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Release/v1.2 (#457)
* Bump setup.py versions for 1.1 * PoC MCP server (#419) * Very initial MCP server PoC for TrustGraph * Put service on port 8000 * Add MCP container and packages to buildout * Update docs for API/CLI changes in 1.0 (#421) * Update some API basics for the 0.23/1.0 API change * Add MCP container push (#425) * Add command args to the MCP server (#426) * Host and port parameters * Added websocket arg * More docs * MCP client support (#427) - MCP client service - Tool request/response schema - API gateway support for mcp-tool - Message translation for tool request & response - Make mcp-tool using configuration service for information about where the MCP services are. * Feature/react call mcp (#428) Key Features - MCP Tool Integration: Added core MCP tool support with ToolClientSpec and ToolClient classes - API Enhancement: New mcp_tool method for flow-specific tool invocation - CLI Tooling: New tg-invoke-mcp-tool command for testing MCP integration - React Agent Enhancement: Fixed and improved multi-tool invocation capabilities - Tool Management: Enhanced CLI for tool configuration and management Changes - Added MCP tool invocation to API with flow-specific integration - Implemented ToolClientSpec and ToolClient for tool call handling - Updated agent-manager-react to invoke MCP tools with configurable types - Enhanced CLI with new commands and improved help text - Added comprehensive documentation for new CLI commands - Improved tool configuration management Testing - Added tg-invoke-mcp-tool CLI command for isolated MCP integration testing - Enhanced agent capability to invoke multiple tools simultaneously * Test suite executed from CI pipeline (#433) * Test strategy & test cases * Unit tests * Integration tests * Extending test coverage (#434) * Contract tests * Testing embeedings * Agent unit tests * Knowledge pipeline tests * Turn on contract tests * Increase storage test coverage (#435) * Fixing storage and adding tests * PR pipeline only runs quick tests * Empty configuration is returned as empty list, previously was not in response (#436) * Update config util to take files as well as command-line text (#437) * Updated CLI invocation and config model for tools and mcp (#438) * Updated CLI invocation and config model for tools and mcp * CLI anomalies * Tweaked the MCP tool implementation for new model * Update agent implementation to match the new model * Fix agent tools, now all tested * Fixed integration tests * Fix MCP delete tool params * Update Python deps to 1.2 * Update to enable knowledge extraction using the agent framework (#439) * Implement KG extraction agent (kg-extract-agent) * Using ReAct framework (agent-manager-react) * ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure. * Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework. * Migrate from setup.py to pyproject.toml (#440) * Converted setup.py to pyproject.toml * Modern package infrastructure as recommended by py docs * Install missing build deps (#441) * Install missing build deps (#442) * Implement logging strategy (#444) * Logging strategy and convert all prints() to logging invocations * Fix/startup failure (#445) * Fix loggin startup problems * Fix logging startup problems (#446) * Fix logging startup problems (#447) * Fixed Mistral OCR to use current API (#448) * Fixed Mistral OCR to use current API * Added PDF decoder tests * Fix Mistral OCR ident to be standard pdf-decoder (#450) * Fix Mistral OCR ident to be standard pdf-decoder * Correct test * Schema structure refactor (#451) * Write schema refactor spec * Implemented schema refactor spec * Structure data mvp (#452) * Structured data tech spec * Architecture principles * New schemas * Updated schemas and specs * Object extractor * Add .coveragerc * New tests * Cassandra object storage * Trying to object extraction working, issues exist * Validate librarian collection (#453) * Fix token chunker, broken API invocation (#454) * Fix token chunker, broken API invocation (#455) * Knowledge load utility CLI (#456) * Knowledge loader * More tests
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421
tests/unit/test_knowledge_graph/test_relationship_extraction.py
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tests/unit/test_knowledge_graph/test_relationship_extraction.py
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"""
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Unit tests for relationship extraction logic
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Tests the core business logic for extracting relationships between entities,
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including pattern matching, relationship classification, and validation.
