I'm looking for the best way to train from my business knowledge base for openAI, Llama, or Claude on my own private business knowledge base.
That data will then be used in a chatbot(maybe whatsapp) on my website that will either create an appointment for call, send follow up email with more information, or directly connect to an real agent.
I will use it to update my CRM information too.
Knowledge comes in all kinds of formats, PDF, Excel, Power Point Slides, Videos.
Looking for some advice on how to do this on a budget. I am a programmer so I do not mind getting my hands dirty or even running my own server that can do most of the work.
But if there is a 3rd party service or open source tool that does most of this, I'm happy to give it a shot too.
Thanks.
I would suggest to avoid training and look into RAG systems, prompt engineering and using OpenAI API for a start.
You can do a small PoC quickly using something like LangChain or LlamaIndex. Their pipelines can ingest unstructured data in all file formats, which is good for getting a quick feel.
Afterwards, if you encounter hallucinations in your tasks - throw out vector DB and embeddings into the trashcan (they are pulling junk information into the context and causing hallucinations). Replace embeddings with a RAG based on full text search and query expansion based on the nuances of your business.
If there are any specific types of questions or requests that you need special handling for - add a lightweight router (request classifier) that will direct user request to a dedicated prompt with dedicated data.
By that time you would’ve probably lost all of RAG, replacing it with a couple of prompt templates, a file based knowledge base in markdown and CSV and a few helpers to pull relevant information into the context.
That’s how most of working LLM-driven workflows end up (in my bubble). Maybe just with PostgreSQL and ES instead of file-based knowledge base. But that’s an implementation detail.
Update: if you really want to try fine-tuning your own LLM - this article links to a Google Collab Notebook for the latest Llama 3.1 8B: https://unsloth.ai/blog/llama3-1
It will not learn new things from your data, though. Might just pick up the style.
Not sure why this would be true. In my experience, semantic search based on a vector index/embeddings pulls in more relevant information than a full-text keyword search. Maybe there is too broad a set of materials in your vector db, or the chunking strategy isn't good?
My problem with similarity search - it is unpredictable. It can sometimes miss really obvious matches or pull completely irrelevant snippets. When this happens - this causes downstream hallucinations that are hard to fix.
My customers don’t tolerate hallucinations.
Query expansion with FTS search works more predictably for me. Especially, if we factor in search scope reduction driven by the request classifier (“agent router”)
The whole point of RAG - we (somehow) retrieve only the relevant information and put it into the context to generate the answer.
What are you using for full text search RAG in production?
Personally for small cases (e.g. under 50k documents and 20GB of text) I like to use SQLite FTS, while linking text fragments to the additional metadata (native or extracted). This way I can really narrow down the search scope to a few case related documents in each conversation path.
But ultimately the flavor of DB and FTS is just an implementation detail. Most of them will do just fine.
Edit: fixed grammar.
- Don't use llamaindex/llangchain etc. - fine to get started quick but you'll quickly get frustrated when you try to do something different
- Suck in all your files using public libraries. convert to text. Remove obvious crap like line breaks etc. Don't worry about it too much.
- Use postgres as vectorDB - cheap.
- OpenAI is fine, and the docs are great - gpt 3.5 gives fine results; cheapest embedding model fine.
- Spend some time optimising the prompts - that's the most important thing.
I wrote up basics for my specific niche here, has cost/time breakdowns and costs about $4 per month for hosting (and only then because I couldn't face setting up postgres on my other server) and < $1 per 50GB of text/xlsx/etc embedded: https://superstarsoftware.co.uk/ai-for-drilling-engineers/
(as in: dirt cheap).
I basically made it as a showcase for potential customers, was half thinking of open sourcing it so people can get up and running quickly including with decent frontend, but not sure if there's much appetite since it's basic.
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.
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.
how would I do this with an ollama model?
