🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.

✨ AI interface for tinkerers (Ollama, Haystack RAG, Python)

Welcome! 😊 This is the official code release of EviNote-RAG, and we’re happy to share it with the community.

Agentic Shopify Chatbot with MCP integration, embedded directly into Shopify via a Theme Extension

Agentic AI framework built using LangGraph and Multi-Agent Control Plane (MCP) for building structured, goal-driven multi-agent systems.

This repository provides core code for managing large volumes of video footage, enabling content understanding, automatic tagging, and vector database storage. It integrates multimodal models and LLMs for accurate descriptions and semantic search. A web interface allows visualization.

BestRAG: A library for hybrid RAG, combining dense, sparse, and late interaction methods for efficient document storage and search.

A Retrieval-Augmented Generation (RAG) Model-Controller-Provider (MCP) server designed to assist AI agents and developers in understanding and navigating codebases.. It supports incremental indexing and multi-language parsing, enabling LLMs to understand and interact with code.

Multimodal Document Processing RAG with LangChain

Dataviz AI is an AI powered web application that enables users to generate animated infographic videos based on input Data ,files. This MVP leverages the gen ai models for video content and incorporates advanced natural language processing (NLP) techniques, including LangChain and stable diffusion techniques, to analyze and create visual impact.

[2026 AAAI] Predicting the Future by Retrieving the Past

An AI-powered email assistant that retrieves emails and generates intelligent responses

Golang RAG/LLM framework with Memory and Transcriber - All-in-One Platform

A self-hosted, privacy-focused RAG (Retrieval-Augmented Generation) interface for intelligent document interaction. Turn any document into a knowledge base you can chat with.

A CLI Template/Framework for creating AI Command line Agents in C

Intent API enables the management of network devices with the help of ChatGPT by utilizing Netmiko for SSH control and NetBox for centralized network data management to perform vendor-agnostic, intent-based operations.

Simple rag implementation for any WordPress blog. Leverages the bootstrap, python, milvus vector dB, and configurable options for LLM providers (Open AI, Anthropic) and embeddings.

RAG (Retrieval-augmented generation) app made with Flutter, Firebase, Gemini, LangChain and Pinecone.

RAG-Complete-Cook-Book is a practical guide and code collection for building Retrieval-Augmented Generation (RAG) pipelines. It provides step-by-step Jupyter notebooks and Python scripts covering the entire RAG workflow.

This is a chatbot QA RAG project implemented using LangChain, which answers based only on the context and is flexible to update and integrate with different LLM models and various vector databases

Just some initial learning for usage of AI models within .NET platform with Semantic Kernel APIs

A question-answering framework empowered with a custom retrieval-augmented generation (RAG) pipeline to answer queries on local documents

[WORK IN PROGRESS] Complete vector search stack • Document processing pipeline • Semantic chunking • Embedding generation • Advanced retrieval strategies • Production-ready microservice

This repo is the comprehensive guide, covering Langchain integration with Huggingface models. Learn to build, deploy, and optimize cutting-edge AI applications through hands-on projects and real-world examples.

This repository contains a Streamlit-based chatbot designed to assist OT (Operational Technology) service and support teams by answering questions based on a library of Standard Operating Procedure (SOP) documents. The system uses a RAG (Retrieval-Augmented Generation) pipeline built with Langflow to provide accurate, context-aware answers.

Efficiently search and retrieve information from PDF documents using a Retrieval-Augmented Generation (RAG) approach. This project leverages DeepSeek-R1 (1.5B) for advanced language understanding, FAISS for high-speed vector search, and Hugging Face’s ecosystem for enhanced NLP capabilities. With an intuitive Streamlit interface and Ollama for mode

AI-powered customer support assistant designed to handle food delivery complaints efficiently. Built using LangChain, RAG (Retrieval-Augmented Generation), PostgreSQL, and email automation, the chatbot provides empathetic, context-aware responses to customer queries regarding order status, payment issues, and refunds.

本地 ONNX 嵌入与向量检索的轻量教学示例，适合快速原型与学习。a lightweight local RAG demo (ONNX embeddings + simple vector search) for quick prototyping and learning.

Build AI-powered document search without OpenAI bills. This RAG system uses Ollama for local LLM inference and LangChain for intelligent retrieval. Free, private, and works offline. Your data never leaves your machine.

Needle components for Haystack projects.