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➕ refactor: summarize error message without line number

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This commit is contained in:
Florian Hönicke
2023-05-29 16:06:24 +02:00
parent 2924ef98f8
commit ba7e9aca1a
4 changed files with 63 additions and 40 deletions
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5
test/integration/test_generator.py
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@@ -136,7 +136,8 @@ data = {{
}}
response = requests.post(url, headers=headers, data=data)
assert response.status_code == 200
print('This is the text from the audio file:', response.text)'''
print('This is the text from the audio file:', response.text)''',
'use any library',
# f'''\
# import openai
# audio_file= open("/path/to/file/audio.mp3", "rb")
@@ -160,7 +161,7 @@ def test_generation_level_4(microservice_dir, mock_input_sequence):
f'''Given an audio file (1min wav) of speech,
1. convert it to text using the Whisper API.
2. Summarize the text while still maintaining the key facts.
3. Create an audio file of the summarized text using a tts library.
3. Create an audio file of the summarized text using via tts.
4. Return the the audio file as base64 encoded binary.
''',
str(microservice_dir),

21
test/unit/test_tools.py
View File

@@ -17,9 +17,24 @@ def test_no_search():
def test_get_used_tools(tmpdir):
os.environ['VERBOSE'] = 'true'
GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
used_tools = PM.get_used_tools('''\
used_tools = PM.get_used_apis('''\
This microservice listens for incoming requests and generates a fixed output of "test" upon receiving a request. \
The response sent back to the requester includes the output as a string parameter. \
No specific request parameters are required, and the response always follows a fixed schema with a single "output" parameter.'''
)
assert used_tools == []
)
assert used_tools == []
def test_get_used_tools_2(tmpdir):
os.environ['VERBOSE'] = 'true'
GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
description = '''\
This microservice accepts a 1-minute WAV audio file of speech, encoded as a base64 binary string, and performs the following tasks:
1. Converts the audio file to text using the Whisper API.
2. Summarizes the text while preserving key facts using gpt_3_5_turbo.
3. Generates an audio file of the summarized text using a text-to-speech (TTS) library.
4. Encodes the resulting audio file as a base64 binary string.
The microservice returns the summarized text converted to audio and encoded as a base64 binary string.'''
used_tools = PM.get_used_apis(description)
assert used_tools == ['Whisper API', 'gpt_3_5_turbo', 'text-to-speech (TTS) library']