Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.

A blazing fast inference solution for text embeddings models

MTEB: Massive Text Embedding Benchmark

Fast State-of-the-Art Static Embeddings

🤖 A Python library for learning and evaluating knowledge graph embeddings

A novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.

AI Inference Operator for Kubernetes. The easiest way to serve ML models in production. Supports VLMs, LLMs, embeddings, and speech-to-text.

Easily compute clip embeddings and build a clip retrieval system with them

KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.

A curated set of agent skills for the Venice AI API, providing SKILL.md instructions for agent runtimes like Cursor, Claude, and Codex.

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.