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https://github.com/frutik/Awesome-RAG


https://github.com/frutik/Awesome-RAG

List: Awesome-RAG

rag rag-evaluation rag-implementation

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README 

        
# Awesome-RAG



> The sections outline is in complete draft form. Everything is in motion (in this document and in my head).



- [General](#general)

  - [Disadvantages of RAG](#disadvantages-of-rag) 

  - [Patterns](#patterns)

- [Dialogue Routing](#dialogue-routing)

- [LLM Models](#llm-models)

  - [Finetuning and Pretraining](#finetuning-and-pretraining)

- [Retrieval](#retrieval)

  -  [Vector retrieval](#vector-retrieval)

     - [Chunking](#chunking)

       - [Positional chunking](#positional-chunking)

       - [Semantic chunking](#semantic-chunking)

     - [Embeddings](#embeddings)

     - [Vector Search](#vector-search)

     - [RAG Fusion](#rag-fusion)

  - [Not Vector retrieval](#not-vector-retrieval)

- [Prompts](#prompts)

  - [Prompting strategies](#prompting-strategies)

    - [Multi-Modal RAG](#multi-modal-rag)

    - [Multi-index RAG](#multi-index-rag)

    - [Multi-Document](#multi-document)

    - [FLARE](#flare)

    - [Chain-of-Verification](#chain-of-verification) 

  -  [Automated prompt optimization](#automated-prompt-optimization)

  - [Context](#context)

    - [Long context RAG](#long-context-rag)

    - [Knowledge and Knowledge Graphs](#knowledge-and-knowledge-graphs)

- [Generation](#generation)

  - [Hallucination](#hallucination)

  - [Guardrails](#guardrails)

- [Evaluation](#evaluation)

- [Performance and cost](#performance-and-cost)

- [Privacy](#privacy)

- [Security](#security)

  - [Overloading context](#overloading-context)

  - [Injections](#security)

- [Applications of RAG](#applications-of-rag)

  - [Chatbots](#chatbots) 

- [Tools](#tools)

- [Vendor-specific examples](#vendor-specific-examples)

  - [Elastcisearch + OpenAI](#elastcisearch--openai)

  - [OpenAI and ChatGPT](#openai-and-chatgpt)

    - [Tools and fucntions](#tools-and-fucntions)

  - [Vespa](#vespa)

  - [Qdrant](#qdrant)

- [Running RAGs in production](#running-rags-in-production)

- [Vectors corner](#vectors-corner)



## General



- [Retrieval Augmented Generation — Intuitively and Exhaustively Explained](https://towardsdatascience.com/retrieval-augmented-generation-intuitively-and-exhaustively-explain-6a39d6fe6fc9)

- [GraphRAG - Microsoft Research Blog Post](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)



### Disadvantages of RAG



- [Disadvantages of RAG](https://medium.com/@kelvin.lu.au/disadvantages-of-rag-5024692f2c53)



### RAG Patterns



- [Generative AI Lifecycle Patterns](https://dr-arsanjani.medium.com/the-generative-ai-lifecycle-1b0c7d9463ec)

- [Why do RAG pipelines fail? Advanced RAG Patterns — Part1

Ozgur Guler](https://cloudatlas.me/why-do-rag-pipelines-fail-advanced-rag-patterns-part1-841faad8b3c2)

- [How to improve RAG peformance — Advanced RAG Patterns — Part2](https://cloudatlas.me/how-to-improve-rag-peformance-advanced-rag-patterns-part2-0c84e2df66e6)

- [Patterns for Building LLM-based Systems & Products](https://eugeneyan.com/writing/llm-patterns/)

- [AI Engineer Summit - Building Blocks for LLM Systems & Products](https://eugeneyan.com/speaking/ai-eng-summit/)

- [Technical Considerations for Complex RAG](https://medium.com/enterprise-rag/a-first-intro-to-complex-rag-retrieval-augmented-generation-a8624d70090f)



