litellm/tests/llm_responses_api_testing/base_responses_api.py

import httpx
import json
import pytest
import sys
from typing import Any, Dict, List
from unittest.mock import MagicMock, Mock, patch
import os
import uuid
import time
import base64

sys.path.insert(
    0, os.path.abspath("../..")
)  # Adds the parent directory to the system path
import litellm
from abc import ABC, abstractmethod

from litellm.integrations.custom_logger import CustomLogger
import json
from litellm.types.utils import StandardLoggingPayload
from litellm.types.llms.openai import (
    ResponseCompletedEvent,
    ResponsesAPIResponse,
    ResponseTextConfig,
    ResponseAPIUsage,
    IncompleteDetails,
)
from openai.types.responses.response_create_params import (
    ResponseInputParam,
)
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler


def validate_responses_api_response(response, final_chunk: bool = False):
    """
    Validate that a response from litellm.responses() or litellm.aresponses()
    conforms to the expected ResponsesAPIResponse structure.

    Args:
        response: The response object to validate

    Raises:
        AssertionError: If the response doesn't match the expected structure
    """
    # Validate response structure
    print("response=", json.dumps(response, indent=4, default=str))
    assert isinstance(
        response, ResponsesAPIResponse
    ), "Response should be an instance of ResponsesAPIResponse"

    # Required fields
    assert "id" in response and isinstance(
        response["id"], str
    ), "Response should have a string 'id' field"
    assert "created_at" in response and isinstance(
        response["created_at"], int
    ), "Response should have an integer 'created_at' field"
    assert "output" in response and isinstance(
        response["output"], list
    ), "Response should have a list 'output' field"
    assert "parallel_tool_calls" in response and isinstance(
        response["parallel_tool_calls"], bool
    ), "Response should have a boolean 'parallel_tool_calls' field"

    # Optional fields with their expected types
    optional_fields = {
        "error": (dict, type(None)),  # error can be dict or None
        "incomplete_details": (IncompleteDetails, type(None)),
        "instructions": (str, type(None)),
        "metadata": dict,
        "model": str,
        "object": str,
        "temperature": (int, float, type(None)),
        "tool_choice": (dict, str),
        "tools": list,
        "top_p": (int, float, type(None)),
        "max_output_tokens": (int, type(None)),
        "previous_response_id": (str, type(None)),
        "reasoning": dict,
        "status": str,
        "text": ResponseTextConfig,
        "truncation": (str, type(None)),
        "usage": ResponseAPIUsage,
        "user": (str, type(None)),
        "store": (bool, type(None)),
    }
    if final_chunk is False:
        optional_fields["usage"] = type(None)

    for field, expected_type in optional_fields.items():
        if field in response:
            assert isinstance(
                response[field], expected_type
            ), f"Field '{field}' should be of type {expected_type}, but got {type(response[field])}"

    # Check if output has at least one item
    if final_chunk is True:
        assert (
            len(response["output"]) > 0
        ), "Response 'output' field should have at least one item"

    return True  # Return True if validation passes



class BaseResponsesAPITest(ABC):
    """
    Abstract base test class that enforces a common test across all test classes.
    """
    @abstractmethod
    def get_base_completion_call_args(self) -> dict:
        """Must return the base completion call args"""
        pass

    def get_base_completion_reasoning_call_args(self) -> dict:
        """Must return the base completion reasoning call args"""
        return None


    @pytest.mark.parametrize("sync_mode", [True, False])
    @pytest.mark.asyncio
    async def test_basic_openai_responses_api(self, sync_mode):
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        try: 
            if sync_mode:
                response = litellm.responses(
                    input="Basic ping", max_output_tokens=20,
                    **base_completion_call_args
                )
            else:
                response = await litellm.aresponses(
                    input="Basic ping", max_output_tokens=20,
                    **base_completion_call_args
                )
        except litellm.InternalServerError: 
            pytest.skip("Skipping test due to litellm.InternalServerError")
        print("litellm response=", json.dumps(response, indent=4, default=str))

        # Use the helper function to validate the response
        validate_responses_api_response(response, final_chunk=True)


    @pytest.mark.parametrize("sync_mode", [True, False])
    @pytest.mark.asyncio
    @pytest.mark.flaky(retries=3, delay=2)
    async def test_basic_openai_responses_api_streaming(self, sync_mode):
        litellm._turn_on_debug()
        base_completion_call_args = self.get_base_completion_call_args()
        collected_content_string = ""
        response_completed_event = None
        if sync_mode:
            response = litellm.responses(
                input="Basic ping",
                stream=True,
                **base_completion_call_args
            )
            for event in response:
                print("litellm response=", json.dumps(event, indent=4, default=str))
                if event.type == "response.output_text.delta":
                    collected_content_string += event.delta
                elif event.type == "response.completed":
                    response_completed_event = event
        else:
            response = await litellm.aresponses(
                input="Basic ping",
                stream=True,
                **base_completion_call_args
            )
            async for event in response:
                print("litellm response=", json.dumps(event, indent=4, default=str))
                if event.type == "response.output_text.delta":
                    collected_content_string += event.delta
                elif event.type == "response.completed":
                    response_completed_event = event

