The Neo4j LLM Knowledge Graph Builder back-end architecture and API overview provides a comprehensive framework for integrating large language models (LLMs) with graph databases, enabling the creation of meaningful knowledge graphs from unstructured data. The system is built on Python with FastAPI, leveraging LangChain's LLM Graph Transformer and various document loaders to process content from diverse inputs, extract entities and relationships, and generate graph documents stored in a Neo4j database. Vector embeddings are used for semantic analysis, ensuring efficient data retrieval and contextual understanding. This modular design makes the back end a versatile foundation for AI-driven conversational interfaces and advanced data interactions, with features such as chatbot systems, knowledge graph visualization, and graph enhancements. The API offers scalable, efficient solutions for loading, processing, and interacting with documents across different sources, with endpoints for uploading documents, extracting content, saving to the database, querying chatbot content, and retrieving processed data.