### Dialogue Routing



- [Routing in RAG-Driven Applications](https://towardsdatascience.com/routing-in-rag-driven-applications-a685460a7220)



## Retrieval



### Vector Retrieval



- [Boosting RAG: Picking the Best Embedding & Reranker models](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83)

- [What We Need to Know Before Adopting a Vector Database](https://medium.com/@kelvin.lu.au/what-we-need-to-know-before-adopting-a-vector-database-85e137570fbb)



#### Chunking



- [Chunking Strategies for LLM Applications](https://www.pinecone.io/learn/chunking-strategies/)

- [Evaluating the Ideal Chunk Size for a RAG System using LlamaIndex](https://blog.llamaindex.ai/evaluating-the-ideal-chunk-size-for-a-rag-system-using-llamaindex-6207e5d3fec5)

- [How to Chunk Text Data — A Comparative Analysis](https://towardsdatascience.com/how-to-chunk-text-data-a-comparative-analysis-3858c4a0997a)



##### Positional chunking



##### Semantic chunking



#### Embeddings



#### Vector Search



- [Awesome Search](https://github.com/frutik/awesome-search)

- [Advanced RAG Retrieval Strategies: Sentence Window Retrieval](https://generativeai.pub/advanced-rag-retrieval-strategies-sentence-window-retrieval-b6964b6e56f7)



#### RAG Fusion



- [Forget RAG, the Future is RAG-Fusion](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1)

- 



### Not Vector Retrieval



- [Vector Search Is Not All You Need](https://towardsdatascience.com/vector-search-is-not-all-you-need-ecd0f16ad65e)

- [Build a search engine, not a vector DB](https://blog.elicit.com/search-vs-vector-db/)

- [Improving RAG (Retrieval Augmented Generation) Answer Quality with Re-ranker](https://medium.com/towards-generative-ai/improving-rag-retrieval-augmented-generation-answer-quality-with-re-ranker-55a19931325)

- [From Search to Synthesis: Enhancing RAG with BM25 and Reciprocal Rank Fusion](https://medium.com/@kachari.bikram42/from-search-to-synthesis-enhancing-rag-with-bm25-and-reciprocal-rank-fusion-872d21dc4ca7)



## Generation



### Prompts



- [Emerging RAG & Prompt Engineering Architectures for LLMs](https://cobusgreyling.medium.com/updated-emerging-rag-prompt-engineering-architectures-for-llms-17ee62e5cbd9)

- [How to Cut RAG Costs by 80% Using Prompt Compression](https://towardsdatascience.com/how-to-cut-rag-costs-by-80-using-prompt-compression-877a07c6bedb)



#### Prompting strategies



##### Multi-Modal RAG



- [Multi-Modal RAG](https://blog.llamaindex.ai/multi-modal-rag-621de7525fea)



##### Multi-index RAG



- [Having all of your data stored in one collection isn't always the best for RAG apps](https://twitter.com/ecardenas300/status/1724829560041038072)



##### Multi-Document



- [Advanced RAG — Multi-Documents Agent with LlamaIndex](https://blog.gopenai.com/advanced-rag-multi-documents-agent-with-llamaindex-43b604f84909)



##### FLARE



- [Better RAG with Active Retrieval Augmented Generation FLARE](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)



##### Chain-of-Verification



- [in-Of-Verification Reduces Hallucination in LLMs](https://cobusgreyling.medium.com/chain-of-verification-reduces-hallucination-in-llms-20af5ea67672)



##### Chain-Of-Thought



- [Chain-Of-Thought Prompting In LLMs](https://cobusgreyling.medium.com/chain-of-thought-prompting-in-llms-1077164edf97)



### Context



- [The Needle In a Haystack Test](https://towardsdatascience.com/the-needle-in-a-haystack-test-a94974c1ad38)

- [Conversational Memory for LLMs with Langchain](https://www.pinecone.io/learn/series/langchain/langchain-conversational-memory/)



#### Long context RAG



- [The next generation of RAG: Long-Context RAG](https://twitter.com/ecardenas300/status/1724129722492142048)

