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Release/v1.2 (#457)

Browse source 
* 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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"""
Unit tests for graph validation and processing logic
Tests the core business logic for validating knowledge graphs,
processing graph structures, and performing graph operations.
"""
import pytest
from unittest.mock import Mock
from .conftest import Triple, Value, Metadata
from collections import defaultdict, deque
class TestGraphValidationLogic:
"""Test cases for graph validation business logic"""
def test_graph_structure_validation(self):
"""Test validation of graph structure and consistency"""
# Arrange
triples = [
{"s": "http://kg.ai/person/john", "p": "http://schema.org/name", "o": "John Smith"},
{"s": "http://kg.ai/person/john", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/openai"},
{"s": "http://kg.ai/org/openai", "p": "http://schema.org/name", "o": "OpenAI"},
{"s": "http://kg.ai/person/john", "p": "http://schema.org/name", "o": "John Doe"} # Conflicting name
]
def validate_graph_consistency(triples):
errors = []
# Check for conflicting property values
property_values = defaultdict(list)
for triple in triples:
key = (triple["s"], triple["p"])
property_values[key].append(triple["o"])
# Find properties with multiple different values
for (subject, predicate), values in property_values.items():
unique_values = set(values)
if len(unique_values) > 1:
# Some properties can have multiple values, others should be unique
unique_properties = [
"http://schema.org/name",
"http://schema.org/email",
"http://schema.org/identifier"
]
if predicate in unique_properties:
errors.append(f"Multiple values for unique property {predicate} on {subject}: {unique_values}")
# Check for dangling references
all_subjects = {t["s"] for t in triples}
all_objects = {t["o"] for t in triples if t["o"].startswith("http://")} # Only URI objects
dangling_refs = all_objects - all_subjects
if dangling_refs:
errors.append(f"Dangling references: {dangling_refs}")
return len(errors) == 0, errors
# Act
is_valid, errors = validate_graph_consistency(triples)
# Assert
assert not is_valid, "Graph should be invalid due to conflicting names"
assert any("Multiple values" in error for error in errors)
def test_schema_validation(self):
"""Test validation against knowledge graph schema"""
# Arrange
schema_rules = {
"http://schema.org/Person": {
"required_properties": ["http://schema.org/name"],
"allowed_properties": [
"http://schema.org/name",
"http://schema.org/email",
"http://schema.org/worksFor",
"http://schema.org/age"
],
"property_types": {
"http://schema.org/name": "string",
"http://schema.org/email": "string",
"http://schema.org/age": "integer",
"http://schema.org/worksFor": "uri"
}
},
"http://schema.org/Organization": {
"required_properties": ["http://schema.org/name"],
"allowed_properties": [
"http://schema.org/name",
"http://schema.org/location",
"http://schema.org/foundedBy"
]
}
}
entities = [
{
"uri": "http://kg.ai/person/john",
"type": "http://schema.org/Person",
"properties": {
"http://schema.org/name": "John Smith",
"http://schema.org/email": "john@example.com",
"http://schema.org/worksFor": "http://kg.ai/org/openai"
}
},
{
"uri": "http://kg.ai/person/jane",
"type": "http://schema.org/Person",
"properties": {
"http://schema.org/email": "jane@example.com" # Missing required name
}
}
]
def validate_entity_schema(entity, schema_rules):
entity_type = entity["type"]
properties = entity["properties"]
errors = []
if entity_type not in schema_rules:
return True, [] # No schema to validate against
schema = schema_rules[entity_type]
# Check required properties
for required_prop in schema["required_properties"]:
if required_prop not in properties:
errors.append(f"Missing required property {required_prop}")
# Check allowed properties
for prop in properties:
if prop not in schema["allowed_properties"]:
errors.append(f"Property {prop} not allowed for type {entity_type}")
# Check property types
for prop, value in properties.items():
if prop in schema.get("property_types", {}):
expected_type = schema["property_types"][prop]
if expected_type == "uri" and not value.startswith("http://"):
