feat: Add AgentInvoke component for agent-to-agent invocation (#11550)
- Implement AgentInvoke component for internal agent-to-agent calls - Enable modular AI portal architecture with specialized agents - Add dynamic routing and parameter passing between agents - Include example templates: portal agent, multi-step workflow, dynamic selection - Support session management and timeout configuration - Direct internal invocation for better performance vs HTTP Resolves #11550
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SmartDever02
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326
agent/component/agent_invoke.py
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326
agent/component/agent_invoke.py
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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AgentInvoke Component
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This component enables agent-to-agent invocation within RAGFlow, allowing a portal
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agent to dynamically delegate tasks to specialized downstream agents/workflows.
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Key Features:
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- Dynamic routing: Route user requests to specialized agents based on intent
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- Parameter passing: Pass inputs and context to downstream agents
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- Result aggregation: Collect and return agent outputs
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- Modular design: Agents can be developed and maintained independently
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Use Case Example:
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Portal Agent receives: "Analyze Q3 sales data"
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-> Invokes Sales Analysis Agent with parameters: {"period": "Q3", ...}
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-> Sales Analysis Agent processes and returns results
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-> Portal Agent formats and returns to user
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"""
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import asyncio
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import json
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import logging
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import os
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import time
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from abc import ABC
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from agent.component.base import ComponentBase, ComponentParamBase
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from common.connection_utils import timeout
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class AgentInvokeParam(ComponentParamBase):
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"""
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Parameters for the AgentInvoke component.
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Attributes:
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agent_id (str): The ID of the agent/workflow to invoke
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agent_name (str): Optional human-readable name for logging
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inputs (dict): Input parameters to pass to the agent
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query (str): Optional query/question to pass to the agent
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timeout_seconds (int): Maximum time to wait for agent response
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create_new_session (bool): Whether to create a new session for each invocation
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"""
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def __init__(self):
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super().__init__()
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self.agent_id = ""
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self.agent_name = ""
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self.inputs = {}
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self.query = ""
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self.timeout_seconds = 300 # 5 minutes default
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self.create_new_session = True
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self.session_id = None
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def check(self):
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"""Validate component parameters"""
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self.check_empty(self.agent_id, "Agent ID")
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self.check_positive_integer(self.timeout_seconds, "Timeout in seconds")
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self.check_boolean(self.create_new_session, "Create new session")
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class AgentInvoke(ComponentBase, ABC):
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"""
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Component for invoking downstream agents/workflows.
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This component allows a portal agent to delegate tasks to specialized
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agents, enabling a modular, maintainable AI portal architecture.
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"""
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component_name = "AgentInvoke"
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@timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 360)))
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def _invoke(self, **kwargs):
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"""
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Invoke a downstream agent and collect its output.
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Args:
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**kwargs: Additional context from the parent workflow
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Returns:
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dict: Agent output including answer, references, and metadata
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"""
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if self.check_if_canceled("AgentInvoke processing"):
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return
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# Get tenant ID from canvas
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tenant_id = self._canvas.get_tenant_id()
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if not tenant_id:
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error_msg = "Tenant ID not available in canvas context"
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logging.error(error_msg)
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self.set_output("_ERROR", error_msg)
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return error_msg
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# Resolve agent_id if it's a variable reference
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agent_id = self._param.agent_id
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if agent_id and agent_id.startswith("{") and agent_id.endswith("}"):
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try:
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agent_id = self._canvas.get_variable_value(agent_id)
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except Exception as e:
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logging.warning(f"Failed to resolve agent_id variable: {e}")
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if not agent_id:
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error_msg = "Agent ID is required but was not provided"
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logging.error(error_msg)
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self.set_output("_ERROR", error_msg)
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return error_msg
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# Resolve query from parameter or variable
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query = self._param.query
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if query and query.startswith("{") and query.endswith("}"):
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try:
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query = self._canvas.get_variable_value(query)
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except Exception as e:
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logging.warning(f"Failed to resolve query variable: {e}")
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query = ""
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# Build inputs dictionary by resolving all variable references
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inputs = {}
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if self._param.inputs:
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for key, value in self._param.inputs.items():
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if isinstance(value, str) and value.startswith("{") and value.endswith("}"):
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try:
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inputs[key] = self._canvas.get_variable_value(value)
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except Exception as e:
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logging.warning(f"Failed to resolve input '{key}' variable: {e}")
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inputs[key] = value
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else:
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inputs[key] = value
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# Determine session_id
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session_id = None
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if not self._param.create_new_session:
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session_id = self._param.session_id
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if session_id and session_id.startswith("{") and session_id.endswith("}"):
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try:
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session_id = self._canvas.get_variable_value(session_id)
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except Exception:
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session_id = None
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agent_name = self._param.agent_name or agent_id
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logging.info(f"AgentInvoke: Invoking agent '{agent_name}' (ID: {agent_id}) with query: {query[:100]}...")
