add knowlege

agentuniverse/agent/action/knowledge/knowledge.py

@@ -5,7 +5,7 @@
 # @Author : wangchongshi
 # @Email : [email protected]
 # @FileName: knowledge.py
-from typing import Optional, Dict, List, Any
+from typing import Optional, Dict, List
 
 from langchain_core.utils.json import parse_json_markdown
 from langchain.tools import Tool as LangchainTool
@@ -94,7 +94,7 @@ def langchain_query(self, query: str) -> str:
  res.append(doc.text)
  return "\n=========================================\n".join(res)
 
- def as_langchain_tool(self) -> Any:
+ def as_langchain_tool(self) -> LangchainTool:
  """Convert the Knowledge object to a LangChain tool.
 
  Returns:

agentuniverse/agent/plan/planner/react_planner/react_planner.py

@@ -11,8 +11,11 @@
 from langchain.tools import Tool as LangchainTool
 from langchain_core.runnables import RunnableConfig
 
+from agentuniverse.agent.action.knowledge.knowledge import Knowledge
+from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
 from agentuniverse.agent.action.tool.tool import Tool
 from agentuniverse.agent.action.tool.tool_manager import ToolManager
+from agentuniverse.agent.agent import Agent
 from agentuniverse.agent.agent_model import AgentModel
 from agentuniverse.agent.input_object import InputObject
 from agentuniverse.agent.memory.chat_memory import ChatMemory
@@ -78,6 +81,15 @@ def acquire_tools(action) -> list[LangchainTool]:
  for tool_name in tool_names:
  tool: Tool = ToolManager().get_instance_obj(tool_name)
  lc_tools.append(tool.as_langchain())
+ knowledge: list = action.get('knowledge') or list()
+ for knowledge_name in knowledge:
+ knowledge_tool: Knowledge = KnowledgeManager().get_instance_obj(knowledge_name)
+ lc_tools.append(knowledge_tool.as_langchain_tool())
+
+ agents: list = action.get('agent') or list()
+ for agent_name in agents:
+ agent_tool: Agent = AgentModel.load_by_name(agent_name)
+ lc_tools.append(agent_tool.as_langchain_tool())
  return lc_tools
 
  def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt:

sample_standard_app/app/test/test_rag_agent.py

@@ -7,6 +7,8 @@
 # @FileName: test_rag_agent.py
 import unittest
 
+from agentuniverse.agent.action.knowledge.knowledge import Knowledge
+from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
 from agentuniverse.agent.agent import Agent
 from agentuniverse.agent.agent_manager import AgentManager
 from agentuniverse.agent.output_object import OutputObject
@@ -23,13 +25,14 @@ def setUp(self) -> None:
 
  def test_rag_agent(self):
  """Test demo rag agent."""
- instance: Agent = AgentManager().get_instance_obj('demo_rag_agent')
- # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因')
- # res_info = f"\nRag agent execution result is :\n"
- # res_info += output_object.get_data('output')
- # print(res_info)
- tool = instance.as_langchain_tool()
- print(tool.description)
+ # instance: Agent = AgentManager().get_instance_obj('demo_rag_agent')
+ # # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因')
+ # # res_info = f"\nRag agent execution result is :\n"
+ # # res_info += output_object.get_data('output')
+ # # print(res_info)
+ # tool = instance.as_langchain_tool()
+ knowledge: Knowledge = KnowledgeManager().get_instance_obj('demo_knowledge')
+ print(knowledge.as_langchain_tool().description)
 
 
 if __name__ == '__main__':