repo_id
stringlengths 15
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stringlengths 34
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| content
stringlengths 2
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| __index_level_0__
int64 0
0
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|---|---|---|---|
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/prompt_tools/samples.json | [
{
"text": "text_1"
},
{
"text": "text_2"
},
{
"text": "text_3"
},
{
"text": "text_4"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/script_with___file__.meta.json | {
"name": "script_with___file__",
"type": "python",
"inputs": {
"input1": {
"type": [
"string"
]
}
},
"source": "script_with___file__.py",
"function": "my_python_tool"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/script_with___file__.py | from pathlib import Path
from promptflow import tool
print(f"The script is {__file__}")
assert Path(__file__).is_absolute(), f"__file__ should be absolute path, got {__file__}"
@tool
def my_python_tool(input1: str) -> str:
from pathlib import Path
assert Path(__file__).name == "script_with___file__.py"
assert __name__ == "__pf_main__"
print(f"Prompt: {input1} {__file__}")
return f"Prompt: {input1} {__file__}"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/flow.dag.yaml | inputs:
text:
type: string
outputs:
output_prompt:
type: string
reference: ${node1.output}
nodes:
- name: node1
type: python
source:
type: code
path: script_with___file__.py
inputs:
input1: ${inputs.text}
- name: node2
type: python
source:
type: code
path: folder/another-tool.py
inputs:
input1: ${node1.output}
- name: node3
type: python
source:
type: code
path: folder/another-tool.py
inputs:
input1: random value | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/samples.json | [
{
"text": "text_1"
},
{
"text": "text_2"
},
{
"text": "text_3"
},
{
"text": "text_4"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__ | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/folder/another-tool.py | from promptflow import tool
print(f"The script is {__file__}")
@tool
def my_python_tool(input1: str) -> str:
from pathlib import Path
assert Path(__file__).as_posix().endswith("folder/another-tool.py")
assert __name__ == "__pf_main__"
return f"Prompt: {input1} {__file__}"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/expected_metrics.json | {"accuracy": 0.67} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/aggregation_assert.py | from typing import List
from promptflow import tool
@tool
def aggregation_assert(input1: List[str], input2: List[str]):
assert isinstance(input1, list)
assert isinstance(input2, list)
assert len(input1) == len(input2)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/calculate_accuracy.py | from typing import List
from promptflow import log_metric, tool
@tool
def calculate_accuracy(grades: List[str], variant_ids: List[str]):
aggregate_grades = {}
for index in range(len(grades)):
grade = grades[index]
variant_id = variant_ids[index]
if variant_id not in aggregate_grades.keys():
aggregate_grades[variant_id] = []
aggregate_grades[variant_id].append(grade)
# calculate accuracy for each variant
for name, values in aggregate_grades.items():
accuracy = round((values.count("Correct") / len(values)), 2)
log_metric("accuracy", accuracy, variant_id=name)
return aggregate_grades
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/expected_status_summary.json | {
"grade.completed": 3,
"calculate_accuracy.completed": 1,
"aggregation_assert.completed": 1
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/grade.py | from promptflow import tool
@tool
def grade(groundtruth: str, prediction: str):
groundtruth = groundtruth.lower().strip('"')
prediction = prediction.lower().strip('"')
return "Correct" if groundtruth == prediction else "Incorrect"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/flow.dag.yaml | inputs:
variant_id:
type: string
groundtruth:
type: string
description: Please specify the groundtruth column, which contains the true label
to the outputs that your flow produces.
prediction:
type: string
description: Please specify the prediction column, which contains the predicted
outputs that your flow produces.