        # assert the delta chunks content had len(collected_content_string) > 0
        # this content is typically rendered on chat ui's
        assert len(collected_content_string) > 0

        # assert the response completed event is not None
        assert response_completed_event is not None

        # assert the response completed event has a response
        assert response_completed_event.response is not None

        # assert the response completed event includes the usage
        assert response_completed_event.response.usage is not None

        # basic test assert the usage seems reasonable
        print("response_completed_event.response.usage=", response_completed_event.response.usage)
        assert response_completed_event.response.usage.input_tokens > 0 and response_completed_event.response.usage.input_tokens < 100
        assert response_completed_event.response.usage.output_tokens > 0 and response_completed_event.response.usage.output_tokens < 2000
        assert response_completed_event.response.usage.total_tokens > 0 and response_completed_event.response.usage.total_tokens < 2000

        # total tokens should be the sum of input and output tokens
        assert response_completed_event.response.usage.total_tokens == response_completed_event.response.usage.input_tokens + response_completed_event.response.usage.output_tokens



    @pytest.mark.parametrize("sync_mode", [False, True])
    @pytest.mark.asyncio
    async def test_basic_openai_responses_delete_endpoint(self, sync_mode):
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        if sync_mode:
            response = litellm.responses(
                input="Basic ping", max_output_tokens=20,
                **base_completion_call_args
            )

            # delete the response
            if isinstance(response, ResponsesAPIResponse):
                litellm.delete_responses(
                    response_id=response.id,
                    **base_completion_call_args
                )
            else:
                raise ValueError("response is not a ResponsesAPIResponse")
        else:
            response = await litellm.aresponses(
                input="Basic ping", max_output_tokens=20,
                **base_completion_call_args
            )

            # async delete the response
            if isinstance(response, ResponsesAPIResponse):
                await litellm.adelete_responses(
                    response_id=response.id,
                    **base_completion_call_args
                )
            else:
                raise ValueError("response is not a ResponsesAPIResponse")
    

    @pytest.mark.parametrize("sync_mode", [True, False])
    @pytest.mark.flaky(retries=3, delay=2)
    @pytest.mark.asyncio
    async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode):
        #litellm._turn_on_debug()
        #litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        response_id = None
        if sync_mode:
            response_id = None
            response = litellm.responses(
                input="Basic ping", max_output_tokens=20,
                stream=True,
                **base_completion_call_args
            )
            for event in response:
                print("litellm response=", json.dumps(event, indent=4, default=str))
                if "response" in event:
                    response_obj = event.get("response")
                    if response_obj is not None:
                        response_id = response_obj.get("id")
            print("got response_id=", response_id)

            # delete the response
            assert response_id is not None
            litellm.delete_responses(
                response_id=response_id,
                **base_completion_call_args
            )
        else:
            response = await litellm.aresponses(
                input="Basic ping", max_output_tokens=20,
                stream=True,
                **base_completion_call_args
            )
            async for event in response:
                print("litellm response=", json.dumps(event, indent=4, default=str))
                if "response" in event:
                    response_obj = event.get("response")
                    if response_obj is not None:
                        response_id = response_obj.get("id")
            print("got response_id=", response_id)

            # delete the response
            assert response_id is not None
            await litellm.adelete_responses(
                response_id=response_id,
                **base_completion_call_args
            )

    @pytest.mark.parametrize("sync_mode", [False, True])
    @pytest.mark.flaky(retries=3, delay=2)
    @pytest.mark.asyncio
    async def test_basic_openai_responses_get_endpoint(self, sync_mode):
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        if sync_mode:
            response = litellm.responses(
                input="Basic ping", max_output_tokens=20,
                **base_completion_call_args
            )

            # get the response
            if isinstance(response, ResponsesAPIResponse):
                result = litellm.get_responses(
                    response_id=response.id,
                    **base_completion_call_args
                )
                assert result is not None
                assert result.id == response.id
                assert result.output == response.output
            else:
                raise ValueError("response is not a ResponsesAPIResponse")
        else:
            response = await litellm.aresponses(
                input="Basic ping", max_output_tokens=20,
                **base_completion_call_args
            )
            # async get the response
            if isinstance(response, ResponsesAPIResponse):
                result = await litellm.aget_responses(
                    response_id=response.id,
                    **base_completion_call_args
                )
                assert result is not None
                assert result.id == response.id
                assert result.output == response.output
            else:
                raise ValueError("response is not a ResponsesAPIResponse")

    @pytest.mark.asyncio
    @pytest.mark.flaky(retries=3, delay=2)
    async def test_basic_openai_list_input_items_endpoint(self):
        """Test that calls the OpenAI List Input Items endpoint"""
        litellm._turn_on_debug()

        response = await litellm.aresponses(
            model="gpt-4o",
            input="Tell me a three sentence bedtime story about a unicorn.",
        )
        print("Initial response=", json.dumps(response, indent=4, default=str))

        response_id = response.get("id")
        assert response_id is not None, "Response should have an ID"
        print(f"Got response_id: {response_id}")

        list_items_response = await litellm.alist_input_items(
            response_id=response_id,
            limit=20,
            order="desc",
        )
        print(
            "List items response=",
            json.dumps(list_items_response, indent=4, default=str),
        )

