To enhance our RAG-enabled car-diagnosis tool, we propose the integration of a knowledge graph. This will map the relationships and dependencies outlined in hardware manuals, improving the tool's ability to direct users through diagnostic procedures efficiently. The addition aims to leverage the interconnected nature of car diagnostics, mimicking the manual navigation process but with added computational insight and efficiency.
Objectives:
Data Collection and Ingestion: Gather and organize data from car hardware manuals for knowledge graph construction.
Knowledge Graph Construction: Utilize Neo4j or a similar graph database tool to build a comprehensive knowledge graph, detailing the relationships between diagnostic procedures and hardware manual content.
RAG Integration: Seamlessly integrate the knowledge graph with the current RAG setup to bolster diagnostic accuracy and user guidance.
Expected Outcomes:
Improved diagnostic accuracy and efficiency.
Enhanced ability to navigate complex diagnostic pathways, akin to manual traversal but optimized through AI.
Resources Needed:
Access to hardware manuals and diagnostic procedure documents.
See blog: https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/
To enhance our RAG-enabled car-diagnosis tool, we propose the integration of a knowledge graph. This will map the relationships and dependencies outlined in hardware manuals, improving the tool's ability to direct users through diagnostic procedures efficiently. The addition aims to leverage the interconnected nature of car diagnostics, mimicking the manual navigation process but with added computational insight and efficiency.
Objectives:
Expected Outcomes:
Resources Needed: