an ambient intelligence library

OpenAI's Structured Outputs with Logprobs

Structured Data Extractor for AI Agents. Search your documents or the web for specific data and get it back in JSON or Markdown in a single tool call.

A high-performance API server that provides OpenAI-compatible endpoints for MLX models. Developed using Python and powered by the FastAPI framework, it provides an efficient, scalable, and user-friendly solution for running MLX-based vision and language models locally with an OpenAI-compatible interface.

Agentic Workflow Platform to do Business-as-Code and deliver valuable Services-as-Software through simple APIs and SDKs

🧰 Some (very) simple helper functions for writing concise JSON Schema — perfect for OpenAI Structured Outputs.

OpenAi.JsonSchema is a lightweight library for generating valid JSON Schema for OpenAI models' Structured Outputs feature. It supports a wide range of types, ensures compatibility with OpenAI's JSON Schema format, and leverages C# descriptions and attributes for schema generation.

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

This project demonstrates how to get structured outputs from Deepseek R1 reasoning model and do tool calling using an OpenAI summarization step. It's made using Next.js serverless routes and uses shadcn/ui for the frontend.

Universal Python library for Structured Outputs with any LLM provider

Swift Macro for OpenAI API Structured Outputs.

An extension of the LLM-based spatial layout generation from image description from https://github.com/Attention-Refocusing/attention-refocusing using OpenAI and Ollama structured outputs

Experiment using JsonSchemaExporter in .NET 9 to improve developer experience with OpenAI's Structured Outputs

This repository covers all the code materials covered within Jose Portilla's Langchain with Python Bootcamp on Udemy.

Fast structured data extraction from text using LLMs. Pre-built templates for common use cases + custom schema support.

Iterate over scans of forms, and have gpt-4o pull data from them into a csv file

Prompt (cue) management and execution for tabular data.

Presentation at PyData Global: Building Knowledge Graph-Based Agents with Structured Text Generation and Open-Weights Models

Job posting parser with structured outputs

Presentación en la conferencia IADevs 2024: Potenciando la Generación Aumentada usando Recuperación con Grafos de Conocimiento