Multi Document RAG Chatbot - Streamlit Langchain Groq ChromaDB - LLAMA 3.1 | Generative AI

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This video is about building a streamlit chatbot for Multi document RAG chatbot using langchain and Groq using Llama 3.1 as the LLM.

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Lets not split the chunk wise. It loses structure. Just structurise it in format. Title, content, tables in document. Also if images.
Generate embeddings of all columns. When user asks query decide whether its based on table or content based on score. We can use bm25 and semantics search scores and then reciprocal rank fusion to calculate all scores. If the score of table col iss more then query is based on table.
When passing data to the llm we will pass all data. Suppose if the query based on table col. We will take high score result on table. And we will pass that table as well as corresponding columns. That is title and content.
This really works great and it maintains the structure as welll.
We can as query like give me content insode the title named “example title “. It will be based on title so we will pass the title as well as corresponding columns.

lnstagrarm

Great contents ❤️
Could you please make videos on lang graph and AI agents ?

Thanks for your valuable tutorials 👍

ruznyma

Great content but this doesn't perform well when we are comparing two documents and asking the questions

kavururajesh

Hey Sid.. Great one. Can u expand this to reading not only pdf but also csv, docx and txt?

KumR

Thanks Sid. What if we want to upload docs & nt just point to data folder ? And what if we have multiple pdf, csv, docx, txt etc where data lies ?

KumR

I have implemented Conversation rag chain taking reference of langchain documentation. I'm not getting how to implement reciprocal rank fusion or reranking when you are using History aware retriever

SUDA-ur

Can Chunksize can be spotted by using "\n" so that chunks will be splitted into paragraph wise. hence it cannot be mess up with the structure.

gauravwankhede

how to implement cache mechanism here using gptcache or langchain inmemory or sqllite cache

gamekhela

Can we use agent based on type of document?

KumR

Also i don’t suggest using langchain. As we cant configure it according to our needs. Also it need so much things in backend to run so it makes responses slower.

lnstagrarm

magic.from_file(file_path, mime=True)
AttributeError: module 'magic' has no attribute 'from_file' even after
pip install python-magic-bin

PraveenYadavgaming

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