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promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/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_v2/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_v2/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_v2/samples.json | [
{
"line_number": 0,
"variant_id": "variant_0",
"groundtruth": "App",
"prediction": "App"
}
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2 | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_v2/.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/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_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/tool_with_assistant_definition/assistant_definition.yaml | model: mock_model
instructions: mock_instructions
tools:
- type: function
tool_type: python
source:
type: code
path: echo.py
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/tool_with_assistant_definition/echo.py | from promptflow import tool
@tool
def echo(message: str):
"""This tool is used to echo the message back.
:param message: The message to echo.
:type message: str
"""
return message
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/tool_with_assistant_definition/test_assistant_definition.py | from promptflow import tool
from promptflow.contracts.types import AssistantDefinition
@tool
def test_assistant_definition(message: str, assistant_definition: AssistantDefinition):
assert assistant_definition.model == "mock_model"
assert assistant_definition.instructions == "mock_instructions"
invoker = assistant_definition.init_tool_invoker()
openai_definition = invoker.to_openai_tools()
assert len(openai_definition) == 1
assert openai_definition[0]["function"]["description"] == "This tool is used to echo the message back."
assert openai_definition[0]["function"]["parameters"]["properties"] == {
"message": {"description": "The message to echo.", "type": "string"}
}
assert openai_definition[0]["function"]["parameters"]["required"] == ["message"]
assert invoker.invoke_tool("echo", {"message": message}) == "Hello World!"
return assistant_definition.serialize()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/tool_with_assistant_definition/flow.dag.yaml | inputs:
message:
type: string
default: Hello World!
outputs:
output:
type: object
reference: ${test_assistant_definition.output}
nodes:
- name: test_assistant_definition
type: python
source:
type: code
path: test_assistant_definition.py
inputs:
message: ${inputs.message}
assistant_definition: assistant_definition.yaml
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/Dockerfile | # syntax=docker/dockerfile:1
FROM docker.io/continuumio/miniconda3:latest
WORKDIR /
COPY ./flow /flow
# create conda environment
RUN conda create -n promptflow-serve python=3.9.16 pip=23.0.1 -q -y && \
conda run -n promptflow-serve \
pip install -r /flow/requirements_txt && \
conda run -n promptflow-serve pip install keyrings.alt && \
conda run -n promptflow-serve pip install gunicorn==20.1.0 && \
conda run -n promptflow-serve pip cache purge && \
conda clean -a -y
RUN apt-get update && apt-get install -y runit
EXPOSE 8080
COPY ./connections/* /connections/
# reset runsvdir
RUN rm -rf /var/runit
COPY ./runit /var/runit
# grant permission
RUN chmod -R +x /var/runit
COPY ./start.sh /
CMD ["bash", "./start.sh"] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/start.sh | #!/bin/bash
# stop services created by runsv and propagate SIGINT, SIGTERM to child jobs
sv_stop() {
echo "$(date -uIns) - Stopping all runsv services"
for s in $(ls -d /var/runit/*); do
sv stop $s
done
}
# register SIGINT, SIGTERM handler
trap sv_stop SIGINT SIGTERM
# start services in background and wait all child jobs
runsvdir /var/runit &
wait
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/README.md | Exported Dockerfile & its dependencies are located in the same folder. The structure is as below:
- flow: the folder contains all the flow files
- ...
- connections: the folder contains yaml files to create all related connections
- ...
- runit: the folder contains all the runit scripts
- ...
- Dockerfile: the dockerfile to build the image
- start.sh: the script used in `CMD` of `Dockerfile` to start the service
- settings.json: a json file to store the settings of the docker image
- README.md: the readme file to describe how to use the dockerfile
Please refer to [official doc](https://microsoft.github.io/promptflow/how-to-guides/deploy-and-export-a-flow.html#export-a-flow)
for more details about how to use the exported dockerfile and scripts.
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/settings.json | {
"CUSTOM_CONNECTION_AZURE_OPENAI_API_KEY": ""
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/connections/custom_connection.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/CustomConnection.schema.json
type: custom
name: custom_connection
configs:
CHAT_DEPLOYMENT_NAME: gpt-35-turbo
AZURE_OPENAI_API_BASE: https://gpt-test-eus.openai.azure.com/
secrets:
AZURE_OPENAI_API_KEY: ${env:CUSTOM_CONNECTION_AZURE_OPENAI_API_KEY}
module: promptflow.connections
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit/promptflow-serve/run | #! /bin/bash
CONDA_ENV_PATH="$(conda info --base)/envs/promptflow-serve"
export PATH="$CONDA_ENV_PATH/bin:$PATH"
ls
ls /connections
pf connection create --file /connections/custom_connection.yaml
echo "start promptflow serving with worker_num: 8, worker_threads: 1"
cd /flow
gunicorn -w 8 --threads 1 -b "0.0.0.0:8080" --timeout 300 "promptflow._sdk._serving.app:create_app()" | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit/promptflow-serve/finish | #!/bin/bash
echo "$(date -uIns) - promptflow-serve/finish $@"
# stop all gunicorn processes
echo "$(date -uIns) - Stopping all Gunicorn processes"
pkill gunicorn
while pgrep gunicorn >/dev/null; do
echo "$(date -uIns) - Gunicorn process is still running, waiting for 1s"
sleep 1
done
echo "$(date -uIns) - Stopped all Gunicorn processes" | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/setup.sh | echo Hello Promptflow!
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/requirements_txt | keyrings.alt
promptflow-tools
promptflow
langchain
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/user_intent_few_shot.jinja2 | You are given a list of orders with item_numbers from a customer and a statement from the customer. It is your job to identify
the intent that the customer has with their statement. Possible intents can be:
"product return", "product exchange", "general question", "product question", "other".
If the intent is product related ("product return", "product exchange", "product question"), then you should also
provide the order id and item that the customer is referring to in their statement.
