Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications
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Updated
Sep 17, 2024 - Python
Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications
Performing a RAG (Retrieval Augmented Generation) assessment using voice-to-voice query resolution. Provide the file containing the queries, ask the questions, and receive the results via voice.
This repository is a demonstration of deploying Qdrant, a high-performance vector database, in a distributed manner.
A RAG implementation on Llama Index using Qdrant vector stores as storage. Take some pdfs, store them in the db, use LLM to inference, enjoy.
A repository containing a modularized implementation of a Retrieval-Augmented Generation (RAG) model as Flask APIs.
A Retrieval-Augmented Generation (RAG) System for PDF Chat using Qdrant Vector Database.
VoicePassport 🎤is an innovative authentication system leveraging voice recognition technology, blockchain ⛓️ security, and vector databases 📊 for robust and seamless user verification.
A Neural Search Tool that helps you find relevant papers to read based on your interests
PaperChat is an AI-powered chat application designed to handle PDF documents through a user-friendly interface. Users can upload PDF files, ask questions related to the content within those documents, and receive responses generated using advanced natural language processing (NLP) techniques.
RAG Backend for Aleph Alpha LLMs.
A blog recommendation engine built for text driven static sites
Qdrant Vector Database + FastAPI
A state-of-the-art Retrieval-Augmented Generation (RAG) application using OpenAI, Qdrant vector store, embeddings, FastAPI, React for the UI, NewsAPI, Word Cloud, and Langchain. This project enables dynamic reading and QA on news articles, focusing on various people of interest, with real-time, personalized news insights.
Backend service of AutoMate Gen-AI application. Includes API endpoints for chat and vision operations.
Build amazing AI and RAG-powered applications, plain and simple🪂
This repository contains the code and resources for deploying a Retrieval-Augmented Generation (RaG) application using Qdrant, Langchain, and OpenAI technologies. The project demonstrates how to integrate these tools to build a robust and scalable application for information retrieval and generation.
RAG (Retrieval Augmented Generation) and vector search to translate natural language into SQL queries for PostgreSQL databases.
A chatbot, built with Streamlit, that download scientific papers and answer to question related to them. Powered by Groq and LLama3
A Streamlit web app for efficient management of Qdrant vector databases. Features include collection creation/deletion, point retrieval/search, and vector data upload, simplifying Qdrant operations through an intuitive interface.
A chat bot trained on a document.
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