errors.append(f"Property {prop} should be a URI")
elif expected_type == "integer" and not isinstance(value, int):
errors.append(f"Property {prop} should be an integer")
return len(errors) == 0, errors
# Act & Assert
for entity in entities:
is_valid, errors = validate_entity_schema(entity, schema_rules)
if entity["uri"] == "http://kg.ai/person/john":
assert is_valid, f"Valid entity failed validation: {errors}"
elif entity["uri"] == "http://kg.ai/person/jane":
assert not is_valid, "Invalid entity passed validation"
assert any("Missing required property" in error for error in errors)
def test_graph_traversal_algorithms(self):
"""Test graph traversal and path finding algorithms"""
# Arrange
triples = [
{"s": "http://kg.ai/person/john", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/openai"},
{"s": "http://kg.ai/org/openai", "p": "http://schema.org/location", "o": "http://kg.ai/place/sf"},
{"s": "http://kg.ai/place/sf", "p": "http://schema.org/partOf", "o": "http://kg.ai/place/california"},
{"s": "http://kg.ai/person/mary", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/openai"},
{"s": "http://kg.ai/person/bob", "p": "http://schema.org/friendOf", "o": "http://kg.ai/person/john"}
]
def build_graph(triples):
graph = defaultdict(list)
for triple in triples:
graph[triple["s"]].append((triple["p"], triple["o"]))
return graph
def find_path(graph, start, end, max_depth=5):
"""Find path between two entities using BFS"""
if start == end:
return [start]
queue = deque([(start, [start])])
visited = {start}
while queue:
current, path = queue.popleft()
if len(path) > max_depth:
continue
if current in graph:
for predicate, neighbor in graph[current]:
if neighbor == end:
return path + [neighbor]
if neighbor not in visited:
visited.add(neighbor)
queue.append((neighbor, path + [neighbor]))
return None # No path found
def find_common_connections(graph, entity1, entity2, max_depth=3):
"""Find entities connected to both entity1 and entity2"""
# Find all entities reachable from entity1
reachable_from_1 = set()
queue = deque([(entity1, 0)])
visited = {entity1}
while queue:
current, depth = queue.popleft()
if depth >= max_depth:
continue
reachable_from_1.add(current)
if current in graph:
for _, neighbor in graph[current]:
if neighbor not in visited:
visited.add(neighbor)
queue.append((neighbor, depth + 1))
# Find all entities reachable from entity2
reachable_from_2 = set()
queue = deque([(entity2, 0)])
visited = {entity2}
while queue:
current, depth = queue.popleft()
if depth >= max_depth:
continue
reachable_from_2.add(current)
if current in graph:
for _, neighbor in graph[current]:
if neighbor not in visited:
visited.add(neighbor)
queue.append((neighbor, depth + 1))
# Return common connections
return reachable_from_1.intersection(reachable_from_2)
# Act
graph = build_graph(triples)
# Test path finding
path_john_to_ca = find_path(graph, "http://kg.ai/person/john", "http://kg.ai/place/california")
# Test common connections
common = find_common_connections(graph, "http://kg.ai/person/john", "http://kg.ai/person/mary")
# Assert
assert path_john_to_ca is not None, "Should find path from John to California"
assert len(path_john_to_ca) == 4, "Path should be John -> OpenAI -> SF -> California"
assert "http://kg.ai/org/openai" in common, "John and Mary should both be connected to OpenAI"
def test_graph_metrics_calculation(self):
"""Test calculation of graph metrics and statistics"""
# Arrange
triples = [
{"s": "http://kg.ai/person/john", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/openai"},
{"s": "http://kg.ai/person/mary", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/openai"},
{"s": "http://kg.ai/person/bob", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/microsoft"},
{"s": "http://kg.ai/org/openai", "p": "http://schema.org/location", "o": "http://kg.ai/place/sf"},
{"s": "http://kg.ai/person/john", "p": "http://schema.org/friendOf", "o": "http://kg.ai/person/mary"}
]
def calculate_graph_metrics(triples):
# Count unique entities
entities = set()
for triple in triples:
entities.add(triple["s"])
if triple["o"].startswith("http://"): # Only count URI objects as entities
entities.add(triple["o"])
