From 0b1d7f43983861877c01cb50e2821769d1e57606 Mon Sep 17 00:00:00 2001 From: Govind Kamtamneni Date: Sun, 19 May 2024 10:47:13 -0700 Subject: [PATCH] add aca sessions example --- .../azure-openai/function-calling-ea.ipynb | 2 +- agents/autogen/notebooks/.env.example | 1 + .../autogen/notebooks/OAI_CONFIG_LIST.example | 9 + agents/autogen/notebooks/aca-sessions.ipynb | 327 ++++++++++++++++++ .../notebooks/retirement-planning.ipynb | 2 +- .../usecases/visual-intelligence/ui-gis.py | 107 ++++++ .../visual-intelligence/weather_data.png | Bin 0 -> 1039064 bytes 7 files changed, 446 insertions(+), 2 deletions(-) create mode 100644 agents/autogen/notebooks/.env.example create mode 100644 agents/autogen/notebooks/OAI_CONFIG_LIST.example create mode 100644 agents/autogen/notebooks/aca-sessions.ipynb create mode 100644 sandbox/usecases/visual-intelligence/ui-gis.py create mode 100644 sandbox/usecases/visual-intelligence/weather_data.png diff --git a/agents/assistants-api/azure-openai/function-calling-ea.ipynb b/agents/assistants-api/azure-openai/function-calling-ea.ipynb index 5de297a0..97fe73f8 100644 --- a/agents/assistants-api/azure-openai/function-calling-ea.ipynb +++ b/agents/assistants-api/azure-openai/function-calling-ea.ipynb @@ -569,7 +569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/agents/autogen/notebooks/.env.example b/agents/autogen/notebooks/.env.example new file mode 100644 index 00000000..bd72a5c2 --- /dev/null +++ b/agents/autogen/notebooks/.env.example @@ -0,0 +1 @@ +ACA_SESSIONS_ENDPOINT= \ No newline at end of file diff --git a/agents/autogen/notebooks/OAI_CONFIG_LIST.example b/agents/autogen/notebooks/OAI_CONFIG_LIST.example new file mode 100644 index 00000000..2d907735 --- /dev/null +++ b/agents/autogen/notebooks/OAI_CONFIG_LIST.example @@ -0,0 +1,9 @@ +[ + { + "model": "gpt-4", + "api_key": "", + "api_type": "azure", + "base_url": "https://.openai.azure.com", + "api_version": "2023-12-01-preview" + } +] diff --git a/agents/autogen/notebooks/aca-sessions.ipynb b/agents/autogen/notebooks/aca-sessions.ipynb new file mode 100644 index 00000000..5caa3679 --- /dev/null +++ b/agents/autogen/notebooks/aca-sessions.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Container Apps' Dynamic Sessions with Autogen\n", + "\n", + "Demonstrates how to use Dynamic Sessions feature of Azure Container Apps to execute code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "! pip -qqq install pyautogen matplotlib yfinance python-dotenv azure-identity" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import List\n", + "\n", + "from autogen import ConversableAgent, config_list_from_json\n", + "from autogen.coding import CodeBlock, CodeExecutor, CodeExtractor, CodeResult, MarkdownCodeExtractor\n", + "\n", + "import requests\n", + "from typing import List" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup Sessions pool and get Sessions endpoint\n", + "\n", + "Azure Container Apps is a serverless platform that enables you to run containerized workloads without managing the underlying infrastructure. Dynamic sessions adds the ability to execute untrusted code in secure, sandboxed environments at scale. [Learn more here](https://techcommunity.microsoft.com/t5/apps-on-azure-blog/new-secure-sandboxes-at-scale-with-azure-container-apps-dynamic/ba-p/4142148). \n", + "\n", + "![sessions](https://techcommunity.microsoft.com/t5/image/serverpage/image-id/581694i61DE419D1A04B7F5/image-size/large?v=v2&px=999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "1. Get the Pool endpoint from [Azure Container Apps - Sessions Code Interpreter](https://learn.microsoft.com/en-us/azure/container-apps/sessions-code-interpreter?tabs=azure-cli).\n", + "\n", + "2. Copy `.env.example` to `.env` and update it with your sessions endpoint.\n", + "\n", + "3. Copy `OAI_CONFIG_LIST.example` to `OAI_CONFIG_LIST` and update it with your Azure OpenAI values." