base test

api/huoshan.py

@@ -560,12 +560,19 @@ class HuoshanAPI:
                 max_tokens=16384, # 16K
                 timeout=600,
                 stream=True,
+                thinking={
+                    "type": "disabled", # 不使用深度思考能力
+                    # "type": "enabled", # 使用深度思考能力
+                    # "type": "auto", # 模型自行判断是否使用深度思考能力
+                },
                 tools=tools # 传入 tools 参数
             )
             for chunk in completion:
                 if chunk.choices and chunk.choices[0].delta.content is not None: # type: ignore
                     yield chunk.choices[0].delta.content # type: ignore
         except Exception as e:
+            import traceback
+            traceback.print_exc()
             raise ValueError(f"火山引擎API流式调用失败: {str(e)}")
     
     def analyze_image(self, image_url: str, prompt: str = "请描述这张图片的内容", model: Optional[str] = None, detail: str = "high") -> Dict[str, Any]:

graph/test/t1.py

@@ -0,0 +1,46 @@
+import os
+import asyncio
+from typing import Annotated
+
+from langchain.chat_models import init_chat_model
+from langchain_community.chat_models import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langgraph.graph.state import RunnableConfig
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+graph_builder = StateGraph(State)
+
+llm = init_chat_model(
+    model="openai:doubao-seed-1.6-250615",
+    temperature=0,
+    base_url="https://ark.cn-beijing.volces.com/api/v3/",
+    api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
+)
+
+def chatbot(state: State):
+    return {"messages": [llm.invoke(state["messages"])]}
+
+
+graph_builder.add_node("chatbot", chatbot)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+graph = graph_builder.compile()
+
+
+# 单对话节点测试
+async def run():
+    config:RunnableConfig = {"configurable": {"thread_id": "1"}}
+    inputs:State = {"messages": [HumanMessage(content="你好")]}
+    result = await graph.ainvoke(inputs, config=config)
+    print(result)
+
+if __name__ == "__main__":    
+    asyncio.run(run())

graph/test/t2.py

@@ -0,0 +1,56 @@
+import os
+import asyncio
+from typing import Annotated
+
+from langchain.chat_models import init_chat_model
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage
+from langgraph.graph.state import RunnableConfig
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+graph_builder = StateGraph(State)
+
+llm = init_chat_model(
+    model="openai:doubao-seed-1.6-250615",
+    temperature=0,
+    base_url="https://ark.cn-beijing.volces.com/api/v3/",
+    api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
+)
+
+chatAgent = create_react_agent(llm, tools=[], prompt="你是一个智能体，你的任务是回答用户的问题。如果用户的输入不是问题，你可以询问用户是否有其他问题。")
+
+graph_builder.add_node("chatbot", chatAgent)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+graph = graph_builder.compile()
+
+# 单智能体节点测试
+async def run():
+    config:RunnableConfig = {"configurable": {"thread_id": "1"}}
+    inputs:State = {"messages": [HumanMessage(content="你好")]}
+    
+    # # 非流式处理查询
+    # result = await graph.ainvoke(inputs, config=config)
+    # print(result)
+
+    # 流式处理查询
+    async for message_chunk, metadata in graph.astream(
+            input=inputs,
+            config=config,
+            stream_mode="messages"
+    ):
+        # 输出最终结果
+        if message_chunk.content: # type: ignore
+            print(message_chunk.content, end="|", flush=True) # type: ignore
+
+
+if __name__ == "__main__":    
+    asyncio.run(run())

graph/test/t3.py

@@ -0,0 +1,68 @@
+import os
+import asyncio
+from typing import Annotated
+
+from langchain.chat_models import init_chat_model
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage
+from langgraph.graph.state import RunnableConfig
+from typing_extensions import TypedDict
+from langchain.tools import tool
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from tools.llm.huoshan_langchain import HuoshanChatModel
+
+@tool
+def QueryLocation():
+    """
+    查询当前所在位置 
+    Returns:
+        Dict: 返回一个包含以下字段的字典：
+            location (str): 当前所在位置。
+    """
+    return {"location": f"杭州"}
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+
+llm = init_chat_model(
+    model="openai:doubao-seed-1.6-250615",
+    temperature=0,
+    base_url="https://ark.cn-beijing.volces.com/api/v3/",
+    api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
+)
+
+# llm = HuoshanChatModel()
+
+chatAgent = create_react_agent(llm, tools=[QueryLocation], prompt="你是一个智能体，你的任务是回答用户的问题。如果用户的输入不是问题，你可以询问用户是否有其他问题。")
+
+graph_builder.add_node("chatbot", chatAgent)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+graph = graph_builder.compile()
+
+# 单智能体节点测试 工具使用 function calling
+async def run():
+    config:RunnableConfig = {"configurable": {"thread_id": "1"}}
+    inputs:State = {"messages": [HumanMessage(content="我现在在哪啊？")]}
+    
+    # # 非流式处理查询
+    # result = await graph.ainvoke(inputs, config=config)
+    # print(result)
+
+    # 流式处理查询
+    async for message_chunk, metadata in graph.astream(
+            input=inputs,
+            config=config,
+            stream_mode="messages"
+    ):
+        # 输出最终结果
+        if message_chunk.content: # type: ignore
+            print(f"type:{message_chunk.type} \n msg:{message_chunk.content}", end="\n", flush=True) # type: ignore
+
+
+if __name__ == "__main__":    
+    asyncio.run(run())

