mirror of
https://github.com/deepset-ai/haystack.git
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81c0cefa41
* Refactor HFAPI Chat Generator * Add component info to generators * Fix type hint * Add reno * Fix unit tests * Remove incorrect dev comment * Move _convert_streaming_chunks_to_chat_message to utils file
760 lines
32 KiB
Python
760 lines
32 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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from unittest.mock import patch, MagicMock
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import pytest
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import logging
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import os
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from openai import OpenAIError
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from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage, ChatCompletionMessageToolCall
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from openai.types.chat.chat_completion import Choice
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from openai.types.completion_usage import CompletionTokensDetails, CompletionUsage, PromptTokensDetails
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from openai.types.chat.chat_completion_message_tool_call import Function
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from openai.types.chat import chat_completion_chunk
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from haystack import component
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from haystack.components.generators.utils import print_streaming_chunk
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from haystack.dataclasses import StreamingChunk
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from haystack.utils.auth import Secret
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from haystack.dataclasses import ChatMessage, ToolCall
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from haystack.tools import ComponentTool, Tool
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from haystack.components.generators.chat.openai import (
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OpenAIChatGenerator,
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_check_finish_reason,
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_convert_chat_completion_chunk_to_streaming_chunk,
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)
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from haystack.tools.toolset import Toolset
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@pytest.fixture
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def chat_messages():
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return [
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ChatMessage.from_system("You are a helpful assistant"),
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ChatMessage.from_user("What's the capital of France"),
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]
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@pytest.fixture
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def mock_chat_completion_chunk_with_tools(openai_mock_stream):
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"""
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Mock the OpenAI API completion chunk response and reuse it for tests
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"""
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with patch("openai.resources.chat.completions.Completions.create") as mock_chat_completion_create:
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completion = ChatCompletionChunk(
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id="foo",
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model="gpt-4",
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object="chat.completion.chunk",
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choices=[
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chat_completion_chunk.Choice(
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finish_reason="tool_calls",
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logprobs=None,
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index=0,
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delta=chat_completion_chunk.ChoiceDelta(
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role="assistant",
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tool_calls=[
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chat_completion_chunk.ChoiceDeltaToolCall(
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index=0,
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id="123",
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type="function",
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function=chat_completion_chunk.ChoiceDeltaToolCallFunction(
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name="weather", arguments='{"city": "Paris"}'
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),
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)
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],
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),
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)
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],
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created=int(datetime.now().timestamp()),
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)
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mock_chat_completion_create.return_value = openai_mock_stream(
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completion, cast_to=None, response=None, client=None
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)
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yield mock_chat_completion_create
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def weather_function(city: str) -> Dict[str, Any]:
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weather_info = {
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"Berlin": {"weather": "mostly sunny", "temperature": 7, "unit": "celsius"},
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"Paris": {"weather": "mostly cloudy", "temperature": 8, "unit": "celsius"},
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"Rome": {"weather": "sunny", "temperature": 14, "unit": "celsius"},
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}
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return weather_info.get(city, {"weather": "unknown", "temperature": 0, "unit": "celsius"})
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@component
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class MessageExtractor:
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@component.output_types(messages=List[str], meta=Dict[str, Any])
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def run(self, messages: List[ChatMessage], meta: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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"""
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Extracts the text content of ChatMessage objects
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:param messages: List of Haystack ChatMessage objects
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:param meta: Optional metadata to include in the response.
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:returns:
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A dictionary with keys "messages" and "meta".