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "load_dotenv()\n", + "\n", + "ACA_SESSIONS_ENDPOINT = os.getenv('ACA_SESSIONS_ENDPOINT')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Why Managed Identity:\n", + "Managed Identity (MI) simplifies Azure service authentication by automatically managing credentials. Using MI, there is no need to store or manage secrets in your application, significantly reducing the risk of credential leakage and enhancing security.\n", + "\n", + "#### How `DefaultAzureCredential` Works:\n", + "`DefaultAzureCredential` attempts to find a best-available credential from a series of providers, including managed identity and developer credentials from Azure CLI or Visual Studio. This makes it both flexible and convenient for development and production environments without code changes.\n", + "\n", + "![Azure Identity](https://raw.githubusercontent.com/Azure/azure-sdk-for-net/main/sdk/identity/Azure.Identity/images/mermaidjs/DefaultAzureCredentialAuthFlow.svg)\n", + "\n", + "#### Setup for Azure OpenAI:\n", + "1. **Azure Managed Identity**:\n", + " - Ensure that the Azure resource (e.g., Azure OpenAI, Logic Apps, AI Search) running in this notebook is assigned a Managed Identity.\n", + " - Configure the Managed Identity to have access to the Azure OpenAI service by setting appropriate role assignments in Azure RBAC.\n", + "\n", + "- **Keys required in .env File**:\n", + " - `OPENAI_ENDPOINT`: The endpoint URL for the Azure OpenAI service.\n", + " - `OPENAI_MODEL_NAME`: The deployed model name in Azure OpenAI\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ACA code executor class implemented by subclassing the `CodeExecutor` protocol and implementing the `execute_code_blocks` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "from typing import List\n", + "from azure.identity import DefaultAzureCredential\n", + "from autogen.coding import CodeBlock, CodeExecutor, CodeExtractor, CodeResult, MarkdownCodeExtractor\n", + "\n", + "class ACASessionsExecutor(CodeExecutor):\n", + "\n", + " @property\n", + " def code_extractor(self) -> CodeExtractor:\n", + " # Extract code from markdown blocks.\n", + " return MarkdownCodeExtractor()\n", + "\n", + " def __init__(self, pool_management_endpoint: str) -> None:\n", + " self.pool_management_endpoint = pool_management_endpoint\n", + " self.access_token = None\n", + "\n", + " def ensure_access_token(self) -> None:\n", + " \"\"\"Ensures that the access token is valid and updates it if necessary.\"\"\"\n", + " if not self.access_token:\n", + " credential = DefaultAzureCredential()\n", + " scope = \"https://dynamicsessions.io\" # \"https://acasessions.io/.default\" could also be used\n", + " self.access_token = credential.get_token(scope).token\n", + "\n", + " def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CodeResult:\n", + " self.ensure_access_token() # Ensure the access token is valid before making calls\n", + " log = \"\"\n", + " print(self.access_token)\n", + " \n", + " exitcode = 0\n", + " headers = {\n", + " \"Authorization\": f\"Bearer {self.access_token}\"\n", + " }\n", + "\n", + " for code_block in code_blocks:\n", + " properties = {\n", + " \"identifier\": \"adslfjlad\",\n", + " \"codeInputType\": \"inline\",\n", + " \"executionType\": \"synchronous\",\n", + " \"pythonCode\": code_block.code,\n", + " \"timeoutInSeconds\": 100\n", + " }\n", + "\n", + " try:\n", + " response = requests.post(\n", + " self.pool_management_endpoint + \"/python/execute\",\n", + " headers=headers,\n", + " json={\"properties\": properties}\n", + " )\n", + " response.raise_for_status() # Raises an HTTPError for bad responses\n", + " data = response.json()\n", + " log += data.get(\"stdout\", \"\") + data.get(\"stderr\", \"\")\n", + " if \"result\" in data and data[\"result\"] is not None:\n", + " log += str(data[\"result\"])\n", + " if \"error\" in data:\n", + " log += f\"\\n{data['error']}\"\n", + " exitcode = 1\n", + " except requests.RequestException as e:\n", + " log += f\"\\nError while sending code block to endpoint: {str(e)}\"\n", + " exitcode = 1\n", + " break\n", + "\n", + " return CodeResult(exit_code=exitcode, output=log)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ACA Code executor used in Agents" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "config_list = config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-4\"],\n", + " },\n", + ")\n", + "\n", + "code_writer_agent = ConversableAgent(\n", + " name=\"CodeWriter\",\n", + " system_message=\"You are a helpful AI assistant.\\n\"\n", + " \"You use your coding skill to solve problems.\\n\"\n", + " \"You have access to a IPython kernel to execute Python code.\\n\"\n", + " \"You output only valid python code. \\n\"\n", + " \"This valid code will be executed in a sandbox, resutling in result, stdout, or stderr. \\n\"\n", + " \"All necessary libraries have already been installed.\\n\"\n", + " \"Once the task is done, returns 'TERMINATE'.\",\n", + " llm_config={\"config_list\":config_list},\n", + ")\n", + "\n", + "aca_pool_management_endpoint = ACA_SESSIONS_ENDPOINT\n", + "aca_sessions_executor = ACASessionsExecutor(aca_pool_management_endpoint)\n", + "\n", + "code_executor_agent = ConversableAgent(\n", + " name=\"CodeExecutor\",\n", + " llm_config=False,\n", + " code_execution_config={\"executor\": aca_sessions_executor},\n", + " is_termination_msg=lambda msg: \"TERMINATE\" in msg.get(\"content\", \"\").strip().upper(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple math task for testing ACA Sessions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mCodeExecutor\u001b[0m (to CodeWriter):\n", + "\n", + "What is 30 plus 20?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mCodeWriter\u001b[0m (to CodeExecutor):\n", + "\n", + "To solve this, you can simply add the two numbers in Python:\n", + "\n", + "```python\n", + "result = 30 + 20\n", + "print(result)\n", + "```\n", + "\n", + "When you run this code, it will output the sum of 30 and 20. \n", + "\n", + "Would you like me to execute this code to get the result?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK (inferred language is python)...\u001b[0m\n", + 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+ "\u001b[33mCodeExecutor\u001b[0m (to CodeWriter):\n", + "\n", + "exitcode: 0 (execution succeeded)\n", + "Code output: 50\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mCodeWriter\u001b[0m (to CodeExecutor):\n", + "\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mCodeExecutor\u001b[0m (to CodeWriter):\n", + "\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mCodeWriter\u001b[0m (to CodeExecutor):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gok\\AppData\\Roaming\\Python\\Python312\\site-packages\\autogen\\agentchat\\conversable_agent.py:1306: UserWarning: Extracted_response from {response} is None.\n", + " warnings.warn(\"Extracted_response from {response} is None.\", UserWarning)\n" + ] + } + ], + "source": [ + "chat_result = code_executor_agent.initiate_chat(\n", + " code_writer_agent,\n", + " message=\"What is 30 plus 20?\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "autogen", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/agents/autogen/notebooks/retirement-planning.ipynb b/agents/autogen/notebooks/retirement-planning.ipynb index 9faf423a..ef0a43aa 100644 --- a/agents/autogen/notebooks/retirement-planning.ipynb +++ b/agents/autogen/notebooks/retirement-planning.ipynb @@ -143,7 +143,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/sandbox/usecases/visual-intelligence/ui-gis.py b/sandbox/usecases/visual-intelligence/ui-gis.py new file mode 100644 index 00000000..6f7263fa --- /dev/null +++ b/sandbox/usecases/visual-intelligence/ui-gis.py @@ -0,0 +1,107 @@ +import streamlit as st +from openai import AzureOpenAI +from PIL import Image +from io import BytesIO +from dotenv import load_dotenv +import os +import base64 +import requests # Added to make REST API calls + +# Load the environment variables +load_dotenv() + +# Load the environment variables +api_base = os.getenv("AZURE_OPENAI_ENDPOINT") +api_key = os.getenv("AZURE_OPENAI_API_KEY") +deployment_name = os.getenv("DEPLOYMENT_NAME") +api_version = '2023-12-01-preview' +vision_endpoint = os.getenv("VISION_ENDPOINT") +vision_key = os.getenv("VISION_KEY") + +# Initialize the Azure OpenAI client +client = AzureOpenAI( + api_key=api_key, + api_version=api_version, + base_url=f"{api_base}/openai/deployments/{deployment_name}/extensions", +) + +def get_country_data(): + # REST API call to get data + url = "https://firms.modaps.eosdis.nasa.gov/api/country/csv/01abe4b8c83aa2f2ee5668d13e01e5a3/MODIS_NRT/USA/1" + response = requests.get(url) + if response.status_code == 200: + return response.text + else: + return "Failed to retrieve data" + +def process_image(image_data): + # Convert the image data to a PIL image and then to a byte array + image = Image.open(BytesIO(image_data)) + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") + + # Get the additional data from the REST API + modis_data_us = get_country_data() + + wildfire_risk_assessment_prompt = f""" + As an AI with advanced natural language understanding and computer vision capabilities who gives risk assessment score for wildfires, you will analyze images to determine risks. Utilize your integrated visual recognition to examine the data to determine what could contribute to wildfire susceptibility. + + Attached are a series of images representing the same geographical location. Each image will be described to you. Your task is to review the data and determine if there is a legitimate risk of a wildfire stemming from electrical equipment based on current status. You must provide your rationale for determining if there is a risk or not in detail. + + ** Consider proximity of currently burning fires to transmission lines and power plants.** + + + Also, factor in current hotspots from MODIS data for US: + {modis_data_us} + + Your output should be a localized wildfire risk score, **highlighting** the factors and concluding with a terse description. DO NOT BE VERBOSE. Think step by step. + """ + + print(wildfire_risk_assessment_prompt) + # Prepare the body for the OpenAI API call + response = client.chat.completions.create( + model=deployment_name, + messages=[ + {"role": "system", "content": wildfire_risk_assessment_prompt}, + {"role": "user", "content": [ + {"type": "text", "text": "Describe this picture:"}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"}} + ]} + ], + extra_body={ + "dataSources": [ + { + "type": "AzureComputerVision", + "parameters": { + "endpoint": vision_endpoint, + "key": vision_key + } + } + ], + "enhancements": { + "ocr": {"enabled": True}, + "grounding": {"enabled": True} + } + }, + max_tokens=4000 + ) + return response + +st.title("Azure OpenAI + AI Vision") + +st.write("This tool allows you to upload an image and receive an AI-generated risk assessment for wildfire susceptibility, taking into account various visual indicators. It uses Azure OpenAI GPT-4 Vision with Azure Computer Vision.") + +with st.container(): + uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) + if uploaded_file is not None: + # Display the uploaded image + st.image(uploaded_file, caption='Uploaded Image', use_column_width=True) + # Process the image + result = process_image(uploaded_file.getvalue()) + # Display results + if result: + st.header("Azure OpenAI with Azure AI Vision Analysis", divider='rainbow') + st.markdown(result.choices[0].message.content) + else: + st.error("Failed to process the image.") diff --git a/sandbox/usecases/visual-intelligence/weather_data.png b/sandbox/usecases/visual-intelligence/weather_data.png new file mode 100644 index 0000000000000000000000000000000000000000..55e3d82c19c029c1328f5de480b75b7dc8c94a93 GIT binary patch literal 1039064 zcmV*EKx@B=P)eh4bY)!^&fNe2AOJ~3K~#90>|JM= z9mTOd)!j35!{%M(oP}}*l!Xu|ATkJn5gB7_FeW%)FeW?<<~e}LIU8XKlY;;uB%y$E zPOBVNtIgr2neMLlshj3|EFL 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