Build Your Own RAG Using Unstructured, Llama3 via Groq, Qdrant & LangChain

In this 5th video in the unstructured playlist, I will explain you how to create your own Retrieval Augmented Generation (RAG) bot using the following tech stack.
- LangChain as framework
- UnstructuredIO for data prep
- Fastembed for embedding
- Qdrant Cloud as vectorstore
- Llama3 via GroqInc

80% of enterprise data exists in difficult-to-use formats like HTML, PDF, CSV, PNG, PPTX, and more. Unstructured effortlessly extracts and transforms complex data for use with every major vector database and LLM framework.

Link ⛓️‍💥
https://unstructured.io/

Code 👨🏻‍💻
https://github.com/sudarshan-koirala/...

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Timestamps ⏰
00:00 Introduction
02:33 Setup
04:58 Preprocess PDF
10:42 Preprocess Markdown (Readme)
14:08 Load the document into the VectorDB
17:27 Now the RAG part
22:24 Qdrant Cloud and LangSmith
25:19 Conclusion

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#unstructureddata ##unstructuredio #rag #langchain #llm #datasciencebasics