Amazon Bedrock과 LangGraph, MLflow를 활용한 서버리스 대화형 AI 에이전트 구축 가이드

class State(TypedDict):
    messages: list
    transcript: list
    session_id: str
    order_number: str
    session_end: bool
    current_turn: int
    next_node: str
    order_confirmed: bool
    order_info_found: bool

def use_tool(messages):
    tool_use = messages[-1]["content"][-1].get("toolUse")
    if tool_use:
        tool_name = tool_use["name"]
        tool_input = tool_use["input"]
        tool_result = _process_tool_call(tool_name, tool_input)
        message = {
            "role": "user",
            "content": [
                {
                    "toolResult": {
                        "toolUseId": tool_use["toolUseId"],
                        "content": [{"text": json.dumps(tool_result)}],
                        "status": "success",
                    }
                }
            ],
        }
        return message

# Save conversation state to DynamoDB
ttl_value = int(time.time()) + (3600 * 4) # 4 hrs
item = {
    'conversationId': session_id,
    'state': json.dumps(state_dict),
    'chat_status': state_dict['session_end'],
    'update_ts_pst': str(datetime.now(pst)),
    'ttl': ttl_value,
    'timestamp': int(time.time())
}
ddb_table.put_item(Item=item)