promptflow

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{
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 "cells": [
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "# Getting started with flex flow in Azure"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "**Learning Objectives** - Upon completing this tutorial, you should be able to:\n",
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    "\n",
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    "- Write LLM application using notebook and visualize the trace of your application.\n",
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    "- Convert the application into a flow and batch run against multi lines of data.\n"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## 0. Install dependent packages"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "%%capture --no-stderr\n",
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    "%pip install -r ./requirements-azure.txt"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## 1. Connection to workspace"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Configure credential\n",
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    "\n",
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    "We are using `DefaultAzureCredential` to get access to workspace. \n",
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    "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. \n",
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    "\n",
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    "Reference for more available credentials if it does not work for you: [configure credential example](https://github.com/microsoft/promptflow/blob/main/examples/configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python)."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n",
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    "\n",
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    "try:\n",
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    "    credential = DefaultAzureCredential()\n",
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    "    # Check if given credential can get token successfully.\n",
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    "    credential.get_token(\"https://management.azure.com/.default\")\n",
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    "except Exception as ex:\n",
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    "    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n",
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    "    credential = InteractiveBrowserCredential()"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Get a handle to the workspace\n",
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    "\n",
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    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](https://github.com/microsoft/promptflow/blob/main/examples/configuration.ipynb)"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from promptflow.azure import PFClient\n",
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    "\n",
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    "# Get a handle to workspace\n",
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    "pf = PFClient.from_config(credential=credential)"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Create necessary connections\n",
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    "Connection helps securely store and manage secret keys or other sensitive credentials required for interacting with LLM and other external tools for example Azure Content Safety.\n",
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    "\n",
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    "In this notebook, we will use flow `basic` & `eval-code-quality` flex flow which uses connection `open_ai_connection` inside, we need to set up the connection if we haven't added it before.\n",
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    "\n",
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    "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.\n",
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    "\n",
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    "Please go to [workspace portal](https://ml.azure.com/), click `Prompt flow` -> `Connections` -> `Create`, then follow the instruction to create your own connections. \n",
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    "Learn more on [connections](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2)."
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## 2. Batch run the function as flow with multi-line data\n",
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    "\n",
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    "Create a `flow.flex.yaml` file to define a flow which entry pointing to the python function we defined.\n"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "# show the flow.flex.yaml content\n",
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    "with open(\"flow.flex.yaml\") as fin:\n",
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    "    print(fin.read())"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Batch run with a data file (with multiple lines of test data)\n"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "flow = \".\"  # path to the flow directory\n",
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    "data = \"./data.jsonl\"  # path to the data file\n",
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    "\n",
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    "# create run with the flow and data\n",
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    "base_run = pf.run(\n",
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    "    flow=flow,\n",
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    "    data=data,\n",
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    "    column_mapping={\n",
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    "        \"text\": \"${data.text}\",\n",
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    "    },\n",
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    "    environment_variables={\n",
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    "        \"AZURE_OPENAI_API_KEY\": \"${open_ai_connection.api_key}\",\n",
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    "        \"AZURE_OPENAI_ENDPOINT\": \"${open_ai_connection.api_base}\",\n",
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    "    },\n",
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    "    stream=True,\n",
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    ")"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "details = pf.get_details(base_run)\n",
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    "details.head(10)"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## 3. Evaluate your flow\n",
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    "Then you can use an evaluation method to evaluate your flow. The evaluation methods are also flows which usually using LLM assert the produced output matches certain expectation. "
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Setup model configuration with connection\n",
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    "\n",
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    "When used in cloud, create a model configuration object with connection name. \n",
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    "The model config will be resolved from connection in runtime."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from promptflow.core import AzureOpenAIModelConfiguration\n",
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    "\n",
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    "model_config = AzureOpenAIModelConfiguration(\n",
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    "    connection=\"open_ai_connection\",\n",
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    "    azure_deployment=\"gpt-35-turbo\",\n",
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    ")"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Run evaluation on the previous batch run\n",
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    "The **base_run** is the batch run we completed in step 2 above, for web-classification flow with \"data.jsonl\" as input."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "eval_flow = \"../eval-code-quality/flow.flex.yaml\"\n",
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    "\n",
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    "eval_run = pf.run(\n",
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    "    flow=eval_flow,\n",
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    "    init={\"model_config\": model_config},\n",
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    "    data=\"./data.jsonl\",  # path to the data file\n",
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    "    run=base_run,  # specify base_run as the run you want to evaluate\n",
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    "    column_mapping={\n",
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    "        \"code\": \"${run.outputs.output}\",\n",
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    "    },\n",
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    "    stream=True,\n",
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    ")"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "details = pf.get_details(eval_run)\n",
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    "details.head(10)"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "import json\n",
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    "\n",
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    "metrics = pf.get_metrics(eval_run)\n",
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    "print(json.dumps(metrics, indent=4))"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "pf.visualize([base_run, eval_run])"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## Next steps\n",
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    "\n",
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    "By now you've successfully run your first prompt flow and even did evaluation on it. That's great!\n",
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    "\n",
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    "You can check out more examples:\n",
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    "- [Basic Chat](https://github.com/microsoft/promptflow/tree/main/examples/flex-flows/chat-basic): demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message."
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   ]
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  }
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 ],
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 "metadata": {
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  "build_doc": {
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   "author": [
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    "D-W-@github.com",
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    "wangchao1230@github.com"
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   ],
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   "category": "azure",
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   "section": "Flow",
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   "weight": 10
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  },
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  "description": "A quickstart tutorial to run a flex flow and evaluate it in azure.",
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  "kernelspec": {
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   "display_name": "prompt_flow",
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   "language": "python",
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   "name": "python3"
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   "version": "3.9.18"
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  "resources": "examples/requirements-azure.txt, examples/flex-flows/basic, examples/flex-flows/eval-code-quality"
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