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promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/pass_through.py | from promptflow import tool
@tool
def pass_through(input1: str="Execution") -> str:
return input1 | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/expected_result.json | [
{
"expected_node_count": 2,
"expected_outputs": {
"output": "Execution"
},
"expected_bypassed_nodes": [
"nodeA"
]
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/flow.dag.yaml | inputs:
text:
type: string
default: hi
outputs:
output:
type: string
reference: ${nodeB.output}
nodes:
- name: nodeA
type: python
source:
type: code
path: pass_through.py
inputs:
input1: ${inputs.text}
activate:
when: ${inputs.text}
is: world
- name: nodeB
type: python
source:
type: code
path: pass_through.py
inputs:
input1: ${nodeA.output}
activate:
when: ${inputs.text}
is: hi
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/pick_an_image.py | import random
from promptflow.contracts.multimedia import Image
from promptflow import tool
@tool
def pick_an_image(image_1: Image, image_2: Image) -> Image:
if random.choice([True, False]):
return image_1
else:
return image_2
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/inputs.jsonl | {"image": {"data:image/png;path":"logo.jpg"}}
{"image": {"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/python_tool_with_simple_image/flow.dag.yaml | inputs:
image:
type: image
default: logo.jpg
outputs:
output:
type: image
reference: ${python_node_2.output}
nodes:
- name: python_node
type: python
source:
type: code
path: pick_an_image.py
inputs:
image_1: ${inputs.image}
image_2: logo_2.png
- name: python_node_2
type: python
source:
type: code
path: pick_an_image.py
inputs:
image_1: ${python_node.output}
image_2: logo_2.png
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/image_inputs/inputs.jsonl | {"image": {"data:image/png;path":"logo_1.png"}}
{"image": {"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/flow_with_ignore_file/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:
# time.sleep(130)
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: "
f"{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/flow_with_ignore_file/.amlignore | ignored_folder
*.ignored | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_ignore_file/flow.dag.yaml | id: web_classification
inputs:
url:
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
is_chat_input: false
type: string
nodes:
- inputs:
url: ${inputs.url}
name: fetch_text_content_from_url
reduce: false
source:
path: fetch_text_content_from_url.py
type: code
type: python
outputs:
text:
evaluation_only: false
is_chat_output: false
reference: ${fetch_text_content_from_url.output}
type: string
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/custom_connection_flow/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}
connection: custom_connection
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/custom_connection_flow/print_env.py | import os
from promptflow import tool
from promptflow.connections import CustomConnection
@tool
def get_env_var(key: str, connection: CustomConnection):
# get from env var
return {"value": os.environ.get(key)}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/custom_connection_flow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/custom_connection_flow/.promptflow/flow.tools.json | {
"package": {},
"code": {
"print_env.py": {
"type": "python",
"inputs": {
"key": {
"type": [
"string"
]
}
},
"function": "get_env_var"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_script_tool_with_custom_strong_type_connection/data.jsonl | {"text": "Hello World!"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_script_tool_with_custom_strong_type_connection/my_script_tool.py | from promptflow import tool
from promptflow.connections import CustomStrongTypeConnection, CustomConnection
from promptflow.contracts.types import Secret
class MyCustomConnection(CustomStrongTypeConnection):
"""My custom strong type connection.
:param api_key: The api key.
:type api_key: String
:param api_base: The api base.
:type api_base: String
"""
api_key: Secret
api_url: str = "This is a fake api url."
