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Hi guys. I’m new to building RAG pipelines. Currently I’m trying to build a RAG that interfaces with my schools cs department info so I can ask it basic questions. I have a couple questions.

I used fire crawl to crawl the website and deeper links, so I have a huge json file. I took that JSON and cleaned that up and made it a txt.

Used hugging face embeddings but from here I’m stuck on what indexing and storing actually means and how I should do it effectively. Right now the Llm given context hallucinates A LOT!

3 comments

WWhiteFang_Jr

Hey,
Indexing is basically a place where you can find embeddings, nodes, query engine, chat engine instance.

Storing would mean where you store the embeddings and node, which is either locally or in a vector database.

For hallucinations, if you are using open source llms then there are chances for it. If you have GPU try llama3.2 they are pretty good

eexternalnoise

Thanks for the response. I guess maybe I thought I could improve the data ingestion portion of my code. I’m going down the rabbit hole of chunking data. Am I on the right track?

WWhiteFang_Jr

Yes if the default chunking is not helping you correctly then you can try diff chunking ways , size

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