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try:
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# Import here to avoid circular dependencies
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from api.db.services.canvas_service import completion as agent_completion
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# Invoke the agent and collect output
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result = self._invoke_agent_sync(
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agent_completion,
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tenant_id=tenant_id,
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agent_id=agent_id,
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query=query,
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inputs=inputs,
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session_id=session_id,
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timeout=self._param.timeout_seconds
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)
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if result.get("error"):
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error_msg = result["error"]
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logging.error(f"AgentInvoke: Agent '{agent_name}' failed: {error_msg}")
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self.set_output("_ERROR", error_msg)
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return error_msg
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# Set outputs
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self.set_output("answer", result.get("answer", ""))
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self.set_output("reference", result.get("reference", {}))
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self.set_output("session_id", result.get("session_id", ""))
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self.set_output("metadata", result.get("metadata", {}))
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self.set_output("result", result.get("answer", "")) # Alias for compatibility
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logging.info(f"AgentInvoke: Agent '{agent_name}' completed successfully")
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return result.get("answer", "")
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except Exception as e:
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error_msg = f"Failed to invoke agent '{agent_name}': {str(e)}"
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logging.exception(error_msg)
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self.set_output("_ERROR", error_msg)
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return error_msg
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def _invoke_agent_sync(self, completion_func, tenant_id, agent_id, query, inputs, session_id, timeout):
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"""
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Synchronously invoke an agent and wait for completion.
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This method handles the async completion generator and collects the full output.
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Args:
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completion_func: The agent completion function to call
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tenant_id (str): Tenant/user ID
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agent_id (str): Agent ID to invoke
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query (str): Query/question for the agent
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inputs (dict): Input parameters for the agent
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session_id (str): Optional session ID to continue conversation
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timeout (int): Timeout in seconds
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Returns:
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dict: Collected agent output
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"""
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import asyncio
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# Create result container
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result = {
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"answer": "",
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"reference": {},
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"session_id": "",
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"metadata": {},
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"error": None
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}
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async def _run_agent():
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"""Async wrapper to run the agent completion"""
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try:
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answer_parts = []
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final_session_id = None
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final_reference = {}
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metadata = {}
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# Run the agent completion generator
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async for event_data in completion_func(
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tenant_id=tenant_id,
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agent_id=agent_id,
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query=query,
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inputs=inputs,
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session_id=session_id
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):
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# Check if task was canceled
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if self.check_if_canceled("AgentInvoke processing"):
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result["error"] = "Task was canceled"
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return
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# Parse SSE format: "data:{json}\n\n"
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if isinstance(event_data, str) and event_data.startswith("data:"):
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json_str = event_data[5:].strip()
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try:
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event = json.loads(json_str)
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except json.JSONDecodeError:
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continue
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else:
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event = event_data
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# Extract session_id if present
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if "session_id" in event:
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final_session_id = event["session_id"]