outputs:
grade:
type: string
reference: ${grade.output}
nodes:
- name: grade
type: python
source:
type: code
path: grade.py
inputs:
groundtruth: ${inputs.groundtruth}
prediction: ${inputs.prediction}
- name: calculate_accuracy
type: python
source:
type: code
path: calculate_accuracy.py
inputs:
grades: ${grade.output}
variant_ids: ${inputs.variant_id}
aggregation: true
- name: aggregation_assert
type: python
source:
type: code
path: aggregation_assert.py
inputs:
input1: ${inputs.groundtruth}
input2: ${inputs.prediction}
aggregation: true
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/samples.json | [
{
"line_number": 0,
"variant_id": "variant_0",
"groundtruth": "App",
"prediction": "App"
},
{
"line_number": 1,
"variant_id": "variant_0",
"groundtruth": "Pdf",
"prediction": "PDF"
},
{
"line_number": 2,
"variant_id": "variant_0",
"groundtruth": "App",
"prediction": "Pdf"
}
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/.promptflow/flow.tools.json | {
"package": {},
"code": {
"grade.py": {
"type": "python",
"inputs": {
"groundtruth": {
"type": [
"string"
]
},
"prediction": {
"type": [
"string"
]
}
},
"function": "grade"
},
"calculate_accuracy.py": {
"type": "python",
"inputs": {
"grades": {
"type": [
"list"
]
},
"variant_ids": {
"type": [
"list"
]
}
},
"function": "calculate_accuracy"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/classify_with_llm.jinja2 | Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = bs4.BeautifulSoup(response.text, "html.parser")
soup.prettify()
return soup.get_text()[:2000]
else:
msg = (
f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: {response.text[:100]}"
)
print(msg)
return "No available content"
except Exception as e:
print("Get url failed with error: {}".format(e))
return "No available content"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/summarize_text_content__variant_1.jinja2 | Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/summarize_text_content.jinja2 | Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/flow.dag.yaml | inputs:
url:
type: string
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
nodes:
- name: convert_to_dict
type: python
source:
type: code
path: convert_to_dict.py
inputs:
input_str: ${classify_with_llm.output}
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '256'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
module: promptflow.tools.aoai
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
url: ${inputs.url}
examples: ${prepare_examples.output}
text_content: ${summarize_text_content.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: ${inputs.url}
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs: {} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/prepare_examples.py | from promptflow import tool
@tool
def prepare_examples():
return [
{
"url": "https://play.google.com/store/apps/details?id=com.spotify.music",
"text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.",
"category": "App",
"evidence": "Both",
},
{
"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
"text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.",
"category": "Channel",
"evidence": "URL",
},
{
"url": "https://arxiv.org/abs/2303.04671",
"text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.",
"category": "Academic",
"evidence": "Text content",
},
{
"url": "https://ab.politiaromana.ro/",
"text_content": "There is no content available for this text.",
"category": "None",
"evidence": "None",
},
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/samples.json | [
{
"line_number": 0,
"variant_id": "variant_0",
"groundtruth": "App",
"prediction": "App"
}
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_requirements_txt/requirements.txt | langchain
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_requirements_txt/flow.dag.yaml | inputs:
key:
type: string
outputs:
output:
type: string
reference: ${print_env.output.value}
nodes:
- name: print_env
type: python
source:
type: code
path: print_env.py
inputs:
key: ${inputs.key}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_requirements_txt/print_env.py | import os
from promptflow import tool
@tool
def get_env_var(key: str):
from langchain import __version__
print(__version__)
print(os.environ.get(key))
# get from env var
return {"value": os.environ.get(key)}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/long_run/long_run.py | import time
from promptflow import tool
def f1():
time.sleep(61)
return 0
def f2():
return f1()
@tool
def long_run_func():
return f2()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/long_run/flow.dag.yaml | inputs: {}
outputs:
output:
type: string
reference: ${long_run_node.output}
nodes:
- name: long_run_node
type: python
inputs: {}
source:
type: code
path: long_run.py
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_python_tool/divide_num.py | from promptflow import tool
@tool
def divide_num(num: int) -> int:
return (int)(num / 2)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_python_tool/flow.dag.yaml | inputs:
num:
type: int
outputs:
content:
type: string
reference: ${divide_num.output}
nodes:
- name: divide_num
type: python
source:
type: code
path: divide_num.py
inputs:
num: ${inputs.num}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_python_tool/inputs.jsonl | {"num": "hello"} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_additional_include/classify_with_llm.jinja2 | system:
Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
user:
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_additional_include/summarize_text_content__variant_1.jinja2 | system:
Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
user:
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_additional_include/flow.dag.yaml | inputs:
url:
type: string
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
nodes:
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: ${inputs.url}
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
use_variants: true
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs: {}
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
url: ${inputs.url}
examples: ${prepare_examples.output}
text_content: ${summarize_text_content.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
- name: convert_to_dict
type: python
source:
type: code
path: convert_to_dict.py
inputs:
input_str: ${classify_with_llm.output}
node_variants:
summarize_text_content:
default_variant_id: variant_1
variants:
variant_0:
node:
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
variant_1:
node:
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '256'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
