#Neo4j

The application efficiently transforms various formats of unstructured data into a structured Neo4j knowledge graph. It utilizes Large Language Models such as OpenAI and Gemini to extract nodes, relationships, and properties via the Langchain framework. Files can be uploaded from local devices, Google Cloud Storage (GCS), or Amazon S3, with the flexibility to select a preferred LLM model. Key features include supporting custom schemas, interactive graph visualization in Bloom, and conducting data interactions using conversational queries within Neo4j. Ensure a Neo4j Database V5.15+ with APOC support. Available for deployment via Docker locally or Google Cloud Platform.

The project implements integration between language models and Neo4j graph databases using a semantic layer. Utilizing OpenAI's function calling, it enables intent-driven interactions, with tools like information retrieval, movie recommendation, and preference storage. The deployment uses Docker containers for services including Neo4j and APIs, with a Streamlit interface. This enhances user interaction by remembering preferences and offering tailored recommendations. Community contributions for further development are welcome.