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Intro-to-RAG-with-CODEGEMMA-7B

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LLM is a very powerful tool. It often performs more than required (hallucinations) and may tend to generate output in a pattern it finds best. We need RAG to harness the power of LLM in a controlled manner. In this work we implement a simple RAG system with Codegemma and an in-memory Vector Database.

codegemmadockerdocker-composegradiollmragsentence-transformersvector-database
Creat：2024-04-23T13:10:22
Update：2024-09-11T23:01:05
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