cyber-security-resources/ai_research/RAG/README.md

# RAG Resources

- [Files from my "Using Retrieval Augmented Generation (RAG), Langchain, and LLMs for Cybersecurity Operations" Course](https://github.com/santosomar/RAG-for-cybersecurity)
- [LangFlow](https://github.com/langflow-ai/langflow) - a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.


### 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)

#### 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)

### 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)


## Research Papers 

### Survey and Benchmark 

**Benchmarking Large Language Models in Retrieval-Augmented Generation** \
*Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun* \
arXiv 2023. [[Paper](https://arxiv.org/abs/2309.01431)][[Github](https://github.com/chen700564/RGB)] \
4 Sep 2023 


### Retrieval-enhanced LLMs


**Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models** \
*Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, Dong Yu* \
arxiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.09210)] 

**REST: Retrieval-Based Speculative Decoding** \
*Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, Di He* \
arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.08252)][[Github](https://github.com/fasterdecoding/rest)]


**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection**  
*Anonymous*  
ICLR 24 – Oct 2023 [[paper](https://openreview.net/forum?id=hSyW5go0v8)]

**Self-Knowledge Guided Retrieval Augmentation for Large Language Models** \
*Yile Wang, Peng Li, Maosong Sun, Yang Liu* \
arXiv - Oct 2023 [[Ppaer](https://arxiv.org/abs/2310.05002)]


**Retrieval meets Long Context Large Language Models** \
*Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro* \
arxiv - Oct 2023 [[Paper](https://arxiv.org/abs/2310.03025)]


**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines**  
*Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts*  
arXiv – Oct 2023 [[paper](https://arxiv.org/abs/2310.03714)] [[code](https://github.com/stanfordnlp/dspy)]

**Adaptive Chameleon  or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts**  
*Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, Yu Su*  
ICLR 24 – May 2023 [[paper](https://arxiv.org/abs/2305.13300)] [[code](https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict)]

**Active Retrieval Augmented Generation**  
*Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig*  
arXiv – May 2023 [[paper](https://arxiv.org/abs/2305.06983)] [[code](https://github.com/jzbjyb/FLARE)]


**REPLUG: Retrieval-Augmented Black-Box Language Models**  
*Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih*  
arXiv – Jan 2023 [[paper](https://arxiv.org/abs/2301.12652)]


**Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks** 
*Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela* 
NeurIPS 2020 - May 2020 [[Paper](https://arxiv.org/abs/2005.11401)]


### RAG Instruction Tuning 

**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**  
*Anonymous*  
ICLR 24 – Oct 23 [[paper](https://openreview.net/forum?id=22OTbutug9)]

**InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining**  
*Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro* \
arXiv -  Oct 23 [[paper](https://openreview.net/forum?id=4stB7DFLp6)]


### RAG In-Context Learning 

**In-Context Retrieval-Augmented Language Models**  
*Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham*  
AI21 Labs – Jan 2023 [[paper](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/63c6c20dec4479564db21819_NEW_In_Context_Retrieval_Augmented_Language_Models.pdf)] [[code](https://github.com/AI21Labs/in-context-ralm)]


### RAG Embeddings 

**RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling** \
*Jingcheng Deng, Liang Pang, Huawei Shen, Xueqi Cheng* \
EMNLP 2023 - Oct 2023 [[Paper](https://arxiv.org/abs/2310.10567)][[Github](https://github.com/TrustedLLM/RegaVAE)] 

**Text Embeddings Reveal (Almost) As Much As Text** \
*John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush*  \
EMNLP 2023 - Oct 2023 [[Paper](https://arxiv.org/abs/2310.06816?ref=upstract.com)][[Github](https://github.com/jxmorris12/vec2text)] 

**Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents** \
*Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao* \
arXiv - Oct 2023. [[Paper](https://arxiv.org/abs/2310.19923)][[Model](https://huggingface.co/jinaai/jina-embeddings-v2-small-en)] 

### RAG Simulators

**KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants** \
*Kaustubh D. Dhole* \
Simulation of Conversational Intelligence in Chat, EACL 2024 [[Paper](https://arxiv.org/abs/2401.16454)]

### RAG Search

### RAG Long-text and Memory 

**HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models** \
*Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, Yu Su* \
arXiv - May 2024 [[paper](https://arxiv.org/abs/2405.14831)] [[GitHub](https://github.com/OSU-NLP-Group/HippoRAG)]

