mirror of
https://github.com/aljazceru/dev-gpt.git
synced 2025-12-20 07:04:20 +01:00
fix: prompts
This commit is contained in:
28
main.py
28
main.py
@@ -7,7 +7,7 @@ from jina import Client
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openai.api_key = os.environ['OPENAI_API_KEY']
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executor_description = "Write an executor that takes an images as byte input (document.blob within a DocumentArray) saves it locally and detects ocr " \
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executor_description = "Write an executor that takes image bytes as input (document.blob within a DocumentArray) and use BytesIO to convert it to PIL and detects ocr " \
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"and returns the texts as output (as DocumentArray). "
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test_description = 'The test downloads the image ' \
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@@ -42,7 +42,7 @@ response = openai.ChatCompletion.create(
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"Here is an example of how a DocumentArray can be defined:"
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'''
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d1 = Document(text='hello')
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d2 = Document(blob=b'\f1')
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d2 = Document(blob=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x03L\x00\x00\x01\x18\x08\x06\x00\x00\x00o...')
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d3 = Document(tensor=numpy.array([1, 2, 3]), chunks=[Document(uri=/local/path/to/file)]
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d4 = Document(
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uri='https://docs.docarray.org',
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@@ -63,14 +63,17 @@ response = openai.ChatCompletion.create(
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"role": "user",
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"content":
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executor_description
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+ "The code you write is production ready. Start from top-level and then fully implement all methods."
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+ "The code you write is production ready. Every file starts with a 5 sentence comment of what the code is doing before the first import. Start from top-level and then fully implement all methods."
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"First, write the executor name. (wrap the code in the string $$$start_executor_name$$$ ... $$$end_executor_name$$$)"
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"Then, write the executor code. (wrap the code in the string $$$start_executor$$$ ... $$$end_executor$$$)"
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"In addition write the content of the requirements.txt file. Make sure to include pytest. (wrap the code in the string $$$start_requirements$$$ ... $$$end_requirements$$$)"
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"Then write a small unit test for the executor. (wrap the code in the string $$$start_test_executor$$$ ... $$$end_test_executor$$$)"
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# "the snipped should take the local file wolf.obj as input and save the output as png files. "
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+ test_description
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+ "Finally write the Dockerfile that defines the environment in which the executor runs. The dockerfile runs the test during the build process (wrap the code in the string $$$start_dockerfile$$$ ... $$$end_dockerfile$$$)"
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+ "Finally write the Dockerfile that defines the environment with all necessary dependencies which the executor uses. "
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"It is important to make sure that all libs are installed that are required by the python packages. "
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"The base image of the Dockerfile is jinaai/jina:3.14.2-dev18-py310-standard. "
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"The Dockerfile runs the test during the build process (wrap the code in the string $$$start_dockerfile$$$ ... $$$end_dockerfile$$$)"
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},
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]
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@@ -91,18 +94,17 @@ def find_between(input_string, start, end):
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def clean_content(content):
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return content.replace('```', '').strip()
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executor_name = find_between(plain_text, f'$$$start_executor_name$$$', f'$$$end_executor_name$$$').strip()
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for tag, file_ending in [['executor', 'py'], ['requirements', 'txt'], ['test_executor', 'py'], ['dockerfile', '']]:
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for tag, file_name in [['executor', f'{executor_name}py'], ['requirements', 'requirements.txt'], ['test_executor', 'test_executor.py'], ['ockerfile', 'Dockerfile']]:
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content = find_between(plain_text, f'$$$start_{tag}$$$', f'$$$end_{tag}$$$')
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clean = clean_content(content)
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file_name = f'{tag}.{file_ending}' if file_ending else tag
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folder = 'executor'
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full_path = os.path.join(folder, file_name)
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os.makedirs(folder, exist_ok=True)
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with open(full_path, 'w') as f:
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f.write(clean)
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executor_name = find_between(plain_text, f'$$$start_executor_name$$$', f'$$$end_executor_name$$$').strip()
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config_content = f'''
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jtype: {executor_name}
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py_modules:
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@@ -113,7 +115,7 @@ metas:
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with open('executor/config.yml', 'w') as f:
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f.write(config_content)
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cmd = 'jina hub push executor/.'
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cmd = 'jina hub push executor/. --verbose'
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os.system(cmd)
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flow = f'''
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@@ -136,14 +138,10 @@ executors:
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JINA_LOG_LEVEL: DEBUG
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jcloud:
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expose: true
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autoscale:
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min: 4
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max: 15
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metric: concurrency
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target: 1
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resources:
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instance: C4
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capacity: spot
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replicas: 1
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'''
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full_flow_path = os.path.join('executor', 'flow.yml')
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with open(full_flow_path, 'w') as f:
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@@ -153,7 +151,9 @@ cloud_flow = CloudFlow(path=full_flow_path).__enter__()
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host = cloud_flow.endpoints['gateway']
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client = Client(host=host)
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response = client.post('/index', inputs=DocumentArray([Document(uri='https://double-rhyme.com/logo_en_white2.png')]))
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d = Document(uri='https://double-rhyme.com/logo_en_white2.png')
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d.load_uri_to_blob()
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response = client.post('/index', inputs=DocumentArray([d]))
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response[0].summary()
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# "Write an executor using open3d that takes 3d models in obj format (within a DocumentArray) as input and returns 3 2d renderings for each 3d model from unique random angles as output (as DocumentArray). Each document of the output DocumentArray has 3 chunks. Each chunk is one of the 2d renderings as png. "
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