additional_includes:
- ../external_files/convert_to_dict.py
- ../external_files/fetch_text_content_from_url.py
- ../external_files/summarize_text_content.jinja2
- ../external_files/summarize_text_content.jinja2
- ../external_files
- ../external_files
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_additional_include/prepare_examples.py | from pathlib import Path
from promptflow import tool
# read file from additional includes
lines = open(r"fetch_text_content_from_url.py", "r").readlines()
@tool
def prepare_examples():
if not Path("summarize_text_content.jinja2").exists():
raise Exception("Cannot find summarize_text_content.jinja2")
return [
{
"url": "https://play.google.com/store/apps/details?id=com.spotify.music",
"text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.",
"category": "App",
"evidence": "Both",
},
{
"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
"text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.",
"category": "Channel",
"evidence": "URL",
},
{
"url": "https://arxiv.org/abs/2303.04671",
"text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.",
"category": "Academic",
"evidence": "Text content",
},
{
"url": "https://ab.politiaromana.ro/",
"text_content": "There is no content available for this text.",
"category": "None",
"evidence": "None",
},
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_additional_include/samples.json | [
{
"line_number": 0,
"variant_id": "variant_0",
"groundtruth": "App",
"prediction": "App"
}
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_nodes_bypassed/test.py | from promptflow import tool
@tool
def test(text: str):
return text + "hello world!"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_nodes_bypassed/flow.dag.yaml | name: all_nodes_bypassed
inputs:
text:
type: string
outputs:
result:
type: string
reference: ${third_node.output}
nodes:
- name: first_node
type: python
source:
type: code
path: test.py
inputs:
text: ${inputs.text}
activate:
when: ${inputs.text}
is: "hello"
- name: second_node
type: python
source:
type: code
path: test.py
inputs:
text: ${first_node.output}
- name: third_node
type: python
source:
type: code
path: test.py
inputs:
text: ${second_node.output}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_nodes_bypassed/inputs.json | {
"text": "bypass"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/print_secret_flow/print_secret.py | import os
from promptflow import tool
from promptflow.connections import CustomConnection
@tool
def print_secret(text: str, connection: CustomConnection):
print(connection["key1"])
print(connection["key2"])
return text
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/print_secret_flow/flow.dag.yaml | inputs:
key:
type: string
default: text
outputs:
output:
type: string
reference: ${print_secret.output}
nodes:
- name: print_secret
type: python
source:
type: code
path: print_secret.py
inputs:
connection: custom_connection
text: ${inputs.key}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/classify_with_llm.jinja2 | Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = bs4.BeautifulSoup(response.text, "html.parser")
soup.prettify()
return soup.get_text()[:2000]
else:
msg = (
f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: {response.text[:100]}"
)
print(msg)
return "No available content"
except Exception as e:
print("Get url failed with error: {}".format(e))
return "No available content"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/summarize_text_content__variant_1.jinja2 | Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/summarize_text_content.jinja2 | Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/flow.dag.yaml | inputs:
url:
type: string
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
nodes:
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: ${inputs.url}
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
module: promptflow.tools.aoai
use_variants: true
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs: {}
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
url: ${inputs.url}
examples: ${prepare_examples.output}
text_content: ${summarize_text_content.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
module: promptflow.tools.aoai
- name: convert_to_dict
type: python
source:
type: code
path: convert_to_dict.py
inputs:
input_str: ${classify_with_llm.output}
node_variants:
summarize_text_content:
default_variant_id: variant_1
variants:
variant_0:
node:
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
module: promptflow.tools.aoai
variant_1:
node:
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '256'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: completion
module: promptflow.tools.aoai
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/prepare_examples.py | from promptflow import tool
@tool
def prepare_examples():
return [
{
"url": "https://play.google.com/store/apps/details?id=com.spotify.music",
"text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.",
"category": "App",
"evidence": "Both",
},
{
"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
"text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.",
"category": "Channel",
"evidence": "URL",
},
{
"url": "https://arxiv.org/abs/2303.04671",
"text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.",
"category": "Academic",
"evidence": "Text content",
},
{
"url": "https://ab.politiaromana.ro/",
"text_content": "There is no content available for this text.",
"category": "None",
"evidence": "None",
},
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/samples.json | [
{
"url": "https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h"
},
{
"url": "https://www.microsoft.com/en-us/windows/"
}
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1 | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/.promptflow/flow.tools.json | {
"package": {},
"code": {
"fetch_text_content_from_url.py": {
"type": "python",
"inputs": {
"url": {
"type": [
"string"
]
}
},
"function": "fetch_text_content_from_url"
},
"summarize_text_content.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
},
"description": "Summarize webpage content into a short paragraph."
},
"summarize_text_content__variant_1.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
}
},
"prepare_examples.py": {
"type": "python",
"function": "prepare_examples"
},
"classify_with_llm.jinja2": {
"type": "llm",
"inputs": {
"url": {
"type": [
"string"
]
},
"examples": {
"type": [
"string"
]
},
"text_content": {
"type": [
"string"
]
}
},
"description": "Multi-class classification of a given url and text content."
