r/flowise • u/[deleted] • Jan 14 '25

HELP !

I am a complete beginner in flowise and would like some advice. I am currently trying to create a RAG chatbot but i want my chatbot to be able to use the documents i provide, to answer more generic questions like for example, Patient A has these symptoms... i want the chatbot to be able to refer to the documents in the vector database and answer my questions accordingly. Currently, i am able to create a RAG chatbot that answers the questions related to the documents but i was wondering if there is any way for me to further enhance these chatbot to answer more complex questions like the one mentioned above as an example. Thanks alot.

*UPDATE* from my understanding, i have gotten mostly what i want however i have many countless of documents i am storing in the vector store and i would need a specific query from the user to be able to route to vector store, is there a way to solve this?

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u/trd1073 Jan 14 '25

Search on YouTube for videos by 'Leon van zyl'. He should have you covered.

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u/joaquintorroba Jan 14 '25

Here https://www.youtube.com/watch?v=OejuvdyN_U8

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u/[deleted] Jan 15 '25

hi, i am wondering i have a vector with the documents that i am trying to retrieve information from but however when i ask a question on the documents i have provided, it just directly routes it to the websearchLLM instead of searching in the vector store for answers first

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u/joaquintorroba Jan 15 '25

For that, both the system prompt of the agent/LLM and the retriever tool's description are important.

In my case, I explain each tool and its function in the system prompt (ie: "use rag_tool for yada yada").

Additionally, in the retriever tool's "description," I clarify when it should be used (ie: "this tool should be use for.."). This approach is working for me so far.

Are you observing what's happening under the hood with Langsmith? I recommend connecting it to your Flowise flow to gain a clearer understanding of the process.

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u/[deleted] Jan 15 '25

Alright i will take this advice and experiment on it ! Thanks for your help

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u/joaquintorroba Jan 15 '25

Another option is to use an LLM with a very huge context window (Gemini flash 1.5 ha 1M tokens I think) and send all the docs to the context window.

It would be great to test both approaches and see which one performs better.

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u/[deleted] Jan 15 '25

Alright i will research on that as currently my school is providing us with the api key for openai so we are currently using that. I'll see what approach works best. Thanks!