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> As far as I can tell you won't be able to use the approach proposed here to create a character matching your above description unless every element of it is e

• YeGoblynQueenne 3y ago
  >> Here's an example jsonschema (a bit handwritten so maybe some errors but it should be clear enough). That'd be nice, but it's not how this tool works. If you look at the repo, there's an example of following a json schema or pydantic model. It's clear that if you wanted a "carrot dagger" in your json, you'd need to define it beforehand: class Weapon(str, Enum): sword = "sword" axe = "axe" mace = "mace" spear = "spear" bow = "bow" crossbow = "crossbow" But perhaps I'm underestimating the tool's capabilities. If so, hopefully remilouf can correct me (and give an example of how the tool can be made to work as you want it). >> I really do not think this is the case. Parsing and understanding arbitrary requests about something like this? Not arbitrary. See my casting-to-type analogy. The point I'm trying really hard to get across is that generating free-form text is all nice and cool, but if you want to give it structure, you need to have the entire structure defined before-hand, otherwise the text that can't be made to conform to it simply won't. So if you haven't got anthropomorphic vegetables in your json schema, your LLM may generate them, they'll never end up in your json.
  • remilouf 3y ago
    You can definitely let the model improvise by defining `weapon` as `Union[Weapon, str]` if that's what you're asking.
  • IanCal 3y ago
    > It's clear that if you wanted a "carrot dagger" in your json, you'd need to define it beforehand: No, only if you want to explicitly limit it to a set of options. You can have freeform fields, just like the jsonschema I provided. If you look at the example there's a character name which has a constrained length but is not limited to a set of options: class Character(BaseModel): name: constr(max_length=10) age: int armor: Armor weapon: Weapon strength: int The name there can be anything you want. This tool is, unfortunately, outrageously slow so I put the json schema above with a few fixes into jsonformer and downloaded a small model and used it to convert the GPT4 description into valid json: { "backstory":"Born in the tranquil meadows of Veggie", "weapons":[ { "name":"Leek Lance", "description":"A long, green and white lance made from a leek", "weapon_type":"distance", "range":100.0, "damage":75.0 }, { "name":"Carrot Dagger", "description":"A short, pointed dagger. It's sharp", "weapon_type":"close", "range":10.0, "damage":50.0 } ], "name":"Sir Turnip Thistlebrook" } > Not arbitrary. Well exactly. If you want to support arbitrary requests while constraining the output, tools like this are an easy approach and I'm not sure what else comes close. An interactive character design flow would have something like the above as the defined output and you could just keep asking for alterations as a human would ("make it more whimsical" or "not a king, something lower class") and have useful structured output > See my casting-to-type analogy. The point I'm trying really hard to get across is that generating free-form text is all nice and cool, but if you want to give it structure, you need to have the entire structure defined before-hand, otherwise the text that can't be made to conform to it simply won't. The structure, sure. But the content can be extremely varied.
    • YeGoblynQueenne 3y ago
      Thanks for the demonstration. Well, maybe I did understimate the tool after all, although I'd prefer to see the entire session (prompt, grammar, and all the interactions) to be fully convinced. I suspect though that the reason the tool was "outrageously slow" in your experiment is that you gave a very general grammar. Constraining it more (by giving exact descriptions of weapons) would perhaps make it work faster. Also, it's obvious that while you'll get valid json like that, you have no guarantee that the contents will always match your request. This time you got a carrot dagger (again- I'd like to see the prompt that led to that, please), next time you might not.