This is something that you can bootstrap into a proof-of-concept in a day and learn the tools you like and don't like along the way.
Basically you'll use any LLM and a vector DB of your choice (I like ChromaDB to date). Write a tool that will walk your source documents and chunk them. Submit the chunks to your LLM with a prompt that asks the LLM to come up with search retrieval questions for the chunk. Store the document and the questions in ChromaDB, cross-referencing the question to the document source (you can add the filename/path as metadata to the question) and the relevant chunk (by it's ID).
Run this tool whenever your docs change - you can automate this. Being intelligent about detecting new/changed content and how you chunk/generate questions can save you time and money and be a place to optimize.
To use it, you need to accept user input, run the input as a text query against your vector DB and submit both the results (with filenames and relevant chunks) and the user's query to a LLM with a prompt designed to elicit a certain kind of response based on input and the relevant chunks. Show the response to the user. Loop if you want.
You can build most of this with as few tools as `litellm`, `langchain` and `huggingface` libraries. You'll be surprised how far you can get with such a dumb setup.
Yes, this is basic RAG. That's how you do it without getting overwhelmed with all the tooling/libraries out there.
Basically you'll use any LLM and a vector DB of your choice (I like ChromaDB to date). Write a tool that will walk your source documents and chunk them. Submit the chunks to your LLM with a prompt that asks the LLM to come up with search retrieval questions for the chunk. Store the document and the questions in ChromaDB, cross-referencing the question to the document source (you can add the filename/path as metadata to the question) and the relevant chunk (by it's ID).
Run this tool whenever your docs change - you can automate this. Being intelligent about detecting new/changed content and how you chunk/generate questions can save you time and money and be a place to optimize.
To use it, you need to accept user input, run the input as a text query against your vector DB and submit both the results (with filenames and relevant chunks) and the user's query to a LLM with a prompt designed to elicit a certain kind of response based on input and the relevant chunks. Show the response to the user. Loop if you want.
You can build most of this with as few tools as `litellm`, `langchain` and `huggingface` libraries. You'll be surprised how far you can get with such a dumb setup.
Yes, this is basic RAG. That's how you do it without getting overwhelmed with all the tooling/libraries out there.