Retrieval Augmented Generation with GPT and Milvus
In this session you'll learn how to get started with Milvus and perform Q&A on some documents using GPT and the RAG technique.
Yujian Tang
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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.관련
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What Is Retrieval Augmented Generation (RAG)? A Complete Guide
Understand how RAG grounds LLM outputs in external data to reduce hallucinations, with a guide to the RAG pipeline, advanced techniques, and real-world applications.
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Boost LLM Accuracy with Retrieval Augmented Generation (RAG) and Reranking
Discover the strengths of LLMs with effective information retrieval mechanisms. Implement a reranking approach and incorporate it into your own LLM pipeline.

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Retrieval Augmented Generation with LlamaIndex
In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.



Large language models (LLMs) like GPT are the must-have technology of the year. Unfortunately, LLMs can't accurately answer questions about your business because they lack enough domain knowledge. The solution is to combine the LLM with a vector database like Milvus—a technique known as retrieval augmented generation (RAG).
In this session you'll learn how to get started with Milvus and perform Q&A on some documents using GPT and the RAG technique.
Key Takeaways:
Notes:
Download and install Visual Studio Code on your local machine. Get started here.
How to get LlamaIndex set up and running with Milvus
Code Along with Us!
Additional Resources
[TUTORIAL] How to Build LLM Applications with LangChain
[TUTORIAL] Using ChatGPT to Moderate ChatGPT Responses
[CODE ALONG] An Introduction to GPT & Whisper with the OpenAI API in Python