@tool
def my_tool(connection: MyCustomConnection, input_param: str) -> str:
# Replace with your tool code.
# Use custom strong type connection like: connection.api_key, connection.api_url
return f"connection_value is MyCustomConnection: {str(isinstance(connection, MyCustomConnection))}"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_script_tool_with_custom_strong_type_connection/flow.dag.yaml | inputs:
text:
type: string
default: this is an input
outputs:
out:
type: string
reference: ${my_script_tool.output}
nodes:
- name: my_script_tool
type: python
source:
type: code
path: my_script_tool.py
inputs:
connection: custom_connection_2
input_param: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/unordered_nodes_with_skip/flow.dag.yaml | name: node_wrong_order
inputs:
text:
type: string
skip:
type: bool
outputs:
result:
type: string
reference: ${third_node}
nodes:
- name: third_node
type: python
source:
type: code
path: test.py
inputs:
text: ${second_node}
- name: first_node
type: python
source:
type: code
path: test.py
inputs:
text: ${inputs.text}
- name: second_node
type: python
source:
type: code
path: test.py
inputs:
text: ${first_node}
skip:
when: ${inputs.skip}
is: true
return: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_aggregation/passthrough.py | from promptflow import tool
@tool
def passthrough(input: str):
return input | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_aggregation/accuracy.py | from promptflow import tool, log_metric
from typing import List
@tool
def accuracy(answer: List[str], groundtruth: List[str]):
assert isinstance(answer, list)
correct = 0
for a, g in zip(answer, groundtruth):
if a == g:
correct += 1
accuracy = float(correct) / len(answer)
log_metric("accuracy", accuracy)
return accuracy
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_aggregation/flow.dag.yaml | inputs:
text:
type: string
default: "play"
outputs:
answer:
type: string
reference: ${passthrough.output}
nodes:
- name: passthrough
type: python
source:
type: code
path: passthrough.py
inputs:
input: ${inputs.text}
- name: accuracy
type: python
source:
type: code
path: accuracy.py
inputs:
answer: ${passthrough.output}
groundtruth: ${inputs.text}
aggregation: True | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/samples.json | [
{"idx": 1, "mod": 3, "mod_2": 5},
{"idx": 2, "mod": 3, "mod_2": 5},
{"idx": 3, "mod": 3, "mod_2": 5},
{"idx": 4, "mod": 3, "mod_2": 5},
{"idx": 5, "mod": 3, "mod_2": 5},
{"idx": 6, "mod": 3, "mod_2": 5},
{"idx": 7, "mod": 3, "mod_2": 5},
{"idx": 8, "mod": 3, "mod_2": 5},
{"idx": 9, "mod": 3, "mod_2": 5},
{"idx": 10, "mod": 3, "mod_2": 5}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/my_python_tool_with_failed_line.py | from promptflow import tool
@tool
def my_python_tool_with_failed_line(idx: int, mod) -> int:
if idx % mod == 0:
raise Exception("Failed")
return idx | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/expected_status_summary.json | {
"my_python_tool_with_failed_line_1.completed": 7,
"my_python_tool_with_failed_line_1.failed": 3,
"my_python_tool_with_failed_line_2.completed": 5,
"my_python_tool_with_failed_line_2.failed": 2
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/flow.dag.yaml | inputs:
idx:
type: int
mod:
type: int
mod_2:
type: int
outputs:
output:
type: int
reference: ${my_python_tool_with_failed_line_2.output}
nodes:
- name: my_python_tool_with_failed_line_1
type: python
source:
type: code
path: my_python_tool_with_failed_line.py
inputs:
idx: ${inputs.idx}
mod: ${inputs.mod}
- name: my_python_tool_with_failed_line_2
type: python
source:
type: code
path: my_python_tool_with_failed_line.py
inputs:
idx: ${my_python_tool_with_failed_line_1.output}
mod: ${inputs.mod_2} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/inputs/data.jsonl | {"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5}
{"mod": 2, "mod_2": 5} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/inputs/output.jsonl | {"idx": 1, "line_number": 0}
{"idx": 2, "line_number": 1}
{"idx": 4, "line_number": 3}
{"idx": 5, "line_number": 4}
{"idx": 7, "line_number": 6}
