Set min/max constraints to float arguments

[pavelkraleu]

Closes this issue.

llm -m chatgpt 'say hello twice' -o temperature -1
Error: temperature
  Input should be greater than or equal to 0

[simonw]

I manually tested this branch.

In main:

llm -m chatgpt 'bye' -o frequency_penalty -4

Goodbye! Take care!

No error message - it turns out OpenAI's API doesn't validate that the number there is between -2 and 2 even though the docs indicate it should:

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo",
    "frequency_penalty": -4.0,
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

{
  "id": "chatcmpl-7gd26ck6ZWrHuaCSo8eHCQgHCCvmn",
  "object": "chat.completion",
  "created": 1690394174,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 19,
    "completion_tokens": 9,
    "total_tokens": 28
  }
}

But if you send the wrong type it DOES return an error:

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo",
    "frequency_penalty": "dog",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

{
  "error": {
    "message": "'dog' is not of type 'number' - 'frequency_penalty'",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

With the branch

gh pr checkout 130

llm -m chatgpt 'bye' -o frequency_penalty -4

Error: frequency_penalty
  Input should be greater than or equal to -2

And the output of llm models list --options is unaffected, which is good:

llm models list --options

OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
  temperature: float
    What sampling temperature to use, between 0 and 2. Higher values like
    0.8 will make the output more random, while lower values like 0.2 will
    make it more focused and deterministic.
  max_tokens: int
    Maximum number of tokens to generate.
  top_p: float
    An alternative to sampling with temperature, called nucleus sampling,
    where the model considers the results of the tokens with top_p
    probability mass. So 0.1 means only the tokens comprising the top 10%
    probability mass are considered. Recommended to use top_p or
    temperature but not both.
  frequency_penalty: float
    Number between -2.0 and 2.0. Positive values penalize new tokens based
    on their existing frequency in the text so far, decreasing the model's
    likelihood to repeat the same line verbatim.
  presence_penalty: float
    Number between -2.0 and 2.0. Positive values penalize new tokens based
    on whether they appear in the text so far, increasing the model's
    likelihood to talk about new topics.
  stop: str
    A string where the API will stop generating further tokens.
  logit_bias: dict, str
    Modify the likelihood of specified tokens appearing in the completion.
    Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
  temperature: float
  max_tokens: int
  top_p: float
  frequency_penalty: float
  presence_penalty: float
  stop: str
  logit_bias: dict, str
...