For instance if you are give the following list of orders:
order_number: 2020230
date: 2023-04-23
store_location: SeattleStore
items:
- description: Roof Rack, color black, price $199.99
item_number: 101010
- description: Running Shoes, size 10, color blue, price $99.99
item_number: 202020
You are given the following customer statements:
- I am having issues with the jobbing shoes I bought.
Then you should answer with in valid yaml format with the fields intent, order_number, item, and item_number like so:
intent: product question
order_number: 2020230
descrption: Running Shoes, size 10, color blue, price $99.99
item_number: 202020
Here is the actual problem you need to solve:
In triple backticks below is the customer information and a list of orders.
```
{{customer_info}}
```
In triple backticks below are the is the chat history with customer statements and replies from the customer service agent:
```
{{chat_history}}
```
What is the customer's `intent:` here?
"product return", "exchange product", "general question", "product question" or "other"?
Reply with only the intent string.
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/intent.py | import os
import pip
def extract_intent(chat_prompt: str):
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
if "AZURE_OPENAI_API_KEY" not in os.environ:
# load environment variables from .env file
try:
from dotenv import load_dotenv
except ImportError:
# This can be removed if user using custom image.
pip.main(["install", "python-dotenv"])
from dotenv import load_dotenv
load_dotenv()
chat = AzureChatOpenAI(
deployment_name=os.environ["CHAT_DEPLOYMENT_NAME"],
openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
openai_api_type="azure",
openai_api_version="2023-03-15-preview",
temperature=0,
)
reply_message = chat([HumanMessage(content=chat_prompt)])
return reply_message.content
def generate_prompt(customer_info: str, history: list, user_prompt_template: str):
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.prompts.prompt import PromptTemplate
chat_history_text = "\n".join(
[message["role"] + ": " + message["content"] for message in history]
)
prompt_template = PromptTemplate.from_template(user_prompt_template)
chat_prompt_template = ChatPromptTemplate.from_messages(
[
HumanMessagePromptTemplate(prompt=prompt_template)
]
)
return chat_prompt_template.format_prompt(customer_info=customer_info, chat_history=chat_history_text).to_string()
if __name__ == "__main__":
import json
with open("./data/denormalized-flat.jsonl", "r") as f:
data = [json.loads(line) for line in f.readlines()]
# only ten samples
data = data[:10]
# load template from file
with open("user_intent_zero_shot.md", "r") as f:
user_prompt_template = f.read()
# each test
for item in data:
chat_prompt = generate_prompt(item["customer_info"], item["history"], user_prompt_template)
reply = extract_intent(chat_prompt)
print("=====================================")
# print("Customer info: ", item["customer_info"])
# print("+++++++++++++++++++++++++++++++++++++")
print("Chat history: ", item["history"])
print("+++++++++++++++++++++++++++++++++++++")
print(reply)
print("+++++++++++++++++++++++++++++++++++++")
print(f"Ground Truth: {item['intent']}")
print("=====================================")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/flow.dag.yaml | inputs:
customer_info:
type: string
chat_history:
type: string
outputs:
output:
type: string
reference: ${extract_intent.output}
nodes:
- name: chat_prompt
type: prompt
source:
type: code
path: user_intent_zero_shot.jinja2
inputs: # Please check the generated prompt inputs
customer_info: ${inputs.customer_info}
chat_history: ${inputs.chat_history}
- name: extract_intent
type: python
source:
type: code
path: extract_intent_tool.py
inputs:
chat_prompt: ${chat_prompt.output}
connection: custom_connection
environment:
python_requirements_txt: requirements_txt
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/.amlignore | *.ipynb
.venv/
.data/
.env
.vscode/
outputs/
connection.json
.gitignore
README.md
eval_cli.md
data/
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/user_intent_zero_shot.jinja2 | You are given a list of orders with item_numbers from a customer and a statement from the customer. It is your job to identify
the intent that the customer has with their statement. Possible intents can be:
"product return", "product exchange", "general question", "product question", "other".
In triple backticks below is the customer information and a list of orders.
```
{{customer_info}}
```
In triple backticks below are the is the chat history with customer statements and replies from the customer service agent:
```
{{chat_history}}
```
What is the customer's `intent:` here?
"product return", "exchange product", "general question", "product question" or "other"?
Reply with only the intent string.
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/extract_intent_tool.py | import os
from promptflow import tool
from promptflow.connections import CustomConnection
from intent import extract_intent
@tool
def extract_intent_tool(
chat_prompt,
connection: CustomConnection) -> str:
# set environment variables
for key, value in dict(connection).items():
os.environ[key] = value
# call the entry function
return extract_intent(
chat_prompt=chat_prompt,
) | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_invalid_default_value/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_invalid_default_value/flow.dag.yaml | inputs:
image:
type: image
default: ""
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 | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/csharp_flow/flow.dag.yaml | language: csharp
inputs:
question:
type: string
default: what is promptflow?
outputs:
answer:
type: string
reference: ${get_answer.output}
nodes:
- name: get_answer
type: csharp
source:
type: package
tool: (Basic)Basic.Flow.HelloWorld
inputs:
question: ${inputs.question}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/csharp_flow/inputs.jsonl | {"question": "What's promptflow1?"}
{"question": "What's promptflow2?"}
{"question": "What's promptflow3?"} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow-with-nan-inf/flow.dag.yaml | inputs:
number:
type: int
outputs:
output:
type: object
reference: ${nan_inf.output}
nodes:
- name: nan_inf
type: python
source:
type: code
path: nan_inf.py
inputs:
number: ${inputs.number}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/flow-with-nan-inf/nan_inf.py | from promptflow import tool
@tool
def nan_inf(number: int):
print(number)
return {"nan": float("nan"), "inf": float("inf")}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/show_answer.py | from promptflow import tool
@tool
def show_answer(chat_answer: str):
raise Exception("mock exception")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/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_exception/flow.dag.yaml | inputs:
chat_history:
type: list
question:
type: string
is_chat_input: true
default: What is ChatGPT?
