the AI-native open-source embedding database

Kubernetes-native package for Weaviate, an AI-native vector database that helps developers create intuitive and reliable AI-powered applications.

Q & A with multiple pdf App is a Python application that allows you to ask questions about the PDFs you upload using natural language model to generate accurate answers to your queries.

Basic Vector DB DataStorage For Images/Audio/Text Embeddings. for nlp with vb.net

A set of Node-RED nodes for interfacing with Couchbase services.

Final Project for Information Retrival, this is an implementation that uses numpy of a vector store and a RAG PoC with ollama

A cloud-native vector database, storage for next generation AI applications

A library for efficient similarity search and clustering of dense vectors.

React Hook for indexed-vector-store package

🤖 An intelligent, context-aware chatbot that can be utilized to answer questions about your own documented data.

Vector Index / Vector Store implemented in go, nginx load balancing and an angular management frontend

LLM powered ChatAI system. Added support for HF Embeddings and Models too

The AI Assistant uses OpenAI's GPT models and Langchain for agent management and memory handling. With a Streamlit interface, it offers interactive responses and supports efficient document search with FAISS. Users can upload and search pdf, docx, and txt files, making it a versatile tool for answering questions and retrieving content.

A Question Generation Application leveraging RAG and Weaviate vector store to be able to retrieve relative contexts and generate a more useful answer-aware questions

Orchestrating the interaction between users and Large Language Models

minimem is a minimal implementation of in-memory vector-store using only numpy

A command-line tool that ingests documents and generates instant answers to your questions about those documents using ChatGPT, giving you the Sheldon Cooper you never had at your fingertips.