rpaframework-sema4ai 1.1.1

Robot Framework

*** Settings ***
Library    RPA.Sema4AI

*** Variables ***
${API_KEY}         %{SEMA4AI_API_KEY}
${API_ENDPOINT}    https://api.sema4.ai/v1

*** Tasks ***
Chat With Agent
    ${response}=    Ask Agent
    ...    message=What is the weather today?
    ...    agent_api_key=${API_KEY}
    ...    agent_api_endpoint=${API_ENDPOINT}
    ...    agent_name=Weather Assistant
    Log    Response: ${response.response}
    Log    Conversation ID: ${response.conversation_id}

Continue Conversation
    ${response}=    Ask Agent
    ...    message=What about tomorrow?
    ...    agent_api_key=${API_KEY}
    ...    agent_api_endpoint=${API_ENDPOINT}
    ...    agent_name=Weather Assistant
    ...    conversation_id=${PREV_CONVERSATION_ID}
    Log    Response: ${response.response}

List Conversations
    ${conversations}=    Get Conversations
    ...    agent_api_key=${API_KEY}
    ...    agent_api_endpoint=${API_ENDPOINT}
    ...    agent_name=Weather Assistant
    FOR    ${conv}    IN    @{conversations}
        Log    ${conv.name} (ID: ${conv.conversation_id})
    END

Python

from RPA.Sema4AI import Sema4AI

client = Sema4AI()

# Send a message to an agent
response = client.ask_agent(
    message="What is the weather today?",
    agent_api_key="your-api-key",
    agent_api_endpoint="https://api.sema4.ai/v1",
    agent_name="Weather Assistant"
)
print(f"Response: {response.response}")
print(f"Conversation ID: {response.conversation_id}")
print(f"Execution time: {response.execution_time}s")

# Continue the conversation
followup = client.ask_agent(
    message="What about tomorrow?",
    agent_api_key="your-api-key",
    agent_api_endpoint="https://api.sema4.ai/v1",
    agent_name="Weather Assistant",
    conversation_id=response.conversation_id
)
print(f"Follow-up: {followup.response}")

# Get all conversations for an agent
conversations = client.get_conversations(
    agent_api_key="your-api-key",
    agent_api_endpoint="https://api.sema4.ai/v1",
    agent_name="Weather Assistant"
)
for conv in conversations:
    print(f"Conversation: {conv.name} (ID: {conv.conversation_id})")

# Get messages in a conversation
messages = client.get_messages(
    agent_api_key="your-api-key",
    agent_api_endpoint="https://api.sema4.ai/v1",
    conversation_id=response.conversation_id,
    agent_name="Weather Assistant"
)
for msg in messages:
    print(f"{msg.get('role')}: {msg.get('content')}")

Async Python

For non-blocking operations in async contexts:

import asyncio
from RPA.Sema4AI import Sema4AI

async def main():
    client = Sema4AI()

    # Concurrent agent requests
    tasks = [
        client.ask_agent_async(
            message=f"Question {i}",
            agent_api_key="your-api-key",
            agent_api_endpoint="https://api.sema4.ai/v1",
            agent_name="My Assistant"
        )
        for i in range(3)
    ]
    responses = await asyncio.gather(*tasks)

    for i, response in enumerate(responses):
        print(f"Response {i}: {response.response}")

asyncio.run(main())

Sema4AI Class

ask_agent(message, agent_api_key, agent_api_endpoint, agent_id=None, agent_name=None, conversation_id=None, conversation_name=None)

Send a message to an agent and receive a response.

• message: The message content to send

• agent_api_key: API key for authentication

• agent_api_endpoint: The agent API endpoint URL

• agent_id: Agent ID (use either agent_id or agent_name)

• agent_name: Agent name (use either agent_id or agent_name)

• conversation_id: Optional, for continuing existing conversations

• conversation_name: Optional, name for new conversations

Returns: MessageResponse object with conversation_id, response, agent_name, agent_id, and execution_time

get_conversations(agent_api_key, agent_api_endpoint, agent_id=None, agent_name=None)

Get all conversations for an agent.

Returns: ConversationsResult containing list of Conversation objects

get_messages(agent_api_key, agent_api_endpoint, conversation_id, agent_id=None, agent_name=None)

Get all messages in a conversation.

Returns: MessagesResult containing list of message dictionaries

Async variants: ask_agent_async, get_conversations_async, get_messages_async

Response Objects

MessageResponse

• conversation_id: ID of the conversation

• response: Text response from the agent

• agent_name: Name of the responding agent

• agent_id: ID of the responding agent

• execution_time: Time taken in seconds

ConversationsResult

• conversations: List of Conversation objects

• execution_time: Time taken in seconds

• Supports iteration and indexing

MessagesResult

• messages: List of message dictionaries

• execution_time: Time taken in seconds

• Supports iteration and indexing

Conversation

• conversation_id: Unique conversation identifier

• name: Conversation name

• agent_id: ID of the associated agent