基于LLM和LangChain实现基于本地文档的QA chatbot

Natural Language Processing (NLP) and Large Language Models (LLM) with Fine-Tuning LLM and make Chatbot Question answering (QA) with LoRA and Flan-T5 Large

ShopBot Pro is an advanced smart chatbot designed to enhance your online shopping experience. Developed as an LLM-based QA (Question-Answering) system, ShopBot Pro is here to make your shopping journey smoother and more informed.

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Medical-QA-Chatbot-Using-PySpur-RAG-and-LLMs

In this project, we are creating a question answer chat bot which will take any document, research paper, book or article as input and will answer any question related to that document. For this purpose, we are using langchain as our framework along with different LLMs (Large language models).

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

An LLM-powered Slack bot built with Langchain.

Multimodal chatbot project using five LLM-based approaches including RAG, PEFT, and prompt engineering, built on the BioASQ dataset for biomedical QA.

This is a chatbot powered by a large langauge model that answers questions about the video game Albion Online, using scraped information from the wiki.

Created a QA Chatbot powered by fine tuned text-to-sql LLM deployed on personal gaming laptop (Nvidia GTX 1650) using Ollama and Ngrok

ChatBotDSinHVAC is a specialized Question-Answering (QA) chatbot tailored for the HVAC domain. Built with Python and powered by a custom domain-specific vector knowledge base, this project was developed to bridge the gap between Large Language Models (LLMs) and specialized engineering disciplines. This is a personal project developed in 2023.

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QA framework for testing conversational AI systems (LLM agents, chatbots, voice assistants) with workflow validation and regression checks

YumQuest is a QA chatbot built using RAG. This is the end-to-end LLM project I developed while participating in the LLM Zoomcamp.

project is on RAG model using LLM model for generating response and integrated to a QA chatbot for interface

Resume QA Chatbot A smart, retrieval-augmented question-answering (QA) system that allows you to query your resume using natural language. Built with LangChain, FAISS, HuggingFace embeddings, and an LLM backend (e.g. Mistral).

A document-based Question Answering (QA) chatbot using FAISS for vector search, SentenceTransformers for embeddings, and HuggingFace LLM Model for answer generation. Supports PDF, DOCX, TXT, XLSX, and CSV file ingestion, chunking, and semantic search.

LLM-PDF QA SEARCH ENGINE🔍 is a Streamlit app utilizing LangChain and OpenAI models to create a chatbot for PDF documents. It enables users to upload PDFs, ask questions, and receive answers, all in one interface.

A Document QA chatbot using LangChain, Pinecone for vector storage, and Amazon Bedrock (mistral.mixtral-8x7b-instruct for LLM and titan-embed-text for embeddings). Built with a Streamlit frontend for document uploads and contextual question answering.

Web-based QA Chatbot that scrapes content from any URL, embeds it using FAISS, and answers user queries using a history-aware RAG pipeline powered by Gemini LLM. Deployed on AWS EC2 with Docker for seamless cloud access.

QA-LLM-Chatbot

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R&D developlemnt of a Chatbot System

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