# SPDX-FileCopyrightText: 2022-present deepset GmbH # # SPDX-License-Identifier: Apache-2.0 from unittest.mock import patch, MagicMock, AsyncMock import pytest import logging import os from datetime import datetime from openai import OpenAIError from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import Choice from openai.types.completion_usage import CompletionTokensDetails, CompletionUsage, PromptTokensDetails from openai.types.chat.chat_completion_message_tool_call import Function from openai.types.chat import chat_completion_chunk from haystack.components.generators.utils import print_streaming_chunk from haystack.dataclasses import StreamingChunk from haystack.utils.auth import Secret from haystack.dataclasses import ChatMessage, ToolCall from haystack.tools import Tool from haystack.components.generators.chat.openai import OpenAIChatGenerator from haystack.tools.toolset import Toolset @pytest.fixture def chat_messages(): return [ ChatMessage.from_system("You are a helpful assistant"), ChatMessage.from_user("What's the capital of France"), ] @pytest.fixture def mock_chat_completion_chunk_with_tools(openai_mock_stream): """ Mock the OpenAI API completion chunk response and reuse it for tests """ with patch("openai.resources.chat.completions.Completions.create") as mock_chat_completion_create: completion = ChatCompletionChunk( id="foo", model="gpt-4", object="chat.completion.chunk", choices=[ chat_completion_chunk.Choice( finish_reason="tool_calls", logprobs=None, index=0, delta=chat_completion_chunk.ChoiceDelta( role="assistant", tool_calls=[ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id="123", type="function", function=chat_completion_chunk.ChoiceDeltaToolCallFunction( name="weather", arguments='{"city": "Paris"}' ), ) ], ), ) ], created=int(datetime.now().timestamp()), ) mock_chat_completion_create.return_value = openai_mock_stream( completion, cast_to=None, response=None, client=None ) yield mock_chat_completion_create def mock_tool_function(x): return x @pytest.fixture def tools(): tool_parameters = {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]} tool = Tool( name="weather", description="useful to determine the weather in a given location", parameters=tool_parameters, function=mock_tool_function, ) return [tool] class TestOpenAIChatGenerator: def test_init_default(self, monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "test-api-key") component = OpenAIChatGenerator() assert component.client.api_key == "test-api-key" assert component.model == "gpt-4o-mini" assert component.streaming_callback is None assert not component.generation_kwargs assert component.client.timeout == 30 assert component.client.max_retries == 5 assert component.tools is None assert not component.tools_strict assert component.http_client_kwargs is None def test_init_fail_wo_api_key(self, monkeypatch): monkeypatch.delenv("OPENAI_API_KEY", raising=False) with pytest.raises(ValueError): OpenAIChatGenerator() def test_init_fail_with_duplicate_tool_names(self, monkeypatch, tools): monkeypatch.setenv("OPENAI_API_KEY", "test-api-key") duplicate_tools = [tools[0], tools[0]] with pytest.raises(ValueError): OpenAIChatGenerator(tools=duplicate_tools) def test_init_with_parameters(self, monkeypatch): tool = Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=lambda x: x) monkeypatch.setenv("OPENAI_TIMEOUT", "100") monkeypatch.setenv("OPENAI_MAX_RETRIES", "10") component = OpenAIChatGenerator( api_key=Secret.from_token("test-api-key"), model="gpt-4o-mini", streaming_callback=print_streaming_chunk, api_base_url="test-base-url", generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"}, timeout=40.0, max_retries=1, tools=[tool], tools_strict=True, http_client_kwargs={"proxy": "http://example.com:8080", "verify": False}, ) assert component.client.api_key == "test-api-key" assert component.model == "gpt-4o-mini" assert component.streaming_callback is print_streaming_chunk assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"} assert component.client.timeout == 40.0 assert component.client.max_retries == 1 assert component.tools == [tool] assert component.tools_strict assert component.http_client_kwargs == {"proxy": "http://example.com:8080", "verify": False} def test_init_with_parameters_and_env_vars(self, monkeypatch): monkeypatch.setenv("OPENAI_TIMEOUT", "100") monkeypatch.setenv("OPENAI_MAX_RETRIES", "10") component = OpenAIChatGenerator( api_key=Secret.from_token("test-api-key"), model="gpt-4o-mini", streaming_callback=print_streaming_chunk, api_base_url="test-base-url", generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"}, ) assert