OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.

All-in-one infrastructure for search, recommendations, RAG, and analytics offered via API

How to create Question-Answering system combining Langchain and OpenAI

Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Qdrant Vector Database on Azure Cloud

Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications

This is Microsoft Fabric Copilot Workshop

A Web app stack written in FastAPI, Qdrant, and React for creating AI projects

Building a Chain of Thought RAG Model with DSPy, Qdrant and Ollama

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.

Bootstrap a Qdrant vector database cluster on Fly.io

Qdrant operator creates and manages Qdrant clusters running in Kubernetes

Text to Image & Reverse Image Search Engine built upon Vector Similarity Search utilizing CLIP VL-Transformer for Semantic Embeddings & Qdrant as the Vector-Store

ChatGPT-like Application using RAG pattern that allows to ask question to my own documents - I Used Semantic Kernel to integrate a LLM (OpenAI) using C# to orchestrate AI pluggins (Azure Cognitive Services). For the document embeddings I used Qdrant for the vector database and Pdfpig to extract the content from the pdfs

Helper package to spin-up a Qdrant instance without Docker

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.

An innovative application designed to help pharmacists and pharmacy students quickly research FDA-approved drugs by retrieving relevant information from drug labels and adverse event datasets, and providing AI-generated summaries to streamline the learning process

Feature extraction chatbot

A repository containing a modularized implementation of a Retrieval-Augmented Generation (RAG) model as Flask APIs.