qdrant-vector-database

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 platform 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

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.

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

Production-ready local deployment of Cheshire Cat AI that delivers enterprise-grade features

Aquiles-RAG is a high-performance Augmented Recovery-Generation (RAG) solution based on Redis or Qdrant. It offers a high-level interface using FastAPI REST APIs

Bootstrap a Qdrant vector database cluster on Fly.io

Qdrant operator creates and manages Qdrant clusters running in Kubernetes

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

This repository is a demonstration of deploying Qdrant, a high-performance vector database, in a distributed manner.

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

A RAG implementation on Llama Index using Qdrant vector stores as storage. Take some pdfs, store them in the db, use LLM to inference.

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