component.client.api_key == "test-api-key" assert component.model == "gpt-4o-mini" assert component.streaming_callback is print_streaming_chunk assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"} assert component.client.timeout == 100.0 assert component.client.max_retries == 10 def test_to_dict_default(self, monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "test-api-key") component = OpenAIChatGenerator() data = component.to_dict() assert data == { "type": "haystack.components.generators.chat.openai.OpenAIChatGenerator", "init_parameters": { "api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"}, "model": "gpt-4o-mini", "organization": None, "streaming_callback": None, "api_base_url": None, "generation_kwargs": {}, "tools": None, "tools_strict": False, "max_retries": None, "timeout": None, "http_client_kwargs": None, }, } def test_to_dict_with_parameters(self, monkeypatch): tool = Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=print) monkeypatch.setenv("ENV_VAR", "test-api-key") component = OpenAIChatGenerator( api_key=Secret.from_env_var("ENV_VAR"), model="gpt-4o-mini", streaming_callback=print_streaming_chunk, api_base_url="test-base-url", generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"}, tools=[tool], tools_strict=True, max_retries=10, timeout=100.0, http_client_kwargs={"proxy": "http://example.com:8080", "verify": False}, ) data = component.to_dict() assert data == { "type": "haystack.components.generators.chat.openai.OpenAIChatGenerator", "init_parameters": { "api_key": {"env_vars": ["ENV_VAR"], "strict": True, "type": "env_var"}, "model": "gpt-4o-mini", "organization": None, "api_base_url": "test-base-url", "max_retries": 10, "timeout": 100.0, "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk", "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"}, "tools": [ { "type": "haystack.tools.tool.Tool", "data": { "description": "description", "function": "builtins.print", "inputs_from_state": None, "name": "name", "outputs_to_state": None, "outputs_to_string": None, "parameters": {"x": {"type": "string"}}, }, } ], "tools_strict": True, "http_client_kwargs": {"proxy": "http://example.com:8080", "verify": False}, }, } def test_from_dict(self, monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key") data = { "type": "haystack.components.generators.chat.openai.OpenAIChatGenerator", "init_parameters": { "api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"}, "model": "gpt-4o-mini", "api_base_url": "test-base-url", "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk", "max_retries": 10, "timeout": 100.0, "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"}, "tools": [ { "type": "haystack.tools.tool.Tool", "data": { "description": "description", "function": "builtins.print", "name": "name", "parameters": {"x": {"type": "string"}}, }, } ], "tools_strict": True, "http_client_kwargs": {"proxy": "http://example.com:8080", "verify": False}, }, } component = OpenAIChatGenerator.from_dict(data) assert isinstance(component, OpenAIChatGenerator) assert component.model == "gpt-4o-mini" assert component.streaming_callback is print_streaming_chunk assert component.api_base_url == "test-base-url" assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"} assert component.api_key == Secret.from_env_var("OPENAI_API_KEY") assert component.tools == [ Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=print) ] assert component.tools_strict assert component.client.timeout == 100.0 assert component.client.max_retries == 10 assert component.http_client_kwargs == {"proxy": "http://example.com:8080", "verify": False} def test_from_dict_fail_wo_env_var(self, monkeypatch): monkeypatch.delenv("OPENAI_API_KEY", raising=False) data = { "type": "haystack.components.generators.chat.openai.OpenAIChatGenerator", "init_parameters": { "api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"}, "model": "gpt-4", "organization": None, "api_base_url": "test-base-url", "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk", "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"}, "tools": None, }, } with pytest.raises(ValueError): OpenAIChatGenerator.from_dict(data) def test_run(self, chat_messages, openai_mock_chat_completion): component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) response = component.run(chat_messages) # check that the component returns the correct ChatMessage response assert isinstance(response, dict) assert "replies" in response assert isinstance(response["replies"], list) assert len(response["replies"]) == 1 assert [isinstance(reply, ChatMessage) for