},
"convert_to_dict.py": {
"type": "python",
"inputs": {
"input_str": {
"type": [
"string"
]
}
},
"function": "convert_to_dict"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml | creation_context:
created_at: xxx
created_by: xxx
created_by_type: xxx
last_modified_at: xxx
last_modified_by: xxx
last_modified_by_type: xxx
description: Create flows that use large language models to classify URLs into multiple
categories.
display_name: web_classification_4
error_threshold: -1
id: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/components/xxx/versions/xxx
input_data: ${{inputs.data}}
inputs:
connections.classify_with_llm.connection:
default: azure_open_ai_connection
optional: true
type: string
connections.classify_with_llm.deployment_name:
default: text-davinci-003
optional: true
type: string
connections.classify_with_llm.model:
enum:
- text-davinci-001
- text-davinci-002
- text-davinci-003
- text-curie-001
- text-babbage-001
- text-ada-001
- code-cushman-001
- code-davinci-002
optional: true
type: string
connections.summarize_text_content.connection:
default: azure_open_ai_connection
optional: true
type: string
connections.summarize_text_content.deployment_name:
default: text-davinci-003
optional: true
type: string
connections.summarize_text_content.model:
enum:
- text-davinci-001
- text-davinci-002
- text-davinci-003
- text-curie-001
- text-babbage-001
- text-ada-001
- code-cushman-001
- code-davinci-002
optional: true
type: string
data:
optional: false
type: uri_folder
run_outputs:
optional: true
type: uri_folder
url:
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
optional: false
type: string
is_deterministic: true
logging_level: INFO
max_concurrency_per_instance: 1
mini_batch_error_threshold: 0
mini_batch_size: '1'
name: web_classification_4
outputs:
debug_info:
type: uri_folder
flow_outputs:
type: uri_folder
retry_settings:
max_retries: 2
timeout: 3600
task:
append_row_to: ${{outputs.flow_outputs}}
code: /subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/codes/xxx/versions/xxx
entry_script: driver/azureml_user/parallel_run/prompt_flow_entry.py
environment: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/environments/xxx/versions/xxx
program_arguments: --amlbi_pf_enabled True --amlbi_pf_run_mode component --amlbi_mini_batch_rows
1 --amlbi_file_format jsonl $[[--amlbi_pf_run_outputs ${{inputs.run_outputs}}]]
--amlbi_pf_debug_info ${{outputs.debug_info}} --amlbi_pf_connections "$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]"
--amlbi_pf_deployment_names "$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]"
--amlbi_pf_model_names "$[[classify_with_llm.model=${{inputs.connections.classify_with_llm.model}},]]$[[summarize_text_content.model=${{inputs.connections.summarize_text_content.model}},]]"
--amlbi_pf_input_url ${{inputs.url}}
type: run_function
type: parallel
version: 1.0.0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/completion.py | from openai.version import VERSION as OPENAI_VERSION
import openai
from promptflow import tool
from promptflow.connections import AzureOpenAIConnection
IS_LEGACY_OPENAI = OPENAI_VERSION.startswith("0.")
def get_client(connection: AzureOpenAIConnection):
api_key = connection.api_key
conn = dict(
api_key=connection.api_key,
)
if api_key.startswith("sk-"):
from openai import OpenAI as Client
else:
from openai import AzureOpenAI as Client
conn.update(
azure_endpoint=connection.api_base,
api_version=connection.api_version,
)
return Client(**conn)
@tool
def completion(connection: AzureOpenAIConnection, prompt: str, stream: bool) -> str:
if IS_LEGACY_OPENAI:
completion = openai.Completion.create(
prompt=prompt,
engine="text-ada-001",
max_tokens=256,
temperature=0.8,
top_p=1.0,
n=1,
stream=stream,
stop=None,
**dict(connection),
)
else:
completion = get_client(connection).completions.create(
prompt=prompt,
model="text-ada-001",
max_tokens=256,
temperature=0.8,
top_p=1.0,
n=1,
stream=stream,
stop=None,
)
if stream:
def generator():
for chunk in completion:
if chunk.choices:
if IS_LEGACY_OPENAI:
yield getattr(chunk.choices[0], "text", "")
else:
yield chunk.choices[0].text or ""
return "".join(generator())
else:
if IS_LEGACY_OPENAI:
return getattr(completion.choices[0], "text", "")
else:
return completion.choices[0].text or ""
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/flow.dag.yaml | inputs:
prompt:
type: string
stream:
type: bool
outputs:
output:
type: string
reference: ${completion.output}
nodes:
- name: completion
type: python
source:
type: code
path: completion.py
inputs:
prompt: ${inputs.prompt}
connection: azure_open_ai_connection
stream: ${inputs.stream}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/inputs.jsonl | {"prompt": "What is the capital of the United States of America?", "stream": true}
{"prompt": "What is the capital of the United States of America?", "stream": false}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/samples.json | {
"prompt": "What is the capital of the United States of America?"