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"""
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if meta is None:
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meta = {}
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return {"messages": [m.text for m in messages], "meta": meta}
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@pytest.fixture
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def tools():
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weather_tool = Tool(
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name="weather",
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description="useful to determine the weather in a given location",
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parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
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function=weather_function,
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)
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# We add a tool that has a more complex parameter signature
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message_extractor_tool = ComponentTool(
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component=MessageExtractor(),
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name="message_extractor",
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description="Useful for returning the text content of ChatMessage objects",
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)
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return [weather_tool, message_extractor_tool]
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class TestOpenAIChatGenerator:
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def test_init_default(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = OpenAIChatGenerator()
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assert component.client.api_key == "test-api-key"
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assert component.model == "gpt-4o-mini"
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assert component.streaming_callback is None
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assert not component.generation_kwargs
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assert component.client.timeout == 30
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assert component.client.max_retries == 5
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assert component.tools is None
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assert not component.tools_strict
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assert component.http_client_kwargs is None
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def test_init_fail_wo_api_key(self, monkeypatch):
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monkeypatch.delenv("OPENAI_API_KEY", raising=False)
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with pytest.raises(ValueError):
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OpenAIChatGenerator()
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def test_init_fail_with_duplicate_tool_names(self, monkeypatch, tools):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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duplicate_tools = [tools[0], tools[0]]
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with pytest.raises(ValueError):
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OpenAIChatGenerator(tools=duplicate_tools)
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def test_init_with_parameters(self, monkeypatch):
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tool = Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=lambda x: x)
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monkeypatch.setenv("OPENAI_TIMEOUT", "100")
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monkeypatch.setenv("OPENAI_MAX_RETRIES", "10")
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component = OpenAIChatGenerator(
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api_key=Secret.from_token("test-api-key"),
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model="gpt-4o-mini",
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streaming_callback=print_streaming_chunk,
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api_base_url="test-base-url",
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generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
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timeout=40.0,
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max_retries=1,
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tools=[tool],
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tools_strict=True,
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http_client_kwargs={"proxy": "http://example.com:8080", "verify": False},
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)
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assert component.client.api_key == "test-api-key"
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assert component.model == "gpt-4o-mini"
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assert component.streaming_callback is print_streaming_chunk
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assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
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assert component.client.timeout == 40.0
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assert component.client.max_retries == 1
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assert component.tools == [tool]
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assert component.tools_strict
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assert component.http_client_kwargs == {"proxy": "http://example.com:8080", "verify": False}
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def test_init_with_parameters_and_env_vars(self, monkeypatch):
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monkeypatch.setenv("OPENAI_TIMEOUT", "100")
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monkeypatch.setenv("OPENAI_MAX_RETRIES", "10")
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component = OpenAIChatGenerator(
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api_key=Secret.from_token("test-api-key"),
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model="gpt-4o-mini",
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streaming_callback=print_streaming_chunk,
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api_base_url="test-base-url",
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generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
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)
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assert component.client.api_key == "test-api-key"
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assert component.model == "gpt-4o-mini"
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assert component.streaming_callback is print_streaming_chunk
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assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
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assert component.client.timeout == 100.0
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assert component.client.max_retries == 10
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def test_to_dict_default(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = OpenAIChatGenerator()
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.generators.chat.openai.OpenAIChatGenerator",
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"init_parameters": {
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"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
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"model": "gpt-4o-mini",
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"organization": None,
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"streaming_callback": None,
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"api_base_url": None,
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"generation_kwargs": {},
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"tools": None,
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"tools_strict": False,
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"max_retries": None,
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"timeout": None,
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"http_client_kwargs": None,
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},
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}
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def test_to_dict_with_parameters(self, monkeypatch):
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tool = Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=print)
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monkeypatch.setenv("ENV_VAR", "test-api-key")
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component = OpenAIChatGenerator(
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api_key=Secret.from_env_var("ENV_VAR"),
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model="gpt-4o-mini",
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streaming_callback=print_streaming_chunk,
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api_base_url="test-base-url",
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generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
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tools=[tool],
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tools_strict=True,
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max_retries=10,
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timeout=100.0,
