A naukri.com initiative
Towards Data Science
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Build and Query Knowledge Graphs with LLMs
- A Knowledge Graph is a structured representation of information connecting concepts, entities, and relationships, enhancing the performance of Large Language Models (LLMs) in Retrieval Augmented Generation applications.
- GraphRAG employs a graph-based representation of knowledge to improve information serving to LLMs compared to standard approaches.
- Challenges in traditional applications include limitations in reasoning at an inter-document level and reliance on vector similarity for retrieval.
- Organizing knowledge bases into graph structures with entities, relationships, and attributes allows for more contextual and implicit references.
- The article details the transformation from vector representations to Knowledge Graphs and extracting key information for building them.
- The technology stack breakdown includes tools like Neo4j for graph databases, LangChain for LLM workflows, and Streamlit for frontend UI.
- Docker is used for containerization, enabling local development and deployment of the project.
- From text corpus to Knowledge Graph involves steps such as loading files, cleaning and chunking content, extracting concepts, embedding chunks, and storing in the graph.
- Graph-informed Retrieval Augmented Generation strategies include Enhanced RAG, Community Reports, Cypher Queries, Community Subgraph, and Cypher + RAG.
- Strategies are compared based on factors like tokens usage, latency, and performance to optimize for accuracy, cost, speed, and scalability.
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