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_python_node_streaming_output/stream.py | from promptflow import tool
from typing import Generator, List
def stream(question: str) -> Generator[str, None, None]:
for word in question:
yield word
@tool
def my_python_tool(chat_history: List[dict], question: str) -> dict:
return {"answer": stream(question)}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_python_node_streaming_output/flow.dag.yaml | inputs:
chat_history:
type: list
is_chat_history: true
question:
type: string
is_chat_input: true
outputs:
answer:
type: string
reference: ${stream.output.answer}
is_chat_output: true
nodes:
- name: stream
type: python
source:
type: code
path: stream.py
inputs:
chat_history: ${inputs.chat_history}
question: ${inputs.question} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_tool/echo.py | from promptflow import tool
@tool
def echo(input: str) -> str:
return input
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_tool/joke.jinja2 | {# Prompt is a jinja2 template that generates prompt for LLM #}
system:
You are a bot can tell good jokes
user:
A joke about {{topic}} please
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_tool/flow.dag.yaml | inputs:
topic:
type: string
default: hello world
is_chat_input: false
stream:
type: bool
default: false
is_chat_input: false
outputs:
joke:
type: string
reference: ${echo.output}
nodes:
- name: echo
type: python
source:
type: code
path: echo.py
inputs:
input: ${joke.output}
use_variants: false
- name: joke
type: llm
source:
type: code
path: joke.jinja2
inputs:
deployment_name: gpt-35-turbo
temperature: 1
top_p: 1
max_tokens: 256
presence_penalty: 0
frequency_penalty: 0
stream: ${inputs.stream}
topic: ${inputs.topic}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
use_variants: false
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_tool_with_init/flow.dag.yaml | inputs:
input:
type: string
default: World
outputs:
output:
type: string
reference: ${script_tool_with_init.output}
nodes:
- name: script_tool_with_init
type: python
source:
type: code
path: script_tool_with_init.py
inputs:
init_input: Hello
input: ${inputs.input}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_tool_with_init/script_tool_with_init.py | from promptflow import ToolProvider, tool
class ScriptToolWithInit(ToolProvider):
def __init__(self, init_input: str):
super().__init__()
self.init_input = init_input
@tool
def call(self, input: str):
return str.join(" ", [self.init_input, input])
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/classify_with_llm.jinja2 | system:
Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
user:
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT: | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = bs4.BeautifulSoup(response.text, "html.parser")
soup.prettify()
return soup.get_text()[:2000]
else:
msg = (
f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: {response.text[:100]}"
)
print(msg)
return "No available content"
except Exception as e:
print("Get url failed with error: {}".format(e))
return "No available content"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/summarize_text_content__variant_1.jinja2 | system:
Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
user:
Text: {{text}}
Summary: | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/summarize_text_content.jinja2 | system:
Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
user:
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/flow.dag.yaml | inputs:
url:
type: string
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
nodes:
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: ${inputs.url}
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '256'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs: {}
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '128'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
url: ${inputs.url}
examples: ${prepare_examples.output}
text_content: ${summarize_text_content.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
- name: convert_to_dict
type: python
source:
type: code
path: convert_to_dict.py
inputs:
input_str: ${classify_with_llm.output}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/prepare_examples.py | from promptflow import tool
@tool
def prepare_examples():
return [
{
"url": "https://play.google.com/store/apps/details?id=com.spotify.music",
"text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.",
"category": "App",
"evidence": "Both",
},
{
"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
"text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.",
"category": "Channel",
"evidence": "URL",
},
{
"url": "https://arxiv.org/abs/2303.04671",
"text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.",
"category": "Academic",
"evidence": "Text content",
},
{
"url": "https://ab.politiaromana.ro/",
"text_content": "There is no content available for this text.",
"category": "None",
"evidence": "None",
},
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/samples.json | [
{
"url": "https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h"
},
{
"url": "https://www.microsoft.com/en-us/windows/"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/flow.tools.json | {
"package": {},
"code": {
"fetch_text_content_from_url.py": {
"type": "python",
"inputs": {
"url": {
"type": [
"string"
]
}
},
"function": "fetch_text_content_from_url"
},
"summarize_text_content.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
},
"description": "Summarize webpage content into a short paragraph."
