How RAG Turns AI Chatbots Into Something Practical

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Retrieval augmented generation, a current popular method to utilize LLMs to retrieve from a database instead of putting everything in a context window. But how does it work? Today I will walk through the most basic idea of RAG and the current meta of how RAG is used, and what it is composed of.

some papers
[Web + RAG] https://arxiv.org/abs/2408.07611
[Vector + KG RAG] https://arxiv.org/abs/2408.04948
[RAG Survey] https://arxiv.org/abs/2404.10981

[Knowledge Graph for RAG] https://docs.llamaindex.ai/en/stable/examples/query_engine/knowledge_graph_rag_query_engine/
[LlamaIndex] https://www.llamaindex.ai/
[LlamaParse] https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse/
[HuggingFace] https://huggingface.co/models
[Cohere Command R+] https://docs.cohere.com/docs/command-r-plus
[Cohere Rerank] https://docs.cohere.com/docs/overview
[Cohere Embedding Models] https://cohere.com/blog/introducing-embed-v3
[GraphRAG] https://github.com/microsoft/graphrag
[RAGAS] https://github.com/explodinggradients/ragas


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