@arizeai/phoenix-mcp

# Agentic RAG Examples ## Overview This folder contains examples for building a Retrieval-Augmented Generation (RAG) agent using the LangChain library. ## Features * Construction of a RAG agent workflow using LangChain * Integration with OpenAI models for language generation and retrieval * Example usage of tools such as web search and response analysis, create rag response * Auto-instrumentation with OpenInference decorators to fully instrument the agent * End-to-end tracing with Phoenix to track agent performance ## Requirements * LangChain library * OpenAI API key * Langgraph * Python 3.x * Gradio (for UI) ## Installation 1. Install the required libraries by running `pip install -r requirements.txt` 2. Run app.py and input the required Keys(OpenAI, Phoenix API Key) ## Usage 1. Run the `app.py` script to start the RAG agent 2. Interact with the agent by providing input and receiving responses ## Files * `app.py`: The main script for starting the application, this will run the web server with default port(7860) * `agent.py`: The main script for the RAG agent * `tools.py`: Contains tools for web search and response analysis, create rag response * `rag.py`: Contains functions for initializing and using the RAG vector store * `requirements.txt`: Lists the required libraries for the project ## Notes * All the Key's must be inputted from the UI application. * RAG will be loaded with default url in the UI, You can update the url and initialize the project with your own data source. * This application will support the HTML based sources.