outputs:
answer:
type: string
reference: ${show_answer.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
type: llm
source:
type: code
path: chat.jinja2
api: chat
provider: AzureOpenAI
connection: azure_open_ai_connection
- name: show_answer
type: python
source:
type: code
path: show_answer.py
inputs:
chat_answer: ${chat.output} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_special_character/script_with_special_character.py | from promptflow import tool
@tool
def print_special_character(input1: str) -> str:
# Add special character to test if file read is working.
return "https://www.bing.com//"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_special_character/script_with_special_character.meta.json | {
"name": "script_with_special_character",
"type": "python",
"inputs": {
"input1": {
"type": [
"string"
]
}
},
"source": "script_with_special_character.py",
"function": "print_special_character"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools/async_passthrough.py | from promptflow import tool
import asyncio
@tool
async def passthrough_str_and_wait(input1: str, wait_seconds=3) -> str:
assert isinstance(input1, str), f"input1 should be a string, got {input1}"
print(f"Wait for {wait_seconds} seconds in async function")
for i in range(wait_seconds):
print(i)
await asyncio.sleep(1)
return input1
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools/flow.dag.yaml | inputs:
input_str:
type: string
default: Hello
outputs:
ouput1:
type: string
reference: ${async_passthrough1.output}
output2:
type: string
reference: ${async_passthrough2.output}
nodes:
- name: async_passthrough
type: python
source:
type: code
path: async_passthrough.py
inputs:
input1: ${inputs.input_str}
wait_seconds: 3
- name: async_passthrough1
type: python
source:
type: code
path: async_passthrough.py
inputs:
input1: ${async_passthrough.output}
wait_seconds: 3
- name: async_passthrough2
type: python
source:
type: code
path: async_passthrough.py
inputs:
input1: ${async_passthrough.output}
wait_seconds: 3
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/sync_tools_failures/sync_fail.py | from promptflow import tool
def raise_exception(s):
msg = f"In raise_exception: {s}"
raise Exception(msg)
@tool
def raise_an_exception(s: str):
try:
raise_exception(s)
except Exception as e:
raise Exception(f"In tool raise_an_exception: {s}") from e
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/sync_tools_failures/flow.dag.yaml | inputs:
text:
type: string
default: dummy_input
outputs:
output_prompt:
type: string
reference: ${sync_fail.output}
nodes:
- name: sync_fail
type: python
source:
type: code
path: sync_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/unordered_nodes/flow.dag.yaml | name: node_wrong_order
inputs:
text:
type: string
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}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image_without_default/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_without_default/flow.dag.yaml | inputs:
image_1:
type: image
image_2:
type: image
outputs:
output:
type: image
reference: ${python_node.output}
nodes:
- name: python_node
type: python
source:
type: code
path: pick_an_image.py
inputs:
image_1: ${inputs.image_1}
image_2: ${inputs.image_2}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_defined_chat_history/show_answer.py | from promptflow import tool
@tool
def show_answer(chat_answer: str):
print("print:", chat_answer)
return chat_answer
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_defined_chat_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_defined_chat_history/flow.dag.yaml | inputs:
user_chat_history:
type: list
is_chat_history: true
question:
type: string
is_chat_input: true
default: What is ChatGPT?
outputs:
answer:
type: string
reference: ${show_answer.output}
is_chat_output: true
nodes:
- inputs:
deployment_name: gpt-35-turbo
max_tokens: "256"
temperature: "0.7"
chat_history: ${inputs.user_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
- name: show_answer
type: python
source:
type: code
path: show_answer.py
inputs:
chat_answer: ${chat_node.output}
node_variants: {}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_input_dir/details.jsonl | {"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url"}
{"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url"}
{"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url"}
| 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/aggregation_node_failed/data.jsonl | {"groundtruth": "Tomorrow's weather will be sunny.","prediction": "The weather will be sunny tomorrow."}
{"groundtruth": "Hello,","prediction": "World."}
{"groundtruth": "Promptflow is a super easy-to-use tool, right?","prediction": "Yes!"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/line_process.py | from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str):
processed_result = groundtruth + prediction
return processed_result
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/aggregate.py | from typing import List
from promptflow import tool
@tool
def aggregate(processed_results: List[str]):
aggregated_results = processed_results
# raise error to test aggregation node failed
num = 1/0
return aggregated_results
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/expected_status_summary.json | {
"line_process.completed": 3,
"aggregate.failed": 1
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/flow.dag.yaml | id: template_eval_flow
name: Template Evaluation Flow
inputs:
groundtruth:
type: string
is_chat_input: false
prediction:
type: string
is_chat_input: false
outputs:
results:
type: string
reference: ${line_process.output}
nodes:
- name: line_process
type: python
source:
type: code
path: line_process.py
inputs:
groundtruth: ${inputs.groundtruth}
prediction: ${inputs.prediction}
use_variants: false
- name: aggregate
type: python
source:
type: code
path: aggregate.py
inputs:
processed_results: ${line_process.output}
aggregation: true
use_variants: false
node_variants: {}
environment:
python_requirements_txt: requirements.txt
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/samples.json | [
{
"groundtruth": "Tomorrow's weather will be sunny.",
"prediction": "The weather will be sunny tomorrow."
},
{
"groundtruth": "Hello,",
"prediction": "World."
},
{
"groundtruth": "Promptflow is a super easy-to-use tool, right?",
"prediction": "Yes!"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/assistant_definition.yaml | model: gpt-4-1106-preview
instructions: You are a helpful assistant.
tools:
- type: code_interpreter
- type: function
source:
type: code
path: get_calorie_by_jogging.py
tool_type: python
- type: function
source:
type: code
path: get_calorie_by_swimming.py
tool_type: python
- type: function
source:
type: code
path: get_current_city.py
tool_type: python
- type: function
source:
type: code
path: get_temperature.py
tool_type: python
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_or_create_thread.py | from openai import AsyncOpenAI
from promptflow import tool
from promptflow.connections import OpenAIConnection
@tool
async def get_or_create_thread(conn: OpenAIConnection, thread_id: str):
if thread_id:
return thread_id
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
thread = await cli.beta.threads.create()
return thread.id
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/data.jsonl | {"chat_history":[], "question": "If I am going to run with 1.5 hours this morning, how many calories will I burn?", "assistant_id": "asst_yWhdFYoCS1UatnRRQZGY85aL", "thread_id": ""}
{"chat_history":[], "question": "I'm going to swim in Guangzhou city today for 30 min, how much calories will I burn?", "assistant_id": "asst_yWhdFYoCS1UatnRRQZGY85aL", "thread_id": ""}
{"chat_history":[], "question": "I'm going to run slowly on local street today, how much calories will I burn?", "assistant_id": "asst_yWhdFYoCS1UatnRRQZGY85aL", "thread_id": ""}
{"chat_history":[], "question": "If I am going to run 1.5 hours under 24 degrees Celsius, how many calories will I burn", "assistant_id": "asst_yWhdFYoCS1UatnRRQZGY85aL", "thread_id": ""}
{"chat_history":[], "question": "I'm going to biking for 2 hours duration today, how much calories will I burn?", "assistant_id": "asst_yWhdFYoCS1UatnRRQZGY85aL", "thread_id": ""}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_calorie_by_jogging.py | import random
import time
from promptflow import tool
@tool
def get_calorie_by_jogging(duration: float, temperature: float):
"""Estimate the calories burned by jogging based on duration and temperature.
:param duration: the length of the jogging in hours.
:type duration: float
:param temperature: the environment temperature in degrees Celsius.
:type temperature: float
"""
print(
f"Figure out the calories burned by jogging, with temperature of {temperature} degrees Celsius, "
f"and duration of {duration} hours."
)
# Generating a random number between 0.2 and 1 for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.randint(50, 100)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_current_city.py | import random
import time
from promptflow import tool
@tool
def get_current_city():
"""Get current city."""
# Generating a random number between 0.2 and 1 for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.choice(["Beijing", "Shanghai"])
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_temperature.py | import random
import time
from promptflow import tool
@tool
def get_temperature(city: str, unit: str = "c"):
"""Estimate the current temperature of a given city.
:param city: city to get the estimated temperature for.
:type city: str
:param unit: the unit of the temperature, either 'c' for Celsius or 'f' for Fahrenheit.
Defaults to Celsius ('c').
:type unit: str
"""
# Generating a random number between 0.2 and 1 for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.uniform(0, 35)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/add_message_and_run.py | import asyncio
import json
from openai import AsyncOpenAI
from openai.types.beta.threads import MessageContentImageFile, MessageContentText
from promptflow import tool, trace
from promptflow.connections import OpenAIConnection
from promptflow.contracts.multimedia import Image
from promptflow.contracts.types import AssistantDefinition
from promptflow.exceptions import SystemErrorException
from promptflow.executor._assistant_tool_invoker import AssistantToolInvoker
URL_PREFIX = "https://platform.openai.com/files/"
RUN_STATUS_POLLING_INTERVAL_IN_MILSEC = 1000
@tool
async def add_message_and_run(
conn: OpenAIConnection,
assistant_id: str,
thread_id: str,
message: list,
assistant_definition: AssistantDefinition,
download_images: bool,
):
cli = await get_openai_api_client(conn)
invoker = await get_assisant_tool_invoker(assistant_definition)
# Check if assistant id is valid. If not, create a new assistant.
# Note: tool registration at run creation, rather than at assistant creation.
if not assistant_id:
assistant = await create_assistant(cli, assistant_definition)
assistant_id = assistant.id
await add_message(cli, message, thread_id)
run = await start_run(cli, assistant_id, thread_id, assistant_definition, invoker)
await wait_for_run_complete(cli, thread_id, invoker, run)
messages = await get_message(cli, thread_id)
file_id_references = await get_openai_file_references(messages.data[0].content, download_images, conn)
return {"content": to_pf_content(messages.data[0].content), "file_id_references": file_id_references}
@trace
async def get_openai_api_client(conn: OpenAIConnection):
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
return cli
@trace
async def get_assisant_tool_invoker(assistant_definition: AssistantDefinition):
invoker = AssistantToolInvoker.init(assistant_definition.tools)
return invoker
@trace
async def create_assistant(cli: AsyncOpenAI, assistant_definition: AssistantDefinition):
assistant = await cli.beta.assistants.create(
instructions=assistant_definition.instructions, model=assistant_definition.model
)
print(f"Created assistant: {assistant.id}")
return assistant
@trace
async def add_message(cli: AsyncOpenAI, message: list, thread_id: str):
content = extract_text_from_message(message)
file_ids = await extract_file_ids_from_message(cli, message)
msg = await cli.beta.threads.messages.create(thread_id=thread_id, role="user", content=content, file_ids=file_ids)
print("Created message message_id: {msg.id}, assistant_id: {assistant_id}, thread_id: {thread_id}")
return msg
@trace
async def start_run(
cli: AsyncOpenAI,
assistant_id: str,
thread_id: str,
assistant_definition: AssistantDefinition,
invoker: AssistantToolInvoker,
):
tools = invoker.to_openai_tools()
run = await cli.beta.threads.runs.create(
assistant_id=assistant_id,
thread_id=thread_id,
model=assistant_definition.model,
instructions=assistant_definition.instructions,
tools=tools,
)
print(f"Assistant_id: {assistant_id}, thread_id: {thread_id}, run_id: {run.id}")
return run
async def wait_for_status_check():
await asyncio.sleep(RUN_STATUS_POLLING_INTERVAL_IN_MILSEC / 1000.0)
async def get_run_status(cli: AsyncOpenAI, thread_id: str, run_id: str):
run = await cli.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)
print(f"Run status: {run.status}")
return run
@trace
async def get_tool_calls_outputs(invoker: AssistantToolInvoker, run):
tool_calls = run.required_action.submit_tool_outputs.tool_calls
tool_outputs = []
for tool_call in tool_calls:
tool_name = tool_call.function.name
tool_args = json.loads(tool_call.function.arguments)
print(f"Invoking tool: {tool_call.function.name} with args: {tool_args}")
output = invoker.invoke_tool(tool_name, tool_args)
tool_outputs.append(
{
"tool_call_id": tool_call.id,
"output": str(output),
}
)
print(f"Tool output: {str(output)}")
return tool_outputs
@trace
async def submit_tool_calls_outputs(cli: AsyncOpenAI, thread_id: str, run_id: str, tool_outputs: list):
await cli.beta.threads.runs.submit_tool_outputs(thread_id=thread_id, run_id=run_id, tool_outputs=tool_outputs)
print(f"Submitted all required resonses for run: {run_id}")
@trace
async def require_actions(cli: AsyncOpenAI, thread_id: str, run, invoker: AssistantToolInvoker):
tool_outputs = await get_tool_calls_outputs(invoker, run)
await submit_tool_calls_outputs(cli, thread_id, run.id, tool_outputs)
@trace
async def wait_for_run_complete(cli: AsyncOpenAI, thread_id: str, invoker: AssistantToolInvoker, run):
while run.status != "completed":
await wait_for_status_check()
run = await get_run_status(cli, thread_id, run.id)
if run.status == "requires_action":
await require_actions(cli, thread_id, run, invoker)
elif run.status == "in_progress" or run.status == "completed":
continue
else:
raise Exception(f"The assistant tool runs in '{run.status}' status. Message: {run.last_error.message}")
@trace
async def get_run_steps(cli: AsyncOpenAI, thread_id: str, run_id: str):
run_steps = await cli.beta.threads.runs.steps.list(thread_id=thread_id, run_id=run_id)
print("step details: \n")
for step_data in run_steps.data:
print(step_data.step_details)
@trace
async def get_message(cli: AsyncOpenAI, thread_id: str):
messages = await cli.beta.threads.messages.list(thread_id=thread_id)
return messages
def extract_text_from_message(message: list):
content = []
for m in message:
if isinstance(m, str):
content.append(m)
continue
message_type = m.get("type", "")
if message_type == "text" and "text" in m:
content.append(m["text"])
return "\n".join(content)
async def extract_file_ids_from_message(cli: AsyncOpenAI, message: list):
file_ids = []
for m in message:
if isinstance(m, str):
continue
message_type = m.get("type", "")
if message_type == "file_path" and "file_path" in m:
path = m["file_path"].get("path", "")
if path:
file = await cli.files.create(file=open(path, "rb"), purpose="assistants")
file_ids.append(file.id)
return file_ids
async def get_openai_file_references(content: list, download_image: bool, conn: OpenAIConnection):
file_id_references = {}
for item in content:
if isinstance(item, MessageContentImageFile):
file_id = item.image_file.file_id
if download_image:
file_id_references[file_id] = {
"content": await download_openai_image(file_id, conn),
"url": URL_PREFIX + file_id,
}
else:
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
elif isinstance(item, MessageContentText):
for annotation in item.text.annotations:
if annotation.type == "file_path":
file_id = annotation.file_path.file_id
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
elif annotation.type == "file_citation":
file_id = annotation.file_citation.file_id
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
else:
raise Exception(f"Unsupported content type: '{type(item)}'.")
return file_id_references
def to_pf_content(content: list):
pf_content = []
for item in content:
if isinstance(item, MessageContentImageFile):
file_id = item.image_file.file_id
pf_content.append({"type": "image_file", "image_file": {"file_id": file_id}})
elif isinstance(item, MessageContentText):
text_dict = {"type": "text", "text": {"value": item.text.value, "annotations": []}}
for annotation in item.text.annotations:
annotation_dict = {
"type": "file_path",
"text": annotation.text,
"start_index": annotation.start_index,
"end_index": annotation.end_index,
}
if annotation.type == "file_path":
annotation_dict["file_path"] = {"file_id": annotation.file_path.file_id}
elif annotation.type == "file_citation":
annotation_dict["file_citation"] = {"file_id": annotation.file_citation.file_id}
text_dict["text"]["annotations"].append(annotation_dict)
pf_content.append(text_dict)
else:
raise SystemErrorException(f"Unsupported content type: {type(item)}")
return pf_content
async def download_openai_image(file_id: str, conn: OpenAIConnection):
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
image_data = await cli.files.content(file_id)
return Image(image_data.read())
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_calorie_by_swimming.py | import random
import time
from promptflow import tool
@tool
def get_calorie_by_swimming(duration: float, temperature: float):
"""Estimate the calories burned by swimming based on duration and temperature.
:param duration: the length of the swimming in hours.
:type duration: float
:param temperature: the environment temperature in degrees Celsius.
:type temperature: float
"""
print(
f"Figure out the calories burned by swimming, with temperature of {temperature} degrees Celsius, "
f"and duration of {duration} hours."
)
# Generating a random number between 0.2 and 1 for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.randint(100, 200)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/README.md | # Chat with Calorie Assistant
This sample demonstrates how to chat with the PromptFlow Assistant tool facilitates calorie calculations by considering your location, the duration of your exercise, and the type of sport. Currently, it supports two types of sports: jogging and swimming.
Tools used in this flow：
- `add_message_and_run` tool, assistant tool, provisioned with below inner functions:
- `get_current_location``: get current city
- `get_temperature(location)``: get temperature of the city
- `get_calorie_by_jogging(duration, temperature)``: calculate calorie for jogging exercise
- `get_calorie_by_jogging(duration, temperature)``: calculate calorie for swimming exercise
## Prerequisites
Install promptflow sdk and other dependencies in this folder:
```sh
pip install -r requirements.txt
```
## What you will learn
In this flow, you will understand how assistant tools within PromptFlow are triggered by user prompts. The assistant tool decides which internal functions or tools to invoke based on the input provided. Your responsibility involves implementing each of these tools and registering them in the `assistant_definition`. Additionally, be aware that the tools may have dependencies on each other, affecting the order and manner of their invocation.
## Getting started
### 1. Create assistant connection (openai)
Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of assistant tool supported connection types and fill in the configurations.
Currently, only "Open AI" connection type are supported for assistant tool. Please refer to [OpenAI](https://platform.openai.com/) for more details.
```bash
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/openai.yml --set api_key=<your_api_key>
```
Note in [flow.dag.yaml](flow.dag.yaml) we are using connection named `open_ai_connection`.
```bash
# show registered connection
pf connection show --name open_ai_connection
```
### 2. Create or get assistant/thread
Navigate to the OpenAI Assistant page and create an assistant if you haven't already. Once created, click on the 'Test' button to enter the assistant's playground. Make sure to note down the assistant_id.
**[Optional]** Start a chat session to create thread automatically. Keep track of the thread_id.
### 3. run the flow
```bash
# run chat flow with default question in flow.dag.yaml
pf flow test --flow . --interactive --multi-modal --user-agent "prompt-flow-extension/1.8.0 (win32; x64) VSCode/1.85.1"
```
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/flow.dag.yaml | environment:
python_requirements_txt: requirements.txt
version: 2
inputs:
chat_history:
type: list
is_chat_history: true
default: []
question:
type: string
is_chat_input: true
default: I am going to swim today for 30 min in Guangzhou city, how much
calories will I burn?
assistant_id:
type: string
default: ""
thread_id:
type: string
default: ""
outputs:
answer:
type: string
reference: ${assistant.output}
is_chat_output: true
thread_id:
type: string
reference: ${get_or_create_thread.output}
nodes:
- name: get_or_create_thread
type: python
source:
type: code
path: get_or_create_thread.py
inputs:
conn: chw-manager-OpenAI
thread_id: ${inputs.thread_id}
- name: assistant
type: python
source:
type: code
path: add_message_and_run.py
inputs:
conn: chw-manager-OpenAI
message: ${inputs.question}
assistant_id: ${inputs.assistant_id}
thread_id: ${get_or_create_thread.output}
download_images: true
assistant_definition: assistant_definition.yaml
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/default_input/test_print_input.py | from promptflow import tool
@tool
def test_print_input(input_str: str, input_bool: bool, input_list: list, input_dict: dict):
assert not input_bool
assert input_list == []
assert input_dict == {}
print(input_str)
return input_str | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/default_input/test_print_aggregation.py | from typing import List
from promptflow import tool
@tool
def test_print_input(input_str: List[str], input_bool: List[bool], input_list: List[List], input_dict: List[dict]):
assert input_bool[0] == False
assert input_list[0] == []
assert input_dict[0] == {}
print(input_str)
return input_str | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/default_input/flow.dag.yaml |
inputs:
input_str:
type: string
default: input value from default
input_bool:
type: bool
default: False
input_list:
type: list
default: []
input_dict:
type: object
default: {}
outputs:
output:
type: string
reference: ${test_print_input.output}
nodes:
- name: test_print_input
type: python
source:
type: code
path: test_print_input.py
inputs:
input_str: ${inputs.input_str}
input_bool: ${inputs.input_bool}
input_list: ${inputs.input_list}
input_dict: ${inputs.input_dict}
- name: aggregate_node
type: python
source:
type: code
path: test_print_aggregation.py
inputs:
input_str: ${inputs.input_str}
input_bool: ${inputs.input_bool}
input_list: ${inputs.input_list}
input_dict: ${inputs.input_dict}
aggregation: true
use_variants: false
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/default_input/samples.json | [
{
"text": "text_1"
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/assistant_definition.yaml | model: gpt-4-1106-preview
instructions: You are a helpful assistant.
tools:
- type: code_interpreter
- type: function
source:
type: code
path: get_stock_eod_price.py
tool_type: python
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/get_or_create_thread.py | from openai import AsyncOpenAI
from promptflow import tool
from promptflow.connections import OpenAIConnection
@tool
async def get_or_create_thread(conn: OpenAIConnection, thread_id: str):
if thread_id:
return thread_id
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
thread = await cli.beta.threads.create()
return thread.id
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/add_message_and_run.py | import asyncio
import json
from openai import AsyncOpenAI
from openai.types.beta.threads import MessageContentImageFile, MessageContentText
from promptflow import tool, trace
from promptflow.connections import OpenAIConnection
from promptflow.contracts.multimedia import Image
from promptflow.contracts.types import AssistantDefinition
from promptflow.exceptions import SystemErrorException
from promptflow.executor._assistant_tool_invoker import AssistantToolInvoker
URL_PREFIX = "https://platform.openai.com/files/"
RUN_STATUS_POLLING_INTERVAL_IN_MILSEC = 1000
@tool
async def add_message_and_run(
conn: OpenAIConnection,
assistant_id: str,
thread_id: str,
message: list,
assistant_definition: AssistantDefinition,
download_images: bool,
):
cli = await get_openai_api_client(conn)
invoker = await get_assisant_tool_invoker(assistant_definition)
