langchain

Querypls🛠️: WebApp that Simplify SQL with Your Prompts. Transforming questions into SQL commands effortlessly.

Deploy open-source LLMs on AWS in minutes — with OpenAI-compatible APIs and a powerful CLI/SDK toolkit.

Collaborative Multi-Agent RAG with CrewAI

Workshop to build and deploy your own Chat Agent using Retrieval Augmented Generation with Astra DB

Hello everyone this repo will contain my journey of machine learning and DeepLearning with some exciting projects

Laozy is a self-hosted web application that helps you build and deploy LLM-based dialogue robots. The robot you built can connect to instant messaging platforms like WeChat, Telegram, etc. You can also integrate it into your app with REST APIs.

Python SDK that enables developers to build and deploy LangGraph agents to the UiPath Cloud Platform

AI-powered arXiv research assistant prototype.

完整的RAG技术教程 - 从基础概念到生产部署，系统化掌握检索增强生成技术。包含4个模块、20章内容、17个Jupyter Notebooks、6个企业级实战案例

[DEPRECATED] Moved to microsoft/agent-governance-toolkit

Simple starting point function to host LangChains with LLMs and other tools in an Azure Function.

A Jupyter python notebook to Execute Zapier Tasks with GPT completion via Langchain

langchain中文网是langchain的python中文文档

Generate full fledged PDF reports using LLMs like GPT, Claude, Llama

Flowchart-like UI to interconnect LLM's and Huggingface models, and deploy them as a REST API with little to no code.

Chat with any website using FastAPI, Next.js, and the latest LangChain version for seamless integration.

Python Streamlit web app utilizing OpenAI (GPT4) and LangChain LLM tools with access to Wikipedia, DuckDuckgo Search, and a ChromaDB with previous research embeddings. Ultimately delivering a research report for a user-specified input, including an introduction, quantitative facts, as well as relevant publications, books, and youtube links.

🌪️ AI research assistant that generates Wikipedia-quality articles through multi-perspective analysis. Based on Stanford's STORM methodology.

Personal Task-Driven Autonomous Agent

ZON → 35-70% cheaper LLM prompts than JSON/TOON. Zero overhead.