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sophia-ramseysophia-ramsey
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create agent in gunicorn if it does not exist (#15)
* in `gunicorn.conf.py`, call `list_agents()` to see if there is an agent called `agent-template-assistant`. If there is not, it will create one * in `main.py`, call `list_agents()` to fetch agent called `agent-template-assistant`. If it is not found, there is an error * in `main.py`, do not delete the agent/resources that are used, so it will be there for the future * edit `readme` to remove import agent section. `main.py` and `gunicorn.conf.py` have functionality to use agent_id once that is enabled * set `workers = (num_cpus * 2) + 1`
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‎README.md‎

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@@ -40,37 +40,6 @@ The default for the model capacity in deployment is 50k tokens. For optimal perf
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* Select Quota, click the GlobalStandard dropdown and select the model and region you are using for this accelerator to see your available quota. Please note GPT-4o mini and text-embedding-ada-002 are used as default.
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* Request more quota or delete any unused model deployments as needed.
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#### Creating and Importing an Agent
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You can create an agent in your project to import to this template. For more information on creating an agent, view [Quickstart: Create a New Agent](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=ai-foundry). There are three agent creation options:
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<details>
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<summary><b>Azure AI Foundry Agents Playground</b></summary>
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In [Azure AI Foundry](https://ai.azure.com/), navigate to the Agents Playground for your project. Here, you can create an agent and customize its name, model deployment, instructions, and tools, as well as upload files for file search.
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</details>
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<details>
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<summary><b>SDK Samples</b></summary>
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You can create an agent using the Azure AI Projects SDK. By following the [instructions to create an agent using Python](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure), you can create an agent using code based on the SDK samples.
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</details>
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<details>
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<summary><b>Assistants Tool</b></summary>
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Using the [Azure AI Assistants Tool](https://github.com/Azure-Samples/azureai-assistant-tool), you can create an agent and customize it with tools and functions. Then, you can export the agent and use the agent_id.
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<!-- TODO: do we need to implement agents.yaml changes in order to use this solution? -->
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</details>
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After creating your agent, import the agent into this template by copying the agent_id and setting the following environment variable:
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```shell
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azd env set AZURE_AI_AGENT_ID "<agent_id>"
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```
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## Development
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#### Code
@@ -83,19 +52,13 @@ At this point you could make changes to the code if required. However, no change
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#### Configure your Agent (optional)
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<!-- TODO where do we want this? probably after downloading the code -->
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If you want to personalize your agent, you can change the default configuration for your agent. This can include changing the model, adding tools, and uploading files to the agent. For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support).
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If you want to personalize your agent, you can change the default configuration for your agent. Additional details on changing your agent can be found in [customizing model deployments](docs/deploy_customization.md#customizing-model-deployments). For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support).
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To specify the model (e.g. gpt-4o-mini, gpt-4o) that is deployed for the agent when `azd up` is called, set the following environment variables:
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```shell
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azd env set AZURE_AI_AGENT_MODEL_NAME <MODEL_NAME>
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azd env set AZURE_AI_AGENT_MODEL_VERSION <MODEL_VERSION>
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```
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To add tools, update the `agents.yaml` file located in the repository.
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```python
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# TODO: add this once it is configured
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```
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<!-- To add files to be used for file search, upload your files to `src/api/files`. -->
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<!-- TODO: do we want this functionality? we would need to slightly alter the py file -->
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#### Logging
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If you want to enable logging to a file, uncomment the following line in Dockerfile located in the src directory:

