A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
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Structured Outputs
- generative-ai
- llms
- prompt-engineering
- symbolic-ai
structured outputs for llms
- openai
- python
- pydantic-v2
- openai-functions
TypeChat is a library that makes it easy to build natural language interfaces using types.
Open-source framework for building agentic apps in JavaScript, Go, Dart, and Python, built and used in production by Google
- ai
- genkit
- vector-database
- embedders
an ambient intelligence library
Repair malformed JSON from LLMs, APIs, logs, and user input in Python.
A Bulletproof Way to Generate Structured JSON from Language Models
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
- chatgpt
- chatgpt-api
- gpt-3
- gpt-3-prompts
The Ruby AI framework.
Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
The pretty much "official" DSPy framework for Typescript
🤖Self-Modifying Framework from the Future 🔮 World's First AMS
Local typed decisions, contrastive data curation, and model evaluation.
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
Extract structured data and insights from documents using LLMs and decision models
- ai
- data-extraction
- document-intelligence
- generative-ai
Fast, Flexible and Portable Structured Generation
- large-language-models
- structured-generation
A neurosymbolic perspective on LLMs
- large-language-models
- neurosymbolic-ai
- probabilistic-programming
Synthetic data curation for post-training and structured data extraction
LLM(😽)
- information-extraction
- llm
- natural-language
- natural-language-processing
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code
- agent
- agent-framework
- python
- framework
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
- calibration
- decision-model
- jev
- jev-model
Open-source platform for extracting structured data from documents using AI.
Unified LLM API, structured data outputs with LLMs, and agent SDK - in PHP