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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.
My own RAG experiment on 1TB of PDFs:
https://youtu.be/KL5Au8pKJ38
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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