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| from typing import Dict, List, Optional | |
| import pytest | |
| from pydantic import BaseModel, Field, ValidationError | |
| from starfish.common.exceptions import PydanticParserError | |
| from starfish.llm.parser.pydantic_parser import PydanticParser | |
| # Define test Pydantic models for use in tests | |
| class Address(BaseModel): | |
| street: str = Field(..., description="Street name") | |
| city: str = Field(..., description="City name") | |
| zip_code: Optional[str] = Field(None, description="Zip code") | |
| class Contact(BaseModel): | |
| name: str = Field(..., description="Contact name") | |
| phone: str = Field(..., description="Phone number") | |
| email: Optional[str] = Field(None, description="Email address") | |
| class Person(BaseModel): | |
| name: str = Field(..., description="Person's name") | |
| age: int = Field(..., description="Person's age") | |
| address: Address = Field(..., description="Person's address") | |
| contacts: List[Contact] = Field(default_factory=list, description="Person's contacts") | |
| class Child(BaseModel): | |
| name: str = Field(..., description="Child's name") | |
| age: int = Field(..., description="Child's age") | |
| hobbies: List[str] = Field(default_factory=list, description="Child's hobbies") | |
| class Spouse(BaseModel): | |
| name: str = Field(..., description="Spouse's name") | |
| occupation: Optional[str] = Field(None, description="Spouse's occupation") | |
| class Family(BaseModel): | |
| spouse: Optional[Spouse] = Field(None, description="Spouse information") | |
| children: List[Child] = Field(default_factory=list, description="Children information") | |
| class PersonWithFamily(BaseModel): | |
| name: str = Field(..., description="Person's name") | |
| age: int = Field(..., description="Person's age") | |
| family: Family = Field(..., description="Family information") | |
| class FactsList(BaseModel): | |
| facts: List[Dict[str, str]] = Field(..., description="A list of facts") | |
| class Fact(BaseModel): | |
| question: str = Field(..., description="The factual question generated") | |
| answer: str = Field(..., description="The corresponding answer") | |
| category: str = Field(..., description="A category for the fact") | |
| class NestedFactsList(BaseModel): | |
| facts: List[Fact] = Field(..., description="A list of facts") | |
| class TestPydanticParser: | |
| """Test cases for the PydanticParser class.""" | |
| # --------------------------------------------------------------------------- | |
| # Tests for schema conversion from Pydantic models | |
| # --------------------------------------------------------------------------- | |
| def test_to_json_schema_basic(self): | |
| """Test converting a basic Pydantic model to JSON schema.""" | |
| # Define a simple model for this test | |
| class SimpleModel(BaseModel): | |
| name: str = Field(..., description="Person's name") | |
| age: int = Field(..., description="Person's age") | |
| is_active: bool = Field(False, description="Activity status") | |
| schema = PydanticParser.to_json_schema(SimpleModel) | |
| # Check schema structure | |
| assert schema["type"] == "object" | |
| assert "properties" in schema | |
| assert "name" in schema["properties"] | |
| assert "age" in schema["properties"] | |
| assert "is_active" in schema["properties"] | |
| # Check property types | |
| assert schema["properties"]["name"]["type"] == "string" | |
| assert schema["properties"]["age"]["type"] == "integer" | |
| assert schema["properties"]["is_active"]["type"] == "boolean" | |
| # Check descriptions | |
| assert schema["properties"]["name"]["description"] == "Person's name" | |
| assert schema["properties"]["age"]["description"] == "Person's age" | |
| assert schema["properties"]["is_active"]["description"] == "Activity status" | |
| # Check required fields | |
| assert "required" in schema | |
| assert "name" in schema["required"] | |
| assert "age" in schema["required"] | |
| assert "is_active" not in schema["required"] # Has default value | |
