# System Architecture & Design ## Overview This document describes the architecture and design principles of the Natural Language to Graph Query Translator system. --- ## System Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ User Input (NL) │ └────────────────┬────────────────────────────────────────────┘ │ ▼ ┌───────────────┐ │ Preprocessing │ │ - Tokenize │ │ - Normalize │ └───────┬───────┘ │ ┌────────────┼────────────┐ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌──────────┐ │ Entity │ │Relation │ │ Intent │ │Extraction│ │Extraction│ │Detection │ └────┬────┘ └────┬────┘ └────┬─────┘ │ │ │ └───────────┼────────────┘ ▼ ┌──────────────────┐ │ Query Generation │ │ - Build Pattern │ │ - Validate │ └────────┬─────────┘ │ ┌────────┴────────┐ ▼ ▼ ┌────────────┐ ┌────────────┐ │ Cypher │ │ SPARQL │ │ Generator │ │ Generator │ └────┬───────┘ └────┬───────┘ │ │ └────────┬────────┘ ▼ ┌──────────────────┐ │ Validation & │ │ Optimization │ └────────┬─────────┘ ▼ ┌──────────────────────┐ │ Query Result │ │ - Query String │ │ - Explanation │ │ - Alternatives │ │ - Confidence Score │ └──────────────────────┘ ``` --- ## Core Components ### 1. Preprocessing Module **Responsibility:** Normalize and prepare input text **Functions:** - Tokenization (split into words) - POS tagging (part-of-speech) - Lemmatization (word normalization) - Sentence segmentation - Case normalization **Example:** ``` Input: "Find employees AT Acme earning > $60K" Output: [ Token("find", "VB", "find"), Token("employees", "NNS", "employee"), Token("at", "IN", "at"), Token("acme", "NNP", "acme"), Token("earning", "VBG", "earn"), Token(">", "SYM", ">"), Token("$60k", "CD", "60000") ] ``` --- ### 2. Entity Recognition Module **Responsibility:** Identify and classify entities **Pipeline:** 1. Named Entity Recognition (NER) 2. Entity Classification 3. Schema Mapping 4. Property Extraction 5. Entity Linking **Output:** ```python [ Entity(text="Acme", label="Company", properties={"name": "Acme"}), Entity(text="employees", label="Person", properties={}), Entity(text="$60K", label="Money", properties={"amount": 60000}) ] ``` --- ### 3. Relationship Extraction Module **Responsibility:** Identify connections between entities **Pipeline:** 1. Verb/Predicate Extraction 2. Relationship Type Mapping 3. Direction Assignment 4. Property Extraction **Output:** ```python [ Relationship( entity1=Entity("employees"), entity2=Entity("Acme"), relation_type="WORKS_AT", direction="forward" ) ] ``` --- ### 4. Intent Detection Module **Responsibility:** Classify the query intent **Intent Types:** - FIND/SELECT: Return entities matching criteria - COUNT/AGGREGATE: Count or aggregate results - EXIST/CHECK: Verify if pattern exists - PATH: Find paths between entities - FILTER: Apply conditional logic - ORDER/SORT: Sort results - LIMIT: Restrict result count **Example:** ``` "How many employees work at Acme?" Intent: COUNT "Find employees earning > $50K" Intent: FIND + FILTER ``` --- ### 5. Query Pattern Builder **Responsibility:** Construct graph patterns **Process:** 1. Map entities to node patterns 2. Map relationships to edge patterns 3. Apply filters and conditions 4. Add aggregations if needed 5. Apply ordering and limits **Output:** ```python GraphPattern( nodes=[ Node("e", "Employee"), Node("c", "Company", {"name": "Acme"}) ], relationships=[ Relationship("e", "WORKS_AT", "c") ], filters=[Filter("e.salary > 60000")], return_fields=["e"], limit=100 ) ``` --- ### 6. Cypher Generator **Responsibility:** Convert patterns to Cypher **Process:** 1. Generate MATCH clause from nodes 2. Generate relationship traversal 3. Add WHERE filters 4. Add aggregations (if needed) 5. Generate RETURN clause 6. Add ORDER BY and LIMIT **Template:** ``` MATCH [WHERE ] [WITH ] RETURN [ORDER BY ] [LIMIT ] ``` --- ### 7. SPARQL Generator **Responsibility:** Convert patterns to SPARQL **Process:** 1. Generate PREFIX declarations 2. Generate SELECT clause 3. Generate WHERE clause with triple patterns 4. Add FILTER clauses 5. Add GROUP BY and aggregations 6. Add ORDER BY and LIMIT **Template:** ``` PREFIX : SELECT WHERE { [FILTER ] } [GROUP BY ] [ORDER BY ] [LIMIT ] ``` --- ### 8. Validation & Optimization Module **Responsibility:** Validate and optimize queries **Validation:** - Check against schema - Verify relationships exist - Validate property types - Check for semantic errors **Optimization:** - Reorder clauses for efficiency - Suggest index hints - Recommend LIMIT for large result sets - Identify expensive patterns --- ## Data Flow Diagram ``` Natural Language Input │ ▼ ┌───────────────────┐ │ Preprocessing │ │ (Tokenize, POS) │ └────┬──────────────┘ │ ├─► Entity Extraction │ │ │ ▼ │ ┌──────────────────┐ │ │ Extracted │ │ │ Entities │ │ └────────┬─────────┘ │ │ ├─► Relationship Extraction │ │ │ ▼ │ ┌──────────────────┐ │ │ Extracted │ │ │ Relationships │ │ └────────┬─────────┘ │ │ └─► Intent Detection │ ▼ ┌──────────────────────┐ │ Query Pattern │ │ Builder │ │ (Combines all info) │ └─────────┬────────────┘ │ ┌────┴────┐ ▼ ▼ ┌─────────┐ ┌──────────┐ │ Cypher │ │ SPARQL │ │Generator│ │Generator │ └────┬────┘ └────┬─────┘ │ │ └─────┬─────┘ ▼ ┌──────────────────────┐ │ Validation & │ │ Optimization │ └─────────┬────────────┘ ▼ ┌──────────────────────┐ │ Query Result │ │ (Query + Metadata) │ └──────────────────────┘ ``` --- ## Design Patterns ### 1. Pipeline Pattern Components are organized as a pipeline where output of one becomes input of the next. **Benefit:** - Easy to insert/remove steps - Clear data flow - Easy to debug --- ### 2. Strategy Pattern Different generators for different query languages (Cypher, SPARQL). **Benefit:** - Easy to add new query languages - Isolated language-specific logic --- ### 3. Builder Pattern Query patterns built step-by-step. **Benefit:** - Flexible query construction - Clear intermediate states --- ### 4. Factory Pattern Create appropriate entity/relationship objects based on context. **Benefit:** - Centralized object creation - Consistent validation --- ### 5. Decorator Pattern Wrap patterns with validation/optimization. **Benefit:** - Non-invasive additions - Composable functionality --- ## Integration Points ### Schema Integration The system integrates with graph schemas to: - Validate entities against schema labels - Verify relationship types exist - Check property names and types - Infer missing information **Schema Interface:** ```python class GraphSchema: def get_node_types(self) -> List[NodeType] def get_relationship_types(self) -> List[RelationType] def validate_entity(entity: Entity) -> bool def validate_relationship(rel: Relationship) -> bool def suggest_properties(label: str) -> List[str] ``` --- ### Database Integration The system can integrate with specific database backends: **Neo4j:** - Cypher syntax validation - Performance profiling - Query execution **RDF Stores (Virtuoso, Jena, etc.):** - SPARQL endpoint integration - Namespace mapping - Query execution --- ### NLP Framework Integration Can use different NLP libraries: **Spacy:** - Fast tokenization and POS tagging - NER models - Dependency parsing **NLTK:** - Lemmatization - POS tagging - Semantic role labeling **Transformers (HuggingFace):** - Advanced NER models - Semantic understanding - Zero-shot classification --- ## Extensibility ### Adding a New Query Language 1. Create new generator class inheriting from `BaseGenerator` 2. Implement `generate_query()` method 3. Register generator in factory 4. Add language-specific tests --- ### Adding Custom Entity Types 1. Extend entity classification rules 2. Update schema mapping 3. Add test cases for new types --- ### Adding Domain-Specific Patterns 1. Create domain pattern library 2. Integrate into pattern builder 3. Add domain-specific entity mappings --- ## Performance Considerations ### Caching Cache expensive operations: - Tokenization results - NER model outputs - Schema queries - Query execution plans ### Lazy Evaluation Generate queries on demand, not eagerly. ### Batch Processing Process multiple queries efficiently. --- ## Error Handling Strategy ``` Try entity extraction ├─ If fails: Try to infer from context │ Try relationship extraction ├─ If fails: Suggest alternatives │ Try query generation ├─ If fails: Return error with suggestions │ Validate query ├─ If invalid: Suggest corrections │ Return result or error ``` --- ## Security Considerations ### Input Validation - Sanitize user input - Check for injection attempts - Validate parameter types ### Query Safety - Parameterize user values - Avoid dynamic query construction - Escape special characters ### Authorization - Check user permissions - Enforce access controls - Log query execution --- ## Testing Strategy ### Unit Testing Test individual components in isolation. ### Integration Testing Test component interactions. ### End-to-End Testing Test full pipeline with sample inputs. ### Regression Testing Maintain test suite for known issues. --- ## Deployment Architecture ``` ┌─────────────────────────┐ │ API Gateway │ └────────────┬────────────┘ │ ┌───────┴───────┐ │ │ ┌────▼────┐ ┌────▼────┐ │ REST │ │ GraphQL │ │Endpoint │ │Endpoint │ └────┬────┘ └────┬────┘ │ │ └───────┬───────┘ │ ┌────────▼─────────┐ │ Translator Core │ │ (Processing) │ └────────┬─────────┘ │ ┌───────┴───────┐ │ │ ┌────▼────┐ ┌────▼────┐ │ Neo4j │ │ RDF │ │ Query │ │ Query │ │Execution│ │Execution│ └─────────┘ └─────────┘ ``` --- ## Monitoring & Observability **Metrics to track:** - Query translation latency - Accuracy/confidence scores - Error rates - Cache hit rates - Query execution times **Logging:** - Translation steps - Extracted entities/relationships - Generated queries - Validation results --- ## See Also - [API Reference](references/api-reference.md) - [Query Patterns](references/query-patterns.md) - [Edge Cases](tests/edge-cases.md)