Building a Graph RAG Pipeline.

Introduction

Graph RAG?

Graph RAG takes traditional vector search one step ahead by treating the provided knowledge base as a network of connected entities instead of isolated text chunks.

Traditional RAG:

Query → Vector Search → Top K chunks → LLM → Answer

Workflow

Query →  Semantic Cache →  (hit)    →  return cached response
                           (miss)   → Vector Search → Initial nodes
                                    → Traverse relationships
                                    → Graph traversal
                                    → Rerank combined context
                                    → LLM → Answer

Building Blocks

Step 1: PDF Extraction

Cleaning text from academic PDFs, there are a lot of python libraries which can help or automate the process. These libraries struggle with figures, numbers, derivations and coordinates in the text. In the current post and demo, I’ve mitigated the issue related to figures by simply acknowledging it in system prompt provided to the QnA LLM.

Step-2: Entity extraction

Tech Stack

• Python 3.14
• PyMuPDF
• sentence-transformers
• DuckDB (VSS)
• NetworkX
• HuggingFace Inference API
• Gradio
• uv

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Additional Resources

Learn More:

• LlamaIndex PropertyGraph Guide
• Microsoft GraphRAG Paper
• Jerry Liu’s Thread on Graph RAG

Tools to Explore:

• Neo4j - If you need full graph DB.
• Microsoft GraphRAG - For enterprise synthesis needs.
• Pinecone + metadata filtering - Simple alternative to graphs.

Questions or want to discuss your specific use case? Connect with me on LinkedIn or Twitter.

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