Mistral Agents
mistral.ai
mistral.ai
What's the difference from sending the system prompt in the api call, as usual?
Edit: Oh, missed that: "We’re working on connecting Agents to tools and data sources."
I agree with the implied statement that 'Agents' doesn't feel right. Reminds me more of the projects that put the model in a loop.
It does feel to me to be a really tough thing to name & market, I'm about to release an app for this across all providers, I call it "Scripts" with "Steps" like chat, search, retrieval, art...
Seems like a lot of the heavy lifting will come from 3rd parties making their APIs compatible with llms.
There should be some sort of extension type app where people can build extensions or "tools" for llms and share them (I guess openAI sort or attempts to do this). Say I want to build one for Toast to order food. I can collect the info needed to run that tool (toast account info or whatever) and an API key for an appropriate llm and then use this configuration info for Toast to build out a middleware that can use natural langauge to build out an order and send the request to Toast via some function call.
This seems very doable and I don't understand why there aren't a million of these "tools" already built into some LLM centric tool aggregator/ web store. What is the hold up? Is it just 3rd parties not wanting to hand out API access for things that require payment to applications controlled by llms? Would these 3rd parties rather have their own assistant tool they run? I'd imagine that some central llm-extension aggregator could have a central mechanism for payment methods that the llm had access to that could be used to implement safegaurds.
Or is it simply that any assistant type tool that could be easily generalized like ordering food, booking a flight or inputing calender events is simply easier to handle doing yourself than asking an llm to do for you?
I imagine the big sites have similar issues and it undermines customer trust when they're given false information.
To clarify more, I see frameworks like CrewAI and similar, with tools even from Microsoft to define these “agents” quickly. But when I tried them, I noticed they are no more than chain of thought CoT functions to ask/extract/generate based on user input and functions output.
As such, they can be quite unpredictable, hence my question of examples of LLM agents being used in production. I just don’t see their value, but I might be missing something so wanted to see examples to understand more.
I might be missing something?
EDIT: I should add that the first step is used to cut down on the number of function definitions I need to send to the model on each user prompt. Navigating a map can be done with as few as four function definitions but styling a map gets out of control fast (google "Mapbox Style Specification" if you want to see why).
Although now that I think about it, a lot of doctors practices have a MyChart-style portal where you can schedule an appointment yourself. Why does an LLM need to be involved in that process? I guess for people who still want to schedule over the phone, the LLM agent makes sense. Kind of, assuming you don't have any special case problems. Which patients most likely do, if they're calling in. Is an LLM actually a good solution here?
https://www.amazon.com/PolyScience-Temperature-Controlled-Co...
That's all you need from the model to be able to use it in an agent. Tell it to output commands in a given JSON format.
I assume that Mistral's API already allowed you to define the system prompt, right?
It's hard to read data with widespread anti-abuse checks (CAPTCHAs), lack of open-format data (RSS support being spotty), and restricted APIs (ex: Twitter API). Companies have all the incentives to prevent bot use, and select for human eyeballs.
If we had a Yahoo Pipes sort of golden age, GenAI agents would have a vaster playground to play in, and would be more useful for us.
Consider building an agent for choosing what to do on weekends for a group of friends. The agent would need to keep state for past activities (X, Y, and Z went upstate to Storm King last week) and users' preferences (ex: liking dosas or Calder, dietary restrictions). This part is easy enough - you could just keep a notebook that's passed as context. Older context gets simply deleted or condensed into high level points.
But would it be easy for the agent to:
1) Look up nearby restaurants and events? (Perhaps Resy/OpenTable allow listing restaurants, but it's likely they have tons of anti-abuse tech. Is there even a place where you could see a list of public events - Google pays a third-party for this feed.)
2) Actuate on behalf of the user? (Do Resy and OpenTable allow authority delegation so the agent could book restaurants for users? There's no standard way to do this across venue types - concerts, museums, cooking classes. Is it realistic for agents to click through these sites on their own?)
we could imagine a data/api marketplace, where such an agent could pay for the data and subscriptions.
Edit: or Stripe.
LLMs themselves are becoming a commodity, plus or minus prompt-following/format-following growing pains. In a year or two, we'll have pretty decent general LLMs that can make use of databases and tools/APIs.
It's a race to see who can integrate all these things in a good way. It really is an execution problem, not an idea problem - it's so obvious.
Mistral is like FitGirl Repacks for LLaMA.
Make sure to put it into your pitch as often as possible.
Meaningless buzzword central.
<<eats gallery peanuts>>
>We’re working on connecting Agents to tools and data sources... So tools and RAG for data sources aren't available yet.
Way behind GPTs/assistants. What's the point of this yet?