Using Qdrant, Fastembed, Google Cloud, OpenAI to build a Question Answer Cloud Based RAG System
-
Updated
Mar 28, 2024 - Jupyter Notebook
Using Qdrant, Fastembed, Google Cloud, OpenAI to build a Question Answer Cloud Based RAG System
RAG (Retrieval Augmented Generation) and vector search to translate natural language into SQL queries for PostgreSQL databases.
findthatbit.com + findthatbit.info
The objective of this project is to create a chatbot that can be used to communicate with users to provide answers to their health issues. This is a RAG implementation using open source stack.
NodeJS Application to ask questions from files by uploading them. I have used open ai chat completion and embeddings. And to store embeddings I have used Qdrant (Vector DB).
QDrant Vector Database with Python Tutorials
Retrieval Augmented Generation QnA application with Azure OpenAI and SpringAI
Youtube GPT
Explore how to perform Role Based Access Control in Qdrant Vector Datase
Parsing PDF, PPT, and Txt documents using LlamaParse, Qdrant, and the Groq model
This app allows users to search for products by either entering text or uploading an image, and retrieves relevant products from a database
News Observatory
This project transform the Bug Frameworks papers into embeddings
Add a description, image, and links to the qdrant topic page so that developers can more easily learn about it.
To associate your repository with the qdrant topic, visit your repo's landing page and select "manage topics."