- [NVIDIA Research: RAG with Long Context LLMs](https://blog.llamaindex.ai/nvidia-research-rag-with-long-context-llms-7d94d40090c4)



#### Knowledge and Knowledge Graphs



- [Graph RAG: Unleashing the Power of Knowledge Graphs with LLM](https://medium.com/@nebulagraph/graph-rag-the-new-llm-stack-with-knowledge-graphs-e1e902c504ed)

- [Embeddings + Knowledge Graphs: The Ultimate Tools for RAG Systems](https://towardsdatascience.com/embeddings-knowledge-graphs-the-ultimate-tools-for-rag-systems-cbbcca29f0fd)

- [The Practical Benefits to Grounding an LLM in a Knowledge Graph

Daniel Bukowski](https://medium.com/@bukowski.daniel/the-practical-benefits-to-grounding-an-llm-in-a-knowledge-graph-919918eb493)

- [Implement RAG with Knowledge Graph and Llama-Index](https://medium.aiplanet.com/implement-rag-with-knowledge-graph-and-llama-index-6a3370e93cdd)



- [Awesome Knowledge Graphs](https://github.com/frutik/awesome-knowledge-graphs)

- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/abs/2405.14831)

  

### Automated prompt optimization



### Hallucination



- [How to Detect Hallucinations in LLMs](https://towardsdatascience.com/real-time-llm-hallucination-detection-9a68bb292698)

- [Measuring Hallucinations in RAG Systems](https://vectara.com/measuring-hallucinations-in-rag-systems/)



### Guardrails



- [Safeguarding LLMs with Guardrails](https://towardsdatascience.com/safeguarding-llms-with-guardrails-4f5d9f57cff2)

- [NeMo Guardrails: The Missing Manual](https://www.pinecone.io/learn/nemo-guardrails-intro/)



## LLM Models



### Finetuning and Pretraining



- [Fine-Tuning Llama 2.0 with Single GPU Magic](https://ai.plainenglish.io/fine-tuning-llama2-0-with-qloras-single-gpu-magic-1b6a6679d436)

- [Practitioners guide to fine-tune LLMs for domain-specific use case](https://cismography.medium.com/practitioners-guide-to-fine-tune-llms-for-domain-specific-use-case-part-1-4561714d874f)

- [Are You Pre-training your RAG Models on Your Raw Text?](https://medium.com/thirdai-blog/are-you-pre-training-your-rag-models-on-your-raw-text-40f832d87703)

- [Combine Multiple LoRA Adapters for Llama 2](https://towardsdatascience.com/combine-multiple-lora-adapters-for-llama-2-ea0bef9025cf)

- [RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?](https://towardsdatascience.com/rag-vs-finetuning-which-is-the-best-tool-to-boost-your-llm-application-94654b1eaba7)



## Evaluation of RAGs



- [RAG Evaluation](https://cobusgreyling.medium.com/rag-evaluation-9813a931b3d4)

- [Evaluating RAG: A journey through metrics](https://www.elastic.co/search-labs/blog/articles/evaluating-rag-metrics)

- [Exploring End-to-End Evaluation of RAG Pipelines](https://betterprogramming.pub/exploring-end-to-end-evaluation-of-rag-pipelines-e4c03221429)

- [Evaluation Driven Development, the Swiss Army Knife for RAG Pipelines](https://levelup.gitconnected.com/evaluation-driven-development-the-swiss-army-knife-for-rag-pipelines-dba24218d47e)

- [Evaluating the Ideal Chunk Size for a RAG System using LlamaIndex](https://blog.llamaindex.ai/evaluating-the-ideal-chunk-size-for-a-rag-system-using-llamaindex-6207e5d3fec5)



## Performance and cost



- [Secrets to Optimizing RAG LLM Apps for Better Performance, Accuracy and Lower Costs!](https://medium.com/madhukarkumar/secrets-to-optimizing-rag-llm-apps-for-better-accuracy-performance-and-lower-cost-da1014127c0a)



