Elysia is an open-source agentic platform that uses a decision tree architecture to dynamically select and execute tools for searching and reasoning over vector databases (Weaviate). The core is a `Tree` class that maintains conversation history, an environment of retrieved objects, and a decision loop: at each step, an LLM (via DSPy) chooses which tool to call based on the current state and user goal. Built-in tools include hybrid/semantic query, aggregate, summarization, visualisation, and linear regression; custom tools can be added via a `@tool` decorator or by subclassing `Tool`. The system preprocesses Weaviate collections to generate LLM-created summaries, field mappings, and statistics, enabling adaptive query formulation. The platform includes a full-stack web application: a FastAPI backend with WebSocket endpoints for real-time query execution, and a built Next.js frontend (served as static files) offering a UI for conversations, collection management, tree visualisation (React Flow), and feedback collection. The backend manages multi-user and multi-conversation state via `UserManager`/`TreeManager`, persists trees to Weaviate, encrypts API keys, and supports feedback-driven few-shot learning. A CLI entry point (`elysia start`) launches the API server.
This repository is a tutorial and research exploration of Graph RAG (Retrieval-Augmented Generation) combining vector databases (Weaviate) and knowledge graphs (RDF/SPARQL). It contains Jupyter notebooks that demonstrate building knowledge graphs from PubMed articles, World Bank documents, and Netflix movies, comparing vector search and structured query performance for retrieval-augmented generation. A Streamlit app provides an interactive demo for medical article search with MeSH term refinement and LLM summarization. The code is primarily educational, with reusable query modules for Weaviate, RDF, and Databricks, but lacks dependency pinning and has hardcoded credentials, limiting reproducibility. The repository is hybrid, with both application (Streamlit app) and data research (notebooks, datasets) elements, but the dominant character is data research as the core value lies in the experimental notebooks and data pipelines.