Interaction Management

# Example 1: Basic Text Conversation (fire-and-forget)client.publish_interaction(    user_id="user_123",    interactions=[        {"role": "User", "content": "What's the weather like?"},        {"role": "Agent", "content": "It's sunny and 72°F today."}    ],    source="support-agent:v2",    session_id="session_001") # Example 2: Publish Retrieved Learnings — attach every Emend profile or# playbook injected into the agent's context to the response that used it.client.publish_interaction(    user_id="user_123",    interactions=[        {"role": "User", "content": "Recommend a laptop for my next work trip."},        {            "role": "Agent",            "content": "Choose a lightweight model with long battery life.",            "retrieved_learnings": [                {"kind": "profile", "learning_id": "prof-abc123"},                {"kind": "user_playbook", "learning_id": "42"},                {"kind": "agent_playbook", "learning_id": "7"},            ],        },    ],    session_id="session_001",    source="support-agent:v2",) # Example 3: Multi-Turn Conversation with Agent Versionclient.publish_interaction(    user_id="customer_456",    interactions=[        {"role": "User", "content": "I need help choosing a laptop"},        {"role": "Agent", "content": "I'd be happy to help! What's your budget?"},        {"role": "User", "content": "Around $1000 for programming work"},        {"role": "Agent", "content": "For programming, I recommend the ThinkPad X1..."}    ],    source="support-agent:v2",    agent_version="v2.1.0",    session_id="purchase_flow_789") # Example 4: Image Interactionsimport base64 def encode_image(path):    with open(path, "rb") as f:        return base64.b64encode(f.read()).decode("utf-8") client.publish_interaction(    user_id="user_123",    interactions=[        {            "role": "User",            "content": "What do you think of this outfit?",            "image_encoding": encode_image("outfit.jpg")        },        {"role": "Agent", "content": "Great choice! The colors complement each other well."}    ],    source="styling_consultation",    session_id="style_session_001") # Example 5: Expert Content for Learning from Expertsclient.publish_interaction(    user_id="user_123",    interactions=[        {"role": "User", "content": "What is your return policy for electronics?"},        {            "role": "Agent",            "content": "You can return electronics within 30 days.",            "expert_content": (                "Electronics can be returned within 30 days with the original receipt. "                "Items must be in original packaging. Opened software is final sale. "                "Defective items have a 90-day return window."            )        }    ],    source="expert_review",    agent_version="v1.0",    session_id="expert_session_001") # Example 6: User Action Trackingclient.publish_interaction(    user_id="shopper_789",    interactions=[        {            "user_action": "click",            "user_action_description": "Clicked 'Add to Cart' for MacBook Pro",            "interacted_image_url": "https://store.com/products/macbook-pro.jpg"        },        {            "user_action": "scroll",            "user_action_description": "Scrolled through laptop accessories"        }    ],    source="ecommerce",    session_id="shopping_session_001")

import time response = client.publish_interaction(    user_id="user_123",    interactions=[{"role": "User", "content": "Help me with X"}],    session_id="sess_001",    wait_for_response=False,)# response.learning_status == "deferred" # The deferred response carries a request_id directly — no need to make a# throwaway wait_for_response=True call first.request_id = response.request_id for _ in range(30):  # poll up to 30 seconds    status = client.get_learning_status(request_id)    if status in ("done", "failed"):        print(f"Learning {status}")        break    time.sleep(1)