**Understanding Retrieval Augmentation for Long-Form Question Answering** \
*Hung-Ting Chen, Fangyuan Xu, Shane A. Arora, Eunsol Choi* \
arXiv - Oct 2023 [[Paper](https://arxiv.org/abs/2310.12150)]

### RAG Evaluation

**ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems** \
*Jon Saad-Falcon, Omar Khattab, Christopher Potts, Matei Zaharia* \
arXiv - Nov 2023. [[Paper](https://arxiv.org/abs/2311.09476)] [[Github](https://github.com/stanford-futuredata/ares)]


### RAG Optimization

**Learning to Filter Context for Retrieval-Augmented Generation** \
*Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig* \
arxiv- Nov 2023 [[Paper](https://arxiv.org/abs/2311.08377)][[Github](https://github.com/zorazrw/filco)] 


**Large Language Models Can Be Easily Distracted by Irrelevant Context** \
*Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou* \
ICML 2023 - Jan 2023 [[Paper](https://arxiv.org/abs/2302.00093)][[Github](https://github.com/google-research-datasets/GSM-IC)] 


**Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks** \
*Akari Asai, Matt Gardner, Hannaneh Hajishirzi* \
NAACL 2022 - Dec 2021 [[Paper](https://arxiv.org/abs/2112.08688)][[Github](https://github.com/akariasai/evidentiality_qa)] 

**When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories** \
*Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi* \
ACL 2023 - Dec 2022 [[Paper](https://arxiv.org/abs/2212.10511)][[Github](https://github.com/alextmallen/adaptive-retrieval)] 


### RAG Application

**Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination** \
*Haoqiang Kang, Xiao-Yang Liu* \
arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.15548)] 


**Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature** \
*Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah* \
arXiv - Oct 2023. [[Paper](https://arxiv.org/abs/2310.16146v1)] 

**PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers** \
*Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi* \
arXiv - Nov 2023. [[Paper](https://arxiv.org/abs/2311.09180)]
-												Create README.md
											
										
										
											2024-08-18 17:44:37 -04:00
+								# RAG Resources
-												Update README.md
											
										
										
