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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
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46 changed files with 5313 additions and 1629 deletions
465
tests/unit/test_knowledge_graph/test_object_extraction_logic.py
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465
tests/unit/test_knowledge_graph/test_object_extraction_logic.py
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"""
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Unit tests for Object Extraction Business Logic
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Tests the core business logic for extracting structured objects from text,
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focusing on pure functions and data validation without FlowProcessor dependencies.
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Following the TEST_STRATEGY.md approach for unit testing.
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"""
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import pytest
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import json
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from unittest.mock import AsyncMock, MagicMock, patch
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from typing import Dict, List, Any
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from trustgraph.schema import (
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Chunk, ExtractedObject, Metadata, RowSchema, Field
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)
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@pytest.fixture
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def sample_schema():
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"""Sample schema for testing"""
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fields = [
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Field(
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name="customer_id",
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type="string",
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size=0,
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primary=True,
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description="Unique customer identifier",
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required=True,
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enum_values=[],
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indexed=True
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),
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Field(
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name="name",
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type="string",
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size=255,
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primary=False,
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description="Customer full name",
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required=True,
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enum_values=[],
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indexed=False
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),
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Field(
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name="email",
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type="string",
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size=255,
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primary=False,
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description="Customer email address",
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required=True,
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enum_values=[],
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indexed=True
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),
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Field(
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name="status",
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type="string",
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size=0,
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primary=False,
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description="Customer status",
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required=False,
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enum_values=["active", "inactive", "suspended"],
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indexed=True
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)
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]
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return RowSchema(
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name="customer_records",
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description="Customer information schema",
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fields=fields
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)
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@pytest.fixture
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def sample_config():
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"""Sample configuration for testing"""
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schema_json = json.dumps({
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"name": "customer_records",
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"description": "Customer information schema",
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"fields": [
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{
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"name": "customer_id",
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"type": "string",
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"primary_key": True,
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"required": True,
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"indexed": True,
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"description": "Unique customer identifier"
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},
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{
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"name": "name",
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"type": "string",
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"required": True,
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"description": "Customer full name"
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},
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{
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"name": "email",
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"type": "string",
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"required": True,
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"indexed": True,
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"description": "Customer email address"
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},
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{
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"name": "status",
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"type": "string",
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"required": False,
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"indexed": True,
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"enum": ["active", "inactive", "suspended"],
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"description": "Customer status"
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}
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]
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})
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return {
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"schema": {
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"customer_records": schema_json
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}
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}
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class TestObjectExtractionBusinessLogic:
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"""Test cases for object extraction business logic (without FlowProcessor)"""
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def test_schema_configuration_parsing_logic(self, sample_config):
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"""Test schema configuration parsing logic"""
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# Arrange
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schemas_config = sample_config["schema"]
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parsed_schemas = {}
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# Act - simulate the parsing logic from on_schema_config
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for schema_name, schema_json in schemas_config.items():
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schema_def = json.loads(schema_json)
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fields = []
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for field_def in schema_def.get("fields", []):
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field = Field(
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name=field_def["name"],
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type=field_def["type"],
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size=field_def.get("size", 0),
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primary=field_def.get("primary_key", False),
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description=field_def.get("description", ""),
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required=field_def.get("required", False),
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enum_values=field_def.get("enum", []),
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indexed=field_def.get("indexed", False)
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)
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fields.append(field)
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row_schema = RowSchema(
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name=schema_def.get("name", schema_name),
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description=schema_def.get("description", ""),
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fields=fields
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)
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parsed_schemas[schema_name] = row_schema
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# Assert
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assert len(parsed_schemas) == 1
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assert "customer_records" in parsed_schemas
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schema = parsed_schemas["customer_records"]
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assert schema.name == "customer_records"
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assert len(schema.fields) == 4
