基于LangChain和ChatGLM-6B等系列LLM的针对本地知识库的自动问答

⚡ Langchain apps in production using Jina & FastAPI

Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure

基于 🦜️🔗 LangChain 与 DeepSeek R1 大语言模型的本地知识库问答 serve 端。 本项目是本地知识库问答应用的 serve 后端。目前实现基本的 RAG 功能。 使用 FastAPI + Uvicorn + SQLModel + SQLite 框架，向量数据库使用 Chroma 。vue 前端服务请详看README

基于 🦜️🔗 LangChain 与 DeepSeek R1 语言模型的本地知识库问答 vue 前端。 本项目是本地知识库问答应用的 web 前端模块，使用 vue3 + typescript + vite + elementplus 框架。serve端可看README说明。

AI Services API: serves langchain, huggingface, & other emergent python AI libraries as a service. This project mainly serves LibreChat, and is in active open development.

LangChain Serve + Blazor chat

Retrieval-Augmented-Generation of over 1700+ pages of Unreal Engine documentation served through FAISS + OpenAI with LangChain

This application serves as a demonstration of the integration of langchain.js, Ollama, and ChromaDB to showcase question-answering capabilities. With a focus on Retrieval Augmented Generation (RAG), this app enables shows you how to build context-aware QA systems with the latest information.

A custom AI Jira agent, with a tool to preform triage on newly created Jira issues. The project makes use of LangChain agents, served via Django (with PostgreSQL) and Google Mesop. Services are provided in Docker to be run locally. The prompting strategy includes a CO-STAR system prompt, Chain-of-Thought (CoT) reasoning with few-shot prompting.

In this article, I will present an open-source AI tool for writing grant applications, using Microsoft AutoGen combined with Retrieval-Augmented Generation (RAG) via a vector database in PostgreSQL/ pgvector and LangChain - served in FastAPI/ Streamlit. Dockerised to run locally.

Leveraging LangChain to call Xunfei SparkLLM to serve multi-round conversation

A Simple Full Stack RAG-bot for Enterprises using React, Qdrant, Langchain, Cohere and FastAPI

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This repository demonstrates a NestJS implementation of the Model Context Protocol (MCP) with a microservice architecture.

About This sample showcases how Purview API can be integrated to audit and secure AI prompts and responses

A Django backend that serves as a thin wrapper around LangChain

Python LLM-RAG deep agent using LangChain, LangGraph and LangSmith built on Quart web microframework and served using Hypercorn ASGI and WSGI web server.

This project combines two functionalities: a Code Interpreter using LLM Agent Orchestration and Tool Utilization, and a ReAct LangChain Agent example.ReAct agent using LangChain, integrated with a custom tool to calculate the length of a given text. It serves as a foundational example for building more complex, tool-augmented LLM applications.

A serverless LangChain agent built with AWS CDK and FastAPI.

This repository contains a case study of the use of Large Language Models to serve as a customer service chatbot based off of Home Depot's website using the Langchain framework, Slack API, and Open AI API. The Langchain agent used is a database agent that can perform SQL queries on any database.

mcp-langchain-client-servers-examples

LangChain + LangGraph client using tools from a MCP server

This project serve as Electronic Supplementary Information for a research paper, comparing the data extraction performance of ChemDataExtractor, BERT-PSIE, ChatExtract, LangChain, and Kimi for bandgap data.

The LangChain Crash Course repository serves as a comprehensive resource for beginners who are ready to learn LangChain, a programming framework designed for creating AI agents, building RAG (Retrieval-Augmented Generation) chatbots, and automating tasks using artificial intelligence.

This tool creates a custom search engine using VertexAI, Langchain and Streamlit. It allows users to input the URL of a website's sitemap XML file, which will serve as the knowledge base. The app then crawls the entire website, refreshes vector embeddings, and uses the information as a knowledge base to answer user queries.

Python API Server (because LangChain needed) using FastAPI to create/document the APIs, Uvicorn to Serve the APIs, and Docker for easy Deployment in an Azure environment.

Build MCP Servers from Scratch with LangChain in Python

MCP with langchain

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