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# Handle different event types
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event_type = event.get("event", "")
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data = event.get("data", {})
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if event_type == "message":
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# Accumulate message content
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content = data.get("content", "")
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if content:
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answer_parts.append(content)
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elif event_type == "message_end":
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# Final message with references
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if data and data.get("reference"):
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final_reference = data["reference"]
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elif event_type == "workflow_finished":
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# Agent completed successfully
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outputs = data.get("outputs", {})
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metadata = {
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"elapsed_time": data.get("elapsed_time", 0),
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"created_at": data.get("created_at", time.time())
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}
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# If outputs contain content, use it as the answer
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if isinstance(outputs, dict) and outputs.get("content"):
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answer_parts = [outputs["content"]]
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break
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elif event_type == "error":
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# Error occurred during agent execution
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result["error"] = data.get("message", "Unknown error")
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return
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# Combine all answer parts
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result["answer"] = "".join(answer_parts)
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result["reference"] = final_reference
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result["session_id"] = final_session_id or session_id or ""
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result["metadata"] = metadata
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except Exception as e:
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result["error"] = str(e)
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logging.exception(f"Error during agent invocation: {e}")
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# Run the async function with timeout
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try:
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# Get or create event loop
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loop = self._canvas._loop if hasattr(self._canvas, '_loop') else None
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if loop and loop.is_running():
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# If we're already in an async context, schedule the coroutine
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future = asyncio.run_coroutine_threadsafe(_run_agent(), loop)
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future.result(timeout=timeout)
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else:
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# Run in a new event loop
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asyncio.run(asyncio.wait_for(_run_agent(), timeout=timeout))
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except asyncio.TimeoutError:
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result["error"] = f"Agent invocation timed out after {timeout} seconds"
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logging.error(result["error"])
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except Exception as e:
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result["error"] = f"Failed to execute agent: {str(e)}"
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logging.exception(result["error"])
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return result
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def thoughts(self) -> str:
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"""Return current component status for UI display"""
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agent_name = self._param.agent_name or "agent"
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return f"Invoking {agent_name}..."
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251
agent/templates/dynamic_agent_selection_example.json
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251
agent/templates/dynamic_agent_selection_example.json
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{
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"description": "Dynamic Agent Selection Based on Document Type",
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"graph": {
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"nodes": [
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{
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"id": "begin",
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"data": {
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"name": "Start",
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"component_name": "Begin",
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"x": 100,
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"y": 300
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}
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},
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{
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"id": "switch_0",
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"data": {
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"name": "Detect Document Type",
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"component_name": "Switch",
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"x": 300,
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"y": 300
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}
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},
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{
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"id": "agent_invoke_pdf",
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"data": {
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"name": "PDF Analyzer",
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"component_name": "AgentInvoke",
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"x": 500,
