This repository is a full-stack application for building knowledge graphs from unstructured data using Large Language Models (LLMs). The backend, built with FastAPI and LangChain, ingests documents from multiple sources (local files, web pages, YouTube, Wikipedia, S3, GCS), chunks them, and uses an LLM to extract entities and relationships, which are stored in a Neo4j graph database. The frontend, a React/TypeScript application, provides a user interface for uploading files, configuring extraction (schema, LLM model, chunking), visualizing the resulting graph, and interacting with the data through a retrieval-augmented generation (RAG) chatbot with multiple retrieval modes (vector, graph, fulltext, hybrid). The system includes post-processing features like community detection, deduplication, and orphan node cleanup, as well as token usage tracking and evaluation metrics (RAGAS). While primarily an application with a web UI, it also contains experimental notebooks (GraphRAG, chunk size evaluation) that serve a data research purpose, making it a hybrid codebase with a dominant application character.

This repository is neo4rs, a production-grade asynchronous Rust driver for the Neo4j graph database. It implements the Bolt binary protocol (versions 4.x with partial 5.x support) and provides a complete client library for querying, transaction management, connection pooling, cluster routing, and serialization of Neo4j's rich data types (nodes, relationships, paths, temporal and spatial values). The architecture is modular: a connection layer handles TCP/TLS handshakes and chunked message framing; a PackStream serializer/deserializer converts between Rust types and the Bolt wire format; a routing module manages cluster membership and load balancing; and a high-level API (Graph, Session, Txn, RowStream) offers async query execution with automatic retry on transient errors. The codebase includes two parallel Bolt protocol implementations—a legacy one and a newer feature-gated version—which introduces maintenance overhead. Despite this, the project is actively maintained with extensive integration tests against multiple Neo4j versions and a robust CI pipeline. The library is suitable for any Rust application that needs to interact with Neo4j, offering both a high-level ergonomic API and low-level control over the protocol.