},
"summarize_text_content__variant_1.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
}
},
"prepare_examples.py": {
"type": "python",
"function": "prepare_examples"
},
"classify_with_llm.jinja2": {
"type": "llm",
"inputs": {
"url": {
"type": [
"string"
]
},
"examples": {
"type": [
"string"
]
},
"text_content": {
"type": [
"string"
]
}
},
"description": "Multi-class classification of a given url and text content."
},
"convert_to_dict.py": {
"type": "python",
"inputs": {
"input_str": {
"type": [
"string"
]
}
},
"function": "convert_to_dict"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/flow.env_files/setup.sh | #!/bin/bash
# Install your packages here.
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/classify_with_llm.jinja2 | Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = bs4.BeautifulSoup(response.text, "html.parser")
soup.prettify()
return soup.get_text()[:2000]
else:
msg = (
f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: {response.text[:100]}"
)
print(msg)
return "No available content"
except Exception as e:
print("Get url failed with error: {}".format(e))
return "No available content"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/summarize_text_content__variant_1.jinja2 | Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/summarize_text_content.jinja2 | Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/prepare_examples.py | from promptflow import tool
@tool
def prepare_examples():
return [
{
"url": "https://play.google.com/store/apps/details?id=com.spotify.music",
"text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.",
"category": "App",
"evidence": "Both",
},
{
"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
"text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.",
"category": "Channel",
"evidence": "URL",
},
{
"url": "https://arxiv.org/abs/2303.04671",
"text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.",
"category": "Academic",
"evidence": "Text content",
},
{
"url": "https://ab.politiaromana.ro/",
"text_content": "There is no content available for this text.",
"category": "None",
"evidence": "None",
},
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/activate_flow/print_input.py | from promptflow import tool
@tool
def print_input(input: str) -> str:
return input
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/activate_flow/flow.dag.yaml | inputs:
text:
type: string
default: world
outputs:
output1:
type: string
reference: ${nodeC.output}
output2:
type: string
reference: ${nodeD.output}
nodes:
- name: nodeA
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${inputs.text}
is: hello
- name: nodeB
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${nodeA.output}
is: hello
- name: nodeC
type: python
source:
type: code
path: print_input.py
inputs:
input: ${nodeB.output}
- name: nodeD
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${inputs.text}
is: world
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/hello_world.py | from promptflow import tool
@tool
def hello_world(name: str) -> str:
return f"Hello World {name}!"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/flow.dag.yaml | inputs:
name:
type: string
default: hod
outputs:
result:
type: string
reference: ${hello_world.output}
nodes:
- name: hello_world
type: python
source:
type: code
path: hello_world.py
inputs:
name: ${inputs.name}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/.promptflow/flow.tools.json | {
"code": {
"hello_world.py": {
"type": "python",
"inputs": {
"name": {
"type": [
"string"
]
}
},
"source": "hello_world.py",
"function": "hello_world"
}
},
"package": {
"promptflow.tools.embedding.embedding": {
"name": "Embedding",
"description": "Use Open AI's embedding model to create an embedding vector representing the input text.",
"type": "python",
"module": "promptflow.tools.embedding",
"function": "embedding",
"inputs": {
"connection": {
"type": [
"AzureOpenAIConnection",
"OpenAIConnection"
]
},
"deployment_name": {
"type": [
"string"
],
"enabled_by": "connection",
"enabled_by_type": [
"AzureOpenAIConnection"
],
"capabilities": {
"completion": false,
"chat_completion": false,
"embeddings": true
},
"model_list": [
"text-embedding-ada-002",
"text-search-ada-doc-001",
"text-search-ada-query-001"
]
},
"model": {
"type": [
"string"
],
"enabled_by": "connection",
"enabled_by_type": [
"OpenAIConnection"
],
"enum": [
"text-embedding-ada-002",
"text-search-ada-doc-001",
"text-search-ada-query-001"
]
},
"input": {
"type": [
"string"
]
}
},
"package": "promptflow-tools",
