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

Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Harnessing the Memory Power of the Camelids

cuVS - a library for vector search and clustering on the GPU

Semantic product search on Databricks

🗲 A high-performance on-disk dictionary.

just testing langchain with llama cpp documents embeddings

Multi-User Chatbot with Langchain and Pinecone in Next.JS

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.

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

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.

Orchestrating the interaction between users and Large Language Models

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

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