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#
# SPDX-License-Identifier: Apache-2.0
import logging
import os
from typing import List
import pytest
from openai import OpenAIError
from haystack.components.generators import OpenAIGenerator
from haystack.components.generators.utils import print_streaming_chunk
from haystack.dataclasses import ChatMessage, StreamingChunk
from haystack.utils.auth import Secret
class TestOpenAIGenerator:
def test_init_default(self, monkeypatch):
monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
component = OpenAIGenerator()
assert component.client.api_key == "test-api-key"
assert component.model == "gpt-4o-mini"
assert component.streaming_callback is None
assert not component.generation_kwargs
assert component.client.timeout == 30
assert component.client.max_retries == 5
def test_init_fail_wo_api_key(self, monkeypatch):
monkeypatch.delenv("OPENAI_API_KEY", raising=False)
with pytest.raises(ValueError, match="None of the .* environment variables are set"):
OpenAIGenerator()
def test_init_with_parameters(self, monkeypatch):
monkeypatch.setenv("OPENAI_TIMEOUT", "100")
monkeypatch.setenv("OPENAI_MAX_RETRIES", "10")
component = OpenAIGenerator(
api_key=Secret.from_token("test-api-key"),
model="gpt-4o-mini",
streaming_callback=print_streaming_chunk,
api_base_url="test-base-url",
generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
timeout=40.0,
max_retries=1,
)
assert component.client.api_key == "test-api-key"
assert component.model == "gpt-4o-mini"
assert component.streaming_callback is print_streaming_chunk
assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
assert component.client.timeout == 40.0
assert component.client.max_retries == 1
def test_to_dict_default(self, monkeypatch):
monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
component = OpenAIGenerator()
data = component.to_dict()
assert data == {
"type": "haystack.components.generators.openai.OpenAIGenerator",
"init_parameters": {
"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
"model": "gpt-4o-mini",
"streaming_callback": None,
"system_prompt": None,
"api_base_url": None,
"organization": None,
"generation_kwargs": {},
},
}
def test_to_dict_with_parameters(self, monkeypatch):
monkeypatch.setenv("ENV_VAR", "test-api-key")
component = OpenAIGenerator(
api_key=Secret.from_env_var("ENV_VAR"),
model="gpt-4o-mini",
streaming_callback=print_streaming_chunk,
api_base_url="test-base-url",
organization="org-1234567",
generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
)
data = component.to_dict()
assert data == {
"type": "haystack.components.generators.openai.OpenAIGenerator",
"init_parameters": {
"api_key": {"env_vars": ["ENV_VAR"], "strict": True, "type": "env_var"},
"model": "gpt-4o-mini",
"system_prompt": None,
"api_base_url": "test-base-url",
"organization": "org-1234567",
"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
},
}
def test_to_dict_with_lambda_streaming_callback(self, monkeypatch):
monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
component = OpenAIGenerator(
model="gpt-4o-mini",
streaming_callback=lambda x: x,
api_base_url="test-base-url",
generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
)
data = component.to_dict()
assert data == {
"type": "haystack.components.generators.openai.OpenAIGenerator",
"init_parameters": {
"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
"model": "gpt-4o-mini",
"system_prompt": None,
"organization": None,
"api_base_url": "test-base-url",
"streaming_callback": "test_openai.<lambda>",
"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
},
}
def test_from_dict(self, monkeypatch):
monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
data = {
"type": "haystack.components.generators.openai.OpenAIGenerator",
"init_parameters": {
"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
"model": "gpt-4o-mini",
"system_prompt": None,
"organization": None,
"api_base_url": "test-base-url",
"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
},
}
component = OpenAIGenerator.from_dict(data)
assert component.model == "gpt-4o-mini"
assert component.streaming_callback is print_streaming_chunk
assert component.api_base_url == "test-base-url"
assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
assert component.api_key == Secret.from_env_var("OPENAI_API_KEY")
def test_from_dict_fail_wo_env_var(self, monkeypatch):
monkeypatch.delenv("OPENAI_API_KEY", raising=False)
data = {
"type": "haystack.components.generators.openai.OpenAIGenerator",
"init_parameters": {
"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
"model": "gpt-4o-mini",
"api_base_url": "test-base-url",
"streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
"generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
},
}
with pytest.raises(ValueError, match="None of the .* environment variables are set"):
OpenAIGenerator.from_dict(data)
def test_run(self, mock_chat_completion):
component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
response = component.run("What's Natural Language Processing?")