{"idx": 8, "line_number": 7}
{"idx": 10, "line_number": 9}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/failed_flow/hello.py | import os
import openai
from dotenv import load_dotenv
from promptflow import tool
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signature per need
def to_bool(value) -> bool:
return str(value).lower() == "true"
@tool
def my_python_tool(input1: str) -> str:
return 'hello '
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/failed_flow/flow.dag.yaml | inputs:
text:
type: string
outputs:
output_prompt:
type: string
reference: ${echo_my_prompt.output}
nodes:
- inputs:
text: ${inputs.text}
name: echo_my_prompt
type: python
source:
type: code
path: hello.py
node_variants: {}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_fetch_url/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:
# time.sleep(130)
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: "
f"{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/simple_fetch_url/flow.dag.yaml | id: web_classification
inputs:
url:
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
is_chat_input: false
type: string
nodes:
- inputs:
url: ${inputs.url}
name: fetch_text_content_from_url
reduce: false
source:
path: fetch_text_content_from_url.py
type: code
type: python
outputs:
text:
evaluation_only: false
is_chat_output: false
reference: ${fetch_text_content_from_url.output}
type: string
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/print_env_var/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/print_env_var/print_env.py | import os
from promptflow import tool
@tool
def get_env_var(key: str):
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/print_env_var | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/print_env_var/.promptflow/flow.tools.json | {
"package": {},
"code": {
"print_env.py": {
"type": "python",
"inputs": {
"key": {
"type": [
"string"
]
}
},
"function": "get_env_var"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/inputs.json | {
"input1": "False",
"input2": "False",
"input3": "False",
"input4": "False"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/wait_short.py | import threading
from time import sleep
from promptflow import tool
@tool
def wait(**kwargs) -> int:
if kwargs["throw_exception"]:
raise Exception("test exception")
for i in range(10):
print(f"Thread {threading.get_ident()} write test log number {i}")
sleep(2)
return 0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/wait_long.py | from time import sleep
from promptflow import tool
@tool
def wait(**args) -> int:
sleep(5)
return str(args)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/flow.dag.yaml | name: TestPythonToolLongWaitTime
inputs:
input1:
type: bool
input2:
type: bool
input3:
type: bool
input4:
type: bool
outputs:
output:
type: int
reference: ${wait_long_1.output}
nodes:
- name: wait_1
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input1}
- name: wait_2
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input2}
- name: wait_3
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input3}
- name: wait_4
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input4}
- name: wait_long_1
type: python
source:
type: code
path: wait_long.py
inputs:
text_1: ${wait_1.output}
text_2: ${wait_2.output}
text_3: ${wait_3.output}
text_4: ${wait_4.output}
| 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/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/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/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/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/flow_with_trace_async/greetings.py | import asyncio
from time import sleep
from promptflow import tool, trace
@trace
async def is_valid_name(name):
await asyncio.sleep(0.5)
return len(name) > 0
@trace
async def get_user_name(user_id):
await asyncio.sleep(0.5)
user_name = f"User {user_id}"
if not await is_valid_name(user_name):
raise ValueError(f"Invalid user name: {user_name}")
return user_name
@trace
async def format_greeting(user_name):
await asyncio.sleep(0.5)
return f"Hello, {user_name}!"