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"y": 100
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}
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},
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{
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"id": "agent_invoke_excel",
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"data": {
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"name": "Spreadsheet Analyzer",
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"component_name": "AgentInvoke",
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"x": 500,
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"y": 250
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}
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},
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{
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"id": "agent_invoke_image",
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"data": {
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"name": "Image Analyzer",
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"component_name": "AgentInvoke",
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"x": 500,
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"y": 400
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}
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},
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{
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"id": "agent_invoke_text",
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"data": {
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"name": "Text Analyzer",
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"component_name": "AgentInvoke",
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"x": 500,
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"y": 550
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}
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},
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{
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"id": "message_0",
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"data": {
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"name": "Analysis Result",
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"component_name": "Message",
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"x": 700,
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"y": 300
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}
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}
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],
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"edges": [
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{
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"source": "begin",
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"target": "switch_0"
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},
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{
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"source": "switch_0",
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"target": "agent_invoke_pdf",
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"label": "pdf"
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},
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{
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"source": "switch_0",
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"target": "agent_invoke_excel",
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"label": "excel"
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},
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{
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"source": "switch_0",
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"target": "agent_invoke_image",
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"label": "image"
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},
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{
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"source": "switch_0",
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"target": "agent_invoke_text",
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"label": "default"
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},
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{
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"source": "agent_invoke_pdf",
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"target": "message_0"
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},
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{
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"source": "agent_invoke_excel",
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"target": "message_0"
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},
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{
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"source": "agent_invoke_image",
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"target": "message_0"
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},
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{
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"source": "agent_invoke_text",
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"target": "message_0"
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}
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]
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},
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"components": {
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"begin": {
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"obj": {
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"component_name": "Begin",
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"params": {
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"prologue": "Upload any document and I'll analyze it using the appropriate specialist.",
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"mode": "conversational"
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}
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},
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"downstream": ["switch_0"],
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"upstream": []
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},
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"switch_0": {
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"obj": {
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"component_name": "Switch",
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"params": {
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"conditions": [
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{
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"case_name": "pdf",
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"expression": "sys.files[0].mime_type.contains('pdf')",
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"goto": ["agent_invoke_pdf"]
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},
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{
