unstructured-data

🦉 Data Versioning and ML Experiments

Refine high-quality datasets and visual AI models

Neo4j graph construction from unstructured data using LLMs

A system for agentic LLM-powered data processing and ETL

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications

An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)

ContextGem: Effortless LLM extraction from documents

Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.

A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.

Optimized Agentic and LLM Bulk Processing Over Your Data

Get clean data from tricky documents, powered by vision-language models ⚡

The open document intelligence platform for builders and hackers - DMS for the agentic world

Embedding Studio is a framework which allows you transform your Vector Database into a feature-rich Search Engine.

Open-source spreadsheets platform for deep research and document processing

Home of the AI workforce - Multi-agent system, AI agents & tools

Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.

RAG-QA-Generator 是一个用于检索增强生成（RAG）系统的自动化知识库构建与管理工具。该工具通过读取文档数据，利用大规模语言模型生成高质量的问答对（QA对），并将这些数据插入数据库中，实现RAG系统知识库的自动化构建和管理。