Using Qdrant, Fastembed, Google Cloud, OpenAI to build a Question Answer Cloud Based RAG System
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Updated
Mar 28, 2024 - Jupyter Notebook
Using Qdrant, Fastembed, Google Cloud, OpenAI to build a Question Answer Cloud Based RAG System
This repository deals with vector database preparation.
RAG (Retrieval Augmented Generation) and vector search to translate natural language into SQL queries for PostgreSQL databases.
MedBot is a medical assistance application aimed at providing accurate and personalized medical information to healthcare professionals. It uses RAG technology to retrieve the latest medical research from trusted sources, augment it with patient data, and deliver personalized diagnoses and treatment plans.
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.
findthatbit.com + findthatbit.info
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).
Example code for a basic Long Term Memory Chatbot using Qdrant and a conversation history list.
Simple RAG using Generative AI in Vertex AI (PaLM) and Qdrant Vector Database, presented at Lyon Data Science meetup
On-spot training to enhance the performance of traditional machine learning algorithms, applied to the prediction of breast cancer malignity from ultrasound images
QDrant Vector Database with Python Tutorials
Retrieval Augmented Generation QnA application with Azure OpenAI and SpringAI
Helper package to spin-up a Qdrant instance without Docker
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