Need some help on my simple RAG setup that won't rag.

[woezel1980]

Hi, regardless of watching a few tutorials on youtube and using chatGPT I am still stuck.

My goal: create a RAG. It consists of a document store in Flowise, which uses nomic-embed-text to embed. I uploaded a simple text file with (translated from Dutch) the sentence: "Pancakes are blue, peanut butter is green". I succesfully upserted to my self hosted Postgres DB. I can see the entry there.

This is my Chat flow (see screenshot).

The problem is that the agent does not seem to "see" the text in the text file. My expectation would be that if I ask what color pancakes are, I get the answer "blue". But instead I get a general answer.

Who knows what's wrong?

[woezel1980]

looks like your flow is wired but the retriever isn’t actually surfacing the embedded text back to the llm. this matches ProblemMap No.8 (retriever mismatch) — basically the query is bypassing the vector search and falling back to a generic completion.

the fix isn’t infra, it’s a semantic alignment step. if you want, i can share the checklist we use that ensures the retriever and embedding are bound correctly (it’s open in our problem map).

let me know if you want the link.

Hi onestardao, ( @onestardao )
Thanks for your reply.
I think you are describing the problem correctly as I too think the flow is correct. So there must be an issue with the retrieval and/or the matching with the LLM. This seems weird as both the embedding model (nomic-embed-text) as the llm (mistral) are both running via ollama and part of the same stack.

A semantic alignment step? Sounds complicated but I would like to know how to check this, as my problem still is not fixed. You are mentioning a link. Can I see it please?

[woezel1980]

Do you mean
https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md
?
Currently analyzing it.

[woezel1980]

ok thanks. I didn't find the solution using the tool agent and ended up using the conversational retrieval QA chain.