[jncraton]

You can actually get these models to do this, but you have to ask:

    >>> lm.do(f"Answer from the context: What is YCombinator? {lm.get_wiki('Python')}")
    'The context does not provide information about YCombinator.'
    >>> lm.do(f"Answer from the context: What is YCombinator? {lm.get_wiki('YCombinator')}")
    'YCombinator is an American technology startup accelerator that has launched over 4,000 companies, including Airbnb, Coinbase, Cruise, DoorDash, Dropbox, Instacart, Quora, PagerDuty, Reddit, Stripe and Twitch.'

Without being told to be grounded, the model will guess. However, it may be able to identify information not available in a provided context.

One of my goals for this package is to provide a way for folks to learn about the basics of grounding and semantic search.

[Animats]

> Without being told to be grounded, the model will guess.

Right. I understand why, but consider the underlying technology flawed unless there's some way to reject wildly wrong results. What's going on here looks like noise fed through layers which generate plausible-looking text from noise. Is it possible to detect that you're not far enough above the noise threshold to generate anything useful?

    >>> lm.do("What is Ycombinator? Do not guess.")
    'Ycombinator is a mathematical formula that states that the sum of 
     two integers multiplied by one are equal to zero.'