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http_client_kwargs={"proxy": "http://example.com:8080", "verify": False},
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)
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.generators.chat.openai.OpenAIChatGenerator",
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"init_parameters": {
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"api_key": {"env_vars": ["ENV_VAR"], "strict": True, "type": "env_var"},
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"model": "gpt-4o-mini",
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"organization": None,
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"api_base_url": "test-base-url",
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"max_retries": 10,
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"timeout": 100.0,
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"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
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"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
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"tools": [
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{
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"type": "haystack.tools.tool.Tool",
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"data": {
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"description": "description",
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"function": "builtins.print",
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"inputs_from_state": None,
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"name": "name",
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"outputs_to_state": None,
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"outputs_to_string": None,
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"parameters": {"x": {"type": "string"}},
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},
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}
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],
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"tools_strict": True,
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"http_client_kwargs": {"proxy": "http://example.com:8080", "verify": False},
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},
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}
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def test_from_dict(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
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data = {
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"type": "haystack.components.generators.chat.openai.OpenAIChatGenerator",
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"init_parameters": {
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"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
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"model": "gpt-4o-mini",
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"api_base_url": "test-base-url",
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"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
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"max_retries": 10,
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"timeout": 100.0,
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"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
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"tools": [
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{
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"type": "haystack.tools.tool.Tool",
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"data": {
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"description": "description",
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"function": "builtins.print",
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"name": "name",
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"parameters": {"x": {"type": "string"}},
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},
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}
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],
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"tools_strict": True,
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"http_client_kwargs": {"proxy": "http://example.com:8080", "verify": False},
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},
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}
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component = OpenAIChatGenerator.from_dict(data)
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assert isinstance(component, OpenAIChatGenerator)
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assert component.model == "gpt-4o-mini"
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assert component.streaming_callback is print_streaming_chunk
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assert component.api_base_url == "test-base-url"
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assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
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assert component.api_key == Secret.from_env_var("OPENAI_API_KEY")
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assert component.tools == [
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Tool(name="name", description="description", parameters={"x": {"type": "string"}}, function=print)
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]
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assert component.tools_strict
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assert component.client.timeout == 100.0
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assert component.client.max_retries == 10
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assert component.http_client_kwargs == {"proxy": "http://example.com:8080", "verify": False}
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def test_from_dict_fail_wo_env_var(self, monkeypatch):
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monkeypatch.delenv("OPENAI_API_KEY", raising=False)
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data = {
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"type": "haystack.components.generators.chat.openai.OpenAIChatGenerator",
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"init_parameters": {
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"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
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"model": "gpt-4",
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"organization": None,
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"api_base_url": "test-base-url",
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"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
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"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
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"tools": None,
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},
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}
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with pytest.raises(ValueError):
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OpenAIChatGenerator.from_dict(data)
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def test_run(self, chat_messages, openai_mock_chat_completion):
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component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"))
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response = component.run(chat_messages)
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# check that the component returns the correct ChatMessage response
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assert isinstance(response, dict)
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assert "replies" in response
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assert isinstance(response["replies"], list)
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assert len(response["replies"]) == 1
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assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
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def test_run_with_params(self, chat_messages, openai_mock_chat_completion):
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component = OpenAIChatGenerator(
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api_key=Secret.from_token("test-api-key"), generation_kwargs={"max_tokens": 10, "temperature": 0.5}
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)
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response = component.run(chat_messages)
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# check that the component calls the OpenAI API with the correct parameters
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_, kwargs = openai_mock_chat_completion.call_args
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assert kwargs["max_tokens"] == 10
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assert kwargs["temperature"] == 0.5
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# check that the tools are not passed to the OpenAI API (the generator is initialized without tools)
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assert "tools" not in kwargs
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# check that the component returns the correct response
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assert isinstance(response, dict)
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assert "replies" in response
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assert isinstance(response["replies"], list)
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assert len(response["replies"]) == 1
