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Web Vector Storage (WVS) is a lightweight and efficient vector database that stores document vectors in the browser's IndexedDB. It supports perform semantic similarity searches on text documents using vector embeddings. Embedding is enabled through use of OpenAI, Ollama or HuggingFace Transformer embedding models.
This project aims to develop a chat platform that leverages embeddings and a vector database to provide personalized and contextually relevant conversations. The platform allows users to engage in chat interactions by providing information such as files or website URLs, enabling an intelligent and tailored conversation experience.
This system contains the API server, neural models, and UI client, a neural search engine for the COVID-19 Open Research Dataset (CORD-19) , and is referred to covidex.
Aplikasi survei ketahanan pangan berbasis AI untuk otomatisasi input dan analisis data konsumsi menggunakan metode RAG, dibangun dengan Next.js, FastAPI, dan Gemini LLM.
Scalable API extension for advanced vector database functions. Enhance machine learning, search, and analytics applications with an API that supports efficient embedding storage and similarity searches.
Autonomous AI pipeline that ingests emails, PDFs, and Excel datasets to detect insurance claim fraud. Uses vector similarity search, FAISS, and AI agents orchestrated via the Modern Concept Protocol (MCP) to generate detailed, explainable reports(Chain of thought).