faiss-vector-search

SCIST x SCAICT Camp 2026 聯合寒訓 AI 系列課程 - 教材與原始碼

Production-ready semantic vector search for Django — searches across FK, M2M, and reverse relations by traversing your model graph. Pluggable backends: ChromaDB, FAISS, Qdrant.

VecFuzz is a lightweight approach to converts words into fixed-size numeric vectors for fast approximate string matching and typo-tolerant search.

Compass, the native AI IDE for macOS. Agentic coding with your own provider and keys, plus on-device RAG, FIM completion and local chat that work fully offline. MIT. No Electron. No lock-in.

基于QQ的ai agent聊天服务

🚗 Complete vehicle re-identification system: YOLO detection, OSNet embeddings, FAISS search. Session-based, no database, modern UI.

Text-to-image search engine for fashion runway photos using CLIP and FAISS.

Competing-risks survival analysis for customer churn — five models from a naive baseline to Aalen-Johansen, Cox, and Random Survival Forest — wrapped in a LangGraph agent that answers natural-language questions, grounded in real evaluation metrics. FastAPI + real IBM Telco data. Real evaluation, real data, deployed live.

RAG-based-on-PDF is a RAG system that lets users ask questions from one web page of VS code documentation which is scraped (given below) using FAISS, HuggingFace embeddings, and a OpenRouter Api via a FastAPI backend and Streamlit as Frontend.

Document Q&A agent template with per-user JWT auth and a pluggable vector store — FAISS or ChromaDB, one env var to switch. SQLite-backed audit trail included. Intermediate tier of the Document Intel Agent Template family — for real users, not yet a full enterprise deployment.

This repository contains a production-ready AI chatbot backend built with a strong emphasis on auditability, traceability, privacy, and compliance. It is designed for enterprise and regulated environments where every AI response must be explainable, reproducible, and verifiable.

AskMyPDF AI is a smart digital assistant that lets you "talk" to your PDF documents to get instant answers without reading through every page.

Enterprise-grade LLM security firewall protecting GenAI applications against prompt injections, jailbreaks, and data leaks using a 3-layer detection engine (Regex, ML, FAISS) and dynamic PII masking.

SynthCerebrum is a fully offline, AI-powered assistant that reads and learns from all types of local files. Using advanced neural networks, embeddings, and RAG, it intelligently retrieves, synthesizes, and generates insights from your data, making your folder a brain-like knowledge hub.

A Vision-Language Model based multimodal retrieval system for semantic Image-to-Text and Text-to-Image search using OpenCLIP, FAISS indexing, and shared embedding space analysis.

Full-stack RAG AI assistant React.js frontend, FastAPI backend, FAISS vector store & Groq LLAMA-4 with PDF-grounded semantic search and multi-turn conversational memory via MongoDB

Automatically turns research papers into fully structured Python projects. Using a multi-agent pipeline with RAG-based context retrieval. With Human-in-the-loop approval. Perfect for quickly prototyping implementations from academic papers.

Multi-document RAG assistant, upload PDFs, ask questions, summarize and compare

A RAG chatbot that answers user questions using Wikipedia articles as context

Full-stack RAG app — upload PDFs, ask questions. FAISS vector search + Groq LLM + Redis caching + per-user session isolation. Deployed with AWS Lightsail.