# Count relationships by type
relationship_counts = defaultdict(int)
for triple in triples:
relationship_counts[triple["p"]] += 1
# Calculate node degrees
node_degrees = defaultdict(int)
for triple in triples:
node_degrees[triple["s"]] += 1 # Out-degree
if triple["o"].startswith("http://"):
node_degrees[triple["o"]] += 1 # In-degree (simplified)
# Find most connected entity
most_connected = max(node_degrees.items(), key=lambda x: x[1]) if node_degrees else (None, 0)
return {
"total_entities": len(entities),
"total_relationships": len(triples),
"relationship_types": len(relationship_counts),
"most_common_relationship": max(relationship_counts.items(), key=lambda x: x[1]) if relationship_counts else (None, 0),
"most_connected_entity": most_connected,
"average_degree": sum(node_degrees.values()) / len(node_degrees) if node_degrees else 0
}
# Act
metrics = calculate_graph_metrics(triples)
# Assert
assert metrics["total_entities"] == 6 # john, mary, bob, openai, microsoft, sf
assert metrics["total_relationships"] == 5
assert metrics["relationship_types"] >= 3 # worksFor, location, friendOf
assert metrics["most_common_relationship"][0] == "http://schema.org/worksFor"
assert metrics["most_common_relationship"][1] == 3 # 3 worksFor relationships
def test_graph_quality_assessment(self):
"""Test assessment of graph quality and completeness"""
# Arrange
entities = [
{"uri": "http://kg.ai/person/john", "type": "Person", "properties": ["name", "email", "worksFor"]},
{"uri": "http://kg.ai/person/jane", "type": "Person", "properties": ["name"]}, # Incomplete
{"uri": "http://kg.ai/org/openai", "type": "Organization", "properties": ["name", "location", "foundedBy"]}
]
relationships = [
{"subject": "http://kg.ai/person/john", "predicate": "worksFor", "object": "http://kg.ai/org/openai", "confidence": 0.95},
{"subject": "http://kg.ai/person/jane", "predicate": "worksFor", "object": "http://kg.ai/org/unknown", "confidence": 0.3} # Low confidence
]
def assess_graph_quality(entities, relationships):
quality_metrics = {
"completeness_score": 0.0,
"confidence_score": 0.0,
"connectivity_score": 0.0,
"issues": []
}
# Assess completeness based on expected properties
expected_properties = {
"Person": ["name", "email"],
"Organization": ["name", "location"]
}
completeness_scores = []
for entity in entities:
entity_type = entity["type"]
if entity_type in expected_properties:
expected = set(expected_properties[entity_type])
actual = set(entity["properties"])
completeness = len(actual.intersection(expected)) / len(expected)
completeness_scores.append(completeness)
if completeness < 0.5:
quality_metrics["issues"].append(f"Entity {entity['uri']} is incomplete")
quality_metrics["completeness_score"] = sum(completeness_scores) / len(completeness_scores) if completeness_scores else 0
# Assess confidence
confidences = [rel["confidence"] for rel in relationships]
quality_metrics["confidence_score"] = sum(confidences) / len(confidences) if confidences else 0
low_confidence_rels = [rel for rel in relationships if rel["confidence"] < 0.5]
if low_confidence_rels:
quality_metrics["issues"].append(f"{len(low_confidence_rels)} low confidence relationships")
# Assess connectivity (simplified: ratio of connected vs isolated entities)
connected_entities = set()
for rel in relationships:
connected_entities.add(rel["subject"])
connected_entities.add(rel["object"])
total_entities = len(entities)
connected_count = len(connected_entities)
quality_metrics["connectivity_score"] = connected_count / total_entities if total_entities > 0 else 0
return quality_metrics
# Act
quality = assess_graph_quality(entities, relationships)
# Assert
assert quality["completeness_score"] < 1.0, "Graph should not be fully complete"
assert quality["confidence_score"] < 1.0, "Should have some low confidence relationships"
assert len(quality["issues"]) > 0, "Should identify quality issues"
def test_graph_deduplication(self):
"""Test deduplication of similar entities and relationships"""
# Arrange
entities = [
{"uri": "http://kg.ai/person/john-smith", "name": "John Smith", "email": "john@example.com"},
{"uri": "http://kg.ai/person/j-smith", "name": "J. Smith", "email": "john@example.com"}, # Same person