| def test_to_json_schema_nested_object(self): | |
| """Test converting a Pydantic model with nested models to JSON schema.""" | |
| schema = PydanticParser.to_json_schema(Person) | |
| # Process the schema to resolve references | |
| processed_schema = PydanticParser._process_schema_for_formatting(schema) | |
| # Check root properties | |
| assert "name" in processed_schema["properties"] | |
| assert "age" in processed_schema["properties"] | |
| assert "address" in processed_schema["properties"] | |
| assert "contacts" in processed_schema["properties"] | |
| # Check nested address properties | |
| assert "properties" in processed_schema["properties"]["address"] | |
| assert "street" in processed_schema["properties"]["address"]["properties"] | |
| assert "city" in processed_schema["properties"]["address"]["properties"] | |
| assert "zip_code" in processed_schema["properties"]["address"]["properties"] | |
| # Check array of contacts properties | |
| assert processed_schema["properties"]["contacts"]["type"] == "array" | |
| assert "items" in processed_schema["properties"]["contacts"] | |
| assert "properties" in processed_schema["properties"]["contacts"]["items"] | |
| assert "name" in processed_schema["properties"]["contacts"]["items"]["properties"] | |
| assert "phone" in processed_schema["properties"]["contacts"]["items"]["properties"] | |
| assert "email" in processed_schema["properties"]["contacts"]["items"]["properties"] | |
| def test_to_json_schema_deeply_nested(self): | |
| """Test converting a deeply nested Pydantic model hierarchy to JSON schema.""" | |
| schema = PydanticParser.to_json_schema(PersonWithFamily) | |
| # Process the schema to resolve references | |
| processed_schema = PydanticParser._process_schema_for_formatting(schema) | |
| # Check first level nesting | |
| assert "family" in processed_schema["properties"] | |
| assert "properties" in processed_schema["properties"]["family"] | |
| # Check second level nesting - spouse and children | |
| family_props = processed_schema["properties"]["family"]["properties"] | |
| assert "spouse" in family_props | |
| assert "children" in family_props | |
| # Check that the schema was processed appropriately | |
| # Even if the exact structure varies, we need to ensure the schema contains | |
| # all the necessary information for generating valid instructions | |
| children_prop = family_props["children"] | |
| assert children_prop["type"] == "array" | |
| assert "items" in children_prop | |
| # --------------------------------------------------------------------------- | |
| # Tests for format instructions generation | |
| # --------------------------------------------------------------------------- | |
| def test_get_format_instructions_basic(self): | |
| """Test generating format instructions for a basic model.""" | |
| class SimpleModel(BaseModel): | |
| name: str = Field(..., description="Person's name") | |
| age: int = Field(..., description="Person's age") | |
| instructions = PydanticParser.get_format_instructions(SimpleModel) | |
| # Check basic elements | |
| assert "[" in instructions | |
| assert "]" in instructions | |
| assert '"name": ""' in instructions | |
| assert '"age": number' in instructions | |
| assert "Person's name (required)" in instructions | |
| assert "Person's age (required)" in instructions | |
| def test_get_format_instructions_nested(self): | |
| """Test generating format instructions for a model with nested objects.""" | |
| instructions = PydanticParser.get_format_instructions(Person) | |
| # Check nested object formatting | |
| assert '"address": {' in instructions | |
| assert '"street": ""' in instructions | |
| assert '"city": ""' in instructions | |
| assert '"zip_code": ""' in instructions | |
| assert "Street name (required)" in instructions | |
| assert "City name (required)" in instructions | |
| assert "Zip code (optional)" in instructions | |
| # Check array of objects formatting | |
| assert '"contacts": [' in instructions | |
| assert '"name": ""' in instructions # Multiple occurrences | |
| assert '"phone": ""' in instructions | |
| assert '"email": ""' in instructions | |
| assert "Contact name (required)" in instructions | |
| assert "Phone number (required)" in instructions | |
| assert "Email address (optional)" in instructions | |
| def test_get_format_instructions_deeply_nested(self): | |
| """Test generating format instructions for deeply nested models.""" | |
| instructions = PydanticParser.get_format_instructions(PersonWithFamily) | |
| # Check family nested object | |
| assert '"family": {' in instructions | |
| # Adjust the test to check for just the key presence without checking exact formatting | |
| assert '"spouse"' in instructions | |
| assert '"children"' in instructions | |
| # Check for name field presence in the output | |
| assert '"name"' in instructions | |
| # Less strict checks for description content | |
| assert "name" in instructions # Just check that "name" is mentioned somewhere | |
| assert "age" in instructions | |
| assert "hobbies" in instructions | |
| def test_nested_fact_model(self): | |
| """Test specific case for NestedFactsList model.""" | |
| instructions = PydanticParser.get_format_instructions(NestedFactsList) | |
| # Check facts array structure | |
| assert '"facts": [' in instructions | |
| # Check fact object properties | |
| assert '"question": ""' in instructions | |
| assert '"answer": ""' in instructions | |
| assert '"category": ""' in instructions | |
| # Check descriptions | |
| assert "The factual question generated (required)" in instructions | |
| assert "The corresponding answer (required)" in instructions | |
| assert "A category for the fact (required)" in instructions | |
| # --------------------------------------------------------------------------- | |
| # Tests for parsing LLM output to Pydantic models | |
| # --------------------------------------------------------------------------- | |
| def test_parse_dict_or_list_single(self): | |
| """Test parsing a single dictionary to a Pydantic model.""" | |
| data = {"name": "John Doe", "age": 35, "address": {"street": "123 Main St", "city": "Anytown", "zip_code": "12345"}} | |
| person = PydanticParser.parse_dict_or_list(data, Person) | |
| assert isinstance(person, Person) | |
| assert person.name == "John Doe" | |
| assert person.age == 35 | |
| assert person.address.street == "123 Main St" | |
| assert person.address.city == "Anytown" | |
| assert person.address.zip_code == "12345" | |
| assert isinstance(person.address, Address) | |
| assert len(person.contacts) == 0 | |
| def test_parse_dict_or_list_list(self): | |
| """Test parsing a list of dictionaries to a list of Pydantic models.""" | |
| data = [{"name": "Alice", "phone": "555-1234"}, {"name": "Bob", "phone": "555-5678", "email": "[email protected]"}] | |
| contacts = PydanticParser.parse_dict_or_list(data, Contact) | |
| assert isinstance(contacts, list) | |
| assert len(contacts) == 2 | |
| assert all(isinstance(contact, Contact) for contact in contacts) | |
| assert contacts[0].name == "Alice" | |
| assert contacts[0].phone == "555-1234" | |
| assert contacts[0].email is None | |
| assert contacts[1].name == "Bob" | |
| assert contacts[1].phone == "555-5678" | |
| assert contacts[1].email == "[email protected]" | |
| def test_parse_dict_or_list_validation_error(self): | |
| """Test validation errors in parse_dict_or_list.""" | |
| # Missing required field | |
| data = {"name": "John"} | |
| with pytest.raises(ValidationError): | |
| PydanticParser.parse_dict_or_list(data, Contact) | |
| # Wrong type for field | |
| data = {"name": "John", "phone": 12345} | |
| with pytest.raises(ValidationError): | |
| PydanticParser.parse_dict_or_list(data, Contact) | |
| def test_parse_llm_output_basic(self): | |
| """Test parsing LLM output into a basic Pydantic model.""" | |
| text = '{"name": "Alice", "phone": "555-1234"}' | |
| result = PydanticParser.parse_llm_output(text, Contact) | |
| assert isinstance(result, Contact) | |
| assert result.name == "Alice" | |
| assert result.phone == "555-1234" | |
| assert result.email is None | |
