Enhancing Retrieval Augmented Generation with Knowledge Graphs and LLMs: A Step-by-Step Guide

Introduction to Knowledge Graphs and Their Role in Enhancing LLMs Knowledge graphs offer a structured representation of information, connecting concepts and entities in a way that mirrors human understanding. They are particularly useful for organizing and integrating data from various sources, enabling more effective retrieval and inference. Traditionally, retrieval-augmented generation (RAG) applications rely on vector similarity to gather context from documents, but this approach can fall short when it comes to cross-referencing and implicit connections between documents. A graph-based approach, referred to as GraphRAG, addresses these limitations by storing concepts and their relationships in a graph structure, allowing large language models (LLMs) to reason at the inter-document level. Tech Stack Overview The project leverages several open-source tools to create a robust GraphRAG application: Python 3.12: The primary programming language used. Neo4j: Acts as both the graph database and vector store. It uses Cypher, a declarative query language, for interaction and allows for storing vector embeddings for semantic search. LangChain: Coordinates interactions between LLMs, vector indices, and the knowledge graph, facilitating the creation of agent workflows. Ollama and Groq APIs: Serve as local and online endpoints for invoking LLMs and generating embeddings. Streamlit: Provides a lightweight, easy-to-deploy frontend for the application, suitable for demos and prototypes. Docker: Ensures consistent and reproducible environments for local development and deployment using containers. Ingestion Pipeline The process of converting a corpus of documents into a knowledge graph involves several steps, each detailed below with references to the corresponding code in the repository. Load Files: The Ingestor class handles different file types by inferring MIME types and using appropriate loaders to convert files into a machine-friendly format. For example, PDFs are loaded using PDFPlumberLoader. Clean and Split Content: The Chunker class splits the document content into manageable text chunks. The size and overlap of these chunks can be configured based on the specific domain requirements. Extract Concepts from Chunks: The GraphExtractor class uses a custom agent powered by an LLM to extract a graph of concepts from each chunk. This agent can be provided with an Ontology, which serves as a blueprint defining the types of entities and relationships that can exist in the graph. Embed Each Chunk: The ChunkEmbedder class generates vector embeddings for each chunk using the chosen embeddings model. These embeddings are essential for performing semantic searches within the graph. Save Embedded Chunks into the Knowledge Graph: The KnowledgeGraph class manages the upload process, creating nodes for documents and chunks, linking them with relationships, and storing embeddings as node properties. Hierarchical clustering is performed to detect communities and summarize them, providing high-level overviews. Querying the Knowledge Graph Once the knowledge graph is populated, various strategies can be employed to query it and generate answers using LLMs: Enhanced RAG: This approach still uses vector similarity to retrieve relevant chunks but enhances the context by including neighboring chunks. For example, a query about the EU's AI strategy might lead to a more detailed answer by incorporating context from adjacent chunks in the document. Community Reports: Community detection algorithms like Leiden or Louvain are applied during ingestion to identify clusters of nodes. These communities are then summarized, and the summaries are stored in the graph. Queries can retrieve both chunks and community reports, providing a high-level overview complemented by specific document details. For instance, an answer about the EU’s AI strategy includes a broad overview and specific points from related documents. Cypher Queries: LLMs can be instructed to write Cypher queries to navigate the graph and retrieve precise information. This is useful for questions that require exact answers, such as identifying a specific person or document. For example, a query for "Thomas Regnier" returns his role in the European Commission, and a query for "Europe Direct" lists documents mentioning it along with contact information. Community Subgraph: This experimental approach combines community reports and Cypher queries. It retrieves a subgraph of nodes within a specific community and uses the LLM to generate an answer. While promising, it currently faces consistency issues and requires further refinement. Cypher + RAG: A hybrid strategy that combines the strengths of Cypher queries and RAG. It retrieves document chunks and intermediate graph traversal results, providing a balanced and detailed answer. For example, a query about "documents mentioning Europe Direct" yields a concise list with relevant information. Comparison of Answering Strategies | Strategy | Accuracy | Cost | Speed | Scalability | Best Use Case | |-------------------|----------|------|-------|------------|--------------| | Enhanced RAG | High | Low | Fast | Good | Detailed answers requiring specific document context | | Community Reports | Medium | Low | Medium| Good | High-level overviews and summaries | | Cypher Queries | High | Medium| Slow | Fair | Precise answers for straightforward questions | | Community Subgraph | Variable | Medium| Slow | Limited | Rich, multifaceted answers but requires further development | | Cypher + RAG | High | Medium| Medium| Good | Balanced and detailed answers | Industry Evaluation and Company Profiles Industry insiders agree that integrating knowledge graphs with LLMs represents a significant advancement in semantic search and contextual understanding. Companies like Neo4j and LangChain are at the forefront of this integration, providing powerful tools and frameworks. Neo4j, with its robust graph database and query capabilities, is ideal for building and maintaining complex knowledge graphs. LangChain, on the other hand, facilitates the management of LLM workflows, making it easier to deploy and scale these applications. The approach outlined in this project is particularly valuable for organizations with vast document repositories, enabling more accurate and context-rich information retrieval. While there are challenges, especially with the consistency of more advanced strategies like the community subgraph, the potential benefits are substantial. By sharing this repository, the author invites feedback and contributions from the community to further refine and expand the capabilities of GraphRAG applications. Whether you are a data scientist, an ML/AI engineer, or simply curious about smarter search systems, this guide offers a practical starting point for building and deploying your own knowledge graph-powered applications.