    
    @pytest.mark.asyncio
    async def test_multiturn_responses_api(self):
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        response_1 = await litellm.aresponses(
            input="Basic ping", max_output_tokens=20, **base_completion_call_args
        )

        # follow up with a second request
        response_1_id = response_1.id
        response_2 = await litellm.aresponses(
            input="Basic ping", 
            max_output_tokens=20, 
            previous_response_id=response_1_id,
            **base_completion_call_args
        )

        # assert the response is not None
        assert response_1 is not None
        assert response_2 is not None
    
    @pytest.mark.asyncio
    async def test_responses_api_with_tool_calls(self):
        """Test that calls the Responses API with tool calls including function call and output"""
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_call_args()
        
        # Define the input with message, function call, and function call output
        input_data: ResponseInputParam = [
            {
                "type": "message",
                "role": "user",
                "content": "How is the weather in São Paulo today ?"
            },
            {
                "type": "function_call",
                "arguments": "{\"location\": \"São Paulo, Brazil\"}",
                "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
                "name": "get_weather",
                "id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
                "status": "completed"
            },
            {
                "type": "function_call_output",
                "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
                "output": "Rainy"
            }
        ]
        
        # Define the tools
        tools = [
            {
                "type": "function",
                "name": "get_weather",
                "description": "Get current temperature for a given location.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City and country e.g. Bogotá, Colombia"
                        }
                    },
                    "required": ["location"],
                    "additionalProperties": False
                }
            }
        ]
        
        try:
            # Make the responses API call
            response = await litellm.aresponses(
                input=input_data,
                store=False,
                tools=tools,
                **base_completion_call_args
            )
        except litellm.InternalServerError:
            pytest.skip("Skipping test due to litellm.InternalServerError")
        
        print("litellm response=", json.dumps(response, indent=4, default=str))
        
        # Validate the response structure
        validate_responses_api_response(response, final_chunk=True)
        
        # Additional assertions specific to tool calls
        assert response is not None
        assert "output" in response
        assert len(response["output"]) > 0
    
    @pytest.mark.asyncio
    async def test_responses_api_multi_turn_with_reasoning_and_structured_output(self):
        """
        Test multi-turn conversation with reasoning, structured output, and tool calls.
        
        This test validates:
        - First call: Model uses reasoning to process a question and makes a tool call
        - Tool call handling: Function call output is properly processed 
        - Second call: Model produces structured output incorporating tool results
        - Structured output: Response conforms to defined Pydantic model schema
        """
        from pydantic import BaseModel
        
        litellm._turn_on_debug()
        litellm.set_verbose = True
        base_completion_call_args = self.get_base_completion_reasoning_call_args()
        if base_completion_call_args is None:
            pytest.skip("Skipping test due to no base completion reasoning call args")
        
        # Define tools for the conversation
        tools = [{"type": "function", "name": "get_today"}]
        
        # Define structured output schema
        class Output(BaseModel):
            today: str
            number_of_r: str
        
        # Initial conversation input
        input_messages = [
            {
                "role": "user", 
                "content": "How many r in strrawberrry? While you're thinking, you should call tool get_today. Then you output the today and number of r",
            }
        ]
        

        # First call - should trigger reasoning and tool call
        response = await litellm.aresponses(
            input=input_messages,
            tools=tools,
            reasoning={"effort": "low", "summary": "detailed"},
            text_format=Output,
            **base_completion_call_args
        )

        print("First call output:")
        print(json.dumps(response.output, indent=4, default=str))
        
        # Validate first response structure
        validate_responses_api_response(response, final_chunk=True)
        assert response.output is not None
        assert len(response.output) > 0
        
        # Extend input with first response output
        input_messages.extend(response.output)
        
        # Process any tool calls and add function outputs
        function_outputs = []
        for item in response.output:
            if hasattr(item, 'type') and item.type in ["function_call", "custom_tool_call"]:
                if hasattr(item, 'name') and item.name == "get_today":
                    function_outputs.append({
                        "type": "function_call_output", 
                        "call_id": item.call_id, 
                        "output": "2025-01-15"
                    })
        
        # Add function outputs to conversation
        input_messages.extend(function_outputs)
        
        print("Second call input:")
        print(json.dumps(input_messages, indent=4, default=str))
        
        # Second call - should produce structured output
        final_response = await litellm.aresponses(
            input=input_messages,
            tools=tools,
            reasoning={"effort": "low", "summary": "detailed"},
            text_format=Output,
            **base_completion_call_args
        )
        
        print("Second call output:")
        print(json.dumps(final_response.output, indent=4, default=str))
        
        # Validate final response structure
        validate_responses_api_response(final_response, final_chunk=True)
        assert final_response.output is not None
        assert len(final_response.output) > 0