Architecture

Proposed Solution for Data Management and Visualization

To address the client’s needs, I propose implementing an approach that combines the following elements:

Utilizing neo4j as a Graph Database and Vector Store: We will leverage neo4j not only as a graph database but also as a Vector Store to import the client’s datasets. This will facilitate advanced graph visualizations, allowing for a clearer understanding of data relationships.
Running neo4j as a Service in Docker: By deploying Neo4j within a Docker container, we can provide a reliable and scalable graph database service. This setup will ensure that the database is easily accessible and manageable, enhancing performance and security.
Integrating the langchain_neo4j library: To enable seamless communication between large language models (LLMs) and the graph database, we will install and integrate the langchain_neo4j library. This integration will allow the LLM to interact with the database using natural language queries, making data retrieval intuitive for users with limited technical skills.
Using ollama to Orchestrate LLM Models: We will employ ollama to orchestrate the LLM models effectively. This tool will help manage interactions between the LLM and the Neo4j database, ensuring smooth operation and efficient processing of user queries.


    


        

actor User
node LLM
database Neo4j

User -> LLM: Ask a question
LLM -> Neo4j: Query the graph database \nthrough `langchain_neo4j`
Neo4j -> LLM: Return the result
LLM -> User: Answer the question

note top of Neo4j: source data imported from \n`income.csv` \n`education.csv`
note bottom of Neo4j: vectorStore
note bottom of LLM: orchstrated by ollama

Figure 7.1: The architecture of the application