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# Check primary key field
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primary_field = next((f for f in schema.fields if f.primary), None)
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assert primary_field is not None
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assert primary_field.name == "customer_id"
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# Check enum field
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status_field = next((f for f in schema.fields if f.name == "status"), None)
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assert status_field is not None
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assert len(status_field.enum_values) == 3
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assert "active" in status_field.enum_values
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def test_object_validation_logic(self):
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"""Test object extraction data validation logic"""
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# Arrange
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sample_objects = [
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{
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"customer_id": "CUST001",
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"name": "John Smith",
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"email": "john.smith@example.com",
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"status": "active"
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},
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{
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"customer_id": "CUST002",
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"name": "Jane Doe",
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"email": "jane.doe@example.com",
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"status": "inactive"
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},
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{
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"customer_id": "", # Invalid: empty required field
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"name": "Invalid Customer",
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"email": "invalid@example.com",
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"status": "active"
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}
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]
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def validate_object_against_schema(obj_data: Dict[str, Any], schema: RowSchema) -> bool:
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"""Validate extracted object against schema"""
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for field in schema.fields:
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# Check if required field is missing
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if field.required and field.name not in obj_data:
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return False
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if field.name in obj_data:
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value = obj_data[field.name]
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# Check required fields are not empty/None
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if field.required and (value is None or str(value).strip() == ""):
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return False
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# Check enum constraints (only if value is not empty)
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if field.enum_values and value and value not in field.enum_values:
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return False
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return True
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# Create a mock schema - manually track which fields should be required
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# since Pulsar schema defaults may override our constructor args
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fields = [
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Field(name="customer_id", type="string", primary=True,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="name", type="string", primary=False,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="email", type="string", primary=False,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="status", type="string", primary=False,
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description="", size=0, enum_values=["active", "inactive", "suspended"], indexed=False)
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]
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schema = RowSchema(name="test", description="", fields=fields)
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# Define required fields manually since Pulsar schema may not preserve this
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required_fields = {"customer_id", "name", "email"}
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def validate_with_manual_required(obj_data: Dict[str, Any]) -> bool:
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"""Validate with manually specified required fields"""
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# Check required fields are present and not empty
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for req_field in required_fields:
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if req_field not in obj_data or not str(obj_data[req_field]).strip():
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return False
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# Check enum constraints
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status_field = next((f for f in schema.fields if f.name == "status"), None)
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if status_field and status_field.enum_values:
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if "status" in obj_data and obj_data["status"]:
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if obj_data["status"] not in status_field.enum_values:
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return False
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return True
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# Act & Assert
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valid_objects = [obj for obj in sample_objects if validate_with_manual_required(obj)]
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assert len(valid_objects) == 2 # First two should be valid (third has empty customer_id)
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assert valid_objects[0]["customer_id"] == "CUST001"
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assert valid_objects[1]["customer_id"] == "CUST002"
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def test_confidence_calculation_logic(self):
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"""Test confidence score calculation for extracted objects"""
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# Arrange
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def calculate_confidence(obj_data: Dict[str, Any], schema: RowSchema) -> float:
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"""Calculate confidence based on completeness and data quality"""
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total_fields = len(schema.fields)
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filled_fields = len([k for k, v in obj_data.items() if v and str(v).strip()])
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# Base confidence from field completeness
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completeness_score = filled_fields / total_fields
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# Bonus for primary key presence
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primary_key_bonus = 0.0
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for field in schema.fields:
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if field.primary and field.name in obj_data and obj_data[field.name]:
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primary_key_bonus = 0.1
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break
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# Penalty for enum violations
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enum_penalty = 0.0
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for field in schema.fields:
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if field.enum_values and field.name in obj_data:
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if obj_data[field.name] not in field.enum_values:
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enum_penalty = 0.2
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break
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confidence = min(1.0, completeness_score + primary_key_bonus - enum_penalty)
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return max(0.0, confidence)
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# Create mock schema
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fields = [
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Field(name="id", type="string", required=True, primary=True,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="name", type="string", required=True, primary=False,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="status", type="string", required=False, primary=False,
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description="", size=0, enum_values=["active", "inactive"], indexed=False)
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]
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schema = RowSchema(name="test", description="", fields=fields)
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# Test cases
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complete_object = {"id": "123", "name": "John", "status": "active"}
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incomplete_object = {"id": "123", "name": ""} # Missing name value
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invalid_enum_object = {"id": "123", "name": "John", "status": "invalid"}
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# Act & Assert
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complete_confidence = calculate_confidence(complete_object, schema)
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incomplete_confidence = calculate_confidence(incomplete_object, schema)
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invalid_enum_confidence = calculate_confidence(invalid_enum_object, schema)
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assert complete_confidence > 0.9 # Should be high
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assert incomplete_confidence < complete_confidence # Should be lower
