lm-sys/FastChat
FastChat is an open platform for training, serving, and evaluating large language model based chatbots, featuring multi-model serving, extensive model support, and large-scale evaluation datasets.
Awesome AI Agents › Chatbot Platforms
run-llama/rags
RAGs is a Streamlit application designed to enable users to build Retrieval-Augmented Generation (RAG) pipelines over their own data using natural language instructions. The app simplifies the process of creating a RAG system by allowing users to describe their task and data source in plain language, such as loading a web page or a local file, and specifying parameters like the number of documents to retrieve. The system then generates a RAG pipeline that can be configured and queried interactively. The app is structured into three main sections: the Home Page, RAG Config, and Generated RAG Agent. On the Home Page, users describe their dataset and task, which initializes the system prompt for the underlying large language model (LLM). The RAG Config section provides a user interface to view and modify parameters such as system prompt, top-k retrieval count, chunk size, embedding model, and whether to include summarization. Users can update the agent configuration dynamically. The Generated RAG Agent section offers a chatbot interface where users can query the RAG system, which intelligently selects retrieval or summarization tools to answer questions based on the data. The app supports multiple LLMs and embedding models, including OpenAI's GPT-4, Anthropic, Replicate, and HuggingFace models, allowing customization of both the builder agent and the generated RAG agent. By default, OpenAI models are used. The project is inspired by OpenAI's GPTs and built using the LlamaIndex Python library. It aims to provide a personalized ChatGPT-like experience over user data, making advanced retrieval and generation capabilities accessible through a simple, natural language-driven interface. Installation involves cloning the repo, setting up a Python virtual environment, installing dependencies with Poetry, and configuring API keys. The project encourages community feedback and support through GitHub issues and Discord.
https://github.com/run-llama/rags
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