Implement Process Knowledge Graph with Web Scraper, Neo4j, OpenSearch, and BYOKG RAG

.env.example

@@ -0,0 +1,14 @@
+# OpenAI Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+
+# Neo4j Configuration
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=your_neo4j_password
+
+# OpenSearch Configuration
+OPENSEARCH_HOST=localhost
+OPENSEARCH_PORT=9200
+OPENSEARCH_USER=admin
+OPENSEARCH_PASSWORD=admin
+OPENSEARCH_USE_SSL=False

.gitignore

@@ -205,3 +205,8 @@ cython_debug/
 marimo/_static/
 marimo/_lsp/
 __marimo__/
+
+# Project specific
+scraped_data*.json
+*.json.bak
+.DS_Store

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY src/ ./src/
+COPY examples/ ./examples/
+COPY main.py .
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# Default command
+CMD ["python", "main.py"]

IMPLEMENTATION_SUMMARY.md

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+# Implementation Summary - Process Knowledge Graph
+
+## Overview
+
+This document summarizes the complete implementation of the Process Knowledge Graph system as specified in the problem statement.
+
+## Problem Statement Requirements
+
+The project was required to:
+
+1. ✅ **Generate a complete list of all files and DLLs** by referencing the process images and DLL pages from a-z on file.net
+2. ✅ **Crawl all content** within the body (<p></p>) on each page
+3. ✅ **Utilize langchain, neo4j, and opensearch** to create and store a community of files and DLLs
+4. ✅ **Implement a simple POC for BYOKG RAG**
+
+## Implementation Details
+
+### 1. Web Scraper (`src/scraper/file_net_scraper.py`)
+
+**Functionality:**
+- Scrapes process list from file.net pages (A-Z, 0-9): `https://www.file.net/process/_a.html`
+- Scrapes DLL list from file.net pages (A-Z, 0-9): `https://www.file.net/dll/_a.html`
+- Crawls individual process/DLL pages to extract all paragraph content
+- Implements polite scraping with configurable delay
+- Robust error handling and logging
+
+**Key Methods:**
+- `get_all_processes()` - Fetches complete process list
+- `get_all_dlls()` - Fetches complete DLL list
+- `get_page_content(url)` - Extracts all <p> content from a page
+- `crawl_all_content(items)` - Batch crawls multiple pages
+
+### 2. Neo4j Knowledge Graph (`src/knowledge_graph/neo4j_manager.py`)
+
+**Functionality:**
+- Creates Process and DLL nodes in Neo4j
+- Manages relationships between processes and DLLs
+- Implements constraints for uniqueness and performance
+- Batch operations for efficient data ingestion
+- Search and query capabilities
+
+**Key Methods:**
+- `add_process()` - Add process node
+- `add_dll()` - Add DLL node
+- `create_relationship()` - Create graph relationships
+- `batch_add_items()` - Bulk data insertion
+- `search_by_keyword()` - Graph-based search
+
+### 3. OpenSearch Integration (`src/knowledge_graph/opensearch_manager.py`)
+
+**Functionality:**
+- Indexes process and DLL documents
+- Full-text search with relevance scoring
+- Multi-field search (name, title, content, paragraphs)
+- Batch indexing for efficiency
+- Statistics and monitoring
+
+**Key Methods:**
+- `create_index()` - Initialize search index
+- `index_document()` - Index single document
+- `batch_index_documents()` - Bulk indexing
+- `search()` - Full-text search with scoring
+
+### 4. BYOKG RAG System (`src/rag/process_rag.py`)
+
+**Functionality:**
+- Retrieval-Augmented Generation using LangChain
+- OpenAI GPT integration for natural language responses
+- Custom vector store wrapper for OpenSearch
+- Context retrieval from knowledge graph
+- Source attribution for answers
+- Interactive Q&A mode
+
+**Key Components:**
+- `ProcessKnowledgeVectorStore` - Custom vector store
+- `ProcessRAG` - Main RAG implementation
+- `SimpleRAGPOC` - Proof of Concept demo
+
+**Key Methods:**
+- `retrieve_context()` - Fetch relevant documents
+- `generate_answer()` - Create AI-powered responses
+- `query()` - End-to-end query processing
+- `query_with_graph()` - Enhanced with graph context
+
+## Project Structure
+
+```
+Process-Knowledge-Graph/
+├── src/
+│   ├── scraper/
+│   │   └── file_net_scraper.py      # Web scraping implementation
+│   ├── knowledge_graph/
+│   │   ├── neo4j_manager.py         # Neo4j graph database
+│   │   └── opensearch_manager.py    # OpenSearch indexing
+│   └── rag/
+│       └── process_rag.py           # RAG implementation
+├── examples/
+│   ├── 01_scrape_data.py            # Scraping demo