## Privacy



- [Masking PII Data in RAG Pipeline](https://betterprogramming.pub/masking-pii-data-in-rag-pipeline-326d2d330336)



## Security



- [Hijacking Chatbots: Dangerous Methods Manipulating GPTs](https://medium.com/@jankammerath/hijacking-chatbots-dangerous-methods-manipulating-gpts-52342f4f88b8)



## Applications of RAG



### Chatbots



## Tools



- [Three Open-Source RAG Tools You Need to Know About](https://medium.com/programmers-journey/three-open-source-rag-tools-you-need-to-know-about-331c3f28ab22)

- [HayStack](https://github.com/deepset-ai/haystack)

- [RAGAS](https://github.com/explodinggradients/ragas)



### DSPy



- [DSPy — Does It Live Up To The Hype?](https://medium.com/emalpha/dspy-does-it-live-up-to-the-hype-6e56c2c6e7a0)



### AutoRAG



- [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG) - AutoML tool for RAG. Automatically optimize RAG pipeline with single YAML file.



### AutoGPT



### Langchain



- [Langchain](https://github.com/langchain-ai/langchain)

- [Langchain is NOT for production use. Here is why ..](https://medium.com/@aldendorosario/langchain-is-not-for-production-use-here-is-why-9f1eca6cce80)



### LlamaIndex



- [LlamaIndex](https://github.com/run-llama/llama_index)



- [Building Production-Ready LLM Apps with LlamaIndex: Document Metadata for Higher Accuracy Retrieval](https://betterprogramming.pub/building-production-ready-llm-apps-with-llamaindex-document-metadata-for-higher-accuracy-retrieval-a8ceca641fb5)

- [Building Production-Ready LLM Apps With LlamaIndex: Recursive Document Agents for Dynamic Retrieval](https://betterprogramming.pub/building-production-ready-llm-apps-with-llamaindex-recursive-document-agents-for-dynamic-retrieval-1f4b25287918)



## Vendor-specific examples



- [RAG Pipeline with Mistral 7B Instruct Model in Colab: A Step-by-Step Guide

Qendel AI GoPenAI](https://blog.gopenai.com/rag-pipeline-with-mistral-7b-instruct-model-a-step-by-step-guide-138df378a0c2)



### Elastcisearch + OpenAI



- [ChatGPT and Elasticsearch: OpenAI meets private data](https://www.elastic.co/search-labs/blog/chatgpt-elasticsearch-openai-meets-private-data)



### OpenAI and ChatGPT



- [Compare PDF Question Answering Systems Build with OpenAI and Google VertexAI](https://medium.com/@kelvin.lu.au/compare-pdf-question-answering-with-openai-and-google-vertexai-46638d62327b)



#### Tools and fucntions



- [Unlocking the Power of the OpenAI API: Master Function-Calling with Practical Examples](https://medium.com/@apollovro/unlocking-the-power-of-the-openai-api-master-function-calling-with-practical-examples-f8b9ab2fceec)

- [penAI/Chat-GPT Function Calling : for Enhanced AI Interactions](https://levelup.gitconnected.com/openai-chat-gpt-function-calling-for-enhanced-ai-interactions-338be974027)



### Vespa



- [Hands-On RAG guide for personal data with Vespa and LLamaIndex](https://blog.vespa.ai/scaling-personal-ai-assistants-with-streaming-mode/)



### Qdrant



## Running RAGs in production



## Vectors corner



- [Similarity Search, Part 2: Product Quantization](https://towardsdatascience.com/similarity-search-product-quantization-b2a1a6397701)

- [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization)

- [Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets

Image of Nils Reimers](https://cohere.com/blog/int8-binary-embeddings)



## Various



- [Improve RAG Pipelines With These 3 Indexing Methods](https://levelup.gitconnected.com/improve-rag-pipelines-with-these-3-indexing-methods-83317e972676)



## LLM Agents