무료로 한국어🇰🇷 파인튜닝 모델 받아서 로컬 LLM 호스팅. LangServe, Ollama, streamlit + RAG

This code is an implementation of a chatbot using LLM chat model API and Langchain.

Interactive notes (Jupyter Notebooks) for building AI-powered applications

👑🦜 Learn the complete LangChain tech stack with this follow-along tutorial series.

A simple NextJS app that streams Langserve (python) streamings on NextJS frontend, using a hook to make it clean on components, and api call to hide backend apis and calls.

LangServe 템플릿 코드

The "lcel-tutorial" repo is designed for mastering LangChain Expression Language (LCEL), offering exercises to build stateful, multi-actor LLM applications. It's a hands-on guide to leveraging LCEL for complex workflows and agent-like behaviors. Perfect for enthusiasts eager to explore LLM's potential.

Demo server for NLUX's website, that uses LangChain and OpenAI

a LLM chatbot trained on data about Salisbury University built for HoyaHacks

✨A gigachat-gigachain based AI research assistant💻📖

Learn Langchain: Run all official examples locally. Updated daily to weekly. Langchain学习：在本地运行所有官方案例。日更到周更。

建立數位銀行RAG增強檢索的問答機器人

A starter repo on how to create your first simple LLM application using LangChain

[📚] Continuously learning and practicing concepts from Langchain + LLMs.

This repository explores the full potential of Langchain-based Large Language Models (LLMs). It covers deployment using Langserve and FastAPI, fine-tuning techniques, integration methods, and API usage with interactive documentation. Ideal for developers looking to implement scalable, efficient LLMs in real-world applications.

A text editor backend based on LLM, implemented using the FastAPI framework

A conversational agent equipped with retrieval-augmented generation capabilities, built upon the foundation of the Pocket Network stack.

Learn the entire LangChain tech-stack fast 🏃‍♀️

Get started with LangChain Agents, part of the zero-to-hero series

The project is a LangChain-based demo application integrated with Google's Gemini API, built using the Streamlit framework. It allows users to input a query or search topic through a text box, which is processed by a language model to generate helpful responses.