‎src/api/main.py‎

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@@ -97,34 +97,14 @@ async def lifespan(app: fastapi.FastAPI):
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logger.error(f"Error fetching agent: {e}", exc_info=True)
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create_new_agent = True
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if create_new_agent:
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logger.info("Creating agent")
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file_names = ["product_info_1.md", "product_info_2.md"]
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for file_name in file_names:
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file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'files', file_name))
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file = await ai_client.agents.upload_file_and_poll(file_path=file_path, purpose=FilePurpose.AGENTS)
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logger.info(f"Uploaded file {file_path}, file ID: {file.id}")
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# Store both file id and the file path using the file name as key.
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files[file_name] = {"id": file.id, "path": file_path}
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# Create the vector store using the file IDs.
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vector_store = await ai_client.agents.create_vector_store_and_poll(
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file_ids=[info["id"] for info in files.values()],
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name="sample_store"
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)
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file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
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toolset = AsyncToolSet()
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toolset.add(file_search_tool)
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agent = await ai_client.agents.create_agent(
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model=os.environ["AZURE_AI_AGENT_DEPLOYMENT_NAME"],
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name="my-assistant",
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instructions="You are helpful assistant",
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toolset=toolset
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)
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logger.info(f"Created agent, agent ID: {agent.id}")
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logger.info(f"Created agent, model name: {agent.model}")
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# Check if a previous agent created by the template exists
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agent_list = await ai_client.agents.list_agents()
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if agent_list.data:
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for agent_object in agent_list.data:
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if agent_object.name == "agent-template-assistant":
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agent = agent_object
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if agent == None:
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raise Exception("Agent not found")
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except Exception as e:
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logger.error(f"Error creating agent: {e}", exc_info=True)
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try:
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yield
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finally:
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# Cleanup on shutdown.
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if create_new_agent:
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try:
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for info in files.values():
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await ai_client.agents.delete_file(info["id"])
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logger.info(f"Deleted file {info['id']}")
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if vector_store:
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await ai_client.agents.delete_vector_store(vector_store.id)
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logger.info(f"Deleted vector store {vector_store.id}")
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if agent:
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await ai_client.agents.delete_agent(agent.id)
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logger.info(f"Deleted agent {agent.id}")
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except Exception as e:
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logger.error(f"Error during cleanup: {e}", exc_info=True)
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try:
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await ai_client.close()
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logger.info("Closed AIProjectClient")

‎src/gunicorn.conf.py‎

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@@ -1,10 +1,76 @@
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import multiprocessing
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import os
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from typing import Dict
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import asyncio
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from azure.ai.projects.aio import AIProjectClient
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from azure.ai.projects.models import FilePurpose, FileSearchTool, AsyncToolSet
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from azure.identity import DefaultAzureCredential
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from dotenv import load_dotenv
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load_dotenv()
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async def list_or_create_agent():
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files: Dict[str, Dict[str, str]] = {} # File name -> {"id": file_id, "path": file_path}
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vector_store = None
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agent = None
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try:
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ai_client = AIProjectClient.from_connection_string(
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credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),
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conn_str=os.environ["AZURE_AIPROJECT_CONNECTION_STRING"],
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)
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if os.environ.get("AZURE_AI_AGENT_ID"):
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try:
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agent = await ai_client.agents.get_agent(os.environ["AZURE_AI_AGENT_ID"])
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return
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except Exception as e:
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print(f"Error fetching agent: {e}")
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# Check if a previous agent created by the template exists
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agent_list = await ai_client.agents.list_agents()
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if agent_list.data:
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for agent_object in agent_list.data:
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if agent_object.name == "agent-template-assistant":
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return
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# Create a new agent with the required resources
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print(f"Creating new agent with resources")
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file_names = ["product_info_1.md", "product_info_2.md"]
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for file_name in file_names:
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file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'files', file_name))
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file = await ai_client.agents.upload_file_and_poll(file_path=file_path, purpose=FilePurpose.AGENTS)
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# Store both file id and the file path using the file name as key.
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files[file_name] = {"id": file.id, "path": file_path}
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# Create the vector store using the file IDs.
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vector_store = await ai_client.agents.create_vector_store_and_poll(
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file_ids=[info["id"] for info in files.values()],
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name="sample_store"
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)
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print(f"agent: file store and vector store success")
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file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
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toolset = AsyncToolSet()
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toolset.add(file_search_tool)
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agent = await ai_client.agents.create_agent(
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model=os.environ["AZURE_AI_AGENT_DEPLOYMENT_NAME"],
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name="agent-template-assistant",
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instructions="You are helpful assistant",
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toolset=toolset
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)
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print(f"Created agent, agent ID: {agent.id}")
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except Exception as e:
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print(f"Error creating agent: {e}", exc_info=True)
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raise RuntimeError(f"Failed to create the agent: {e}")
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def on_starting(server):
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"""This code runs once before the workers will start."""
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asyncio.get_event_loop().run_until_complete(list_or_create_agent())
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max_requests = 1000
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max_requests_jitter = 50
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log_file = "-"
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if not os.getenv("RUNNING_IN_PRODUCTION"):
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reload = True
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preload_app = True
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num_cpus = multiprocessing.cpu_count()
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workers = 1 #(num_cpus * 2) + 1
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workers = (num_cpus * 2) + 1
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worker_class = "uvicorn.workers.UvicornWorker"
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timeout = 120

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