@tool
async def greetings(user_id):
user_name = await get_user_name(user_id)
greeting = await format_greeting(user_name)
print(greeting)
return {"greeting": greeting}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_trace_async/flow.dag.yaml | inputs:
user_id:
type: int
default: 1
outputs:
output:
type: string
reference: ${greetings.output.greeting}
nodes:
- name: greetings
type: python
source:
type: code
path: greetings.py
inputs:
user_id: ${inputs.user_id}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/get_dict_val.py | from promptflow import tool
@tool
def get_dict_val(key):
# get from env var
print(key)
if not isinstance(key, dict):
raise TypeError(f"key must be a dict, got {type(key)}")
return {"value": f"{key}: {type(key)}", "origin_value": key}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/flow.dag.yaml | inputs:
key:
type: object
outputs:
output:
type: string
reference: ${get_dict_val.output.value}
nodes:
- name: get_dict_val
type: python
source:
type: code
path: get_dict_val.py
inputs:
key: ${inputs.key}
- name: print_val
type: python
source:
type: code
path: print_val.py
inputs:
val: ${get_dict_val.output.value}
origin_val: ${get_dict_val.output.origin_value}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/print_val.py | from promptflow import tool
@tool
def print_val(val, origin_val):
print(val)
print(origin_val)
if not isinstance(origin_val, dict):
raise TypeError(f"key must be a dict, got {type(origin_val)}")
return val
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/.promptflow/flow.tools.json | {
"package": {},
"code": {
"print_val.py": {
"name": "print_val.py",
"type": "python",
"inputs": {
"key": {
"type": [
"object"
]
}
},
"source": "print_val.py",
"function": "get_val"
}
}
} | 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/no_inputs_outputs/say_hello.py | from promptflow import tool
@tool
def stringify_num():
print("hello world")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/no_inputs_outputs/flow.dag.yaml | outputs:
nodes:
- name: say_hello
type: python
source:
type: code
path: say_hello.py
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/inputs.json | [
{
"incident_id": 1,
"incident_content": "Incident 418856448 : Stale App Deployment for App promptflow"
},
{
"incident_id": 3,
"incident_content": "Incident 418856448 : Stale App Deployment for App promptflow"
},
{
"incident_id": 0,
"incident_content": "Incident 418856448 : Stale App Deployment for App promptflow"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/investigation_method.py | from promptflow import tool
@tool
def choose_investigation_method(method1="Skip job info extractor", method2="Skip incident info extractor"):
method = {}
if method1:
method["first"] = method1
if method2:
method["second"] = method2
return method
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/icm_retriever.py | from promptflow import tool
@tool
def icm_retriever(content: str) -> str:
return "ICM: " + content | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/expected_result.json | [
{
"expected_node_count": 9,
"expected_outputs":{
"investigation_method": {
"first": "Skip job info extractor",
"second": "Execute incident info extractor"
}
},
"expected_bypassed_nodes":["job_info_extractor", "icm_retriever"]
},
{
"expected_node_count": 9,
"expected_outputs":{
"investigation_method": {
"first": "Execute job info extractor",
"second": "Skip incident info extractor"
}
},
"expected_bypassed_nodes":["incident_info_extractor", "icm_retriever", "kql_tsg_retriever", "tsg_retriever", "investigation_steps", "retriever_summary"]
},
{
"expected_node_count": 9,
"expected_outputs":{
"investigation_method": {
"first": "Skip job info extractor",
"second": "Execute incident info extractor"
}
},
"expected_bypassed_nodes":["job_info_extractor", "kql_tsg_retriever", "tsg_retriever"]
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/retriever_summary.py | from promptflow import tool
@tool
def retriever_summary(summary) -> str:
print(f"Summary: {summary}")
return "Execute incident info extractor"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/kql_tsg_retriever.py | from promptflow import tool
@tool
def kql_retriever(content: str) -> str:
return "KQL: " + content | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/tsg_retriever.py | from promptflow import tool
@tool
def tsg_retriever(content: str) -> str:
return "TSG: " + content | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/incident_id_extractor.py | from promptflow import tool
@tool
def extract_incident_id(incident_content: str, incident_id: int):
if incident_id >= 0 and incident_id < 3:
return {
"has_incident_id": True,
"incident_id": incident_id,
"incident_content": incident_content
}
return {
"has_incident_id": False,
"incident_id": incident_id,
"incident_content": incident_content
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/expected_status_summary.json | {
"incident_id_extractor.completed": 3,
"job_info_extractor.completed": 1,
"job_info_extractor.bypassed": 2,
"incident_info_extractor.completed": 2,
"incident_info_extractor.bypassed": 1,
"icm_retriever.completed": 1,
"icm_retriever.bypassed": 2,
"tsg_retriever.completed": 1,
"tsg_retriever.bypassed": 2,
"kql_tsg_retriever.completed": 1,
"kql_tsg_retriever.bypassed": 2,
"investigation_steps.completed": 2,
"investigation_steps.bypassed": 1,
"retriever_summary.completed": 2,
"retriever_summary.bypassed": 1,
"investigation_method.completed": 3
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/investigation_steps.jinja2 | system:
You are a helpful assistant.
user:
When an incident occurs, there have 3 ways to deal with it, please choose one.
