🦉 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.

🔮 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

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

Interact, analyze and structure massive text, image, embedding, audio and video datasets

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.

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

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

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

LOTUS: A semantic query engine for fast and easy LLM-powered data processing

ContextGem: Effortless LLM extraction from documents

Interactively explore unstructured datasets from your dataframe.

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

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

Curate better data for LLMs

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

NucliaDB, The AI Search database for RAG