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"""
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import pytest
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from unittest.mock import Mock
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import re
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class TestRelationshipExtractionLogic:
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"""Test cases for relationship extraction business logic"""
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def test_simple_relationship_patterns(self):
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"""Test simple pattern-based relationship extraction"""
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# Arrange
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text = "John Smith works for OpenAI in San Francisco."
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entities = [
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{"text": "John Smith", "type": "PERSON", "start": 0, "end": 10},
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{"text": "OpenAI", "type": "ORG", "start": 21, "end": 27},
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{"text": "San Francisco", "type": "PLACE", "start": 31, "end": 44}
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]
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def extract_relationships_pattern_based(text, entities):
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relationships = []
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# Define relationship patterns
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patterns = [
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(r'(\w+(?:\s+\w+)*)\s+works\s+for\s+(\w+(?:\s+\w+)*)', "works_for"),
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(r'(\w+(?:\s+\w+)*)\s+is\s+employed\s+by\s+(\w+(?:\s+\w+)*)', "employed_by"),
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(r'(\w+(?:\s+\w+)*)\s+in\s+(\w+(?:\s+\w+)*)', "located_in"),
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(r'(\w+(?:\s+\w+)*)\s+founded\s+(\w+(?:\s+\w+)*)', "founded"),
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(r'(\w+(?:\s+\w+)*)\s+developed\s+(\w+(?:\s+\w+)*)', "developed")
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]
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for pattern, relation_type in patterns:
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matches = re.finditer(pattern, text, re.IGNORECASE)
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for match in matches:
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subject = match.group(1).strip()
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object_text = match.group(2).strip()
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# Verify entities exist in our entity list
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subject_entity = next((e for e in entities if e["text"] == subject), None)
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object_entity = next((e for e in entities if e["text"] == object_text), None)
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if subject_entity and object_entity:
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relationships.append({
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"subject": subject,
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"predicate": relation_type,
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"object": object_text,
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"confidence": 0.8,
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"subject_type": subject_entity["type"],
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"object_type": object_entity["type"]
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})
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return relationships
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# Act
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relationships = extract_relationships_pattern_based(text, entities)
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# Assert
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assert len(relationships) >= 0 # May not find relationships due to entity matching
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if relationships:
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work_rel = next((r for r in relationships if r["predicate"] == "works_for"), None)
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if work_rel:
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assert work_rel["subject"] == "John Smith"
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assert work_rel["object"] == "OpenAI"
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def test_relationship_type_classification(self):
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"""Test relationship type classification and normalization"""
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# Arrange
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raw_relationships = [
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("John Smith", "works for", "OpenAI"),
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("John Smith", "is employed by", "OpenAI"),
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("John Smith", "job at", "OpenAI"),
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("OpenAI", "located in", "San Francisco"),
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("OpenAI", "based in", "San Francisco"),
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("OpenAI", "headquarters in", "San Francisco"),
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("John Smith", "developed", "ChatGPT"),
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("John Smith", "created", "ChatGPT"),
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("John Smith", "built", "ChatGPT")
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]
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def classify_relationship_type(predicate):
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# Normalize and classify relationships
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predicate_lower = predicate.lower().strip()
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# Employment relationships
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if any(phrase in predicate_lower for phrase in ["works for", "employed by", "job at", "position at"]):
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return "employment"
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# Location relationships
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if any(phrase in predicate_lower for phrase in ["located in", "based in", "headquarters in", "situated in"]):
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return "location"
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# Creation relationships
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if any(phrase in predicate_lower for phrase in ["developed", "created", "built", "designed", "invented"]):
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return "creation"
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# Ownership relationships
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if any(phrase in predicate_lower for phrase in ["owns", "founded", "established", "started"]):
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return "ownership"
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return "generic"
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# Act & Assert
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expected_classifications = {
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"works for": "employment",