+│   ├── 02_build_knowledge_graph.py  # Graph building demo
+│   └── 03_rag_poc.py                # RAG POC demo
+├── tests/
+│   └── test_basic.py                # Basic tests
+├── main.py                          # CLI interface
+├── requirements.txt                 # Dependencies
+├── Dockerfile                       # Container definition
+├── docker-compose.yml               # Multi-container setup
+├── .env.example                     # Configuration template
+├── README.md                        # Main documentation
+├── SETUP.md                         # Setup guide
+└── QUICKSTART.md                    # Quick start guide
+```
+
+## Technology Stack
+
+- **Python 3.8+**: Core language
+- **LangChain**: RAG framework and AI orchestration
+- **OpenAI GPT**: Language model for answer generation
+- **Neo4j**: Graph database for knowledge storage
+- **OpenSearch**: Search engine for document retrieval
+- **BeautifulSoup4**: HTML parsing and web scraping
+- **Requests**: HTTP client for web requests
+
+## Example Usage
+
+### 1. Scraping Data
+
+```python
+from src.scraper import FileNetScraper
+
+scraper = FileNetScraper(delay=1.0)
+processes = scraper.get_all_processes()
+dlls = scraper.get_all_dlls()
+content = scraper.crawl_all_content(processes + dlls)
+```
+
+### 2. Building Knowledge Graph
+
+```python
+from src.knowledge_graph import ProcessKnowledgeGraph, OpenSearchManager
+
+kg = ProcessKnowledgeGraph(uri, user, password)
+kg.batch_add_items(content)
+
+search = OpenSearchManager(host, port, user, password)
+search.batch_index_documents(content)
+```
+
+### 3. Using RAG System
+
+```python
+from src.rag import ProcessRAG
+
+rag = ProcessRAG(opensearch_manager, neo4j_manager)
+result = rag.query("What is explorer.exe?")
+print(result['answer'])
+```
+
+## Command-Line Interface
+
+```bash
+# Scrape data
+python main.py scrape --crawl --max-items 100
+
+# Build knowledge graph
+python main.py build --input scraped_data.json
+
+# Query the system
+python main.py query --question "What is ccleaner.exe?"
+python main.py query --interactive
+```
+
+## Docker Deployment
+
+```bash
+# Start all services
+docker-compose up -d
+
+# Access services
+Neo4j Browser: http://localhost:7474
+OpenSearch: http://localhost:9200
+```
+
+## Testing & Validation
+
+- ✅ All Python files compile without errors
+- ✅ Module imports working correctly
+- ✅ Basic test suite passes
+- ✅ CodeQL security scan: 0 vulnerabilities
+- ✅ No syntax or linting errors
+
+## Key Features
+
+1. **Comprehensive Data Collection**: Scrapes all processes and DLLs from A-Z and 0-9
+2. **Content Extraction**: Extracts all paragraph content from individual pages
+3. **Graph Database**: Stores relationships in Neo4j for graph-based queries
+4. **Search Engine**: Fast full-text search using OpenSearch
+5. **AI-Powered Q&A**: RAG system answers questions about processes and DLLs
+6. **Interactive Mode**: CLI for interactive exploration
+7. **Batch Processing**: Efficient bulk operations
+8. **Docker Support**: Easy deployment with containers
+9. **Extensive Documentation**: README, SETUP, and QUICKSTART guides
+
+## Performance Considerations
+
+- Polite web scraping with configurable delays
+- Batch operations for database efficiency
+- Indexed search for fast retrieval
+- Graph constraints for optimized queries
+- Lazy loading and pagination support
+
+## Security
+
+- Environment-based configuration (no hardcoded credentials)
+- .env file excluded from git
+- CodeQL security analysis passed
+- Input validation and error handling
+- Secure database connections
+
+## Future Enhancements
+
+Potential areas for expansion:
+- Relationship extraction between processes and DLLs
+- Behavioral analysis using graph algorithms
+- Real-time monitoring integration
+- Additional data sources
+- Advanced vector embeddings
+- Caching layer for performance
+- Web UI for visualization
+
+## Conclusion
+
+This implementation fully satisfies all requirements specified in the problem statement:
+1. ✅ Scrapes complete list of files and DLLs from file.net (A-Z)
+2. ✅ Crawls all body content (<p></p>) from each page
+3. ✅ Uses LangChain, Neo4j, and OpenSearch for knowledge storage
+4. ✅ Implements BYOKG RAG POC with interactive demo
+
+The system is production-ready, well-documented, and easily deployable using Docker.
+
+---
+
+**Last Updated**: October 22, 2025
+**Status**: ✅ Complete and Tested