Document Splitting](/nirdiamant/rag_techniques/implementing-semantic-chunking-for-rag)

Learn semantic chunking for RAG to split documents by meaning. Enhance retrieval relevance in your RAG pipelines with this essential guide.

- Tags: how-to-guide
- Published: 2026-02-19

### [Context Window Enhancement in RAG: Techniques for Improving Retrieval Context](/nirdiamant/rag_techniques/techniques-for-context-window-enhancement-in-rag)

Enhance RAG context window with neighboring segments for better narrative flow and answer coherence. Discover techniques implemented in NirDiamant/RAG_Techniques.

- Tags: deep-dive
- Published: 2026-02-19

### [How to Use Relevant Segment Extraction in RAG: A Complete Implementation Guide](/nirdiamant/rag_techniques/how-to-use-relevant-segment-extraction-in-rag)

Master Relevant Segment Extraction in RAG. This guide explains how RSE orders scattered chunks into cohesive segments, boosting LLM context coherence. Implement RAG techniques effectively.

- Tags: how-to-guide
- Published: 2026-02-19

### [What is HyDE and How to Implement It in RAG Systems](/nirdiamant/rag_techniques/what-is-hyde-and-how-to-implement-it-in-rag)

Discover HyDE (Hypothetical Document Embedding) and learn how to implement it in RAG systems. Enhance retrieval accuracy by generating synthetic answers with LLMs for richer vector representations.

- Tags: deep-dive
- Published: 2026-02-19

### [How to Use Query Transformations for Better RAG: A Complete Implementation Guide](/nirdiamant/rag_techniques/how-to-use-query-transformations-for-better-rag)

Master Query Transformations in RAG Improve document relevance and reduce hallucinations in your RAG pipelines with LLM-driven query rewriting. Implement advanced RAG techniques now.

- Tags: how-to-guide
- Published: 2026-02-19

### [Implementing Proposition Chunking for RAG: A Complete Technical Guide](/nirdiamant/rag_techniques/implementing-proposition-chunking-for-rag)

Learn proposition chunking for RAG and boost retrieval precision. This guide details how to decompose documents into factual statements for higher accuracy.

- Tags: how-to-guide
- Published: 2026-02-19

### [Advanced RAG Techniques for Improved Retrieval: A Comprehensive Implementation Guide](/nirdiamant/rag_techniques/what-are-advanced-rag-techniques-for-improved-retrieval)

Discover advanced RAG techniques for improved retrieval. Learn how hybrid fusion, cross-encoder reranking, and synthetic queries boost performance. Get the implementation guide.

- Tags: how-to-guide
- Published: 2026-02-19

### [How to Implement Basic RAG with LangChain: A Step-by-Step Guide](/nirdiamant/rag_techniques/how-to-implement-basic-rag-with-langchain)

Learn to implement basic RAG with LangChain using PyPDFLoader, FAISS, and other essential tools. This guide provides a step-by-step walkthrough for augmenting LLM queries with retrieved documents. Get started today!

- Tags: getting-started
- Published: 2026-02-19