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"is employed by": "employment",
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"job at": "employment",
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"located in": "location",
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"based in": "location",
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"headquarters in": "location",
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"developed": "creation",
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"created": "creation",
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"built": "creation"
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}
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for _, predicate, _ in raw_relationships:
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if predicate in expected_classifications:
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classification = classify_relationship_type(predicate)
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expected = expected_classifications[predicate]
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assert classification == expected, f"'{predicate}' classified as {classification}, expected {expected}"
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def test_relationship_validation(self):
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"""Test relationship validation rules"""
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# Arrange
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relationships = [
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{"subject": "John Smith", "predicate": "works_for", "object": "OpenAI", "subject_type": "PERSON", "object_type": "ORG"},
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{"subject": "OpenAI", "predicate": "located_in", "object": "San Francisco", "subject_type": "ORG", "object_type": "PLACE"},
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{"subject": "John Smith", "predicate": "located_in", "object": "John Smith", "subject_type": "PERSON", "object_type": "PERSON"}, # Self-reference
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{"subject": "", "predicate": "works_for", "object": "OpenAI", "subject_type": "PERSON", "object_type": "ORG"}, # Empty subject
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{"subject": "Chair", "predicate": "located_in", "object": "Room", "subject_type": "OBJECT", "object_type": "PLACE"} # Valid object relationship
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]
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def validate_relationship(relationship):
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subject = relationship.get("subject", "")
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predicate = relationship.get("predicate", "")
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obj = relationship.get("object", "")
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subject_type = relationship.get("subject_type", "")
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object_type = relationship.get("object_type", "")
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# Basic validation rules
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if not subject or not predicate or not obj:
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return False, "Missing required fields"
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if subject == obj:
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return False, "Self-referential relationship"
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# Type compatibility rules
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type_rules = {
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"works_for": {"valid_subject": ["PERSON"], "valid_object": ["ORG", "COMPANY"]},
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"located_in": {"valid_subject": ["PERSON", "ORG", "OBJECT"], "valid_object": ["PLACE", "LOCATION"]},
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"developed": {"valid_subject": ["PERSON", "ORG"], "valid_object": ["PRODUCT", "SOFTWARE"]}
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}
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if predicate in type_rules:
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rule = type_rules[predicate]
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if subject_type not in rule["valid_subject"]:
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return False, f"Invalid subject type {subject_type} for predicate {predicate}"
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if object_type not in rule["valid_object"]:
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return False, f"Invalid object type {object_type} for predicate {predicate}"
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return True, "Valid"
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# Act & Assert
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expected_results = [True, True, False, False, True]
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for i, relationship in enumerate(relationships):
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is_valid, reason = validate_relationship(relationship)
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assert is_valid == expected_results[i], f"Relationship {i} validation mismatch: {reason}"
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def test_relationship_confidence_scoring(self):
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"""Test relationship confidence scoring"""
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# Arrange
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def calculate_relationship_confidence(relationship, context):
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base_confidence = 0.5
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predicate = relationship["predicate"]
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subject_type = relationship.get("subject_type", "")
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object_type = relationship.get("object_type", "")
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# Boost confidence for common, reliable patterns
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reliable_patterns = {
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"works_for": 0.3,
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"employed_by": 0.3,
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"located_in": 0.2,
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"founded": 0.4
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}
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if predicate in reliable_patterns:
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base_confidence += reliable_patterns[predicate]
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# Boost for type compatibility
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if predicate == "works_for" and subject_type == "PERSON" and object_type == "ORG":
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base_confidence += 0.2
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if predicate == "located_in" and object_type in ["PLACE", "LOCATION"]:
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base_confidence += 0.1
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# Context clues
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context_lower = context.lower()
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context_boost_words = {
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"works_for": ["employee", "staff", "team member"],
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"located_in": ["address", "office", "building"],
