Building an assistant for Boletin Oficial del Estado (BOE) using Retrieval Augmented Generation (RAG)

Sample code that uses data from FAO stats to build a RAG over LLM

Easily build AI workflows using LLM agents in TypeScript

Welcome to the Falcon 40B Chatbot Demo repository! This project demonstrates how to utilize the Falcon-40B-Instruct model from Hugging Face in combination with the LangChain framework. The code showcases how to set up and interact with the model, while also integrating it into the LangChain library for advanced conversational capabilities.

Bash scripts for in-line CLI review, editing and execution of Bash commands suggested by an LLM in response to a user's requests for assistance.

GenesisZERO : potential applications for MCTS agents with LLMs for Sequential decision-making

DocAI is a custom medical agent that can help patients to access medical information more effectively.

LLM Agent Builder Toolkit - File

Evaluate open-source language models on Agent, formatted output, command following, long text, multilingual, coding, and custom task capabilities. 开源语言模型在Agent，格式化输出，指令追随，长文本，多语言，代码，自定义任务的能力基准测试。

Stock market AI agent

LLM experiments with LangChain

A discord bot which can call LLMs using either Hugging Face or vLLM on Windows platform. Combined with function calling.

Testing out OpenAI, Langchain, and LLMs while building a portfolio bot.

This repository contains a Python script that uses a pre-trained NBART (Neural Bidirectional AutoRegressive Transformer) model to perform multi-lingual translation tasks between several languages. The model was trained on multiple language pairs using data parallelism, allowing it to learn representations across all languages simultaneously.

Agent based modelling of LLMs power actors for thermal comfort

Use a RAG Langchain agent that connects to your supermarket's inventory database and get information about product stocks and locations.

LLM Agent Builder

Search for, compare, and list Forex Data chat (coming soon)

UnOfficial Gradio Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.