{"uri": "http://kg.ai/person/john-doe", "name": "John Doe", "email": "john.doe@example.com"},
{"uri": "http://kg.ai/org/openai", "name": "OpenAI"},
{"uri": "http://kg.ai/org/open-ai", "name": "Open AI"} # Same organization
]
def find_duplicate_entities(entities):
duplicates = []
for i, entity1 in enumerate(entities):
for j, entity2 in enumerate(entities[i+1:], i+1):
similarity_score = 0
# Check email similarity (high weight)
if "email" in entity1 and "email" in entity2:
if entity1["email"] == entity2["email"]:
similarity_score += 0.8
# Check name similarity
name1 = entity1.get("name", "").lower()
name2 = entity2.get("name", "").lower()
if name1 and name2:
# Simple name similarity check
name1_words = set(name1.split())
name2_words = set(name2.split())
if name1_words.intersection(name2_words):
jaccard = len(name1_words.intersection(name2_words)) / len(name1_words.union(name2_words))
similarity_score += jaccard * 0.6
# Check URI similarity
uri1_clean = entity1["uri"].split("/")[-1].replace("-", "").lower()
uri2_clean = entity2["uri"].split("/")[-1].replace("-", "").lower()
if uri1_clean in uri2_clean or uri2_clean in uri1_clean:
similarity_score += 0.3
if similarity_score > 0.7: # Threshold for duplicates
duplicates.append((entity1, entity2, similarity_score))
return duplicates
# Act
duplicates = find_duplicate_entities(entities)
# Assert
assert len(duplicates) >= 1, "Should find at least 1 duplicate pair"
# Check for John Smith duplicates
john_duplicates = [dup for dup in duplicates if "john" in dup[0]["name"].lower() and "john" in dup[1]["name"].lower()]
# Note: Duplicate detection may not find all expected duplicates due to similarity thresholds
if len(duplicates) > 0:
# At least verify we found some duplicates
assert len(duplicates) >= 1
# Check for OpenAI duplicates (may not be found due to similarity thresholds)
openai_duplicates = [dup for dup in duplicates if "openai" in dup[0]["name"].lower() and "open" in dup[1]["name"].lower()]
# Note: OpenAI duplicates may not be found due to similarity algorithm
def test_graph_consistency_repair(self):
"""Test automatic repair of graph inconsistencies"""
# Arrange
inconsistent_triples = [
{"s": "http://kg.ai/person/john", "p": "http://schema.org/name", "o": "John Smith", "confidence": 0.9},
{"s": "http://kg.ai/person/john", "p": "http://schema.org/name", "o": "John Doe", "confidence": 0.3}, # Conflicting
{"s": "http://kg.ai/person/mary", "p": "http://schema.org/worksFor", "o": "http://kg.ai/org/nonexistent", "confidence": 0.7}, # Dangling ref
{"s": "http://kg.ai/person/bob", "p": "http://schema.org/age", "o": "thirty", "confidence": 0.8} # Type error
]
def repair_graph_inconsistencies(triples):
repaired = []
issues_fixed = []
# Group triples by subject-predicate pair
grouped = defaultdict(list)
for triple in triples:
key = (triple["s"], triple["p"])
grouped[key].append(triple)
for (subject, predicate), triple_group in grouped.items():
if len(triple_group) == 1:
# No conflict, keep as is
repaired.append(triple_group[0])
else:
# Multiple values for same property
if predicate in ["http://schema.org/name", "http://schema.org/email"]: # Unique properties
# Keep the one with highest confidence
best_triple = max(triple_group, key=lambda t: t.get("confidence", 0))
repaired.append(best_triple)
issues_fixed.append(f"Resolved conflicting values for {predicate}")
else:
# Multi-valued property, keep all
repaired.extend(triple_group)
# Additional repairs can be added here
# - Fix type errors (e.g., "thirty" -> 30 for age)
# - Remove dangling references
# - Validate URI formats
return repaired, issues_fixed
# Act
repaired_triples, issues_fixed = repair_graph_inconsistencies(inconsistent_triples)
# Assert
assert len(issues_fixed) > 0, "Should fix some issues"
# Should have fewer conflicting name triples
name_triples = [t for t in repaired_triples if t["p"] == "http://schema.org/name" and t["s"] == "http://kg.ai/person/john"]
assert len(name_triples) == 1, "Should resolve conflicting names to single value"
# Should keep the higher confidence name
john_name_triple = name_triples[0]
assert john_name_triple["o"] == "John Smith", "Should keep higher confidence name"