There are a lot of tools to build customer faci g chatbots that scan the webpage, load additional pdfs etc, but I would like to build an internal use chatbot assistant that is trained on sensitive internal data so I would need APIs to push the training data to train the AI on the specifics of the business. Any suggestion on the tools to use in order to not build the bicycle twice?
Thanks Nick for the offer. I built RAG based chatbot concept already to try if the idea could work. And also to understand there is way too much to do for the project to be financially feasable. Thatās why I am looking for some ready made tos for that
Copilot Studio can be ātrainedā on sharepoint sites, and publicly available sites. As of right now, I have not way of trying out hooking atlassian products to it, as a data source (requiring a login).
From a technical standpoint it should be easily doable by forwarding an API Key, however I am not aware if this is implemented (yet).
There are other solutions on the atlassian marketplace available. Iām just not sure if thatās the way you want to take (Microsoft seems to protect your data where as GPT is rather unclear)
For what itās worth, itās the best pretrained thing available now, as far as I can tell. Itās pre trained (GPT) that hooks to your data sources and lets you interact with it. You can then deploy the chat to many different interfaces, e.g. a Teams User. If itās not what youāre looking for, keep an eye on it, still. They will surely build on top of that.
Thanks Matz. We are testing Copilit Studio, but yeah - we would need something we can train over API. So have to wait when they add that or maybe there is smth like that already - thatās why I asked
The problem with using RAG for this is that youāre not really training the LLM, and any amount of RAG context you feed the LLM eats up the context window, which reduces the effectiveness of the response as the prompt size changes. The OP is looking for tools to incrementally train an LLM so it retains the knowledge. I think LoRAs that get incrementally built, with periodic merging with an off-the-shelf open source LLM base like Llama2 or Mistral.
I considered fine tuning LLM yes and just supplement with RAG to be able to show exact sources/links with additional information. However, I have amjor challange - the materials for fine tuning are not in English but Latvian and I think that strikes fine tuning out. So I think I need to stick with an LLM with sufficiently big context window. And them again - I could try sifferent approaches, but I am looking for ready made frameworks/tools to not build the whole boilerplate myself
Unfortunately, Latvian is a small language soā¦ have not found any. And translating would mean to translate also the requests and that would almost guarantee that the specific industry related questions would ālost in translationā
Assuming the user query is in english and base llm works good with english, have the documents translated to english via nllb. It does carry over for semantic meaning of the sentence. If you find a better model, you can go ahead.
if your user query is in latvian, Convert user query to english, do a RAG over your data that you have converted to english.
Approach 2:(If you really really really dont want to use RAG)
Do a frankenmerger or a weight merge of nllb and a base model such as mistral. Lazy Merge Kit (Colab) Mergekit (Github)