aymenfurter/polyclaw/foundry-code-interpreter

python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, CodeInterpreterToolAuto
 
endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "<ENDPOINT>")
model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "<MODEL>")
 
project_client = AIProjectClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential(),
)
 
with project_client:
    openai_client = project_client.get_openai_client()
 
    # Create agent with code interpreter
    agent = project_client.agents.create_version(
        agent_name="CodeInterpreterAgent",
        definition=PromptAgentDefinition(
            model=model,
            instructions="You are a data analysis and computation assistant. Write and execute Python code to solve the user's problem. Show results clearly.",
            tools=[CodeInterpreterTool(container=CodeInterpreterToolAuto())],
        ),
        description="Ad-hoc code interpreter agent.",
    )
    print(f"Agent created: {agent.name} v{agent.version}")
 
    # Create a conversation
    conversation = openai_client.conversations.create()
    print(f"Conversation: {conversation.id}")
 
    # Send the user's request
    response = openai_client.responses.create(
        conversation=conversation.id,
        input="USER_PROMPT_HERE",
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )
 
    # Print the response
    for item in response.output:
        if item.type == "message":
            for content in item.content:
                if content.type == "output_text":
                    print(content.text)
 
    # Clean up
    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
    print("Agent cleaned up.")
PYEOF

python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, CodeInterpreterToolAuto
 
endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "<ENDPOINT>")
model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "<MODEL>")
file_path = "FILE_PATH_HERE"
 
project_client = AIProjectClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential(),
)
 
with project_client:
    openai_client = project_client.get_openai_client()
 
    # Upload the file
    with open(file_path, "rb") as f:
        uploaded_file = openai_client.files.create(purpose="assistants", file=f)
    print(f"File uploaded: {uploaded_file.id}")
 
    # Create agent with code interpreter and file access
    agent = project_client.agents.create_version(
        agent_name="DataAnalysisAgent",
        definition=PromptAgentDefinition(
            model=model,
            instructions="You are a data analysis assistant. Analyze the uploaded file and fulfil the user's request. Generate charts or output files when appropriate.",
            tools=[CodeInterpreterTool(container=CodeInterpreterToolAuto(file_ids=[uploaded_file.id]))],
        ),
        description="Ad-hoc data analysis agent with file.",
    )
    print(f"Agent created: {agent.name} v{agent.version}")
 
    # Create a conversation
    conversation = openai_client.conversations.create()
 
    # Send the user's request
    response = openai_client.responses.create(
        conversation=conversation.id,
        input="USER_PROMPT_HERE",
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )
 
    # Print response and download generated files
    for item in response.output:
        if item.type == "message":
            for content in item.content:
                if content.type == "output_text":
                    print(content.text)
                    # Check for file citations
                    if hasattr(content, "annotations") and content.annotations:
                        for ann in content.annotations:
                            if ann.type == "container_file_citation":
                                file_content = openai_client.containers.files.content.retrieve(
                                    file_id=ann.file_id, container_id=ann.container_id
                                )
                                safe_name = os.path.basename(ann.filename)
                                with open(safe_name, "wb") as out:
                                    out.write(file_content.read())
                                print(f"Downloaded: {safe_name}")
 
    # Clean up
    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
    print("Agent cleaned up.")
PYEOF