reply in response["replies"]] def test_run_with_params(self, chat_messages, openai_mock_chat_completion): component = OpenAIChatGenerator( api_key=Secret.from_token("test-api-key"), generation_kwargs={"max_tokens": 10, "temperature": 0.5} ) response = component.run(chat_messages) # check that the component calls the OpenAI API with the correct parameters _, kwargs = openai_mock_chat_completion.call_args assert kwargs["max_tokens"] == 10 assert kwargs["temperature"] == 0.5 # check that the tools are not passed to the OpenAI API (the generator is initialized without tools) assert "tools" not in kwargs # check that the component returns the correct response assert isinstance(response, dict) assert "replies" in response assert isinstance(response["replies"], list) assert len(response["replies"]) == 1 assert [isinstance(reply, ChatMessage) for reply in response["replies"]] def test_run_with_params_streaming(self, chat_messages, openai_mock_chat_completion_chunk): streaming_callback_called = False def streaming_callback(chunk: StreamingChunk) -> None: nonlocal streaming_callback_called streaming_callback_called = True component = OpenAIChatGenerator( api_key=Secret.from_token("test-api-key"), streaming_callback=streaming_callback ) response = component.run(chat_messages) # check we called the streaming callback assert streaming_callback_called # check that the component still returns the correct response assert isinstance(response, dict) assert "replies" in response assert isinstance(response["replies"], list) assert len(response["replies"]) == 1 assert [isinstance(reply, ChatMessage) for reply in response["replies"]] assert "Hello" in response["replies"][0].text # see openai_mock_chat_completion_chunk def test_run_with_streaming_callback_in_run_method(self, chat_messages, openai_mock_chat_completion_chunk): streaming_callback_called = False def streaming_callback(chunk: StreamingChunk) -> None: nonlocal streaming_callback_called streaming_callback_called = True component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) response = component.run(chat_messages, streaming_callback=streaming_callback) # check we called the streaming callback assert streaming_callback_called # check that the component still returns the correct response assert isinstance(response, dict) assert "replies" in response assert isinstance(response["replies"], list) assert len(response["replies"]) == 1 assert [isinstance(reply, ChatMessage) for reply in response["replies"]] assert "Hello" in response["replies"][0].text # see openai_mock_chat_completion_chunk def test_run_with_wrapped_stream_simulation(self, chat_messages, openai_mock_stream): streaming_callback_called = False def streaming_callback(chunk: StreamingChunk) -> None: nonlocal streaming_callback_called streaming_callback_called = True assert isinstance(chunk, StreamingChunk) chunk = ChatCompletionChunk( id="id", model="gpt-4", object="chat.completion.chunk", choices=[chat_completion_chunk.Choice(index=0, delta=chat_completion_chunk.ChoiceDelta(content="Hello"))], created=int(datetime.now().timestamp()), ) # Here we wrap the OpenAI stream in a MagicMock # This is to simulate the behavior of some tools like Weave (https://github.com/wandb/weave) # which wrap the OpenAI stream in their own stream wrapped_openai_stream = MagicMock() wrapped_openai_stream.__iter__.return_value = iter([chunk]) component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) with patch.object( component.client.chat.completions, "create", return_value=wrapped_openai_stream ) as mock_create: response = component.run(chat_messages, streaming_callback=streaming_callback) mock_create.assert_called_once() assert streaming_callback_called assert "replies" in response assert "Hello" in response["replies"][0].text def test_check_abnormal_completions(self, caplog): caplog.set_level(logging.INFO) component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) messages = [ ChatMessage.from_assistant( "", meta={"finish_reason": "content_filter" if i % 2 == 0 else "length", "index": i} ) for i, _ in enumerate(range(4)) ] for m in messages: component._check_finish_reason(m.meta) # check truncation warning message_template = ( "The completion for index {index} has been truncated before reaching a natural stopping point. " "Increase the max_tokens parameter to allow for longer completions." ) for index in [1, 3]: assert caplog.records[index].message == message_template.format(index=index) # check content filter warning message_template = "The completion for index {index} has been truncated due to the content