1. {{first_method}}
2. {{second_method}}
3. {{third_method}} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/flow.dag.yaml | id: template_standard_flow
name: Template Standard Flow
inputs:
incident_content:
type: string
incident_id:
type: int
outputs:
investigation_method:
type: string
reference: ${investigation_method.output}
nodes:
- name: incident_id_extractor
type: python
source:
type: code
path: incident_id_extractor.py
inputs:
incident_content: ${inputs.incident_content}
incident_id: ${inputs.incident_id}
- name: job_info_extractor
type: python
source:
type: code
path: job_info_extractor.py
inputs:
incident_content: ${incident_id_extractor.output.incident_content}
activate:
when: ${incident_id_extractor.output.has_incident_id}
is: false
- name: incident_info_extractor
type: python
source:
type: code
path: incident_info_extractor.py
inputs:
incident: ${incident_id_extractor.output}
activate:
when: ${incident_id_extractor.output.has_incident_id}
is: true
- name: tsg_retriever
type: python
source:
type: code
path: tsg_retriever.py
inputs:
content: ${incident_info_extractor.output.incident_content}
activate:
when: ${incident_info_extractor.output.retriever}
is: tsg
- name: icm_retriever
type: python
source:
type: code
path: icm_retriever.py
inputs:
content: ${incident_info_extractor.output.incident_content}
activate:
when: ${incident_info_extractor.output.retriever}
is: icm
- name: kql_tsg_retriever
type: python
source:
type: code
path: kql_tsg_retriever.py
inputs:
content: ${incident_info_extractor.output.incident_content}
activate:
when: ${incident_info_extractor.output.retriever}
is: tsg
- name: investigation_steps
type: llm
source:
type: code
path: investigation_steps.jinja2
inputs:
deployment_name: gpt-35-turbo
temperature: 0.7
top_p: 1
stop: ""
max_tokens: 256
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
first_method: ${icm_retriever.output}
second_method: ${tsg_retriever.output}
third_method: ${kql_tsg_retriever.output}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
- name: retriever_summary
type: python
source:
type: code
path: retriever_summary.py
inputs:
summary: ${investigation_steps.output}
- name: investigation_method
type: python
source:
type: code
path: investigation_method.py
inputs:
method1: ${job_info_extractor.output}
method2: ${retriever_summary.output}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/incident_info_extractor.py | from promptflow import tool
@tool
def extract_incident_info(incident: dict) -> str:
retriever_type = ["icm", "tsg", "kql"]
return {
"retriever": retriever_type[incident["incident_id"]],
"incident_content": incident["incident_content"]
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/job_info_extractor.py | from promptflow import tool
@tool
def extract_job_info(incident_content: str) -> str:
print(f"Incident: {incident_content}")
return "Execute job info extractor"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/inputs.json | [
{
"case": "double",
"value": 1
},
{
"case": "double",
"value": 2
},
{
"case": "square",
"value": 3
},
{
"case": "square",
"value": 4
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/double.py | from promptflow import tool
@tool
def double(input: int) -> int:
return 2*input
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/aggregation_node.py | from promptflow import tool
from promptflow import log_metric
@tool
def average(input: list):
avg, cnt = 0, 0
for num in input:
if num!=None:
avg += num
cnt += 1
if len(input) > 0:
avg = avg/cnt
log_metric("average", avg)
return avg
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/expected_result.json | [
{
"expected_node_count": 3,
"expected_outputs":{
"output":{
"double": 2,
"square": ""
}
},
"expected_bypassed_nodes":["square"]
},
{
"expected_node_count": 3,
"expected_outputs":{
"output":{
"double": 4,
"square": ""
}
},
"expected_bypassed_nodes":["square"]
},
{
"expected_node_count": 3,
"expected_outputs":{
"output":{
"double": null,
"square": 9
}
},
"expected_bypassed_nodes":["double"]
},
{
"expected_node_count": 3,
"expected_outputs":{
"output":{
"double": null,
"square": 16
}
},
"expected_bypassed_nodes":["double"]
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/square.py | from promptflow import tool
@tool
def square(input: int) -> int:
return input*input
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/expected_status_summary.json | {