# 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, str) for reply in response["replies"]]
def test_run_with_params_streaming(self, mock_chat_completion_chunk):
streaming_callback_called = False
def streaming_callback(chunk: StreamingChunk) -> None:
nonlocal streaming_callback_called
streaming_callback_called = True
component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"), streaming_callback=streaming_callback)
response = component.run("Come on, stream!")
# 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 "Hello" in response["replies"][0] # see mock_chat_completion_chunk
def test_run_with_streaming_callback_in_run_method(self, mock_chat_completion_chunk):
streaming_callback_called = False
def streaming_callback(chunk: StreamingChunk) -> None:
nonlocal streaming_callback_called
streaming_callback_called = True
# pass streaming_callback to run()
component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
response = component.run("Come on, stream!", 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 "Hello" in response["replies"][0] # see mock_chat_completion_chunk
def test_run_with_params(self, mock_chat_completion):
component = OpenAIGenerator(
api_key=Secret.from_token("test-api-key"), generation_kwargs={"max_tokens": 10, "temperature": 0.5}
)
response = component.run("What's Natural Language Processing?")
# check that the component calls the OpenAI API with the correct parameters
_, kwargs = mock_chat_completion.call_args
assert kwargs["max_tokens"] == 10
assert kwargs["temperature"] == 0.5
# 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, str) for reply in response["replies"]]
def test_check_abnormal_completions(self, caplog):
caplog.set_level(logging.INFO)
component = OpenAIGenerator(api_key=Secret.from_token("test-api-key"))
# underlying implementation uses ChatMessage objects so we have to use them here
messages: List[ChatMessage] = []
for i, _ in enumerate(range(4)):
message = ChatMessage.from_assistant("Hello")
metadata = {"finish_reason": "content_filter" if i % 2 == 0 else "length", "index": i}
message.meta.update(metadata)
messages.append(message)
for m in messages:
component._check_finish_reason(m)
# check truncation warning
message_template = (
"The completion for index {index} has been truncated before reaching a natural stopping point. "
"Increase the max_tokens parameter to allow for longer completions."
)
for index in [1, 3]:
assert caplog.records[index].message == message_template.format(index=index)
# check content filter warning
message_template = "The completion for index {index} has been truncated due to the content filter."
for index in [0, 2]:
assert caplog.records[index].message == message_template.format(index=index)
@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):
component = OpenAIGenerator()
results = component.run("What's the capital of France?")
assert len(results["replies"]) == 1
assert len(results["meta"]) == 1
response: str = results["replies"][0]
assert "Paris" in response
metadata = results["meta"][0]
assert "gpt-4o-mini" in metadata["model"]
assert metadata["finish_reason"] == "stop"
assert "usage" in metadata
assert "prompt_tokens" in metadata["usage"] and metadata["usage"]["prompt_tokens"] > 0
assert "completion_tokens" in metadata["usage"] and metadata["usage"]["completion_tokens"] > 0
assert "total_tokens" in metadata["usage"] and metadata["usage"]["total_tokens"] > 0
@pytest.mark.skipif(
not os.environ.get("OPENAI_API_KEY", None),
reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
)
@pytest.mark.integration
def test_live_run_wrong_model(self):
component = OpenAIGenerator(model="something-obviously-wrong")
with pytest.raises(OpenAIError):
component.run("Whatever")
@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 = OpenAIGenerator(streaming_callback=callback)
results = component.run("What's the capital of France?")
assert len(results["replies"]) == 1
assert len(results["meta"]) == 1
response: str = results["replies"][0]
assert "Paris" in response
metadata = results["meta"][0]
assert "gpt-4o-mini" in metadata["model"]
assert metadata["finish_reason"] == "stop"
# unfortunately, the usage is not available for streaming calls
# we keep the key in the metadata for compatibility
assert "usage" in metadata and len(metadata["usage"]) == 0
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_run_with_system_prompt(self):
generator = OpenAIGenerator(
model="gpt-4o-mini",
system_prompt="You answer in Portuguese, regardless of the language on which a question is asked",
)
result = generator.run("Can you explain the Pitagoras therom?")
assert "teorema" in result["replies"][0].lower()
result = generator.run(
"Can you explain the Pitagoras therom?",
system_prompt="You answer in German, regardless of the language on which a question is asked.",
)
assert "pythagoras".lower() in result["replies"][0].lower()
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