Multi AI agents for customer support email automation built with Langchain & Langgraph
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Updated
Feb 13, 2025 - Python
Multi AI agents for customer support email automation built with Langchain & Langgraph
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
This python powered AI based RAG Scraper allows you to ask question based on PDF/URL provided to the software using local Ollama powered LLMs
pdfKotha.AI - Interact with PDFs using AI! Upload, ask questions, and get instant answers from Google's Gemini model. Streamline your research and information retrieval tasks effortlessly
RAG-API: A production-ready Retrieval Augmented Generation API leveraging LLMs, vector databases, and hybrid search for accurate, context-aware responses with citation support.
ML Bot is a RAG Application built using google/gemma-2b-it local LLM
BetterRAG: Powerful RAG evaluation toolkit for LLMs. Measure, analyze, and optimize how your AI processes text chunks with precision metrics. Perfect for RAG systems, document processing, and embedding quality assessment.
A supportive server to handle telegram messages using telegram bot API, return back the response to the user with RAG application techniques
AI-powered platform that turns study notes into podcast episodes with two hosts and lets you chat with documents.
A basic RAG application for inventory management. Provides real-time stock updates, checks availability, suggests similar products, and generates responses to both customer and manager queries .
A command-line RAG Chatbot application built from scratch
A simple Retrieval-Augmented Generation (RAG) web application chatbot called Raggy 🤖
This project utilizes advanced Large Language Models (LLMs) and vector database technologies to extract structured information about characters from literary texts. It is designed to analyze a given text, identify key characters, and determine their summaries, relationships, and roles (e.g., Protagonist, Antagonist, or Side character)
A dynamic web application made using MERN stack
LLM based rag application that embed given web page to vector db and answer given query using vector similarity cosine.
A Customizable RAG (Retrieval Augmented Generation) App
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