Filesystem Librarian Agent

This is a LlamaIndex project bootstrapped with create-llama and instrumented using OpenInference.

Overview

The Filesystem Librarian Agent is a simple application that allows you to chat with a librarian that can answer questions about a set of documents stored in a filesystem.

Architecture Overview

• A NextJS frontend that provides an interface to a basic RAG chat application
• A Python FastAPI backend that serves a simple LlamaIndex RAG application. The LlamaIndex framework is instrumented using OpenInference to produce traces.
• A Phoenix server that acts as both a collector for OpenInference traces and as a trace UI for observability.

Getting Started with Local Development

First, startup the backend as described in the backend README.

• If you'd like, include your own data to build an index in the data directory
• Build a simple index using LlamaIndex
• Ensure that your OpenAI API key is available to the application, either via the OPENAI_API_KEY environment variable or a .env file
• Start the backend server

Second, run the development server of the frontend as described in the frontend README.

Open http://localhost:3000 with your browser to use the chat interface to your RAG application.

Traces can be viewed using the Phoenix UI.

Getting Started with Docker-Compose

1. If you'd like, add your own PDFs to ./backend/data to build indexes over.
2. Follow the instructions in backend/README.md to install LlamaIndex using poetry and generate an index.
3. Ensure that your OpenAI API key is available to the application, either via the OPENAI_API_KEY environment variable or a .env file alongside compose.yml.
4. Ensure that Docker is installed and running.
5. Run the command docker compose up --build to spin up services for the frontend, backend, and Phoenix.
6. Once those services are running, open http://localhost:3000 to use the chat interface.
7. Traces can be viewed using the Phoenix UI.
8. When you're finished, run docker compose down to spin down the services.