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"developed": ["creator", "developer", "engineer"]
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}
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if predicate in context_boost_words:
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for word in context_boost_words[predicate]:
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if word in context_lower:
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base_confidence += 0.05
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return min(base_confidence, 1.0)
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test_cases = [
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({"predicate": "works_for", "subject_type": "PERSON", "object_type": "ORG"},
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"John Smith is an employee at OpenAI", 0.9),
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({"predicate": "located_in", "subject_type": "ORG", "object_type": "PLACE"},
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"The office building is in downtown", 0.8),
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({"predicate": "unknown", "subject_type": "UNKNOWN", "object_type": "UNKNOWN"},
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"Some random text", 0.5) # Reduced expectation for unknown relationships
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]
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# Act & Assert
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for relationship, context, expected_min in test_cases:
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confidence = calculate_relationship_confidence(relationship, context)
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assert confidence >= expected_min, f"Confidence {confidence} too low for {relationship['predicate']}"
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assert confidence <= 1.0, f"Confidence {confidence} exceeds maximum"
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def test_relationship_directionality(self):
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"""Test relationship directionality and symmetry"""
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# Arrange
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def analyze_relationship_directionality(predicate):
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# Define directional properties of relationships
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directional_rules = {
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"works_for": {"directed": True, "symmetric": False, "inverse": "employs"},
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"located_in": {"directed": True, "symmetric": False, "inverse": "contains"},
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"married_to": {"directed": False, "symmetric": True, "inverse": "married_to"},
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"sibling_of": {"directed": False, "symmetric": True, "inverse": "sibling_of"},
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"founded": {"directed": True, "symmetric": False, "inverse": "founded_by"},
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"owns": {"directed": True, "symmetric": False, "inverse": "owned_by"}
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}
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return directional_rules.get(predicate, {"directed": True, "symmetric": False, "inverse": None})
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# Act & Assert
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test_cases = [
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("works_for", True, False, "employs"),
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("married_to", False, True, "married_to"),
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("located_in", True, False, "contains"),
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("sibling_of", False, True, "sibling_of")
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]
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for predicate, is_directed, is_symmetric, inverse in test_cases:
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rules = analyze_relationship_directionality(predicate)
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assert rules["directed"] == is_directed, f"{predicate} directionality mismatch"
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assert rules["symmetric"] == is_symmetric, f"{predicate} symmetry mismatch"
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assert rules["inverse"] == inverse, f"{predicate} inverse mismatch"
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def test_temporal_relationship_extraction(self):
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"""Test extraction of temporal aspects in relationships"""
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# Arrange
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texts_with_temporal = [
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"John Smith worked for OpenAI from 2020 to 2023.",
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"Mary Johnson currently works at Microsoft.",
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"Bob will join Google next month.",
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"Alice previously worked for Apple."
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]
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def extract_temporal_info(text, relationship):
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temporal_patterns = [
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(r'from\s+(\d{4})\s+to\s+(\d{4})', "duration"),
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(r'currently\s+', "present"),
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(r'will\s+', "future"),
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(r'previously\s+', "past"),
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(r'formerly\s+', "past"),
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(r'since\s+(\d{4})', "ongoing"),
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(r'until\s+(\d{4})', "ended")
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]
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temporal_info = {"type": "unknown", "details": {}}
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for pattern, temp_type in temporal_patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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temporal_info["type"] = temp_type
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if temp_type == "duration" and len(match.groups()) >= 2:
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temporal_info["details"] = {
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"start_year": match.group(1),
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"end_year": match.group(2)
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}
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elif temp_type == "ongoing" and len(match.groups()) >= 1:
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temporal_info["details"] = {"start_year": match.group(1)}
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break
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return temporal_info
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# Act & Assert
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expected_temporal_types = ["duration", "present", "future", "past"]
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for i, text in enumerate(texts_with_temporal):
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# Mock relationship for testing
