Rust App on Qdrant - Vector database
-
Updated
Aug 3, 2023 - Rust
Rust App on Qdrant - Vector database
A Neural Search Tool that helps you find relevant papers to read based on your interests
Tutorials and references to get started with Qdrant vector databases
Implemented vector embeddings on the dataset and store them in Qdrant a vector DB. Implemented LLM on the DB that can give contextual answers to the queries. Wraped this LLM as an API using FastAPI and streamlit frontend
This repository holds a query engine LLM based on the vector DB of BigBasket products list
News Observatory
All CPU efficient GPU-less Financial Analysis RAG Model with Qdrant, Langchain and GPT4All x Mistral-7B, run RAG without any GPU support!
Building a Chain of Thought RAG Model with DSPy, Qdrant and Ollama
QDrant Vector Database with Python Tutorials
This repository is a demonstration of deploying Qdrant, a high-performance vector database, in a distributed manner.
RAG powered AI chatbot for Indian Language (Hindi) using LangChain, Ollama, Qdrant, and MLFlow
Explore how to perform Role Based Access Control in Qdrant Vector Datase
We are going to showcase how to build a superhero character AI - where users can chat with their favourite superheroes.
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.
Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files
VoicePassport 🎤is an innovative authentication system leveraging voice recognition technology, blockchain ⛓️ security, and vector databases 📊 for robust and seamless user verification.
此存储库展示了用于检索增强生成(RAG)系统的各种先进技术。RAG 系统将信息检索与生成模型相结合,以提供准确且上下文丰富的响应。
This is a RAG (Retrieval-Augmented Generation) model that leverages Qdrant as a vector store and Google Gemini for intelligent document retrieval and context-aware response generation. It efficiently processes PDF documents to provide detailed answers to user queries based on the extracted context.
Official Java client for Qdrant
LlamaIndex&LangChine wiht LLM
Add a description, image, and links to the qdrant-client topic page so that developers can more easily learn about it.
To associate your repository with the qdrant-client topic, visit your repo's landing page and select "manage topics."