"package_version": "0.1.0b5"
},
"promptflow.tools.serpapi.SerpAPI.search": {
"name": "Serp API",
"description": "Use Serp API to obtain search results from a specific search engine.",
"inputs": {
"connection": {
"type": [
"SerpConnection"
]
},
"engine": {
"default": "google",
"enum": [
"google",
"bing"
],
"type": [
"string"
]
},
"location": {
"default": "",
"type": [
"string"
]
},
"num": {
"default": "10",
"type": [
"int"
]
},
"query": {
"type": [
"string"
]
},
"safe": {
"default": "off",
"enum": [
"active",
"off"
],
"type": [
"string"
]
}
},
"type": "python",
"module": "promptflow.tools.serpapi",
"class_name": "SerpAPI",
"function": "search",
"package": "promptflow-tools",
"package_version": "0.1.0b5"
},
"my_tool_package.tools.my_tool_1.my_tool": {
"function": "my_tool",
"inputs": {
"connection": {
"type": [
"CustomConnection"
],
"custom_type": [
"MyFirstConnection",
"MySecondConnection"
]
},
"input_text": {
"type": [
"string"
]
}
},
"module": "my_tool_package.tools.my_tool_1",
"name": "My First Tool",
"description": "This is my first tool",
"type": "python",
"package": "test-custom-tools",
"package_version": "0.0.2"
},
"my_tool_package.tools.my_tool_2.MyTool.my_tool": {
"class_name": "MyTool",
"function": "my_tool",
"inputs": {
"connection": {
"type": [
"CustomConnection"
],
"custom_type": [
"MySecondConnection"
]
},
"input_text": {
"type": [
"string"
]
}
},
"module": "my_tool_package.tools.my_tool_2",
"name": "My Second Tool",
"description": "This is my second tool",
"type": "python",
"package": "test-custom-tools",
"package_version": "0.0.2"
},
"my_tool_package.tools.my_tool_with_custom_strong_type_connection.my_tool": {
"function": "my_tool",
"inputs": {
"connection": {
"custom_type": [
"MyCustomConnection"
],
"type": [
"CustomConnection"
]
},
"input_param": {
"type": [
"string"
]
}
},
"module": "my_tool_package.tools.my_tool_with_custom_strong_type_connection",
"name": "Tool With Custom Strong Type Connection",
"description": "This is my tool with custom strong type connection.",
"type": "python",
"package": "test-custom-tools",
"package_version": "0.0.2"
}
}
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/echo.py | from promptflow import tool
@tool
def echo(text):
"""yield the input string."""
echo_text = "Echo - " + text
for word in echo_text.split():
yield word | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/flow.dag.yaml | inputs:
text:
type: string
outputs:
answer:
type: string
reference: ${echo_generator.output}
nodes:
- name: echo_generator
type: python
source:
type: code
path: echo.py
inputs:
text: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools_failures/async_fail.py | from promptflow import tool
async def raise_exception_async(s):
msg = f"In raise_exception_async: {s}"
raise Exception(msg)
@tool
async def raise_an_exception_async(s: str):
try:
await raise_exception_async(s)
except Exception as e:
raise Exception(f"In tool raise_an_exception_async: {s}") from e
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools_failures/flow.dag.yaml | inputs:
text:
type: string
default: dummy_input
outputs:
output_prompt:
type: string
reference: ${async_fail.output}
nodes:
- name: async_fail
type: python
source:
type: code
path: async_fail.py
inputs:
s: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_additional_include_req/flow.dag.yaml | inputs:
key:
type: string
outputs:
output:
type: string
reference: ${print_env.output.value}
nodes:
- name: print_env
type: python
source:
type: code
path: print_env.py
inputs:
key: ${inputs.key}
additional_includes:
- ../flow_with_environment/requirements
environment:
python_requirements_txt: requirements
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_additional_include_req/print_env.py | import os
from promptflow import tool
@tool
def get_env_var(key: str):
from tensorflow import __version__
print(__version__)
print(os.environ.get(key))
# get from env var
return {"value": os.environ.get(key)}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_default_history/chat.jinja2 | system:
You are a helpful assistant.
{% for item in chat_history %}
user:
{{item.inputs.question}}
assistant:
{{item.outputs.answer}}
{% endfor %}
user:
{{question}} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_default_history/flow.dag.yaml | inputs:
chat_history:
type: list
is_chat_history: true
default:
- inputs:
question: hi
outputs:
answer: hi
- inputs:
question: who are you
outputs:
answer: who are you
question:
type: string
is_chat_input: true
default: What is ChatGPT?
