FalkorDB · Copilot · Nov 24, 2025 · Nov 24, 2025 · Nov 24, 2025 · Nov 24, 2025
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+# GraphRAG-SDK Improvements
+
+This document outlines the improvements made to the GraphRAG-SDK to enhance knowledge graph generation, reduce duplication, improve entity extraction accuracy, and overall system quality.
+
+## Table of Contents
+1. [Entity Resolution & Deduplication](#entity-resolution--deduplication)
+2. [Extraction Validation](#extraction-validation)
+3. [Enhanced Prompts](#enhanced-prompts)
+4. [Integration with Knowledge Graph](#integration-with-knowledge-graph)
+5. [Usage Examples](#usage-examples)
+6. [Best Practices](#best-practices)
+
+## Entity Resolution & Deduplication
+
+### Overview
+The new `EntityResolver` class provides sophisticated entity deduplication and normalization capabilities to reduce redundancy in the knowledge graph.
+
+### Features
+
+#### 1. String Normalization
+- Converts text to lowercase
+- Removes extra whitespace
+- Strips special characters for comparison
+- Ensures consistent formatting
+
+#### 2. Date Normalization
+- Supports multiple date formats (YYYY-MM-DD, MM/DD/YYYY, etc.)
+- Converts all dates to standard YYYY-MM-DD format
+- Handles various separators (-, /, etc.)
+
+#### 3. Fuzzy Matching
+- Uses sequence matching to compute similarity scores
+- Configurable similarity threshold (default: 0.85)
+- Identifies entities that are semantically similar
+
+#### 4. Entity Deduplication
+- Compares entities based on unique attributes
+- Merges duplicate entities intelligently
+- Preserves the most complete information
+
+#### 5. Coreference Resolution
+- Resolves pronouns and abbreviated names to full identifiers
+- Maintains entity consistency across documents
+- Uses context-aware matching
+
+### Usage
+
+```python
+from graphrag_sdk import EntityResolver
+
+# Initialize with custom threshold
+resolver = EntityResolver(similarity_threshold=0.85)
+
+# Normalize strings
+normalized = resolver.normalize_string("  John  Doe  ")  # "john doe"
+
+# Normalize dates
+date = resolver.normalize_date("12/25/2023")  # "2023-12-25"
+
+# Deduplicate entities
+entities = [
+    {"label": "Person", "attributes": {"name": "John Doe"}},
+    {"label": "Person", "attributes": {"name": "John  Doe"}},  # duplicate
+]
+deduplicated, count = resolver.deduplicate_entities(entities, ["name"])
+# Returns: 1 entity, count = 1
+```
+
+## Extraction Validation
+
+### Overview
+The `ExtractionValidator` class validates extracted entities and relations against the ontology, ensuring data quality and consistency.
+
+### Features
+
+#### 1. Entity Validation
+- Checks if entity labels exist in ontology
+- Validates required attributes are present
+- Ensures unique attributes are provided
+- Verifies attribute types match schema
+
+#### 2. Relation Validation
+- Validates relation labels against ontology
+- Checks source and target entity compatibility
+- Verifies relation direction correctness
+- Validates relation attributes
+
+#### 3. Quality Scoring
+- Assigns quality scores (0.0 - 1.0) to extractions
+- Provides detailed error reporting
+- Enables filtering based on quality thresholds
+
+#### 4. Extraction Reports
+- Generates comprehensive validation reports
+- Tracks valid vs invalid extractions
+- Reports average quality scores
+- Lists validation errors for debugging
+
+### Usage
+
+```python
+from graphrag_sdk import ExtractionValidator
+
+# Initialize with ontology
+validator = ExtractionValidator(ontology, strict_mode=False)
+
+# Validate a single entity
+entity = {"label": "Person", "attributes": {"name": "John Doe"}}
+is_valid, errors, quality_score = validator.validate_entity(entity)
+
+# Validate complete extraction
+data = {
+    "entities": [...],
+    "relations": [...]
