llama-index

GPTCache is a semantic cache library for LLM models and multi-models, which seamlessly integrates with 🦜️🔗LangChain and 🦙llama_index, making it accessible to 🌎 developers working in any language.

🧠 AI-powered enterprise search engine 🔎

💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram

ChatGPT for lecture slides

🤖 DocsMind allows you to chat with your docs and summarize your docs, support pdf, md.

Load a PDF file and ask questions via llama_index and GPT

This open source chatbot project lets you create a chatbot that uses your own data to answer questions, thanks to the power of the OpenAI GPT-3.5 model.

A Flask Server Demo Application showing off some llama-index LLM prompt magic, including file upload and parsing :)

Intelligent search engine/QA module that uses GPT models to provide accurate, relevant & recent answers from Google News/Web, & can directly answer user queries using GPT's knowledge.

A simple Streamlit web app for using LlamaIndex, an interface to connect LLM’s with external data.

A Web-UI for Llama_index. Allow ChatGPT to access your own database.

Experiments with Langchain using different approaches on Google colab

a QA bot on contents of given docs 用所给文档进行问答的聊天机器人

Taking advantage of LlamaIndex's in-context learning paradigm, LlamaDoc empowers users to input PDF documents and pose any questions related to the content. The tool leverages the LLama Index's reasoning capabilities to provide intelligent responses based on the contextual understanding of the LLM.

因为这两个库迭代太快➕看不懂大佬们写的代码➕英语不好➕踩坑太多➕环境配置总是冲突，所以自己造一个简单的仓库方便自己造福和我一样的小可怜

An experimental architecture for composing actions from simple learning agents to produce complex behavior.

Concepts and examples on using and training LLMs

PromptPal: An AI-powered command-line assistant built using LlamaIndex & OpenAI Chat Model over custom data

FastAPI + Hugging Face Transformers + LlamaIndex

Generate documentation using Hugging Face embeddings and local LLMs