filter." for index in [0, 2]: assert caplog.records[index].message == message_template.format(index=index) def test_run_with_tools(self, tools): with patch("openai.resources.chat.completions.Completions.create") as mock_chat_completion_create: completion = ChatCompletion( id="foo", model="gpt-4", object="chat.completion", choices=[ Choice( finish_reason="tool_calls", logprobs=None, index=0, message=ChatCompletionMessage( role="assistant", tool_calls=[ ChatCompletionMessageToolCall( id="123", type="function", function=Function(name="weather", arguments='{"city": "Paris"}'), ) ], ), ) ], created=int(datetime.now().timestamp()), usage=CompletionUsage( completion_tokens=40, prompt_tokens=57, total_tokens=97, completion_tokens_details=CompletionTokensDetails( accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0 ), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0), ), ) mock_chat_completion_create.return_value = completion component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"), tools=tools, tools_strict=True) response = component.run([ChatMessage.from_user("What's the weather like in Paris?")]) # ensure that the tools are passed to the OpenAI API function_spec = {**tools[0].tool_spec} function_spec["strict"] = True function_spec["parameters"]["additionalProperties"] = False assert mock_chat_completion_create.call_args[1]["tools"] == [{"type": "function", "function": function_spec}] assert len(response["replies"]) == 1 message = response["replies"][0] assert not message.texts assert not message.text assert message.tool_calls tool_call = message.tool_call assert isinstance(tool_call, ToolCall) assert tool_call.tool_name == "weather" assert tool_call.arguments == {"city": "Paris"} assert message.meta["finish_reason"] == "tool_calls" assert message.meta["usage"]["completion_tokens"] == 40 def test_run_with_tools_streaming(self, mock_chat_completion_chunk_with_tools, tools): streaming_callback_called = False def streaming_callback(chunk: StreamingChunk) -> None: nonlocal streaming_callback_called streaming_callback_called = True component = OpenAIChatGenerator( api_key=Secret.from_token("test-api-key"), streaming_callback=streaming_callback ) chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")] response = component.run(chat_messages, tools=tools) # check we called the streaming callback assert streaming_callback_called # check that the component still returns the correct response assert isinstance(response, dict) assert "replies" in response assert isinstance(response["replies"], list) assert len(response["replies"]) == 1 assert [isinstance(reply, ChatMessage) for reply in response["replies"]] message = response["replies"][0] assert message.tool_calls tool_call = message.tool_call assert isinstance(tool_call, ToolCall) assert tool_call.tool_name == "weather" assert tool_call.arguments == {"city": "Paris"} assert message.meta["finish_reason"] == "tool_calls" def test_invalid_tool_call_json(self, tools, caplog): caplog.set_level(logging.WARNING) with patch("openai.resources.chat.completions.Completions.create") as mock_create: mock_create.return_value = ChatCompletion( id="test", model="gpt-4o-mini", object="chat.completion", choices=[ Choice( finish_reason="tool_calls", index=0, message=ChatCompletionMessage( role="assistant", tool_calls=[ ChatCompletionMessageToolCall( id="1", type="function", function=Function(name="weather", arguments='"invalid": "json"'), ) ], ), ) ], created=1234567890, usage=CompletionUsage( completion_tokens=47, prompt_tokens=540, total_tokens=587, completion_tokens_details=CompletionTokensDetails( accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0 ), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0), ), ) component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"), tools=tools) response = component.run([ChatMessage.from_user("What's the weather in Paris?")]) assert len(response["replies"]) == 1 message = response["replies"][0] assert len(message.tool_calls) == 0 assert "OpenAI returned a malformed JSON string for tool call arguments" in caplog.text assert message.meta["finish_reason"] == "tool_calls" assert message.meta["usage"]["completion_tokens"] == 47 def test_convert_streaming_chunks_to_chat_message_tool_calls_in_any_chunk(self): component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) chunk = chat_completion_chunk.ChatCompletionChunk( id="chatcmpl-B2g1XYv1WzALulC5c8uLtJgvEB48I", choices=[ chat_completion_chunk.Choice( delta=chat_completion_chunk.ChoiceDelta( content=None, function_call=None, refusal=None, role=None, tool_calls=None ), finish_reason="tool_calls", index=0, logprobs=None, ) ], created=1739977895, model="gpt-4o-mini-2024-07-18", object="chat.completion.chunk", service_tier="default", system_fingerprint="fp_00428b782a", usage=None, ) chunks = [ StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": None, "finish_reason": None, "received_at": "2025-02-19T16:02:55.910076", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id="call_ZOj5l67zhZOx6jqjg7ATQwb6", function=chat_completion_chunk.ChoiceDeltaToolCallFunction( arguments="", name="rag_pipeline_tool" ), type="function", ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.913919", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='{"qu', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.914439", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='ery":', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.924146", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments=' "Wher', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.924420", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="e do", name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.944398", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="es Ma", name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.944958", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments="rk liv", name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.945507", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=0, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='e?"}', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.946018", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=1, id="call_STxsYY69wVOvxWqopAt3uWTB", function=chat_completion_chunk.ChoiceDeltaToolCallFunction( arguments="", name="get_weather" ), type="function", ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.946578", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=1, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='{"ci', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.946981", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=1, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='ty": ', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.947411", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=1, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='"Berli', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.947643", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": [ chat_completion_chunk.ChoiceDeltaToolCall( index=1, id=None, function=chat_completion_chunk.ChoiceDeltaToolCallFunction(arguments='n"}', name=None), type=None, ) ], "finish_reason": None, "received_at": "2025-02-19T16:02:55.947939", }, ), StreamingChunk( content="", meta={ "model": "gpt-4o-mini-2024-07-18", "index": 0, "tool_calls": None, "finish_reason": "tool_calls", "received_at": "2025-02-19T16:02:55.948772", }, ), ] # Convert chunks to a chat message result = component._convert_streaming_chunks_to_chat_message(chunk, chunks) assert not result.texts assert not result.text # Verify both tool calls were found and processed assert len(result.tool_calls) == 2 assert result.tool_calls[0].id == "call_ZOj5l67zhZOx6jqjg7ATQwb6" assert result.tool_calls[0].tool_name == "rag_pipeline_tool" assert result.tool_calls[0].arguments == {"query": "Where does Mark live?"} assert result.tool_calls[1].id == "call_STxsYY69wVOvxWqopAt3uWTB" assert result.tool_calls[1].tool_name == "get_weather" assert result.tool_calls[1].arguments == {"city": "Berlin"} # Verify meta information assert result.meta["model"] == "gpt-4o-mini-2024-07-18" assert result.meta["finish_reason"] == "tool_calls" assert result.meta["index"] == 0 assert result.meta["completion_start_time"] == "2025-02-19T16:02:55.910076" def test_convert_usage_chunk_to_streaming_chunk(self): component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key")) chunk = ChatCompletionChunk( id="chatcmpl-BC1y4wqIhe17R8sv3lgLcWlB4tXCw", choices=[], created=1742207200, model="gpt-4o-mini-2024-07-18", object="chat.completion.chunk", service_tier="default", system_fingerprint="fp_06737a9306", usage=CompletionUsage( completion_tokens=8, prompt_tokens=13, total_tokens=21, completion_tokens_details=CompletionTokensDetails( accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0 ), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0), ), ) result = component._convert_chat_completion_chunk_to_streaming_chunk(chunk) assert result.content == "" assert result.meta["model"] == "gpt-4o-mini-2024-07-18" assert result.meta["received_at"] is not None @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run(self): chat_messages = [ChatMessage.from_user("What's the capital of France")] component = OpenAIChatGenerator(generation_kwargs={"n": 1}) results = component.run(chat_messages) assert len(results["replies"]) == 1 message: ChatMessage = results["replies"][0] assert "Paris" in message.text assert "gpt-4o" in message.meta["model"] assert message.meta["finish_reason"] == "stop" assert message.meta["usage"]["prompt_tokens"] > 0 @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run_wrong_model(self, chat_messages): component = OpenAIChatGenerator(model="something-obviously-wrong") with pytest.raises(OpenAIError): component.run(chat_messages) @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run_streaming(self): class Callback: def __init__(self): self.responses = "" self.counter = 0 def __call__(self, chunk: StreamingChunk) -> None: self.counter += 1 self.responses += chunk.content if chunk.content else "" callback = Callback() component = OpenAIChatGenerator( streaming_callback=callback, generation_kwargs={"stream_options": {"include_usage": True}} ) results = component.run([ChatMessage.from_user("What's the capital of France?")]) assert len(results["replies"]) == 1 message: ChatMessage = results["replies"][0] assert "Paris" in message.text assert "gpt-4o" in message.meta["model"] assert message.meta["finish_reason"] == "stop" assert callback.counter > 1 assert "Paris" in callback.responses # check that the completion_start_time is set and valid ISO format assert "completion_start_time" in message.meta assert datetime.fromisoformat(message.meta["completion_start_time"]) <= datetime.now() assert isinstance(message.meta["usage"], dict) assert message.meta["usage"]["prompt_tokens"] > 0 assert message.meta["usage"]["completion_tokens"] > 0 assert message.meta["usage"]["total_tokens"] > 0 @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run_with_tools(self, tools): chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")] component = OpenAIChatGenerator(tools=tools) results = component.run(chat_messages) assert len(results["replies"]) == 1 message = results["replies"][0] assert not message.texts assert not message.text assert message.tool_calls tool_call = message.tool_call assert isinstance(tool_call, ToolCall) assert tool_call.tool_name == "weather" assert tool_call.arguments == {"city": "Paris"} assert message.meta["finish_reason"] == "tool_calls" @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run_with_tools_streaming(self, tools): chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")] component = OpenAIChatGenerator(tools=tools, streaming_callback=print_streaming_chunk) results = component.run(chat_messages) assert len(results["replies"]) == 1 message = results["replies"][0] assert not message.texts assert not message.text assert message.tool_calls tool_call = message.tool_call assert isinstance(tool_call, ToolCall) assert tool_call.tool_name == "weather" assert tool_call.arguments == {"city": "Paris"} assert message.meta["finish_reason"] == "tool_calls" def test_openai_chat_generator_with_toolset_initialization(self, tools, monkeypatch): """Test that the OpenAIChatGenerator can be initialized with a Toolset.""" monkeypatch.setenv("OPENAI_API_KEY", "test-api-key") toolset = Toolset(tools) generator = OpenAIChatGenerator(tools=toolset) assert generator.tools == toolset def test_from_dict_with_toolset(self, tools, monkeypatch): """Test that the OpenAIChatGenerator can be deserialized from a dictionary with a Toolset.""" monkeypatch.setenv("OPENAI_API_KEY", "test-api-key") toolset = Toolset(tools) component = OpenAIChatGenerator(tools=toolset) data = component.to_dict() deserialized_component = OpenAIChatGenerator.from_dict(data) assert isinstance(deserialized_component.tools, Toolset) assert len(deserialized_component.tools) == len(tools) assert all(isinstance(tool, Tool) for tool in deserialized_component.tools) @pytest.mark.skipif( not os.environ.get("OPENAI_API_KEY", None), reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.", ) @pytest.mark.integration def test_live_run_with_toolset(self, tools): chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")] toolset = Toolset(tools) component = OpenAIChatGenerator(tools=toolset) results = component.run(chat_messages) assert len(results["replies"]) == 1 message = results["replies"][0] assert not message.texts assert not message.text assert message.tool_calls tool_call = message.tool_call assert isinstance(tool_call, ToolCall) assert tool_call.tool_name == "weather" assert tool_call.arguments == {"city": "Paris"} assert message.meta["finish_reason"] == "tool_calls"