+}
+validated_data, report = validator.validate_extraction(data)
+
+print(f"Valid entities: {report['valid_entities']}/{report['total_entities']}")
+print(f"Average quality: {report['entity_quality_avg']:.2f}")
+```
+
+## Enhanced Prompts
+
+### Improvements to Data Extraction Prompts
+
+1. **Entity Consistency Guidelines**
+   - Clear instructions on avoiding duplicates
+   - Use of canonical forms (full names, complete titles)
+   - Consistent entity references across text
+
+2. **Format Consistency**
+   - Standardized date format (YYYY-MM-DD)
+   - Consistent name formatting and capitalization
+   - Normalized numbers with units
+
+3. **Accuracy Guidelines**
+   - Extract only high-confidence information
+   - Preserve exact meaning from source
+   - No inference beyond stated facts
+
+4. **Enhanced Documentation**
+   - Clearer examples and guidelines
+   - Better structure and organization
+   - Explicit constraints and requirements
+
+### Improvements to Ontology Creation Prompts
+
+1. **Attribute Extraction**
+   - Emphasis on unique identifiers
+   - Distinction between required and optional attributes
+   - Clear attribute type specifications
+
+2. **Design Principles**
+   - Focus on general, timeless concepts
+   - Avoid redundancy in entities and relations
+   - Balance between simplicity and completeness
+
+## Integration with Knowledge Graph
+
+### Enhanced `process_sources` Method
+
+The `KnowledgeGraph.process_sources()` method now supports deduplication and validation:
+
+```python
+kg.process_sources(
+    sources=sources,
+    enable_deduplication=True,  # Enable entity deduplication
+    enable_validation=True,      # Enable extraction validation
+)
+```
+
+### Configuration Options
+
+The `ExtractDataStep` now accepts additional configuration:
+
+```python
+config = {
+    "max_workers": 16,
+    "max_input_tokens": 500000,
+    "max_output_tokens": 8192,
+    "similarity_threshold": 0.85,  # Deduplication threshold
+}
+```
+
+## Usage Examples
+
+### Example 1: Basic Usage with Deduplication
+
+```python
+from graphrag_sdk import KnowledgeGraph, Ontology
+from graphrag_sdk.source import URL
+from graphrag_sdk.models.litellm import LiteModel
+from graphrag_sdk.model_config import KnowledgeGraphModelConfig
+
+# Setup
+model = LiteModel(model_name="openai/gpt-4.1")
+sources = [URL("https://example.com/article")]
+
+# Create ontology
+ontology = Ontology.from_sources(sources=sources, model=model)
+
+# Create knowledge graph with deduplication enabled
+kg = KnowledgeGraph(
+    name="my_kg",
+    model_config=KnowledgeGraphModelConfig.with_model(model),
+    ontology=ontology,
+)
+
+# Process sources with deduplication and validation
+kg.process_sources(
+    sources=sources,
+    enable_deduplication=True,
+    enable_validation=True,
+)
+```
+
+### Example 2: Custom Similarity Threshold
+
+```python
+from graphrag_sdk.steps.extract_data_step import ExtractDataStep
+
+# Configure with custom similarity threshold
+config = {
+    "max_workers": 16,
+    "max_input_tokens": 500000,
+    "max_output_tokens": 8192,
+    "similarity_threshold": 0.90,  # Higher threshold for stricter matching
+}
+
+step = ExtractDataStep(
+    sources=sources,
+    ontology=ontology,
+    model=model,
+    graph=graph,
+    config=config,
+    enable_deduplication=True,
+    enable_validation=True,
+)
+```
+
+### Example 3: Standalone Entity Resolution
+
+```python
+from graphrag_sdk import EntityResolver
+
+resolver = EntityResolver(similarity_threshold=0.85)
+
+# Normalize entity attributes
+entities = [
+    {"label": "Person", "attributes": {"name": "John Doe", "birth_date": "12/25/1990"}},
+    {"label": "Person", "attributes": {"name": "Jane Smith", "birth_date": "1985-03-15"}},
+]
+
