Embeddings

The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.

Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.

SeaTunnel is a multimodal, high-performance, distributed, massive data integration tool.

100+ Chinese Word Vectors 上百种预训练中文词向量

A library for transfer learning by reusing parts of TensorFlow models.

A vector index built on TurboQuant, written in Rust with Python bindings

Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/

The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.

Retrieval and Retrieval-augmented LLMs

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

A curated list of Generative AI tools, works, models, and references

Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..

This repository is deprecated and will be archived

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

MTEB: State-of-the-art evaluation of embeddings across languages and modalities

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

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.