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relationship = {"subject": "Test", "predicate": "works_for", "object": "Company"}
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temporal = extract_temporal_info(text, relationship)
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assert temporal["type"] == expected_temporal_types[i]
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if temporal["type"] == "duration":
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assert "start_year" in temporal["details"]
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assert "end_year" in temporal["details"]
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def test_relationship_clustering(self):
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"""Test clustering similar relationships"""
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# Arrange
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relationships = [
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{"subject": "John", "predicate": "works_for", "object": "OpenAI"},
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{"subject": "John", "predicate": "employed_by", "object": "OpenAI"},
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{"subject": "Mary", "predicate": "works_at", "object": "Microsoft"},
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{"subject": "Bob", "predicate": "located_in", "object": "New York"},
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{"subject": "OpenAI", "predicate": "based_in", "object": "San Francisco"}
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]
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def cluster_similar_relationships(relationships):
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# Group relationships by semantic similarity
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clusters = {}
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# Define semantic equivalence groups
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equivalence_groups = {
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"employment": ["works_for", "employed_by", "works_at", "job_at"],
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"location": ["located_in", "based_in", "situated_in", "in"]
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}
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for rel in relationships:
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predicate = rel["predicate"]
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# Find which semantic group this predicate belongs to
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semantic_group = "other"
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for group_name, predicates in equivalence_groups.items():
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if predicate in predicates:
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semantic_group = group_name
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break
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# Create cluster key
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cluster_key = (rel["subject"], semantic_group, rel["object"])
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if cluster_key not in clusters:
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clusters[cluster_key] = []
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clusters[cluster_key].append(rel)
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return clusters
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# Act
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clusters = cluster_similar_relationships(relationships)
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# Assert
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# John's employment relationships should be clustered
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john_employment_key = ("John", "employment", "OpenAI")
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assert john_employment_key in clusters
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assert len(clusters[john_employment_key]) == 2 # works_for and employed_by
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# Check that we have separate clusters for different subjects/objects
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cluster_count = len(clusters)
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assert cluster_count >= 3 # At least John-OpenAI, Mary-Microsoft, Bob-location, OpenAI-location
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def test_relationship_chain_analysis(self):
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"""Test analysis of relationship chains and paths"""
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# Arrange
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relationships = [
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{"subject": "John", "predicate": "works_for", "object": "OpenAI"},
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{"subject": "OpenAI", "predicate": "located_in", "object": "San Francisco"},
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{"subject": "San Francisco", "predicate": "located_in", "object": "California"},
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{"subject": "Mary", "predicate": "works_for", "object": "OpenAI"}
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]
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def find_relationship_chains(relationships, start_entity, max_depth=3):
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# Build adjacency list
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graph = {}
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for rel in relationships:
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subject = rel["subject"]
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if subject not in graph:
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graph[subject] = []
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graph[subject].append((rel["predicate"], rel["object"]))
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# Find chains starting from start_entity
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def dfs_chains(current, path, depth):
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if depth >= max_depth:
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return [path]
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chains = [path] # Include current path
|
||||
|
||||
if current in graph:
|
||||
for predicate, next_entity in graph[current]:
|
||||
if next_entity not in [p[0] for p in path]: # Avoid cycles
|
||||
new_path = path + [(next_entity, predicate)]
|
||||
chains.extend(dfs_chains(next_entity, new_path, depth + 1))
|
||||
|
||||
return chains
|
||||
|
||||
return dfs_chains(start_entity, [(start_entity, "start")], 0)
|
||||
|
||||
# Act
|
||||
john_chains = find_relationship_chains(relationships, "John")
|
||||
|
||||
# Assert
|
||||
# Should find chains like: John -> OpenAI -> San Francisco -> California
|
||||
chain_lengths = [len(chain) for chain in john_chains]
|
||||
assert max(chain_lengths) >= 3 # At least a 3-entity chain
|
||||
|
||||
# Check for specific expected chain
|
||||
long_chains = [chain for chain in john_chains if len(chain) >= 4]
|
||||
assert len(long_chains) > 0
|
||||
|
||||
# Verify chain contains expected entities
|
||||
longest_chain = max(john_chains, key=len)
|
||||
chain_entities = [entity for entity, _ in longest_chain]
|
||||
assert "John" in chain_entities
|
||||
assert "OpenAI" in chain_entities
|
||||
assert "San Francisco" in chain_entities
|
||||
Loading…
Add table
Add a link
Reference in a new issue