| def test_parse_llm_output_nested(self): | |
| """Test parsing LLM output into a nested Pydantic model structure.""" | |
| text = """ | |
| { | |
| "name": "John Smith", | |
| "age": 42, | |
| "address": { | |
| "street": "123 Main St", | |
| "city": "Anytown" | |
| }, | |
| "contacts": [ | |
| { | |
| "name": "Jane Smith", | |
| "phone": "555-1234", | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "name": "Bob Jones", | |
| "phone": "555-5678" | |
| } | |
| ] | |
| } | |
| """ | |
| result = PydanticParser.parse_llm_output(text, Person) | |
| assert isinstance(result, Person) | |
| assert result.name == "John Smith" | |
| assert result.age == 42 | |
| assert result.address.street == "123 Main St" | |
| assert result.address.city == "Anytown" | |
| assert len(result.contacts) == 2 | |
| assert result.contacts[0].name == "Jane Smith" | |
| assert result.contacts[0].phone == "555-1234" | |
| assert result.contacts[0].email == "[email protected]" | |
| assert result.contacts[1].name == "Bob Jones" | |
| assert result.contacts[1].phone == "555-5678" | |
| assert result.contacts[1].email is None | |
| def test_parse_llm_output_with_markdown_code_blocks(self): | |
| """Test parsing LLM output with markdown formatting.""" | |
| text = """ | |
| Here's the information you requested: | |
| ```json | |
| { | |
| "name": "John Smith", | |
| "age": 42, | |
| "address": { | |
| "street": "123 Main St", | |
| "city": "Anytown" | |
| } | |
| } | |
| ``` | |
| Is there anything else you need? | |
| """ | |
| result = PydanticParser.parse_llm_output(text, Person) | |
| assert isinstance(result, Person) | |
| assert result.name == "John Smith" | |
| assert result.age == 42 | |
| assert result.address.street == "123 Main St" | |
| assert result.address.city == "Anytown" | |
| def test_parse_llm_output_error_handling(self): | |
| """Test error handling in parse_llm_output.""" | |
| # Missing required field | |
| text = '{"name": "John"}' | |
| # Should raise error in strict mode | |
| with pytest.raises(PydanticParserError): | |
| PydanticParser.parse_llm_output(text, Person, strict=True) | |
| # Should return None in non-strict mode | |
| result = PydanticParser.parse_llm_output(text, Person, strict=False) | |
| assert result is None | |
| def test_parse_llm_output_with_wrapper(self): | |
| """Test parsing with a JSON wrapper key.""" | |
| text = """ | |
| { | |
| "results": [ | |
| { | |
| "name": "John Doe", | |
| "phone": "555-1234" | |
| }, | |
| { | |
| "name": "Jane Smith", | |
| "phone": "555-5678", | |
| "email": "[email protected]" | |
| } | |
| ] | |
| } | |
| """ | |
| result = PydanticParser.parse_llm_output(text, Contact, json_wrapper_key="results") | |
| assert isinstance(result, list) | |
| assert len(result) == 2 | |
| assert all(isinstance(item, Contact) for item in result) | |
| assert result[0].name == "John Doe" | |
| assert result[1].name == "Jane Smith" | |
| assert result[1].email == "[email protected]" | |
| def test_nested_facts_list_parsing(self): | |
| """Test parsing the specific NestedFactsList example.""" | |
| text = """ | |
| { | |
| "facts": [ | |
| { | |
| "question": "What is the tallest building in New York?", | |
| "answer": "One World Trade Center", | |
| "category": "Architecture" | |
| }, | |
| { | |
| "question": "What is the largest park in New York?", | |
| "answer": "Pelham Bay Park", | |
| "category": "Geography" | |
| } | |
| ] | |
| } | |
| """ | |
| result = PydanticParser.parse_llm_output(text, NestedFactsList) | |
| assert isinstance(result, NestedFactsList) | |
| assert len(result.facts) == 2 | |
| assert all(isinstance(fact, Fact) for fact in result.facts) | |
| assert result.facts[0].question == "What is the tallest building in New York?" | |
| assert result.facts[0].answer == "One World Trade Center" | |
| assert result.facts[0].category == "Architecture" | |
| assert result.facts[1].question == "What is the largest park in New York?" | |
| assert result.facts[1].answer == "Pelham Bay Park" | |
| assert result.facts[1].category == "Geography" | |