"square.bypassed": 2,
"double.completed": 2,
"collect_node.completed": 4,
"double.bypassed": 2,
"square.completed": 2,
"aggregation_double.completed": 1,
"aggregation_square.completed": 1
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/collect_node.py | from promptflow import tool
@tool
def collect(input1, input2: str="") -> str:
return {'double': input1, 'square': input2}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/flow.dag.yaml | inputs:
case:
type: string
default: double
is_chat_input: false
value:
type: int
default: 1
outputs:
output:
type: string
reference: ${collect_node.output}
evaluation_only: false
is_chat_output: false
nodes:
- name: double
type: python
source:
type: code
path: double.py
inputs:
input: ${inputs.value}
activate:
when: ${inputs.case}
is: double
aggregation: false
- name: square
type: python
source:
type: code
path: square.py
inputs:
input: ${inputs.value}
activate:
when: ${inputs.case}
is: square
aggregation: false
- name: aggregation_double
type: python
source:
type: code
path: aggregation_node.py
inputs:
input: ${double.output}
aggregation: true
- name: aggregation_square
type: python
source:
type: code
path: aggregation_node.py
inputs:
input: ${square.output}
aggregation: true
- name: collect_node
type: python
source:
type: code
path: collect_node.py
inputs:
input1: ${double.output}
input2: ${square.output}
aggregation: false
| 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/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/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/flow_with_non_english_input/data.jsonl | {"text": "Hello 123 日本語"}
{"text": "World 123 日本語"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_non_english_input/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
text:
type: string
default: Hello 日本語
outputs:
output:
type: string
reference: ${hello_prompt.output}
nodes:
- name: hello_prompt
type: prompt
source:
type: code
path: hello.jinja2
inputs:
text: ${inputs.text} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow_with_non_english_input/hello.jinja2 | {{text}} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/data.jsonl | {"input": "atom", "index": 0}
{"input": "atom", "index": 6}
{"input": "atom", "index": 12}
{"input": "atom", "index": 18}
{"input": "atom", "index": 24}
{"input": "atom", "index": 30}
{"input": "atom", "index": 36}
{"input": "atom", "index": 42}
{"input": "atom", "index": 48}
{"input": "atom", "index": 54}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/samples.json | [
{
"input": "atom",
"index": 0
},
{
"input": "atom",
"index": 6
},
{
"input": "atom",
"index": 12
},{
"input": "atom",
"index": 18
},{
"input": "atom",
"index": 24
},{
"input": "atom",
"index": 30
},{
"input": "atom",
"index": 36
},{
"input": "atom",
"index": 42
},{
"input": "atom",
"index": 48
},{
"input": "atom",
"index": 54
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/python_node.py | from promptflow import tool
import time
@tool
def python_node(input: str, index: int) -> str:
time.sleep(index + 5)
return input
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/flow.dag.yaml | id: template_standard_flow
name: Template Standard Flow
inputs:
input:
type: string
is_chat_input: false
index:
type: int
is_chat_input: false
outputs:
output:
type: string
reference: ${python_node.output}
nodes:
- name: python_node
type: python
source:
type: code
path: python_node.py
inputs:
index: ${inputs.index}
input: ${inputs.input}
use_variants: false
node_variants: {}
| 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 | 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/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/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/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/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/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/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/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/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/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/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/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/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 |
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