mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-06-26 22:00:13 +00:00
409 lines
17 KiB
Python
409 lines
17 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
|
|
#
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from unittest.mock import AsyncMock, patch, MagicMock
|
|
|
|
from openai import AsyncOpenAI, OpenAIError
|
|
import pytest
|
|
from datetime import datetime
|
|
import os
|
|
|
|
from openai.types.chat import ChatCompletion, ChatCompletionMessage, ChatCompletionMessageToolCall, ChatCompletionChunk
|
|
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.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
|
|
|
|
|
|
@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_async):
|
|
"""
|
|
Mock the OpenAI API completion chunk response and reuse it for tests
|
|
"""
|
|
|
|
with patch(
|
|
"openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock
|
|
) 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()),
|
|
usage=None,
|
|
)
|
|
mock_chat_completion_create.return_value = openai_mock_stream_async(completion)
|
|
yield mock_chat_completion_create
|
|
|
|
|
|
@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=lambda x: x,
|
|
)
|
|
return [tool]
|
|
|
|
|
|
class TestOpenAIChatGeneratorAsync:
|
|
def test_init_should_also_create_async_client_with_same_args(self, monkeypatch):
|
|
monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
|
|
component = OpenAIChatGenerator(
|
|
api_key=Secret.from_token("test-api-key"),
|
|
api_base_url="test-base-url",
|
|
organization="test-organization",
|
|
timeout=30,
|
|
max_retries=5,
|
|
)
|
|
|
|
assert isinstance(component.async_client, AsyncOpenAI)
|
|
assert component.async_client.api_key == "test-api-key"
|
|
assert component.async_client.organization == "test-organization"
|
|
assert component.async_client.base_url == "test-base-url/"
|
|
assert component.async_client.timeout == 30
|
|
assert component.async_client.max_retries == 5
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_async(self, chat_messages, openai_mock_async_chat_completion):
|
|
component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"))
|
|
response = await component.run_async(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"]]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_params_async(self, chat_messages, openai_mock_async_chat_completion):
|
|
component = OpenAIChatGenerator(
|
|
api_key=Secret.from_token("test-api-key"), generation_kwargs={"max_tokens": 10, "temperature": 0.5}
|
|
)
|
|
response = await component.run_async(chat_messages)
|
|
|
|
# check that the component calls the OpenAI API with the correct parameters
|
|
_, kwargs = openai_mock_async_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"]]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_params_streaming_async(self, chat_messages, openai_mock_async_chat_completion_chunk):
|
|
streaming_callback_called = False
|
|
|
|
async 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 = await component.run_async(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
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_streaming_callback_in_run_method_async(
|
|
self, chat_messages, openai_mock_async_chat_completion_chunk
|
|
):
|
|
streaming_callback_called = False
|
|
|
|
async 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 = await component.run_async(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
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_tools_async(self, tools):
|
|
with patch(
|
|
"openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock
|
|
) 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 = await component.run_async([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
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_tools_streaming_async(self, mock_chat_completion_chunk_with_tools, tools):
|
|
streaming_callback_called = False
|
|
|
|
async 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 = await component.run_async(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"
|
|
|
|
@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
|
|
@pytest.mark.asyncio
|
|
async def test_live_run_async(self):
|
|
chat_messages = [ChatMessage.from_user("What's the capital of France")]
|
|
component = OpenAIChatGenerator(generation_kwargs={"n": 1})
|
|
results = await component.run_async(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"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_run_with_wrong_model_async(self):
|
|
mock_client = MagicMock()
|
|
mock_client.chat.completions.create.side_effect = OpenAIError("Invalid model name")
|
|
|
|
generator = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"), model="something-obviously-wrong")
|
|
|
|
generator.client = mock_client
|
|
|
|
with pytest.raises(OpenAIError):
|
|
await generator.run_async([ChatMessage.from_user("irrelevant")])
|
|
|
|
@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
|
|
@pytest.mark.asyncio
|
|
async def test_live_run_streaming_async(self):
|
|
counter = 0
|
|
responses = ""
|
|
|
|
async def callback(chunk: StreamingChunk):
|
|
nonlocal counter
|
|
nonlocal responses
|
|
counter += 1
|
|
responses += chunk.content if chunk.content else ""
|
|
|
|
component = OpenAIChatGenerator(
|
|
streaming_callback=callback, generation_kwargs={"stream_options": {"include_usage": True}}
|
|
)
|
|
results = await component.run_async([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 counter > 1
|
|
assert "Paris" in 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
|
|
@pytest.mark.asyncio
|
|
async def test_live_run_with_tools_async(self, tools):
|
|
chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")]
|
|
component = OpenAIChatGenerator(tools=tools)
|
|
results = await component.run_async(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.asyncio
|
|
async def test_run_with_wrapped_stream_simulation_async(self, chat_messages, openai_mock_stream_async):
|
|
streaming_callback_called = False
|
|
|
|
async 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 async stream in an AsyncMock
|
|
# This is to simulate the behavior of some tools like Weave (https://github.com/wandb/weave)
|
|
# which wrap the OpenAI async stream in their own stream
|
|
wrapped_openai_async_stream = AsyncMock()
|
|
wrapped_openai_async_stream.__aiter__.return_value = iter([chunk])
|
|
|
|
component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"))
|
|
|
|
# Patch the async client's create method
|
|
with patch.object(
|
|
component.async_client.chat.completions,
|
|
"create",
|
|
return_value=wrapped_openai_async_stream,
|
|
new_callable=AsyncMock,
|
|
) as mock_create:
|
|
response = await component.run_async(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
|