[kingkongjaffa]

If you’re on a budget you don’t want to “Train” the model. I.e fine tune.

Since you have multi format data you likely want a pipeline to convert it all to text using various tools, make sure it’s structured and then shove it in a RAG system for the LLM chatbot to work with.

You can get started with lang chain and openAI’s API

Experiment with gpt4o mini for a while to keep costs down and then test if cranking up to gpt4o proper is worth it.

That’s the LLM part solved. You’ll need to control the logic after that depending on controls in your chatbot pop-up window to be able to arrange the calls/send emails etc.

[ndr_]

Depends on the budget, I’d say. If it’s less than 10¢, that may be true. If it‘s 15¢ or more, you could try training with OpenAI: https://ndurner.github.io/training-own-model-finetuning

[crazymoka]

I can put all the content into text. Would putting it all into json or cvs with headers be helpful? Break down content by topic, categories, links to other pages if it needs to read other content?

[kingkongjaffa]

Yeah whatever will give you accurate vector embeddings.

You’ll combine those json properties into one vector.

For links you might hard code those as an extra part of each object or potentially require another API call to retrieve relevant links from your “link store”.

It’s useful to have benchmarks in place like:

input “should equal” output And start building a suite of test cases to evaluate your set up.

[cranberryturkey]

> shove it in a RAG system for the LLM chatbot to work with

how would I do this with an ollama model?

[kingkongjaffa]

You want to ingest your input documents and generate embeddings using an embedding model like https://ollama.com/blog/embedding-models