outputs:
answer:
type: string
reference: ${chat_node.output}
is_chat_output: true
nodes:
- inputs:
deployment_name: gpt-35-turbo
max_tokens: "256"
temperature: "0.7"
chat_history: ${inputs.chat_history}
question: ${inputs.question}
name: chat_node
type: llm
source:
type: code
path: chat.jinja2
api: chat
provider: AzureOpenAI
connection: azure_open_ai_connection | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/mock_chat.py | from promptflow import tool
from promptflow.contracts.multimedia import Image
@tool
def mock_chat(chat_history: list, question: list):
ensure_image_in_list(question, "question")
for item in chat_history:
ensure_image_in_list(item["inputs"]["question"], "inputs of chat history")
ensure_image_in_list(item["outputs"]["answer"], "outputs of chat history")
res = []
for item in question:
if isinstance(item, Image):
res.append(item)
res.append("text response")
return res
def ensure_image_in_list(value: list, name: str):
include_image = False
for item in value:
if isinstance(item, Image):
include_image = True
if not include_image:
raise Exception(f"No image found in {name}, you should include at least one image in your {name}.")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/flow.dag.yaml | inputs:
chat_history:
type: list
default:
- inputs:
question:
- the first question
- data:image/jpg;path: logo.jpg
outputs:
answer:
- data:image/jpg;path: logo.jpg
- inputs:
question:
- the second question
- data:image/png;path: logo_2.png
outputs:
answer:
- data:image/png;path: logo_2.png
is_chat_history: true
question:
type: list
default:
- the third question
- data:image/jpg;path: logo.jpg
- data:image/png;path: logo_2.png
is_chat_input: true
outputs:
answer:
type: string
reference: ${mock_chat_node.output}
is_chat_output: true
nodes:
- name: mock_chat_node
type: python
source:
type: code
path: mock_chat.py
inputs:
chat_history: ${inputs.chat_history}
question: ${inputs.question}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/inputs.jsonl | {"chat_history":[{"inputs": {"question": ["the first question",{"data:image/jpg;path": "logo.jpg"}]},"outputs": {"answer": [{"data:image/jpg;path": "logo.jpg"}]}},{"inputs": {"question": ["the second question",{"data:image/png;path": "logo_2.png"}]},"outputs": {"answer": [{"data:image/png;path": "logo_2.png"}]}}],"question": ["the third question",{"data:image/jpg;path": "logo.jpg"},{"data:image/png;path": "logo_2.png"}]}
{"chat_history":[{"inputs": {"question": ["the first question",{"data:image/jpg;path": "logo.jpg"}]},"outputs": {"answer": [{"data:image/jpg;path": "logo.jpg"}]}},{"inputs": {"question": ["the second question",{"data:image/png;path": "logo_2.png"}]},"outputs": {"answer": [{"data:image/png;path": "logo_2.png"}]}}],"question": ["the third question",{"data:image/jpg;path": "logo.jpg"},{"data:image/png;path": "logo_2.png"}]} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_connection_override/conn_tool.py | from promptflow import tool
from promptflow.connections import AzureOpenAIConnection
@tool
def conn_tool(conn: AzureOpenAIConnection):
assert isinstance(conn, AzureOpenAIConnection)
return conn.api_base | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_connection_override/connection_arm_template.json | {
"id": "/subscriptions/xxxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/connections/azure_open_ai_connection",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {
"key": "api_key"
},
"category": "AzureOpenAI",
"expiryTime": null,
"target": "api_base",
"createdByWorkspaceArmId": null,
"isSharedToAll": false,
"sharedUserList": [],
"metadata": {
"azureml.flow.connection_type": "AzureOpenAI",
"azureml.flow.module": "promptflow.connections",
"ApiType": "azure",
"ApiVersion": "2023-03-15-preview"
}
},
"systemData": {
"createdAt": "2023-06-14T09:40:51.1117116Z",
"createdBy": "[email protected]",
"createdByType": "User",
"lastModifiedAt": "2023-06-14T09:40:51.1117116Z",
"lastModifiedBy": "[email protected]",
"lastModifiedByType": "User"
}
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/llm_connection_override/flow.dag.yaml | inputs: {}
outputs:
output:
type: string
reference: ${conn_node.output}
nodes:
- name: conn_node
type: python
source:
type: code
path: conn_tool.py
inputs:
conn: aoai connection
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/classify_with_llm.jinja2 | Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = bs4.BeautifulSoup(response.text, "html.parser")
soup.prettify()
return soup.get_text()[:2000]
else:
msg = (
f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: {response.text[:100]}"
)
print(msg)
return "No available content"
except Exception as e:
print("Get url failed with error: {}".format(e))
return "No available content"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/summarize_text_content__variant_1.jinja2 | Please summarize some keywords of this paragraph and have some details of each keywords.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
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