A cloud-native vector database, storage for next generation AI applications
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Updated
Jun 13, 2024 - Go
A cloud-native vector database, storage for next generation AI applications
cuVS - a library for vector search and clustering on the GPU
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.
🗲 A high-performance on-disk dictionary.
Kubernetes-native package for Weaviate, an AI-native vector database that helps developers create intuitive and reliable AI-powered applications.
Final Project for Information Retrival, this is an implementation that uses numpy of a vector store and a RAG PoC with ollama
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.
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
React Hook for indexed-vector-store package
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
Semantic product search on Databricks
Orchestrating the interaction between users and Large Language Models
minimem is a minimal implementation of in-memory vector-store using only numpy
LLM powered ChatAI system. Added support for HF Embeddings and Models too
A set of Node-RED nodes for interfacing with Couchbase services.
Harnessing the Memory Power of the Camelids
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.
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