											2024-08-18 20:11:07 -04:00
+								- [Files from my "Using Retrieval Augmented Generation (RAG), Langchain, and LLMs for Cybersecurity Operations" Course](https://github.com/santosomar/RAG-for-cybersecurity)
 								- [LangFlow](https://github.com/langflow-ai/langflow) - a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
 								### 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)
 								#### 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)
 								### 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)
 								## Research Papers
 								### Survey and Benchmark
 								**Benchmarking Large Language Models in Retrieval-Augmented Generation** \
 								*Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun* \
 								arXiv 2023. [[Paper](https://arxiv.org/abs/2309.01431)][[Github](https://github.com/chen700564/RGB)] \
 Sep 2023
 								### Retrieval-enhanced LLMs
 								**Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models** \
 								*Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, Dong Yu* \
 								arxiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.09210)]
 								**REST: Retrieval-Based Speculative Decoding** \
 								*Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, Di He* \
 								arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.08252)][[Github](https://github.com/fasterdecoding/rest)]
 								**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection**
 								*Anonymous*
 								ICLR 24 – Oct 2023 [[paper](https://openreview.net/forum?id=hSyW5go0v8)]
 								**Self-Knowledge Guided Retrieval Augmentation for Large Language Models** \
 								*Yile Wang, Peng Li, Maosong Sun, Yang Liu* \
 								arXiv - Oct 2023 [[Ppaer](https://arxiv.org/abs/2310.05002)]
 								**Retrieval meets Long Context Large Language Models** \
 								*Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro* \
 								arxiv - Oct 2023 [[Paper](https://arxiv.org/abs/2310.03025)]
 								**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines**
 								*Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts*
 								arXiv – Oct 2023 [[paper](https://arxiv.org/abs/2310.03714)] [[code](https://github.com/stanfordnlp/dspy)]
 								**Adaptive Chameleon  or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts**
 								*Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, Yu Su*
 								ICLR 24 – May 2023 [[paper](https://arxiv.org/abs/2305.13300)] [[code](https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict)]
 								**Active Retrieval Augmented Generation**
 								*Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig*
 								arXiv – May 2023 [[paper](https://arxiv.org/abs/2305.06983)] [[code](https://github.com/jzbjyb/FLARE)]
 								**REPLUG: Retrieval-Augmented Black-Box Language Models**
 								*Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih*
 								arXiv – Jan 2023 [[paper](https://arxiv.org/abs/2301.12652)]
 								**Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks**
 								*Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela*
 								NeurIPS 2020 - May 2020 [[Paper](https://arxiv.org/abs/2005.11401)]
 								### RAG Instruction Tuning
 								**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**
 								*Anonymous*
 								ICLR 24 – Oct 23 [[paper](https://openreview.net/forum?id=22OTbutug9)]
 								**InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining**
 								*Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro* \
 								arXiv -  Oct 23 [[paper](https://openreview.net/forum?id=4stB7DFLp6)]
 								### RAG In-Context Learning
 								**In-Context Retrieval-Augmented Language Models**
 								*Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham*
 								AI21 Labs – Jan 2023 [[paper](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/63c6c20dec4479564db21819_NEW_In_Context_Retrieval_Augmented_Language_Models.pdf)] [[code](https://github.com/AI21Labs/in-context-ralm)]
 								### RAG Embeddings
 								**RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling** \
 								*Jingcheng Deng, Liang Pang, Huawei Shen, Xueqi Cheng* \
 								EMNLP 2023 - Oct 2023 [[Paper](https://arxiv.org/abs/2310.10567)][[Github](https://github.com/TrustedLLM/RegaVAE)]
 								**Text Embeddings Reveal (Almost) As Much As Text** \
 								*John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush*  \
 								EMNLP 2023 - Oct 2023 [[Paper](https://arxiv.org/abs/2310.06816?ref=upstract.com)][[Github](https://github.com/jxmorris12/vec2text)]
 								**Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents** \
 								*Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao* \
 								arXiv - Oct 2023. [[Paper](https://arxiv.org/abs/2310.19923)][[Model](https://huggingface.co/jinaai/jina-embeddings-v2-small-en)]
 								### RAG Simulators
 								**KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants** \
 								*Kaustubh D. Dhole* \
 								Simulation of Conversational Intelligence in Chat, EACL 2024 [[Paper](https://arxiv.org/abs/2401.16454)]
 								### RAG Search
 								### RAG Long-text and Memory
 								**HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models** \
 								*Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, Yu Su* \
 								arXiv - May 2024 [[paper](https://arxiv.org/abs/2405.14831)] [[GitHub](https://github.com/OSU-NLP-Group/HippoRAG)]
 								**Understanding Retrieval Augmentation for Long-Form Question Answering** \
 								*Hung-Ting Chen, Fangyuan Xu, Shane A. Arora, Eunsol Choi* \
 								arXiv - Oct 2023 [[Paper](https://arxiv.org/abs/2310.12150)]
 								### RAG Evaluation
 								**ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems** \
 								*Jon Saad-Falcon, Omar Khattab, Christopher Potts, Matei Zaharia* \
 								arXiv - Nov 2023. [[Paper](https://arxiv.org/abs/2311.09476)] [[Github](https://github.com/stanford-futuredata/ares)]
 								### RAG Optimization
 								**Learning to Filter Context for Retrieval-Augmented Generation** \
 								*Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig* \
 								arxiv- Nov 2023 [[Paper](https://arxiv.org/abs/2311.08377)][[Github](https://github.com/zorazrw/filco)]
 								**Large Language Models Can Be Easily Distracted by Irrelevant Context** \
 								*Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou* \
 								ICML 2023 - Jan 2023 [[Paper](https://arxiv.org/abs/2302.00093)][[Github](https://github.com/google-research-datasets/GSM-IC)]
 								**Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks** \
 								*Akari Asai, Matt Gardner, Hannaneh Hajishirzi* \
 								NAACL 2022 - Dec 2021 [[Paper](https://arxiv.org/abs/2112.08688)][[Github](https://github.com/akariasai/evidentiality_qa)]
 								**When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories** \
 								*Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi* \
 								ACL 2023 - Dec 2022 [[Paper](https://arxiv.org/abs/2212.10511)][[Github](https://github.com/alextmallen/adaptive-retrieval)]
 								### RAG Application
 								**Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination** \
 								*Haoqiang Kang, Xiao-Yang Liu* \
 								arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.15548)]
 								**Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature** \
 								*Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah* \
 								arXiv - Oct 2023. [[Paper](https://arxiv.org/abs/2310.16146v1)]
 								**PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers** \
 								*Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi* \
 								arXiv - Nov 2023. [[Paper](https://arxiv.org/abs/2311.09180)]