structured-logprobs 0.1.5

Logprobs for OpenAI Structured Outputs

This Python library is designed to enhance OpenAI chat completion responses by adding detailed information about token log probabilities. This library works with OpenAI Structured Outputs, which is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value. It provides utilities to analyze and incorporate token-level log probabilities into structured outputs, helping developers understand the reliability of structured data extracted from OpenAI models.

Objective

The primary goal of structured-logprobs is to provide insights into the reliability of extracted data. By analyzing token-level log probabilities, the library helps assess how likely each value generated from an LLM's structured outputs is.

Key Features

The module contains a function for mapping characters to token indices (map_characters_to_token_indices) and two methods for incorporating log probabilities:

1. Adding log probabilities as a separate field in the response (add_logprobs).
2. Embedding log probabilities inline within the message content (add_logprobs_inline).

Example

To use this library, first create a chat completion response with the OpenAI Python SDK, then enhance the response with log probabilities. Here is an example of how to do that:

from openai import OpenAI
from openai.types import ResponseFormatJSONSchema
from structured_logprobs import add_logprobs, add_logprobs_inline

# Initialize the OpenAI client
client = OpenAI(api_key="your-api-key")

schema_path = "path-to-your-json-schema"
with open(schema_path) as f:
        schema_content = json.load(f)

# Validate the schema content
response_schema = ResponseFormatJSONSchema.model_validate(schema_content)

# Create a chat completion request
completion = client.chat.completions.create(
    model="gpt-4o-2024-08-06",
    messages = [
            {
                "role": "system",
                "content": (
                    "I have three questions. The first question is: What is the capital of France? "
                    "The second question is: Which are the two nicest colors? "
                    "The third question is: Can you roll a die and tell me which number comes up?"
                ),
            }
        ],
    logprobs=True,
    response_format=response_schema.model_dump(by_alias=True),
)

chat_completion = add_logprobs(completion)
chat_completion_inline = add_logprobs_inline(completion)
print(chat_completion.log_probs[0])
{'capital_of_France': -5.5122365e-07, 'the_two_nicest_colors': [-0.0033997903, -0.011364183612649998], 'die_shows': -0.48048785}
print(chat_completion_inline.choices[0].message.content)
{"capital_of_France": "Paris", "capital_of_France_logprob": -6.704273e-07, "the_two_nicest_colors": ["blue", "green"], "die_shows": 5.0, "die_shows_logprob": -2.3782086}

Example JSON Schema

The response_format in the request body is an object specifying the format that the model must output. Setting to { "type": "json_schema", "json_schema": {...} } ensures the model will match your supplied JSON schema.

Below is the example of the JSON file that defines the schema used for validating the responses.

{
    "type": "json_schema",
    "json_schema": {
        "name": "answears",
        "description": "Response to questions in JSON format",
        "schema": {
            "type": "object",
            "properties": {
                "capital_of_France": { "type": "string" },
                "the_two_nicest_colors": {
                    "type": "array",
                    "items": {
                        "type": "string",
                        "enum": ["red", "blue", "green", "yellow", "purple"]
                    }
                },
                "die_shows": { "type": "number" }
            },
            "required": ["capital_of_France", "the_two_nicest_colors", "die_shows"],
            "additionalProperties": false
        },
        "strict": true
    }
}