Add Knowledge Graph RAG with Verifiable Citations example

2026-04-30 23:31:31 -05:00 · 2026-01-03 03:10:24 -05:00
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+# 🔍 Knowledge Graph RAG with Verifiable Citations
+
+A Streamlit application demonstrating how **Knowledge Graph-based Retrieval-Augmented Generation (RAG)** provides multi-hop reasoning with fully verifiable source attribution.
+
+## 🎯 What Makes This Different?
+
+Traditional vector-based RAG finds similar text chunks, but struggles with:
+- Questions requiring information from multiple documents
+- Complex reasoning chains
+- Providing verifiable sources for each claim
+
+**Knowledge Graph RAG** solves these by:
+1. **Building a structured graph** of entities and relationships from documents
+2. **Traversing connections** to find related information (multi-hop reasoning)
+3. **Tracking provenance** so every claim links back to its source
+
+## ✨ Features
+
+| Feature | Description |
+|---------|-------------|
+| 🔗 **Multi-hop Reasoning** | Traverse entity relationships to answer complex questions |
+| 📚 **Verifiable Citations** | Every claim includes source document and text |
+| 🧠 **Reasoning Trace** | See exactly how the answer was derived |
+| 🏠 **Fully Local** | Uses Ollama for LLM, Neo4j for graph storage |
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+1. **Ollama** - Local LLM inference
+   ```bash
+   # Install from https://ollama.ai
+   ollama pull llama3.2
+   ```
+
+2. **Neo4j** - Knowledge graph database
+   ```bash
+   # Using Docker
+   docker run -d \
+     --name neo4j \
+     -p 7474:7474 -p 7687:7687 \
+     -e NEO4J_AUTH=neo4j/password \
+     neo4j:latest
+   ```
+
+### Installation
+
+```bash
+# Clone and navigate
+cd knowledge_graph_rag_with_citations
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the app
+streamlit run knowledge_graph_rag.py
+```
+
+## 📖 How It Works
+
+### Step 1: Document → Knowledge Graph
+
+```
+┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
+│   Document      │ ──► │  LLM Extraction  │ ──► │ Knowledge Graph │
+│   (Text/PDF)    │     │  (Entities+Rels) │     │    (Neo4j)      │
+└─────────────────┘     └──────────────────┘     └─────────────────┘
+```
+
+The LLM extracts:
+- **Entities**: People, organizations, concepts, technologies
+- **Relationships**: How entities connect (e.g., "works_for", "created", "uses")
+- **Provenance**: Source document and chunk for each extraction
+
+### Step 2: Query → Multi-hop Traversal
+
+```
+┌─────────┐     ┌─────────────┐     ┌─────────────┐     ┌───────────┐
+│  Query  │ ──► │  Find Start │ ──► │  Traverse   │ ──► │  Context  │
+│         │     │   Entities  │     │  Relations  │     │  + Sources│
+└─────────┘     └─────────────┘     └─────────────┘     └───────────┘
+```
+
+### Step 3: Answer → Verified Citations
+
+```
+┌─────────────┐     ┌─────────────┐     ┌──────────────────┐
+│   Context   │ ──► │  Generate   │ ──► │  Answer with     │
+│ + Sources   │     │   Answer    │     │  [1][2] Citations│
+└─────────────┘     └─────────────┘     └──────────────────┘
+                                                │
+                                                ▼
+                                        ┌──────────────────┐
+                                        │ Citation Details │
+                                        │ • Source Doc     │
+                                        │ • Source Text    │
+                                        │ • Reasoning Path │
+                                        └──────────────────┘
+```
+
+## 🖥️ Usage Example
+
+### 1. Add a Document
+
+Paste or select a sample document. The system extracts entities and relationships:
+
+```
+Document: "GraphRAG was developed by Microsoft Research. 
+           Darren Edge led the project..."
+
+Extracted:
+  ├── Entity: GraphRAG (TECHNOLOGY)
+  ├── Entity: Microsoft Research (ORGANIZATION)  
+  ├── Entity: Darren Edge (PERSON)
+  └── Relationship: Darren Edge --[WORKS_FOR]--> Microsoft Research
+```
+
+### 2. Ask a Question
+
+```
+Question: "Who developed GraphRAG and what organization are they from?"
+```
+
+### 3. Get Verified Answer
+
+```
+Answer: GraphRAG was developed by researchers at Microsoft Research [1], 
+        with Darren Edge leading the project [2].
+
+Citations:
+  [1] Source: AI Research Paper
+      Text: "GraphRAG is a technique developed by Microsoft Research..."
+      
+  [2] Source: AI Research Paper  
+      Text: "...introduced by researchers including Darren Edge..."
+```
+
+## 🔧 Configuration
+
+| Setting | Default | Description |
+|---------|---------|-------------|
+| Neo4j URI | `bolt://localhost:7687` | Neo4j connection string |
+| Neo4j User | `neo4j` | Database username |
+| Neo4j Password | - | Database password |
+| LLM Model | `llama3.2` | Ollama model for extraction/generation |
+
+## 🏗️ Architecture
+
+```
+knowledge_graph_rag_with_citations/
+├── knowledge_graph_rag.py   # Main Streamlit application
+├── requirements.txt         # Python dependencies
+└── README.md               # This file
+```
+
+### Key Components
+
+- **`KnowledgeGraphManager`**: Neo4j interface for graph operations
+- **`extract_entities_with_llm()`**: LLM-based entity/relationship extraction
+- **`generate_answer_with_citations()`**: Multi-hop RAG with provenance tracking
+
+## 🎓 Learn More
+
+This example is inspired by [VeritasGraph](https://github.com/bibinprathap/VeritasGraph), an enterprise-grade framework for:
+- On-premise knowledge graph RAG
+- Visual reasoning traces (Veritas-Scope)
+- LoRA-tuned LLM integration
+
+## 📝 License
+
+MIT License