embedding-models

MineContext is your proactive context-aware AI partner（Context-Engineering+ChatGPT Pulse）

A curated list of awesome embedding models tutorials, projects and communities.

This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

Program that lets you ask questions about your documents, audio, and video files.

A client side vector search library that can embed, store, search, and cache vectors. Works on the browser and node. It outperforms OpenAI's text-embedding-ada-002 and is way faster than Pinecone and other VectorDBs.

Optimize Document Retrieval with Fine-Tuned KnowledgeBases

Train an adapter for any embedding model in under a minute

AI Engineering Specially Topics- Agentic AI & GenAI Explanation

GenAI/RAG Optimizer and Toolkit for experimentation using Oracle Database AI Vector Search and NL2SQL

Code and resources showcasing the Retrieval-Augmented Generation (RAG) technique, a solution for enhancing data freshness in Large Language Models (LLMs). Incorporate up-to-date external knowledge into LLM-generated responses. Additionally, this repository includes a Gradio-based user interface for seamless model deployment.

ONNX implementation of the BGE-M3 multilingual embedding model and tokenizer with native C#, Java, and Python implementations. Generates all three embedding types: dense, sparse, and ColBERT vectors.

Convert Hugging Face tokenizers to ONNX models for cross-language compatibility (.NET, Java, Python) with embedding models

Fine-tuning black-box OpenAI embedding models

ToucanDB is a brand-new micro ML-first database engine 🦜

Code for the paper [SIGIR'26]“Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation”