chore: import upstream snapshot with attribution

examples/flows/standard/basic/.env.example

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+AZURE_OPENAI_API_KEY=<your_AOAI_key>
+AZURE_OPENAI_API_BASE=<your_AOAI_endpoint>
+AZURE_OPENAI_API_TYPE=azure

examples/flows/standard/basic/README.md

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+# Basic standard flow
+A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables.
+
+Tools used in this flow：
+- `prompt` tool
+- custom `python` Tool
+
+Connections used in this flow:
+- None
+
+## Prerequisites
+
+Install promptflow sdk and other dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Run flow
+
+- Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
+
+- Setup environment variables
+
+Ensure you have put your azure OpenAI endpoint key in [.env](.env) file. You can create one refer to this [example file](.env.example).
+
+```bash
+cat .env
+```
+
+- Test flow/node
+```bash
+# test with default input value in flow.dag.yaml
+pf flow test --flow .
+
+# test with flow inputs
+pf flow test --flow . --inputs text="Java Hello World!"
+
+# test node with inputs
+pf flow test --flow . --node llm --inputs prompt="Write a simple Hello World program that displays the greeting message."
+```
+
+- Create run with multiple lines data
+```bash
+# using environment from .env file (loaded in user code: hello.py)
+pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --stream
+```
+
+You can also skip providing `column-mapping` if provided data has same column name as the flow.
+Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
+
+- List and show run meta
+```bash
+# list created run
+pf run list
+
+# get a sample run name
+name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_variant_0")) | .name'| head -n 1 | tr -d '"')
+
+# show specific run detail
+pf run show --name $name
+
+# show output
+pf run show-details --name $name
+
+# visualize run in browser
+pf run visualize --name $name
+```
+
+## Run flow with connection
+Storing connection info in .env with plaintext is not safe. We recommend to use `pf connection` to guard secrets like `api_key` from leak.
+
+- Show or create `open_ai_connection`
+```bash
+# create connection from `azure_openai.yml` file
+# Override keys with --set to avoid yaml file changes
+pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
+
+# check if connection exists
+pf connection show -n open_ai_connection
+```
+
+- Test using connection secret specified in environment variables
+**Note**: we used `'` to wrap value since it supports raw value without escape in powershell & bash. For windows command prompt, you may remove the `'` to avoid it become part of the value.
+
+```bash
+# test with default input value in flow.dag.yaml
+pf flow test --flow . --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}'
+```
+
+- Create run using connection secret binding specified in environment variables, see [run.yml](run.yml)
+```bash
+# create run
+pf run create --flow . --data ./data.jsonl --stream --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}'
+# create run using yaml file
+pf run create --file run.yml --stream
+
+# show outputs
+name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_variant_0")) | .name'| head -n 1 | tr -d '"')
+pf run show-details --name $name
+```
+
+## Run flow in cloud with connection
+- Assume we already have a connection named `open_ai_connection` in workspace.
+```bash
+# set default workspace
+az account set -s <your_subscription_id>
+az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
+```
+
+- Create run
+```bash
+# run with environment variable reference connection in azureml workspace
+pfazure run create --flow . --data ./data.jsonl --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}' --stream
+# run using yaml file
+pfazure run create --file run.yml --stream
+```
+
+- List and show run meta
+```bash
+# list created run
+pfazure run list -r 3
+
+# get a sample run name
+name=$(pfazure run list -r 100 | jq '.[] | select(.name | contains("basic_variant_0")) | .name'| head -n 1 | tr -d '"')
+
+# show specific run detail
+pfazure run show --name $name
+
+# show output
+pfazure run show-details --name $name
+
+# visualize run in browser
+pfazure run visualize --name $name
+```

examples/flows/standard/basic/data.jsonl

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+{"text": "Python Hello World!"}
+{"text": "C Hello World!"}
+{"text": "C# Hello World!"}

examples/flows/standard/basic/flow.dag.yaml

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+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
+environment:
+  python_requirements_txt: requirements.txt
+inputs:
+  text:
+    type: string
+    default: Hello World!
+outputs:
+  output:
+    type: string
+    reference: ${llm.output}
+nodes:
+- name: hello_prompt
+  type: prompt
+  source:
+    type: code
+    path: hello.jinja2
+  inputs:
+    text: ${inputs.text}
+- name: llm
+  type: python
+  source:
+    type: code
+    path: hello.py
+  inputs:
+    prompt: ${hello_prompt.output}
+    deployment_name: gpt-35-turbo-instruct
+    max_tokens: "120"

examples/flows/standard/basic/hello.jinja2

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+{# Please replace the template with your own prompt. #}
+Write a simple {{text}} program that displays the greeting message.

examples/flows/standard/basic/hello.py

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+import os
+from openai.version import VERSION as OPENAI_VERSION
+
+from dotenv import load_dotenv
+from promptflow.core 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"
+
+
+def get_client():
+    if OPENAI_VERSION.startswith("0."):
+        raise Exception(
+            "Please upgrade your OpenAI package to version >= 1.0.0 or using the command: pip install --upgrade openai."
+        )
+    api_key = os.environ["AZURE_OPENAI_API_KEY"]
+    conn = dict(
+        api_key=os.environ["AZURE_OPENAI_API_KEY"],
+    )
+    if api_key.startswith("sk-"):
+        from openai import OpenAI as Client
+    else:
+        from openai import AzureOpenAI as Client
+        conn.update(
+            azure_endpoint=os.environ.get("AZURE_OPENAI_API_BASE", "azure"),
+            api_version=os.environ.get("OPENAI_API_VERSION", "2023-07-01-preview"),
+        )
+    return Client(**conn)
+
+
+@tool
+def my_python_tool(
+    prompt: str,
+    # for AOAI, deployment name is customized by user, not model name.
+    deployment_name: str,
+    suffix: str = None,
+    max_tokens: int = 120,
+    temperature: float = 1.0,
+    top_p: float = 1.0,
+    n: int = 1,
+    logprobs: int = None,
+    echo: bool = False,
+    stop: list = None,
+    presence_penalty: float = 0,
+    frequency_penalty: float = 0,
+    best_of: int = 1,
+    logit_bias: dict = {},
+    user: str = "",
+    **kwargs,
+) -> str:
+    if "AZURE_OPENAI_API_KEY" not in os.environ or "AZURE_OPENAI_API_BASE" not in os.environ:
+        # load environment variables from .env file
+        load_dotenv()
+
+    if "AZURE_OPENAI_API_KEY" not in os.environ:
+        raise Exception("Please specify environment variables: AZURE_OPENAI_API_KEY")
+
+    # TODO: remove below type conversion after client can pass json rather than string.
+    echo = to_bool(echo)
+
+    response = get_client().completions.create(
+        prompt=prompt,
+        model=deployment_name,
+        # empty string suffix should be treated as None.
+        suffix=suffix if suffix else None,
+        max_tokens=int(max_tokens),
+        temperature=float(temperature),
+        top_p=float(top_p),
+        n=int(n),
+        logprobs=int(logprobs) if logprobs else None,
+        echo=echo,
+        # fix bug "[] is not valid under any of the given schemas-'stop'"
+        stop=stop if stop else None,
+        presence_penalty=float(presence_penalty),
+        frequency_penalty=float(frequency_penalty),
+        best_of=int(best_of),
+        # Logit bias must be a dict if we passed it to openai api.
+        logit_bias=logit_bias if logit_bias else {},
+        user=user,
+    )
+
+    # get first element because prompt is single.
+    return response.choices[0].text

examples/flows/standard/basic/requirements.txt

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+promptflow[azure]
+promptflow-tools
+python-dotenv

examples/flows/standard/basic/run.yml

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+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
+flow: .
+data: data.jsonl
+environment_variables:
+  # environment variables from connection
+  AZURE_OPENAI_API_KEY: ${open_ai_connection.api_key}
+  AZURE_OPENAI_API_BASE: ${open_ai_connection.api_base}
+  AZURE_OPENAI_API_TYPE: azure
+column_mapping:
+  text: ${data.text}