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

A curated list of pretrained sentence and word embedding models

Python library for knowledge graph embedding and representation learning.

Plugin that lets you ask questions about your documents including audio and video files.

Implementations of Embedding-based methods for Knowledge Base Completion tasks

Image search engine

Word Embeddings for Information Retrieval

Neural Code Comprehension: A Learnable Representation of Code Semantics

Web-ify your word2vec: framework to serve distributional semantic models online

Optimize Document Retrieval with Fine-Tuned KnowledgeBases

tensorflow prediction using c++ api

Train an adapter for any embedding model in under a minute

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.

Code for KaLM-Embedding models

langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.

A monolingual and cross-lingual meta-embedding generation and evaluation framework

Generates a set of property-specific entity embeddings from knowledge graphs using node2vec

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

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.

Learning node representation using edge semantics