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"case_name": "excel",
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"expression": "sys.files[0].mime_type.contains('spreadsheet') or sys.files[0].name.endswith('.xlsx')",
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"goto": ["agent_invoke_excel"]
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},
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{
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"case_name": "image",
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"expression": "sys.files[0].mime_type.contains('image')",
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"goto": ["agent_invoke_image"]
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}
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],
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"default_goto": ["agent_invoke_text"]
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}
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},
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"downstream": ["agent_invoke_pdf", "agent_invoke_excel", "agent_invoke_image", "agent_invoke_text"],
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"upstream": ["begin"]
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},
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"agent_invoke_pdf": {
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"obj": {
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"component_name": "AgentInvoke",
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"params": {
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"agent_id": "YOUR_PDF_ANALYZER_AGENT_ID",
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"agent_name": "PDF Analysis Agent",
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"query": "{sys.query}",
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"inputs": {
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"files": "{sys.files}",
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"extract_tables": true,
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"ocr_enabled": true
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},
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"timeout_seconds": 300,
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"create_new_session": true
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}
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},
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"downstream": ["message_0"],
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"upstream": ["switch_0"]
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},
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"agent_invoke_excel": {
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"obj": {
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"component_name": "AgentInvoke",
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"params": {
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"agent_id": "YOUR_EXCEL_ANALYZER_AGENT_ID",
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"agent_name": "Spreadsheet Analysis Agent",
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"query": "{sys.query}",
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"inputs": {
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"files": "{sys.files}",
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"analyze_formulas": true,
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"generate_charts": true
|
||||
},
|
||||
"timeout_seconds": 240,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["switch_0"]
|
||||
},
|
||||
"agent_invoke_image": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_IMAGE_ANALYZER_AGENT_ID",
|
||||
"agent_name": "Image Analysis Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {
|
||||
"files": "{sys.files}",
|
||||
"detect_objects": true,
|
||||
"extract_text": true
|
||||
},
|
||||
"timeout_seconds": 180,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["switch_0"]
|
||||
},
|
||||
"agent_invoke_text": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_TEXT_ANALYZER_AGENT_ID",
|
||||
"agent_name": "Text Analysis Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {
|
||||
"files": "{sys.files}",
|
||||
"summarize": true,
|
||||
"extract_entities": true
|
||||
},
|
||||
"timeout_seconds": 120,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["switch_0"]
|
||||
},
|
||||
"message_0": {
|
||||
"obj": {
|
||||
"component_name": "Message",
|
||||
"params": {
|
||||
"content": "{agent_invoke_pdf@answer}{agent_invoke_excel@answer}{agent_invoke_image@answer}{agent_invoke_text@answer}"
|
||||
}
|
||||
},
|
||||
"downstream": [],
|
||||
"upstream": ["agent_invoke_pdf", "agent_invoke_excel", "agent_invoke_image", "agent_invoke_text"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"path": ["begin"],
|
||||
"retrieval": {
|
||||
"chunks": [],
|
||||
"doc_aggs": []
|
||||
},
|
||||
"globals": {
|
||||
"sys.query": "",
|
||||
"sys.user_id": "",
|
||||
"sys.conversation_turns": 0,
|
||||
"sys.files": []
|
||||
}
|
||||
}
|
||||
|
||||
161
agent/templates/multi_step_workflow_example.json
Normal file
161
agent/templates/multi_step_workflow_example.json
Normal file
|
|
@ -0,0 +1,161 @@
|
|||
{
|
||||
"description": "Multi-Step Workflow - Sentiment Analysis → Issues → Action Items",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": "begin",
|
||||
"data": {
|
||||
"name": "Start",
|
||||
"component_name": "Begin",
|
||||
"x": 100,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_sentiment",
|
||||
"data": {
|
||||
"name": "Sentiment Analysis",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 300,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_issues",
|
||||
"data": {
|
||||
"name": "Extract Key Issues",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 500,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_actions",
|
||||
"data": {
|
||||
"name": "Generate Action Items",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 700,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "message_0",
|
||||
"data": {
|
||||
"name": "Final Report",
|
||||
"component_name": "Message",
|
||||
"x": 900,
|
||||
"y": 200
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "begin",
|
||||
"target": "agent_invoke_sentiment"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_sentiment",
|
||||
"target": "agent_invoke_issues"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_issues",
|
||||
"target": "agent_invoke_actions"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_actions",
|
||||
"target": "message_0"
|
||||
}
|
||||
]
|
||||
},
|
||||
"components": {
|
||||
"begin": {
|
||||
"obj": {
|
||||
"component_name": "Begin",
|
||||
"params": {
|
||||
"prologue": "I'll analyze customer feedback and generate actionable insights for you.",
|
||||
"mode": "workflow"
|
||||
}
|
||||
},
|
||||
"downstream": ["agent_invoke_sentiment"],
|
||||
"upstream": []
|
||||
},
|
||||
"agent_invoke_sentiment": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_SENTIMENT_ANALYSIS_AGENT_ID",
|
||||