+# Normalize dates and format
+for entity in entities:
+    entity = resolver.normalize_entity_attributes(entity)
+
+# Deduplicate
+deduplicated, dup_count = resolver.deduplicate_entities(entities, ["name"])
+print(f"Removed {dup_count} duplicates")
+```
+
+## Best Practices
+
+### 1. Entity Deduplication
+
+**Do:**
+- Enable deduplication for sources with potential duplicates
+- Use appropriate similarity thresholds (0.80-0.90 range)
+- Define clear unique attributes in your ontology
+- Normalize data formats consistently
+
+**Don't:**
+- Set similarity threshold too low (< 0.75) - may merge distinct entities
+- Set similarity threshold too high (> 0.95) - may miss duplicates
+- Skip defining unique attributes in ontology
+
+### 2. Extraction Validation
+
+**Do:**
+- Enable validation to catch extraction errors early
+- Review validation reports to understand quality issues
+- Use non-strict mode for flexibility with diverse sources
+- Monitor average quality scores over time
+
+**Don't:**
+- Use strict mode with noisy or diverse data sources
+- Ignore validation errors without investigation
+- Disable validation without good reason
+
+### 3. Ontology Design
+
+**Do:**
+- Define at least one unique attribute per entity
+- Specify required vs optional attributes clearly
+- Use general, reusable entity types
+- Include sufficient attributes for meaningful queries
+
+**Don't:**
+- Create overly specific entity types
+- Duplicate entity types or relations
+- Omit unique identifiers
+
+### 4. Performance Optimization
+
+**Do:**
+- Adjust `max_workers` based on available resources
+- Use appropriate token limits for your use case
+- Monitor processing time and adjust config
+- Use progress bars for long-running operations
+
+**Don't:**
+- Set `max_workers` too high (may exhaust resources)
+- Use unlimited token limits (may hit API limits)
+- Process all sources in a single batch for large datasets
+
+## Performance Considerations
+
+### Memory Usage
+- Entity deduplication requires holding entities in memory
+- For large datasets, consider batch processing
+- Monitor memory usage with many concurrent workers
+
+### Processing Time
+- Deduplication adds minimal overhead (< 5% typically)
+- Validation is very fast (< 1% overhead)
+- Main bottleneck is still LLM API calls
+
+### Accuracy vs Speed Trade-off
+- Higher similarity thresholds are faster but less accurate
+- Validation adds minimal time but improves quality significantly
+- Disable features selectively if speed is critical
+
+## Future Enhancements
+
+Potential areas for future improvement:
+
+1. **Advanced Coreference Resolution**
+   - Integration with NLP models (spaCy, AllenNLP)
+   - Multi-document entity tracking
+   - Cross-reference resolution
+
+2. **Machine Learning-Based Deduplication**
+   - Train custom similarity models
+   - Learn from user feedback
+   - Context-aware matching
+
+3. **Incremental Validation**
+   - Real-time validation during extraction
+   - Immediate feedback to LLM
+   - Iterative refinement
+
+4. **Enhanced Ontology Evolution**
+   - Automatic ontology updates from validated extractions
+   - Conflict resolution for schema changes
+   - Version control for ontologies
+
+5. **Quality Metrics Dashboard**
+   - Visualization of extraction quality
+   - Historical quality trends
+   - Entity-level quality scores
+
+## Conclusion
+
+These improvements significantly enhance the GraphRAG-SDK's ability to:
+- Generate high-quality knowledge graphs
+- Reduce entity duplication and redundancy
+- Improve extraction accuracy and reliability
+- Provide better visibility into data quality
+- Enable more consistent and maintainable knowledge graphs
+
+For questions or issues, please refer to the main README or open an issue on GitHub.