# Check if assistant id is valid. If not, create a new assistant.
# Note: tool registration at run creation, rather than at assistant creation.
if not assistant_id:
assistant = await create_assistant(cli, assistant_definition)
assistant_id = assistant.id
await add_message(cli, message, thread_id)
run = await start_run(cli, assistant_id, thread_id, assistant_definition, invoker)
await wait_for_run_complete(cli, thread_id, invoker, run)
messages = await get_message(cli, thread_id)
file_id_references = await get_openai_file_references(messages.data[0].content, download_images, conn)
return {"content": to_pf_content(messages.data[0].content), "file_id_references": file_id_references}
@trace
async def get_openai_api_client(conn: OpenAIConnection):
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
return cli
@trace
async def get_assisant_tool_invoker(assistant_definition: AssistantDefinition):
invoker = AssistantToolInvoker.init(assistant_definition.tools)
return invoker
@trace
async def create_assistant(cli: AsyncOpenAI, assistant_definition: AssistantDefinition):
assistant = await cli.beta.assistants.create(
instructions=assistant_definition.instructions, model=assistant_definition.model
)
print(f"Created assistant: {assistant.id}")
return assistant
@trace
async def add_message(cli: AsyncOpenAI, message: list, thread_id: str):
content = extract_text_from_message(message)
file_ids = await extract_file_ids_from_message(cli, message)
msg = await cli.beta.threads.messages.create(thread_id=thread_id, role="user", content=content, file_ids=file_ids)
print("Created message message_id: {msg.id}, assistant_id: {assistant_id}, thread_id: {thread_id}")
return msg
@trace
async def start_run(
cli: AsyncOpenAI,
assistant_id: str,
thread_id: str,
assistant_definition: AssistantDefinition,
invoker: AssistantToolInvoker,
):
tools = invoker.to_openai_tools()
run = await cli.beta.threads.runs.create(
assistant_id=assistant_id,
thread_id=thread_id,
model=assistant_definition.model,
instructions=assistant_definition.instructions,
tools=tools,
)
print(f"Assistant_id: {assistant_id}, thread_id: {thread_id}, run_id: {run.id}")
return run
async def wait_for_status_check():
await asyncio.sleep(RUN_STATUS_POLLING_INTERVAL_IN_MILSEC / 1000.0)
async def get_run_status(cli: AsyncOpenAI, thread_id: str, run_id: str):
run = await cli.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)
print(f"Run status: {run.status}")
return run
@trace
async def get_tool_calls_outputs(invoker: AssistantToolInvoker, run):
tool_calls = run.required_action.submit_tool_outputs.tool_calls
tool_outputs = []
for tool_call in tool_calls:
tool_name = tool_call.function.name
tool_args = json.loads(tool_call.function.arguments)
print(f"Invoking tool: {tool_call.function.name} with args: {tool_args}")
output = invoker.invoke_tool(tool_name, tool_args)
tool_outputs.append(
{
"tool_call_id": tool_call.id,
"output": str(output),
}
)
print(f"Tool output: {str(output)}")
return tool_outputs
@trace
async def submit_tool_calls_outputs(cli: AsyncOpenAI, thread_id: str, run_id: str, tool_outputs: list):
await cli.beta.threads.runs.submit_tool_outputs(thread_id=thread_id, run_id=run_id, tool_outputs=tool_outputs)
print(f"Submitted all required resonses for run: {run_id}")
@trace
async def require_actions(cli: AsyncOpenAI, thread_id: str, run, invoker: AssistantToolInvoker):
tool_outputs = await get_tool_calls_outputs(invoker, run)
await submit_tool_calls_outputs(cli, thread_id, run.id, tool_outputs)
@trace
async def wait_for_run_complete(cli: AsyncOpenAI, thread_id: str, invoker: AssistantToolInvoker, run):
while run.status != "completed":
await wait_for_status_check()
run = await get_run_status(cli, thread_id, run.id)
if run.status == "requires_action":
await require_actions(cli, thread_id, run, invoker)
elif run.status == "in_progress" or run.status == "completed":
continue
else:
raise Exception(f"The assistant tool runs in '{run.status}' status. Message: {run.last_error.message}")
@trace
async def get_run_steps(cli: AsyncOpenAI, thread_id: str, run_id: str):
run_steps = await cli.beta.threads.runs.steps.list(thread_id=thread_id, run_id=run_id)
print("step details: \n")
for step_data in run_steps.data:
print(step_data.step_details)
@trace
async def get_message(cli: AsyncOpenAI, thread_id: str):
messages = await cli.beta.threads.messages.list(thread_id=thread_id)
return messages
def extract_text_from_message(message: list):
content = []
for m in message:
if isinstance(m, str):
content.append(m)
continue
message_type = m.get("type", "")
if message_type == "text" and "text" in m:
content.append(m["text"])
return "\n".join(content)
async def extract_file_ids_from_message(cli: AsyncOpenAI, message: list):
file_ids = []
for m in message:
if isinstance(m, str):
continue
message_type = m.get("type", "")
if message_type == "file_path" and "file_path" in m:
path = m["file_path"].get("path", "")
if path:
file = await cli.files.create(file=open(path, "rb"), purpose="assistants")
file_ids.append(file.id)
return file_ids
async def get_openai_file_references(content: list, download_image: bool, conn: OpenAIConnection):
file_id_references = {}
for item in content:
if isinstance(item, MessageContentImageFile):
file_id = item.image_file.file_id
if download_image:
file_id_references[file_id] = {
"content": await download_openai_image(file_id, conn),
"url": URL_PREFIX + file_id,
}
else:
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
elif isinstance(item, MessageContentText):
for annotation in item.text.annotations:
if annotation.type == "file_path":
file_id = annotation.file_path.file_id
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
elif annotation.type == "file_citation":
file_id = annotation.file_citation.file_id
file_id_references[file_id] = {"url": URL_PREFIX + file_id}
else:
raise Exception(f"Unsupported content type: '{type(item)}'.")
return file_id_references
def to_pf_content(content: list):
pf_content = []
for item in content:
if isinstance(item, MessageContentImageFile):
file_id = item.image_file.file_id
pf_content.append({"type": "image_file", "image_file": {"file_id": file_id}})
elif isinstance(item, MessageContentText):
text_dict = {"type": "text", "text": {"value": item.text.value, "annotations": []}}
for annotation in item.text.annotations:
annotation_dict = {
"type": "file_path",
"text": annotation.text,
"start_index": annotation.start_index,
"end_index": annotation.end_index,
}
if annotation.type == "file_path":
annotation_dict["file_path"] = {"file_id": annotation.file_path.file_id}
elif annotation.type == "file_citation":
annotation_dict["file_citation"] = {"file_id": annotation.file_citation.file_id}
text_dict["text"]["annotations"].append(annotation_dict)
pf_content.append(text_dict)
else:
raise SystemErrorException(f"Unsupported content type: {type(item)}")
return pf_content
async def download_openai_image(file_id: str, conn: OpenAIConnection):
cli = AsyncOpenAI(api_key=conn.api_key, organization=conn.organization)
image_data = await cli.files.content(file_id)
return Image(image_data.read())
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/README.md | # Stock EOD Price Analyzer
This sample demonstrates how the PromptFlow Assistant tool help with time series data (stock EOD price) retrieval, plot and consolidation.
Tools used in this flow：
- `get_or_create_thread` tool, python tool, used to provide assistant thread information if absent
- `add_message_and_run` tool, assistant tool, provisioned with below inner functions:
- `get_stock_eod_price``: get the stock eod price based on date and company name
## Prerequisites
Install promptflow sdk and other dependencies in this folder:
```bash
pip install -r requirements.txt
```
## What you will learn
In this flow, you will understand how assistant tools within PromptFlow are triggered by user prompts. The assistant tool decides which internal functions or tools to invoke based on the input provided. Your responsibility involves implementing each of these tools and registering them in the `assistant_definition`. Additionally, be aware that the tools may have dependencies on each other, affecting the order and manner of their invocation.
## Getting started
### 1. Create assistant connection (openai)
Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
Currently, only "Open AI" connection type are supported for assistant tool. Please refer to [OpenAI](https://platform.openai.com/) for more details.
```bash
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key>
```
Note in [flow.dag.yaml](flow.dag.yaml) we are using connection named `open_ai_connection`.
```bash
# show registered connection
pf connection show --name open_ai_connection
```
### 2. Create or get assistant/thread
Navigate to the OpenAI Assistant page and create an assistant if you haven't already. Once created, click on the 'Test' button to enter the assistant's playground. Make sure to note down the assistant_id.
**[Optional]** Start a chat session to create thread automatically. Keep track of the thread_id.
### 3. run the flow
```bash
# run chat flow with default question in flow.dag.yaml
pf flow test --flow .
```
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/flow.dag.yaml | version: 2
inputs:
assistant_input:
type: list
default:
- type: text
text: The provided file contains end-of-day (EOD) stock prices for companies A
and B across various dates in March. However, it does not include the
EOD stock prices for Company C.
- type: file_path
file_path:
path: ./stock_price.csv
- type: text
text: Please draw a line chart with the stock price of the company A, B and C
and return a CVS file with the data.
assistant_id:
type: string
default: asst_eHO2rwEYqGH3pzzHHov2kBCG
thread_id:
type: string
default: ""
outputs:
assistant_output:
type: string
reference: ${add_message_and_run.output}
thread_id:
type: string
reference: ${get_or_create_thread.output}
nodes:
- name: get_or_create_thread
type: python
source:
type: code
path: get_or_create_thread.py
inputs:
conn: chw_openai
thread_id: ${inputs.thread_id}
- name: add_message_and_run
type: python
source:
type: code
path: add_message_and_run.py
inputs:
conn: chw_openai
message: ${inputs.assistant_input}
assistant_id: ${inputs.assistant_id}
thread_id: ${get_or_create_thread.output}
assistant_definition: assistant_definition.yaml
download_images: true
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/get_stock_eod_price.py | import random
import time
from promptflow import tool
@tool
def get_stock_eod_price(date: str, company: str):
"""Get the stock end of day price by date and symbol.
:param date: the date of the stock price. e.g. 2021-01-01
:type date: str
:param company: the company name like A, B, C
:type company: str
"""
print(f"Try to get the stock end of day price by date {date} and company {company}.")
# Sleep a random number between 0.2s and 1s for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.uniform(110, 130)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/stock_price.csv | Date,A,B
2023-03-15,100.25,110.50
2023-03-16,102.75,114.35
2023-03-17,101.60,120.10
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/echo_input.py | from promptflow import tool
@tool
def my_python_tool(input: str) -> str:
yield "Echo: "
for word in input.split():
yield word + " " | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/flow.dag.yaml | inputs:
text:
type: string
outputs:
output_echo:
type: string
reference: ${echo_my_input.output}
nodes:
- name: echo_my_input
type: python
source:
type: code
path: echo_input.py
inputs:
input: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/inputs.json | {
"text": "Hello World!"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_exception/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_exception/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_with_exception/convert_to_dict.py |
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
raise Exception("mock exception")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_exception/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_exception/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_with_exception/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
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: '0.1'
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: '0.3'
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
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_with_exception/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_with_exception/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/script_with_import/fail.py | from aaa import bbb # noqa: F401 | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import/flow.dag.yaml | inputs:
text:
type: string
outputs:
output:
type: string
reference: ${node1.output}
nodes:
- name: node1
type: python
source:
type: code
path: dummy_utils/main.py
inputs:
x: ${inputs.text}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import/dummy_utils/main.meta.json | {
"name": "main",
"type": "python",
"inputs": {
"x": {
"type": [
"string"
]
}
},
"source": "dummy_utils/main.py",
"function": "main"
} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import/dummy_utils/main.py | from promptflow import tool
from dummy_utils.util_tool import passthrough
@tool
def main(x: str):
return passthrough(x)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_with_import/dummy_utils/util_tool.py | from promptflow import tool
@tool
def passthrough(x: str):
return x
| 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/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/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/eval_flow_with_composite_image/merge_images.py | from promptflow import tool
from promptflow.contracts.multimedia import Image
@tool
def merge_images(image_list: list, image_dict: list):
res = set()
for item in image_list[0]:
res.add(item)
for _, v in image_dict[0].items():
res.add(v)
assert all(isinstance(item, Image) for item in res)
return list(res)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval_flow_with_composite_image/passthrough_list.py | from promptflow import tool
from promptflow.contracts.multimedia import Image
@tool
def passthrough_list(image_list: list, image_dict: dict):
assert all(isinstance(item, Image) for item in image_list)
return image_list
| 0 |
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