1. Abstract

The Knowledge-Graph-Enhanced Retrieval-Augmented Generation (RAG) System is an AI-powered application that combines knowledge graphs, semantic search, and Large Language Models (LLMs) to generate accurate, context-aware answers from interconnected information sources. Traditional RAG systems retrieve relevant text passages using vector similarity, whereas the proposed system additionally represents entities and their relationships in a knowledge graph. This enables the application to retrieve information involving connected concepts, related entities, and multi-step relationships across documents. The system extracts entities and relationships from source documents, stores them as graph structures, and combines graph-based retrieval with vector search. The retrieved information is supplied to an LLM to generate responses supported by relevant evidence. The application can provide source references, relationship explanations, and conversational question answering for domains such as healthcare information, legal research, education, and enterprise knowledge management.

2. Objectives

1. To develop a RAG system enhanced with a knowledge graph.
2. To extract entities, facts, and relationships from unstructured documents.
3. To represent interconnected information as graph nodes and edges.
4. To combine vector similarity search with graph-based retrieval.
5. To improve answers to questions involving multiple related entities.
6. To generate context-aware responses using an LLM.
7. To provide source references for retrieved information.
8. To support multi-hop reasoning across connected information.
9. To compare graph-enhanced retrieval with conventional vector-based RAG.
10. To evaluate retrieval relevance, answer correctness, and citation accuracy.

3. Existing System

Conventional RAG systems typically divide documents into chunks, generate embeddings, and retrieve semantically similar passages using a vector database. This approach is effective for many question-answering tasks but can struggle when a question requires connecting facts scattered across multiple documents. Pure keyword search may miss semantically related information, while vector retrieval may return relevant individual passages without fully representing the relationships among entities.

Disadvantages

1. Limited explicit representation of relationships between entities.
2. Difficulty retrieving information that requires multiple connected facts.
3. Relevant details may be distributed across separate documents.
4. Vector similarity alone may not capture relationship structure.
5. Complex questions can receive incomplete answers.
6. Identifying how facts are connected can be difficult.
7. Generated responses may contain unsupported conclusions if retrieved evidence is insufficient.

4. Proposed System

The proposed system integrates a knowledge graph with a vector-based retrieval pipeline. Source documents are processed to extract entities, relationships, and supporting text. The entities become graph nodes, while their relationships become edges. Document chunks are also converted into embeddings and stored in a vector database.

When a user submits a query, the system identifies relevant concepts, retrieves semantically similar passages, and explores related graph nodes and edges. The retrieved passages and relationship information are combined into a context package for the LLM. The model generates an answer using this evidence, with source references where available.

Key Features

1. Automated entity and relationship extraction.
2. Knowledge graph construction from documents.
3. Graph-based and vector-based hybrid retrieval.
4. Multi-hop relationship exploration.
5. LLM-based question answering.
6. Source-grounded response generation.
7. Relationship visualization.
8. Document and entity search.
9. Knowledge graph updates when new documents are added.
10. Comparison of conventional RAG and graph-enhanced RAG.
11. Configurable retrieval and ranking methods.

5. Implementation Procedure

1. Collect source documents: Gather relevant PDFs, text files, reports, manuals, or other authorized knowledge sources.

2. Extract document text: Use PyMuPDF, pdfplumber, or compatible document-processing libraries to extract text and metadata.

3. Preprocess and chunk the documents: Clean the extracted text and divide it into manageable chunks while preserving document references and section information.

4. Extract entities: Use an LLM, spaCy, or another NLP model to identify entities such as people, organizations, products, concepts, and locations.

5. Extract relationships: Identify meaningful relationships between entities and associate them with supporting passages from the original documents.

6. Build the knowledge graph: Create graph nodes and edges using a graph database such as Neo4j or a suitable graph library.

7. Generate vector embeddings: Convert document chunks into embeddings using a compatible embedding model.

8. Create the vector index: Store the embeddings and metadata in FAISS, ChromaDB, or another vector database.

9. Implement hybrid retrieval: Process user queries through semantic search and graph traversal, then combine and rank the retrieved evidence.

10. Integrate the LLM: Supply the relevant passages, graph relationships, and source metadata to an LLM with instructions to answer only when sufficient evidence is available.

11. Generate answers with citations: Display the response alongside supporting documents and relevant entity relationships.

12. Build the dashboard: Create a user interface for document uploads, conversational queries, search results, and knowledge graph visualization.

13. Evaluate the system: Compare it with a conventional vector-only RAG baseline using retrieval metrics, answer quality, citation accuracy, latency, and multi-hop question performance.

14. Deploy and maintain: Add new documents, update graph relationships, handle duplicate entities, and protect confidential source information.

6. Software Requirements

7. Hardware Requirements

Note: A small-scale prototype can run on a standard development computer using hosted models. Larger knowledge graphs and locally hosted LLMs may require additional memory and computing resources.

8. Advantages of the Project

1. Relationship-aware retrieval: Uses explicit entity relationships alongside semantic similarity.
2. Multi-hop question answering: Helps connect information spread across different documents.
3. Improved context: Combines graph relationships with relevant text passages.
4. Source traceability: Supports verification of answers against original documents.
5. Knowledge visualization: Makes relationships among entities easier to explore.
6. Flexible knowledge integration: Supports multiple document collections and domains.
7. Better complex-query support: Can help answer questions that require connecting related facts.
8. Extensible architecture: Allows new entities, relationships, and documents to be incorporated.
9. Comparative evaluation: Enables benchmarking against conventional RAG approaches.
10. Centralized knowledge discovery: Organizes document information into searchable, interconnected structures.