"agent_name": "Sentiment Analysis Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {
|
||||
"analysis_depth": "detailed",
|
||||
"include_emotions": true
|
||||
},
|
||||
"timeout_seconds": 120,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["agent_invoke_issues"],
|
||||
"upstream": ["begin"]
|
||||
},
|
||||
"agent_invoke_issues": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_ISSUE_EXTRACTION_AGENT_ID",
|
||||
"agent_name": "Key Issues Extraction Agent",
|
||||
"query": "Based on the sentiment analysis, extract the top 5 key issues:\n\n{agent_invoke_sentiment@answer}",
|
||||
"inputs": {
|
||||
"max_issues": 5,
|
||||
"prioritize": "negative_sentiment"
|
||||
},
|
||||
"timeout_seconds": 120,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["agent_invoke_actions"],
|
||||
"upstream": ["agent_invoke_sentiment"]
|
||||
},
|
||||
"agent_invoke_actions": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_ACTION_ITEMS_AGENT_ID",
|
||||
"agent_name": "Action Items Generator Agent",
|
||||
"query": "Generate actionable recommendations for these issues:\n\n{agent_invoke_issues@answer}",
|
||||
"inputs": {
|
||||
"format": "actionable",
|
||||
"include_timeline": true,
|
||||
"include_ownership": true
|
||||
},
|
||||
"timeout_seconds": 150,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["agent_invoke_issues"]
|
||||
},
|
||||
"message_0": {
|
||||
"obj": {
|
||||
"component_name": "Message",
|
||||
"params": {
|
||||
"content": "# Customer Feedback Analysis Report\n\n## Sentiment Analysis\n{agent_invoke_sentiment@answer}\n\n## Key Issues Identified\n{agent_invoke_issues@answer}\n\n## Recommended Actions\n{agent_invoke_actions@answer}"
|
||||
}
|
||||
},
|
||||
"downstream": [],
|
||||
"upstream": ["agent_invoke_actions"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"path": ["begin"],
|
||||
"retrieval": {
|
||||
"chunks": [],
|
||||
"doc_aggs": []
|
||||
},
|
||||
"globals": {
|
||||
"sys.query": "",
|
||||
"sys.user_id": "",
|
||||
"sys.conversation_turns": 0,
|
||||
"sys.files": []
|
||||
}
|
||||
}
|
||||
|
||||
205
agent/templates/portal_agent_example.json
Normal file
205
agent/templates/portal_agent_example.json
Normal file
|
|
@ -0,0 +1,205 @@
|
|||
{
|
||||
"description": "Portal Agent Example - Agent-to-Agent Invocation",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": "begin",
|
||||
"data": {
|
||||
"name": "Begin",
|
||||
"component_name": "Begin",
|
||||
"x": 100,
|
||||
"y": 100
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "categorize_0",
|
||||
"data": {
|
||||
"name": "Intent Classifier",
|
||||
"component_name": "Categorize",
|
||||
"x": 300,
|
||||
"y": 100
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_sales",
|
||||
"data": {
|
||||
"name": "Sales Analysis Agent",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 500,
|
||||
"y": 50
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_customer",
|
||||
"data": {
|
||||
"name": "Customer Service Agent",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 500,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "agent_invoke_docs",
|
||||
"data": {
|
||||
"name": "Document Processing Agent",
|
||||
"component_name": "AgentInvoke",
|
||||
"x": 500,
|
||||
"y": 350
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "message_0",
|
||||
"data": {
|
||||
"name": "Response",
|
||||
"component_name": "Message",
|
||||
"x": 700,
|
||||
"y": 200
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "begin",
|
||||
"target": "categorize_0"
|
||||
},
|
||||
{
|
||||
"source": "categorize_0",
|
||||
"target": "agent_invoke_sales",
|
||||
"label": "sales"
|
||||
},
|
||||
{
|
||||
"source": "categorize_0",
|
||||
"target": "agent_invoke_customer",
|
||||
"label": "customer_service"
|
||||
},
|
||||
{
|
||||
"source": "categorize_0",
|
||||
"target": "agent_invoke_docs",
|
||||
"label": "documents"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_sales",
|
||||
"target": "message_0"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_customer",
|
||||
"target": "message_0"
|
||||
},
|
||||
{
|
||||
"source": "agent_invoke_docs",
|
||||
"target": "message_0"
|
||||
}
|
||||
]
|
||||
},
|
||||
"components": {
|
||||
"begin": {
|
||||
"obj": {
|
||||
"component_name": "Begin",
|
||||
"params": {
|
||||
"prologue": "Welcome to our AI Portal! I can help you with sales analysis, customer service, or document processing.",
|
||||
"mode": "conversational"
|
||||
}
|
||||
},
|
||||
"downstream": ["categorize_0"],
|
||||
"upstream": []
|
||||
},
|
||||
"categorize_0": {
|
||||
"obj": {
|
||||
"component_name": "Categorize",
|
||||
"params": {
|
||||
"description": "Classify user intent to route to appropriate specialized agent",
|
||||
"llm_id": "",
|
||||
"items": [
|
||||
{
|
||||
"name": "sales",
|
||||
"description": "Questions about sales data, revenue, reports, or business metrics"
|
||||
},
|
||||
{
|
||||
"name": "customer_service",
|
||||
"description": "Customer support requests, complaints, or general inquiries"
|
||||
},
|
||||
{
|
||||
"name": "documents",
|
||||
"description": "Document processing, analysis, or extraction tasks"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"downstream": ["agent_invoke_sales", "agent_invoke_customer", "agent_invoke_docs"],
|
||||
"upstream": ["begin"]
|
||||
},
|
||||
"agent_invoke_sales": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_SALES_AGENT_ID_HERE",
|
||||
"agent_name": "Sales Analysis Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {
|
||||
"period": "current",
|
||||
"metrics": ["revenue", "units", "growth"]
|
||||
},
|
||||
"timeout_seconds": 180,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["categorize_0"]
|
||||
},
|
||||
"agent_invoke_customer": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_CUSTOMER_SERVICE_AGENT_ID_HERE",
|
||||
"agent_name": "Customer Service Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {},
|
||||
"timeout_seconds": 120,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["categorize_0"]
|
||||
},
|
||||
"agent_invoke_docs": {
|
||||
"obj": {
|
||||
"component_name": "AgentInvoke",
|
||||
"params": {
|
||||
"agent_id": "YOUR_DOCUMENT_PROCESSING_AGENT_ID_HERE",
|
||||
"agent_name": "Document Processing Agent",
|
||||
"query": "{sys.query}",
|
||||
"inputs": {
|
||||
"files": "{sys.files}"
|
||||
},
|
||||
"timeout_seconds": 300,
|
||||
"create_new_session": true
|
||||
}
|
||||
},
|
||||
"downstream": ["message_0"],
|
||||
"upstream": ["categorize_0"]
|
||||
},
|
||||
"message_0": {
|
||||
"obj": {
|
||||
"component_name": "Message",
|
||||
"params": {
|
||||
"content": "{agent_invoke_sales@answer}{agent_invoke_customer@answer}{agent_invoke_docs@answer}"
|
||||
}
|
||||
},
|
||||
"downstream": [],
|
||||
"upstream": ["agent_invoke_sales", "agent_invoke_customer", "agent_invoke_docs"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"path": ["begin"],
|
||||
"retrieval": {
|
||||
"chunks": [],
|
||||
"doc_aggs": []
|
||||
},
|
||||
"globals": {
|
||||
"sys.query": "",
|
||||
"sys.user_id": "",
|
||||
"sys.conversation_turns": 0,
|
||||
"sys.files": []
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Add table
Reference in a new issue