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Comprehensive Guide to RAG
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Retrieve - Augment - Generate is a chain of activities to feed LLM with your own data, providing accurate context and based on that generate output specific to your flow.
You can feed your documents, APIs and have RAG flow autonomously interact with systems and humans.
Let's say you want to scan over your legal documents and ...
What is RAG?
How to Build a RAG?
Libraries
- https://www.llamaindex.ai/ (opens in a new tab)
- https://python.langchain.com/v0.2/docs/introduction/ (opens in a new tab)
- https://haystack.deepset.ai/ (opens in a new tab)
- https://flowiseai.com/ (opens in a new tab)
- https://blog.n8n.io/langchain-alternatives/?utm_source=perplexity (opens in a new tab)
- https://www.langflow.org/ (opens in a new tab)
- https://neuml.github.io/txtai/ (opens in a new tab)
- https://www.crewai.com/ (opens in a new tab)
- https://superagi.com/ (opens in a new tab)
- https://langroid.github.io/langroid/ (opens in a new tab)
- https://rivet.ironcladapp.com/ (opens in a new tab)
- https://outlines-dev.github.io/outlines/ (opens in a new tab)
- https://r2r-docs.sciphi.ai/introduction (opens in a new tab)
Similar Tutorials
- https://hackernoon.com/build-your-own-rag-app-a-step-by-step-guide-to-setup-llm-locally-using-ollama-python-and-chromadb?utm_source=perplexity (opens in a new tab)
- https://docs.llamaindex.ai/en/stable/examples/low_level/oss_ingestion_retrieval/?utm_source=perplexity (opens in a new tab)
- https://github.com/llmware-ai/llmware (opens in a new tab)
- https://www.pinecone.io/learn/retrieval-augmented-generation/ (opens in a new tab)
- https://docs.pinecone.io/guides/get-started/build-a-rag-chatbot (opens in a new tab)
Extra Reading
- https://nebius.ai/solutions/rag (opens in a new tab)
- https://nuclia.com/ (opens in a new tab)
- https://pcg.io/insights/introduction-to-retrieval-augmented-generation-rag/ (opens in a new tab)
- https://www.ibm.com/blog/the-recipe-for-rag-how-cloud-services-enable-generative-ai-outcomes-across-industries/ (opens in a new tab)
- https://www.nvidia.com/en-eu/glossary/retrieval-augmented-generation/ (opens in a new tab)
- https://en.getmaia.ai/ (opens in a new tab)
- https://tinyml.substack.com/p/the-rise-of-ragaas-rag-as-a-service (opens in a new tab)
- https://www.ragu.ai/rag-systems-retrieval-augmented-generation (opens in a new tab)
- https://greatwave.ai/platform/llm-rag/ (opens in a new tab)
- https://www.openxcell.com/rag-as-a-service/ (opens in a new tab)
Challenging RAG Classic Approach
- https://blog.metamirror.io/stop-using-a-single-rag-approach-48ec9d93b80b (opens in a new tab)
- https://blog.jayanthk.in/types-of-rag-an-overview-0e2b3ed71b82 (opens in a new tab)
- https://gradientflow.com/best-practices-in-retrieval-augmented-generation/?utm_source=perplexity (opens in a new tab)
Research
- https://arxiv.org/abs/2407.01219 (opens in a new tab)
- https://arxiv.org/html/2405.06211v1?utm_source=perplexity (opens in a new tab)
- https://arxiv.org/abs/2312.10997?utm_source=perplexity (opens in a new tab)
- https://arxiv.org/abs/2407.08223 (opens in a new tab)
- https://arxiv.org/abs/2408.02545 (opens in a new tab)
- https://www.arxiv.org/abs/2407.16833 (opens in a new tab)
- https://arxiv.org/abs/2409.01666 (opens in a new tab)
- https://arxiv.org/abs/2005.11401 (opens in a new tab)
- https://arxiv.org/abs/2407.01219 (opens in a new tab)
- https://arxiv.org/abs/2406.15319 (opens in a new tab)
- https://arxiv.org/abs/2407.18553 (opens in a new tab)
- https://arxiv.org/abs/2408.04948 (opens in a new tab)
- https://arxiv.org/abs/2406.12430 (opens in a new tab)
- https://selfrag.github.io/ (opens in a new tab)
- https://arxiv.org/abs/2401.14887 (opens in a new tab)
- https://arxiv.org/abs/2406.13629 (opens in a new tab)
- https://microsoft.github.io/graphrag/ (opens in a new tab)
- https://arxiv.org/abs/2408.04187 (opens in a new tab)
- https://dl.acm.org/doi/10.1145/3626772.3657923 (opens in a new tab)
- https://arxiv.org/abs/2404.16130 (opens in a new tab)
- https://www.marktechpost.com/2024/08/29/table-augmented-generation-tag-a-unified-approach-for-enhancing-natural-language-querying-over-databases/ (opens in a new tab)
- https://www.researchgate.net/publication/383120066_WeKnow-RAG_An_Adaptive_Approach_for_Retrieval-Augmented_Generation_Integrating_Web_Search_and_Knowledge_Graphs (opens in a new tab)
- https://arxiv.org/abs/2406.07348 (opens in a new tab)
- https://arxiv.org/abs/2403.14403 (opens in a new tab)
Fine Tuning
- https://github.com/unslothai/unsloth (opens in a new tab)
- https://mlabonne.github.io/blog/posts/2024-07-29_Finetune_Llama31.html (opens in a new tab)
Memory
Advanced RAG
- https://div.beehiiv.com/p/advanced-rag-series-indexing?utm_source=perplexity (opens in a new tab)
- https://betterprogramming.pub/fine-tuning-gpt-3-5-rag-pipeline-with-gpt-4-training-data-49ac0c099919 (opens in a new tab)
Deployment
Research assistant
LLM Tools
- https://github.com/mendableai/firecrawl (opens in a new tab)
- https://vectorize.io/ (opens in a new tab)
Function calling
- https://github.com/TengHu/ActionWeaver (opens in a new tab)
- https://www.promptingguide.ai/applications/function_calling (opens in a new tab)