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
สร้างบัญชีฟรีของคุณ
ฝึกฝนและตรวจสอบความก้าวหน้าของคุณ
ดำเนินการต่อด้วย Googleเมื่อดำเนินการต่อ ถือว่าคุณยอมรับ ข้อกำหนดการใช้งานและ นโยบายความเป็นส่วนตัว และยอมรับว่าข้อมูลของคุณจะถูกจัดเก็บในสหรัฐอเมริกาที่เกี่ยวข้อง
บล็อก
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.
บทแนะนำ
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.

code-along
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