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

Semantic product search on Databricks

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

just testing langchain with llama cpp documents embeddings

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.

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

Orchestrating the interaction between users and Large Language Models

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

the AI-native open-source embedding database

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.

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

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

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