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assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
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def test_run_with_params_streaming(self, chat_messages, openai_mock_chat_completion_chunk):
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streaming_callback_called = False
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def streaming_callback(chunk: StreamingChunk) -> None:
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nonlocal streaming_callback_called
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streaming_callback_called = True
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component = OpenAIChatGenerator(
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api_key=Secret.from_token("test-api-key"), streaming_callback=streaming_callback
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)
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response = component.run(chat_messages)
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# check we called the streaming callback
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assert streaming_callback_called
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# check that the component still returns the correct response
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assert isinstance(response, dict)
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assert "replies" in response
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assert isinstance(response["replies"], list)
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assert len(response["replies"]) == 1
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assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
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assert "Hello" in response["replies"][0].text # see openai_mock_chat_completion_chunk
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def test_run_with_streaming_callback_in_run_method(self, chat_messages, openai_mock_chat_completion_chunk):
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streaming_callback_called = False
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def streaming_callback(chunk: StreamingChunk) -> None:
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nonlocal streaming_callback_called
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streaming_callback_called = True
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component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"))
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response = component.run(chat_messages, streaming_callback=streaming_callback)
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# check we called the streaming callback
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assert streaming_callback_called
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# check that the component still returns the correct response
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assert isinstance(response, dict)
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assert "replies" in response
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assert isinstance(response["replies"], list)
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assert len(response["replies"]) == 1
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assert [isinstance(reply, ChatMessage) for reply in response["replies"]]
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assert "Hello" in response["replies"][0].text # see openai_mock_chat_completion_chunk
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def test_run_with_wrapped_stream_simulation(self, chat_messages, openai_mock_stream):
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streaming_callback_called = False
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def streaming_callback(chunk: StreamingChunk) -> None:
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nonlocal streaming_callback_called
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streaming_callback_called = True
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assert isinstance(chunk, StreamingChunk)
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chunk = ChatCompletionChunk(
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id="id",
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model="gpt-4",
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object="chat.completion.chunk",
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choices=[chat_completion_chunk.Choice(index=0, delta=chat_completion_chunk.ChoiceDelta(content="Hello"))],
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created=int(datetime.now().timestamp()),
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)
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# Here we wrap the OpenAI stream in a MagicMock
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# This is to simulate the behavior of some tools like Weave (https://github.com/wandb/weave)
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# which wrap the OpenAI stream in their own stream
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wrapped_openai_stream = MagicMock()
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wrapped_openai_stream.__iter__.return_value = iter([chunk])
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component = OpenAIChatGenerator(api_key=Secret.from_token("test-api-key"))
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with patch.object(
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component.client.chat.completions, "create", return_value=wrapped_openai_stream
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) as mock_create:
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response = component.run(chat_messages, streaming_callback=streaming_callback)
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mock_create.assert_called_once()
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assert streaming_callback_called
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assert "replies" in response
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assert "Hello" in response["replies"][0].text
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def test_check_abnormal_completions(self, caplog):
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caplog.set_level(logging.INFO)
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messages = [
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ChatMessage.from_assistant(
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"", meta={"finish_reason": "content_filter" if i % 2 == 0 else "length", "index": i}
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)
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for i, _ in enumerate(range(4))
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]
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for m in messages:
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_check_finish_reason(m.meta)
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# check truncation warning
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message_template = (
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"The completion for index {index} has been truncated before reaching a natural stopping point. "
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"Increase the max_tokens parameter to allow for longer completions."
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)
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for index in [1, 3]:
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assert caplog.records[index].message == message_template.format(index=index)
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# check content filter warning
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message_template = "The completion for index {index} has been truncated due to the content filter."
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for index in [0, 2]:
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assert caplog.records[index].message == message_template.format(index=index)
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|
|
|
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[:1], 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_usage_chunk_to_streaming_chunk(self):
|
|
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 = _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
|
|
|
|
def test_run_with_wrong_model(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):
|
|
generator.run([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
|
|
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?")])
|
|
|
|
# Basic response checks
|
|
assert "replies" in results
|
|
assert len(results["replies"]) == 1
|
|
message: ChatMessage = results["replies"][0]
|
|
assert "Paris" in message.text
|
|
assert isinstance(message.meta, dict)
|
|
|
|
# Metadata checks
|
|
metadata = message.meta
|
|
assert "gpt-4o" in metadata["model"]
|
|
assert metadata["finish_reason"] == "stop"
|
|
|
|
# Usage information checks
|
|
assert isinstance(metadata.get("usage"), dict), "meta.usage not a dict"
|
|
usage = metadata["usage"]
|
|
assert "prompt_tokens" in usage and usage["prompt_tokens"] > 0
|
|
assert "completion_tokens" in usage and usage["completion_tokens"] > 0
|
|
|
|
# Detailed token information checks
|
|
assert isinstance(usage.get("completion_tokens_details"), dict), "usage.completion_tokens_details not a dict"
|
|
assert isinstance(usage.get("prompt_tokens_details"), dict), "usage.prompt_tokens_details not a dict"
|
|
|
|
# Streaming callback verification
|
|
assert callback.counter > 1
|
|
assert "Paris" in callback.responses
|
|
|
|
@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"
|