weaviate/structured-rag
Experimental Code for StructuredRAG: JSON Response Formatting with Large Language Models
This project helps evaluate how well large language models can follow instructions to produce answers in a specific, structured JSON format. You input a question and some context, and the output is an answer formatted precisely as required (e.g., a score, a list of paraphrased questions, or an answer with a confidence rating). This is for engineers and developers who build AI systems and need reliable, structured outputs from LLMs for tasks like data extraction or report generation.
117 stars. No commits in the last 6 months.
Use this if you are building an AI application and need to ensure your large language model consistently outputs data in a precise JSON structure.
Not ideal if you are looking for a plug-and-play solution for end-users or if your primary concern is generating free-form, unstructured text.
Stars
117
Forks
8
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 09, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/weaviate/structured-rag"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Renumics/renumics-rag
Visualization for a Retrieval-Augmented Generation (RAG) Assistant 🤖❤️📚
VectorInstitute/retrieval-augmented-generation
Reference Implementations for the RAG bootcamp
naver/bergen
Benchmarking library for RAG
KalyanKS-NLP/rag-zero-to-hero-guide
Comprehensive guide to learn RAG from basics to advanced.
alan-turing-institute/t0-1
Application of Retrieval-Augmented Reasoning on a domain-specific body of knowledge