How to Build a GenAI Chatbot From Technical Documents Using Neo4j and Unstructured.io

The blog post outlines the process of building a GenAI chatbot capable of interpreting technical documents using Neo4j and Unstructured.io, focusing on the energy industry. It describes how Unstructured.io facilitates high-resolution chunking of documents, extracting tables and images, and how a Neo4j knowledge graph maintains the chunk sequence for context. The chatbot, built with Neo4j's Needle Starter Kit, utilizes the Neo4j GraphRAG Package to perform retrieval and generation tasks by implementing vector and full-text indexing on document chunks and entities. The system enhances GenAI accuracy by enabling semantic searches, full-text searches, and nearest-neighbor traversals through a lexical graph structure, which allows for detailed and inspectable responses. The post also highlights the use of OpenAI for embedding and entity extraction, and the use of metadata to filter out irrelevant images, thereby enhancing the chatbot's reliability and specificity. The approach, while demonstrated on energy documents, is adaptable to any technical document corpus with minimal code modification.

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