openai-embeddings

RAG on codebases using treesitter and LanceDB

openai-whisper-talk is a sample voice conversation application powered by OpenAI technologies such as Whisper, Completions, Embeddings, and the latest Text-to-Speech. The application is built using Nuxt, a Javascript framework based on Vue.js.

AI Github assistant for your repo. Your proactive GitHub bot that auto-detects duplicates using OpenAI embeddings and Supabase magic!

Harness the power of Retrieval-Augmented Generation with the Personal AI Assistant, an innovative tool designed to extract and synthesize information from web and PDF sources efficiently. This cutting-edge solution transforms complex data into concise, actionable insights, making it indispensable for researchers and professionals alike.

Insurance AI Assistant A smart system combining PostgreSQL, Milvus, and specialized AI agents (Life/Home/Auto) to answer insurance queries accurately. Features real-time sync, semantic search via OpenAI embeddings, and a Streamlit UI. Perfect for insurance tech demos or customer service augmentation.

Automate your customer support using Retrieval Augmented Generation (RAG): OpenAI + Pinecone

A Question Answering(Q/A) Chatbot on Insurance Documents. Powered by Retrieval Augmented Generation(RAG), LlamaIndex and LangGraph. Inspired from my Upgrad_IIITB PG Course.

Nodejs a REST API is designed to provide users with an interactive chat interface where they can ask questions and receive responses generated by an AI model. The application utilizes OpenAI embeddings and Langchain to process the user's input and generate relevant responses based on the context of the conversation.

Forecasting Private Capital Market using published research and patents. Project developed at Michigan State University under the guidance of Dr. Mohammed Ghassemi for JP Morgan Chase.

Implementing LangChain concepts and building meaningful stuffs

TenderFlow, an AI-powered tender response agent that transforms complex RFPs into structured, human-reviewed drafts in hours, not days. Leverages LLMs, vector search (pgvector), and a LangGraph workflow with human-in-the-loop review to assemble high-quality, compliant tender responses from your internal knowledge base.

Successfully designed and developed a customer support chatbot that leverages LangChain and Pinecone for efficient retrieval-augmented generation (RAG), enabling intelligent and context-aware responses to user queries.

[WORK IN PROGRESS] Complete vector search stack • Document processing pipeline • Semantic chunking • Embedding generation • Advanced retrieval strategies • Production-ready microservice

Make multiple Collections on Qdrant with Langchain & Openai

Node Proxima converts your repo into embeddings using OpenAI.

🚀 slackAgent: Your AI-powered Slack assistant! Built with LlamaIndex, ChromaDB, and OpenAI embeddings, it delivers instant answers from your documents via a sleek Slack bot or Streamlit web UI. Automate workflows with n8n, expose local APIs with ngrok, and query with ease using FastAPI. Join the future of intelligent chatbots! 🤖💬

GPT 3.5 Turbo LLM and MongoDB Atlas Vector Search for fast and performant Retrieval Augmented Generation (RAG) with LlamaIndex

Detailed description given in the README

Creates mapping table to merge 2 tables