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363 lines
16 KiB
Python
363 lines
16 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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import logging
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import os
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from typing import List
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from datetime import datetime
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import pytest
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from openai import OpenAIError
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from openai.types.chat import ChatCompletionChunk, chat_completion_chunk
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from unittest.mock import MagicMock, patch
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from haystack.components.generators import OpenAIGenerator
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from haystack.components.generators.utils import print_streaming_chunk
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from haystack.dataclasses import ChatMessage, StreamingChunk
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from haystack.utils.auth import Secret
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class TestOpenAIGenerator:
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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 = OpenAIGenerator()
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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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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, match="None of the .* environment variables are set"):
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OpenAIGenerator()
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def test_init_with_parameters(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 = OpenAIGenerator(
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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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)
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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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def test_to_dict_default(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = OpenAIGenerator()
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.generators.openai.OpenAIGenerator",
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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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"streaming_callback": None,
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"system_prompt": None,
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"api_base_url": None,
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"organization": None,
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"http_client_kwargs": None,
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"generation_kwargs": {},
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},
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}
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def test_to_dict_with_parameters(self, monkeypatch):
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monkeypatch.setenv("ENV_VAR", "test-api-key")
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component = OpenAIGenerator(
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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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organization="org-1234567",
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http_client_kwargs={"proxy": "http://localhost:8080"},
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generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
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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.openai.OpenAIGenerator",
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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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"system_prompt": None,
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"api_base_url": "test-base-url",
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"organization": "org-1234567",
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"http_client_kwargs": {"proxy": "http://localhost:8080"},
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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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},
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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.openai.OpenAIGenerator",
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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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"system_prompt": None,
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"organization": None,
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"api_base_url": "test-base-url",
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"http_client_kwargs": None,
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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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},
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}
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component = OpenAIGenerator.from_dict(data)
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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.http_client_kwargs is None
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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.openai.OpenAIGenerator",
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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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"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
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},
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}
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with pytest.raises(ValueError, match="None of the .* environment variables are set"):
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OpenAIGenerator.from_dict(data)
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def test_run(self, openai_mock_chat_completion):
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component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
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response = component.run("What's Natural Language Processing?")
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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, str) for reply in response["replies"]]
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def test_run_with_params_streaming(self, 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 = OpenAIGenerator(api_key=Secret.from_token("test-api-key"), streaming_callback=streaming_callback)
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response = component.run("Come on, stream!")
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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 "Hello" in response["replies"][0] # see openai_mock_chat_completion_chunk
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def test_run_with_streaming_callback_in_run_method(self, 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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# pass streaming_callback to run()
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component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
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response = component.run("Come on, stream!", 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 "Hello" in response["replies"][0] # see openai_mock_chat_completion_chunk
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def test_run_with_params(self, openai_mock_chat_completion):
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component = OpenAIGenerator(
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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("What's Natural Language Processing?")
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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 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, str) for reply in response["replies"]]
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def test_check_abnormal_completions(self, caplog):
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caplog.set_level(logging.INFO)
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component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
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# underlying implementation uses ChatMessage objects so we have to use them here
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messages: List[ChatMessage] = []
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for i, _ in enumerate(range(4)):
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message = ChatMessage.from_assistant("Hello")
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metadata = {"finish_reason": "content_filter" if i % 2 == 0 else "length", "index": i}
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message.meta.update(metadata)
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messages.append(message)
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for m in messages:
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component._check_finish_reason(m)
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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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@pytest.mark.skipif(
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not os.environ.get("OPENAI_API_KEY", None),
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reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
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)
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@pytest.mark.integration
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def test_live_run(self):
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component = OpenAIGenerator()
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results = component.run("What's the capital of France?")
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assert len(results["replies"]) == 1
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assert len(results["meta"]) == 1
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response: str = results["replies"][0]
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assert "Paris" in response
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metadata = results["meta"][0]
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assert "gpt-4o-mini" in metadata["model"]
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assert metadata["finish_reason"] == "stop"
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assert "usage" in metadata
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assert "prompt_tokens" in metadata["usage"] and metadata["usage"]["prompt_tokens"] > 0
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assert "completion_tokens" in metadata["usage"] and metadata["usage"]["completion_tokens"] > 0
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assert "total_tokens" in metadata["usage"] and metadata["usage"]["total_tokens"] > 0
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def test_run_with_wrong_model(self):
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mock_client = MagicMock()
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mock_client.chat.completions.create.side_effect = OpenAIError("Invalid model name")
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generator = OpenAIGenerator(api_key=Secret.from_token("test-api-key"), model="something-obviously-wrong")
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generator.client = mock_client
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with pytest.raises(OpenAIError):
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generator.run("Whatever")
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@pytest.mark.skipif(
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not os.environ.get("OPENAI_API_KEY", None),
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reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
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)
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@pytest.mark.integration
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def test_run_with_system_prompt(self):
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generator = OpenAIGenerator(model="gpt-4o-mini", system_prompt="Answer in Italian using only one word.")
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result = generator.run("What's the capital of Italy?")
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assert "roma" in result["replies"][0].lower()
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@pytest.mark.skipif(
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not os.environ.get("OPENAI_API_KEY", None),
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reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
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)
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@pytest.mark.integration
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def test_live_run_streaming_with_include_usage(self):
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class Callback:
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def __init__(self):
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self.responses = ""
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self.counter = 0
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def __call__(self, chunk: StreamingChunk) -> None:
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self.counter += 1
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self.responses += chunk.content if chunk.content else ""
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callback = Callback()
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component = OpenAIGenerator(
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streaming_callback=callback, generation_kwargs={"stream_options": {"include_usage": True}}
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)
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results = component.run("What's the capital of France?")
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# Basic response validation
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assert len(results["replies"]) == 1
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assert len(results["meta"]) == 1
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response: str = results["replies"][0]
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assert "Paris" in response
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# Metadata validation
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metadata = results["meta"][0]
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assert "gpt-4o-mini" in metadata["model"]
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assert metadata["finish_reason"] == "stop"
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# Basic usage validation
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assert isinstance(metadata.get("usage"), dict), "meta.usage not a dict"
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usage = metadata["usage"]
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assert "prompt_tokens" in usage and usage["prompt_tokens"] > 0
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assert "completion_tokens" in usage and usage["completion_tokens"] > 0
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# Detailed token information validation
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assert isinstance(usage.get("completion_tokens_details"), dict), "usage.completion_tokens_details not a dict"
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assert isinstance(usage.get("prompt_tokens_details"), dict), "usage.prompt_tokens_details not a dict"
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# Streaming callback validation
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assert callback.counter > 1
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assert "Paris" in callback.responses
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def test_run_with_wrapped_stream_simulation(self, 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=[
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chat_completion_chunk.Choice(
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index=0, delta=chat_completion_chunk.ChoiceDelta(content="Hello"), finish_reason="stop"
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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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# 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 = OpenAIGenerator(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(prompt="test prompt", 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]
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assert response["meta"][0]["finish_reason"] == "stop"
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