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assert invalid_enum_confidence < complete_confidence # Should be penalized
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def test_extracted_object_creation(self):
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"""Test ExtractedObject creation and properties"""
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# Arrange
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metadata = Metadata(
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id="test-extraction-001",
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user="test_user",
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collection="test_collection",
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metadata=[]
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)
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values = {
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"customer_id": "CUST001",
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"name": "John Doe",
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"email": "john@example.com",
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"status": "active"
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}
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# Act
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extracted_obj = ExtractedObject(
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metadata=metadata,
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schema_name="customer_records",
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values=values,
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confidence=0.95,
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source_span="John Doe (john@example.com) ID: CUST001"
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)
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# Assert
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assert extracted_obj.schema_name == "customer_records"
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assert extracted_obj.values["customer_id"] == "CUST001"
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assert extracted_obj.confidence == 0.95
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assert "John Doe" in extracted_obj.source_span
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assert extracted_obj.metadata.user == "test_user"
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def test_config_parsing_error_handling(self):
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"""Test configuration parsing with invalid JSON"""
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# Arrange
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invalid_config = {
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"schema": {
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"invalid_schema": "not valid json",
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"valid_schema": json.dumps({
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"name": "valid_schema",
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"fields": [{"name": "test", "type": "string"}]
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})
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}
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}
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parsed_schemas = {}
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# Act - simulate parsing with error handling
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for schema_name, schema_json in invalid_config["schema"].items():
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try:
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schema_def = json.loads(schema_json)
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# Only process valid JSON
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if "fields" in schema_def:
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parsed_schemas[schema_name] = schema_def
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except json.JSONDecodeError:
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# Skip invalid JSON
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continue
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# Assert
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assert len(parsed_schemas) == 1
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assert "valid_schema" in parsed_schemas
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assert "invalid_schema" not in parsed_schemas
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def test_multi_schema_parsing(self):
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"""Test parsing multiple schemas from configuration"""
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# Arrange
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multi_config = {
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"schema": {
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"customers": json.dumps({
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"name": "customers",
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"fields": [{"name": "id", "type": "string", "primary_key": True}]
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}),
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"products": json.dumps({
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"name": "products",
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"fields": [{"name": "sku", "type": "string", "primary_key": True}]
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})
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}
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}
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parsed_schemas = {}
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# Act
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for schema_name, schema_json in multi_config["schema"].items():
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schema_def = json.loads(schema_json)
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parsed_schemas[schema_name] = schema_def
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# Assert
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assert len(parsed_schemas) == 2
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assert "customers" in parsed_schemas
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assert "products" in parsed_schemas
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assert parsed_schemas["customers"]["fields"][0]["name"] == "id"
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assert parsed_schemas["products"]["fields"][0]["name"] == "sku"
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class TestObjectExtractionDataTypes:
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"""Test the data types used in object extraction"""
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def test_field_schema_with_all_properties(self):
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"""Test Field schema with all new properties"""
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# Act
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field = Field(
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name="status",
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type="string",
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size=50,
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primary=False,
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description="Customer status field",
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required=True,
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enum_values=["active", "inactive", "pending"],
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indexed=True
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)
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# Assert - test the properties that work correctly
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assert field.name == "status"
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assert field.type == "string"
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assert field.size == 50
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assert field.primary is False
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assert field.indexed is True
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assert len(field.enum_values) == 3
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assert "active" in field.enum_values
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# Note: required field may have Pulsar schema default behavior
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assert hasattr(field, 'required') # Field exists
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def test_row_schema_with_multiple_fields(self):
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"""Test RowSchema with multiple field types"""
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# Arrange
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fields = [
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Field(name="id", type="string", primary=True, required=True,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="name", type="string", primary=False, required=True,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="age", type="integer", primary=False, required=False,
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description="", size=0, enum_values=[], indexed=False),
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Field(name="status", type="string", primary=False, required=False,
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description="", size=0, enum_values=["active", "inactive"], indexed=True)
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]
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# Act
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schema = RowSchema(
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name="user_profile",
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description="User profile information",
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fields=fields
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)
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# Assert
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assert schema.name == "user_profile"
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assert len(schema.fields) == 4
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# Check field types
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id_field = next(f for f in schema.fields if f.name == "id")
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status_field = next(f for f in schema.fields if f.name == "status")
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assert id_field.primary is True
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assert len(status_field.enum_values) == 2
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assert status_field.indexed is True
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