🦉 Data Versioning and ML Experiments

Refine high-quality datasets and visual AI models

No-code LLM Platform to launch APIs and ETL Pipelines to structure unstructured documents

Neo4j graph construction from unstructured data using LLMs

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

A system for agentic LLM-powered data processing and ETL

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

Nomic Developer API SDK

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

ContextGem: Effortless LLM extraction from documents

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.

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

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

AI-Powered Data Processing: Use LOTUS to process all of your datasets with LLMs and embeddings. Enjoy up to 1000x speedups with fast, accurate query processing, that's as simple as writing Pandas code

A curated list of resources for Document Understanding (DU) topic

Visual Data Preparation and Transformation. Low-Code Python-based ETL.

Interactively explore unstructured datasets from your dataframe.

Enterprise-grade and API-first LLM workspace for unstructured documents, including data extraction, redaction, rights management, prompt playground, and more!

Curate better data for LLMs