Advanced RAG Techniques: Elevating Your Retrieval-Augmented Generation Systems 🚀
I am pleased to present this comprehensive collection of advanced Retrieval-Augmented Generation (RAG) techniques. The aim is to provide a valuable resource for researchers and practitioners seeking to enhance the accuracy, efficiency, and contextual richness of their RAG systems.
Table of Contents
Introduction
Retrieval-Augmented Generation (RAG) is revolutionizing the way we combine information retrieval with generative AI. This repository showcases a curated collection of advanced techniques designed to supercharge your RAG systems, enabling them to deliver more accurate, contextually relevant, and comprehensive responses.
Key Features
- 🧠 State-of-the-art RAG enhancements
- 📚 Comprehensive documentation for each technique
- 🛠️ Practical implementation guidelines
- 🌟 Regular updates with the latest advancements
Advanced Techniques
Explore the extensive list of cutting-edge RAG techniques:
1. Context Enrichment Techniques 📝
Overview 🔎
Enhancing retrieval accuracy by embedding individual sentences and extending context to neighboring sentences.
Implementation 🛠️
Retrieve the most relevant sentence while also accessing the sentences before and after it in the original text.
2. Multi-faceted Filtering 🔍
Overview 🔎
Applying various filtering techniques to refine and improve the quality of retrieved results.
Implementation 🛠️
- 🏷️ Metadata Filtering: Apply filters based on attributes like date, source, author, or document type.
- 📊 Similarity Thresholds: Set thresholds for relevance scores to keep only the most pertinent results.
- 📄 Content Filtering: Remove results that don't match specific content criteria or essential keywords.
- 🌈 Diversity Filtering: Ensure result diversity by filtering out near-duplicate entries.
3. Fusion Retrieval 🔗
Overview 🔎
Optimizing search results by combining different retrieval methods.
Implementation 🛠️
Combine keyword-based search with vector-based search for more comprehensive and accurate retrieval.
4. Reference Citations 📚
Overview 🔎
Boosting credibility and accuracy of information by providing reliable source attributions.
Implementation 🛠️
Use direct source mentions and fuzzy matching techniques to ensure accurate citations.
5. Intelligent Reranking 📈
Overview 🔎
Applying advanced scoring mechanisms to improve the relevance ranking of retrieved results.
Implementation 🛠️
- 🧠 LLM-based Scoring: Use a language model to score the relevance of each retrieved chunk.
- 🔀 Cross-Encoder Models: Re-encode both the query and retrieved documents jointly for similarity scoring.
- 🏆 Metadata-enhanced Ranking: Incorporate metadata into the scoring process for more nuanced ranking.
6. Query Transformations 🔄
Overview 🔎
Modifying and expanding queries to improve retrieval effectiveness.
Implementation 🛠️
- ✍️ Query Rewriting: Reformulate queries to improve retrieval.
- 🔙 Step-back Prompting: Generate broader queries for better context retrieval.
- 🧩 Sub-query Decomposition: Break complex queries into simpler sub-queries.
7. Hierarchical Indices 🗂️
Overview 🔎
Creating a multi-tiered system for efficient information navigation and retrieval.
Implementation 🛠️
Implement a two-tiered system for document summaries and detailed chunks, both containing metadata pointing to the same location in the data.
8. Hypothetical Questions (HyDE Approach) ❓
Overview 🔎
Generating hypothetical questions to improve alignment between queries and data.
Implementation 🛠️
Create hypothetical questions that point to relevant locations in the data, enhancing query-data matching.
9. Dynamic Chunk Sizing 📏
Overview 🔎
Adaptively adjusting the size of text chunks based on content complexity.
Implementation 🛠️
Vary chunk sizes to preserve context in complex sections while reducing redundancy in simpler parts.
10. Semantic Chunking 🧠
Overview 🔎
Dividing documents based on semantic coherence rather than fixed sizes.
Implementation 🛠️
Use NLP techniques to identify topic boundaries or coherent sections within documents for more meaningful retrieval units.
11. Contextual Compression 🗜️
Overview 🔎
Compressing retrieved information while preserving query-relevant content.
Implementation 🛠️
Use an LLM to compress or summarize retrieved chunks, preserving key information relevant to the query.
12. Explainable Retrieval 🔍
Overview 🔎
Providing transparency in the retrieval process to enhance user trust and system refinement.
Implementation 🛠️
Explain why certain pieces of information were retrieved and how they relate to the query.
13. Retrieval with Feedback Loops 🔁
Overview 🔎
Implementing mechanisms to learn from user interactions and improve future retrievals.
Implementation 🛠️
Collect and utilize user feedback on the relevance and quality of retrieved documents and generated responses to fine-tune retrieval and ranking models.
14. Adaptive Retrieval 🎯
Overview 🔎
Dynamically adjusting retrieval strategies based on query types and user contexts.
Implementation 🛠️
Classify queries into different categories and use tailored retrieval strategies for each, considering user context and preferences.
15. Iterative Retrieval 🔄
Overview 🔎
Performing multiple rounds of retrieval to refine and enhance result quality.
Implementation 🛠️
Use the LLM to analyze initial results and generate follow-up queries to fill in gaps or clarify information.
16. Ensemble Retrieval 🎭
Overview 🔎
Combining multiple retrieval models or techniques for more robust and accurate results.
Implementation 🛠️
Apply different embedding models or retrieval algorithms and use voting or weighting mechanisms to determine the final set of retrieved documents.
17. Knowledge Graph Integration 🕸️
Overview 🔎
Incorporating structured data from knowledge graphs to enrich context and improve retrieval.
Implementation 🛠️
Retrieve entities and their relationships from a knowledge graph relevant to the query, combining this structured data with unstructured text for more informative responses.
18. Multi-modal Retrieval 📽️
Overview 🔎
Extending RAG capabilities to handle diverse data types for richer responses.
Implementation 🛠️
Integrate models that can retrieve and understand different data modalities, combining insights from text, images, and videos.
19. RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval 🌳
Overview 🔎
Implementing a recursive approach to process and organize retrieved information in a tree structure.
Implementation 🛠️
Use abstractive summarization to recursively process and summarize retrieved documents, organizing the information in a tree structure for hierarchical context.
Getting Started
To start implementing these advanced RAG techniques in your projects:
- Clone this repository:
git clone https://github.com/NirDiamant/RAG_Techniques.git - Navigate to the technique you're interested in:
cd rag-techniques/technique-name - Follow the detailed implementation guide in each technique's directory
Contributing
We welcome contributions from the community! If you have a new technique or improvement to suggest:
- Fork the repository
- Create your feature branch:
git checkout -b feature/AmazingFeature - Commit your changes:
git commit -m 'Add some AmazingFeature' - Push to the branch:
git push origin feature/AmazingFeature - Open a pull request
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
⭐️ If you find this repository helpful, please consider giving it a star!
Keywords: RAG, Retrieval-Augmented Generation, NLP, AI, Machine Learning, Information Retrieval, Natural Language Processing