structured-logprobs

OpenAI's Structured Outputs with Logprobs

Stars: 128

Visit

This Python library enhances OpenAI chat completion responses by providing detailed information about token log probabilities. It works with OpenAI Structured Outputs to ensure model-generated responses adhere to a JSON Schema. Developers can analyze and incorporate token-level log probabilities to understand the reliability of structured data extracted from OpenAI models.

README:

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:

Adding log probabilities as a separate field in the response (add_logprobs).
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
    }
}

For Tasks:

Click tags to check more tools for each tasks

analyze data reliability enhance chat responses incorporate log probabilities validate json schema understand model outputs

For Jobs:

data scientist ai engineer machine learning engineer nlp researcher ai product manager

Alternative AI tools for structured-logprobs

Similar Open Source Tools

structured-logprobs

github

: 128

firecrawl

Firecrawl is an API service that takes a URL, crawls it, and converts it into clean markdown. It crawls all accessible subpages and provides clean markdown for each, without requiring a sitemap. The API is easy to use and can be self-hosted. It also integrates with Langchain and Llama Index. The Python SDK makes it easy to crawl and scrape websites in Python code.

github

: 27.5k

npi

NPi is an open-source platform providing Tool-use APIs to empower AI agents with the ability to take action in the virtual world. It is currently under active development, and the APIs are subject to change in future releases. NPi offers a command line tool for installation and setup, along with a GitHub app for easy access to repositories. The platform also includes a Python SDK and examples like Calendar Negotiator and Twitter Crawler. Join the NPi community on Discord to contribute to the development and explore the roadmap for future enhancements.

github

: 206

sparrow

Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images. It seamlessly handles forms, invoices, receipts, and other unstructured data sources. Sparrow stands out with its modular architecture, offering independent services and pipelines all optimized for robust performance. One of the critical functionalities of Sparrow - pluggable architecture. You can easily integrate and run data extraction pipelines using tools and frameworks like LlamaIndex, Haystack, or Unstructured. Sparrow enables local LLM data extraction pipelines through Ollama or Apple MLX. With Sparrow solution you get API, which helps to process and transform your data into structured output, ready to be integrated with custom workflows. Sparrow Agents - with Sparrow you can build independent LLM agents, and use API to invoke them from your system. **List of available agents:** * **llamaindex** - RAG pipeline with LlamaIndex for PDF processing * **vllamaindex** - RAG pipeline with LLamaIndex multimodal for image processing * **vprocessor** - RAG pipeline with OCR and LlamaIndex for image processing * **haystack** - RAG pipeline with Haystack for PDF processing * **fcall** - Function call pipeline * **unstructured-light** - RAG pipeline with Unstructured and LangChain, supports PDF and image processing * **unstructured** - RAG pipeline with Weaviate vector DB query, Unstructured and LangChain, supports PDF and image processing * **instructor** - RAG pipeline with Unstructured and Instructor libraries, supports PDF and image processing. Works great for JSON response generation

github

: 4.4k

AIGODLIKE-ComfyUI-Translation

A plugin for multilingual translation of ComfyUI, This plugin implements translation of resident menu bar/search bar/right-click context menu/node, etc

github

: 1.3k

redis-vl-python

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

github

: 253

AICentral

AI Central is a powerful tool designed to take control of your AI services with minimal overhead. It is built on Asp.Net Core and dotnet 8, offering fast web-server performance. The tool enables advanced Azure APIm scenarios, PII stripping logging to Cosmos DB, token metrics through Open Telemetry, and intelligent routing features. AI Central supports various endpoint selection strategies, proxying asynchronous requests, custom OAuth2 authorization, circuit breakers, rate limiting, and extensibility through plugins. It provides an extensibility model for easy plugin development and offers enriched telemetry and logging capabilities for monitoring and insights.

github

: 76

promptic

Promptic is a tool designed for LLM app development, providing a productive and pythonic way to build LLM applications. It leverages LiteLLM, allowing flexibility to switch LLM providers easily. Promptic focuses on building features by providing type-safe structured outputs, easy-to-build agents, streaming support, automatic prompt caching, and built-in conversation memory.

github

: 223

VectorETL

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

github

: 72

llm2vec

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

github

: 1.2k

llm-structured-output

This repository contains a library for constraining LLM generation to structured output, enforcing a JSON schema for precise data types and property names. It includes an acceptor/state machine framework, JSON acceptor, and JSON schema acceptor for guiding decoding in LLMs. The library provides reference implementations using Apple's MLX library and examples for function calling tasks. The tool aims to improve LLM output quality by ensuring adherence to a schema, reducing unnecessary output, and enhancing performance through pre-emptive decoding. Evaluations show performance benchmarks and comparisons with and without schema constraints.

github

: 69

parakeet

Parakeet is a Go library for creating GenAI apps with Ollama. It enables the creation of generative AI applications that can generate text-based content. The library provides tools for simple completion, completion with context, chat completion, and more. It also supports function calling with tools and Wasm plugins. Parakeet allows users to interact with language models and create AI-powered applications easily.

github

: 88

CredSweeper

CredSweeper is a tool designed to detect credentials like tokens, passwords, and API keys in directories or files. It helps users identify potential exposure of sensitive information by scanning lines, filtering, and utilizing an AI model. The tool reports lines containing possible credentials, their location, and the expected type of credential.

github

: 117

oramacore

OramaCore is a database designed for AI projects, answer engines, copilots, and search functionalities. It offers features such as a full-text search engine, vector database, LLM interface, and various utilities. The tool is currently under active development and not recommended for production use due to potential API changes. OramaCore aims to provide a comprehensive solution for managing data and enabling advanced search capabilities in AI applications.

github

: 156

ruby-openai

Use the OpenAI API with Ruby! 🤖🩵 Stream text with GPT-4, transcribe and translate audio with Whisper, or create images with DALL·E... Hire me | 🎮 Ruby AI Builders Discord | 🐦 Twitter | 🧠 Anthropic Gem | 🚂 Midjourney Gem ## Table of Contents * Ruby OpenAI * Table of Contents * Installation * Bundler * Gem install * Usage * Quickstart * With Config * Custom timeout or base URI * Extra Headers per Client * Logging * Errors * Faraday middleware * Azure * Ollama * Counting Tokens * Models * Examples * Chat * Streaming Chat * Vision * JSON Mode * Functions * Edits * Embeddings * Batches * Files * Finetunes * Assistants * Threads and Messages * Runs * Runs involving function tools * Image Generation * DALL·E 2 * DALL·E 3 * Image Edit * Image Variations * Moderations * Whisper * Translate * Transcribe * Speech * Errors * Development * Release * Contributing * License * Code of Conduct

github

: 2.8k

superagent-py

Superagent is an open-source framework that enables developers to integrate production-ready AI assistants into any application quickly and easily. It provides a Python SDK for interacting with the Superagent API, allowing developers to create, manage, and invoke AI agents. The SDK simplifies the process of building AI-powered applications, making it accessible to developers of all skill levels.

github

: 99

For similar tasks

structured-logprobs

github

: 128

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 608

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k