graph/test/t4.py

@@ -0,0 +1,161 @@
+import os
+import asyncio
+import json
+
+from typing import Annotated
+from typing_extensions import NotRequired, TypedDict
+
+from langchain.chat_models import init_chat_model
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage, human
+from langgraph.graph.state import RunnableConfig
+from typing_extensions import TypedDict
+from langchain.tools import tool
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from tools.llm.huoshan_langchain import HuoshanChatModel
+
+@tool
+def QueryLocation():
+    """
+    查询当前所在位置 
+    Returns:
+        Dict: 返回一个包含以下字段的字典：
+            location (str): 当前所在位置。
+    """
+    return {"location": f"杭州"}
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+    agent: NotRequired[str]
+
+graph_builder = StateGraph(State)
+
+llm = init_chat_model(
+    model="openai:doubao-seed-1.6-250615",
+    temperature=0,
+    base_url="https://ark.cn-beijing.volces.com/api/v3/",
+    api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
+)
+
+# llm = HuoshanChatModel()
+
+routerAgent = create_react_agent(llm, tools=[], 
+    prompt=f"""
+        你的任务是根据用户的输入，判断使用哪个智能体来继续执行任务
+        下面是智能体名称与任务描述:
+        taskA：用于回答位置相关的问题
+        taskB：用于给用户讲一个笑话
+        taskC：用于回复用户的其他问题
+        
+        # 输出规范
+        请严格按照下列JSON结构返回数据，不要有其他任何多余的信息和描述：
+        {{
+            "agent": "taskA", # 智能体名称
+        }}
+    """)
+taskAgentA = create_react_agent(llm, tools=[QueryLocation], 
+    prompt=f"""
+        你的任务是回答位置相关的问题
+    """)
+taskAgentB = create_react_agent(llm, tools=[], 
+    prompt=f"""
+        你的任务是给用户讲一个笑话
+    """)
+taskAgentC = create_react_agent(llm, tools=[], 
+    prompt=f"""
+        你的任务是礼貌的回复用户
+    """)
+def node_router(state: State):
+    """
+    路由函数，根据输入的状态判断下一个节点
+    """
+
+    last_msg = state["messages"][-1].content
+    # print(f"last_msg:{last_msg}")
+    try:
+        data = json.loads(last_msg)
+        nextAgent = data.get("agent", "taskC")
+    except json.JSONDecodeError:
+        nextAgent = "taskC"
+    return nextAgent
+
+def taskAgentB_node(state: State):
+    """
+    taskAgentB节点，用于给用户讲一个笑话
+    """
+    human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
+    # print(f"taskAgentB_node state:{state}")
+    res = taskAgentB.invoke({"messages": human_msgs})
+    state["messages"].append(res["messages"][-1])
+    return state
+
+def taskAgentA_node(state: State):
+    """
+    taskAgentA节点，用于回答位置相关的问题
+    """
+    human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
+    # print(f"taskAgentA_node state:{state}")
+    res = taskAgentA.invoke({"messages": human_msgs})
+    state["messages"].append(res["messages"][-1])
+    return state
+
+def taskAgentC_node(state: State):
+    """
+    taskAgentC节点，用于回复用户的其他问题
+    """
+    human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
+    # print(f"taskAgentC_node state:{state}")
+    res = taskAgentC.invoke({"messages": human_msgs})
+    state["messages"].append(res["messages"][-1])
+    return state
+    
+graph_builder.add_node("router", routerAgent)
+graph_builder.add_node("taskA", taskAgentA_node)
+graph_builder.add_node("taskB", taskAgentB_node)
+graph_builder.add_node("taskC", taskAgentC_node)
+
+graph_builder.add_edge(START, "router")
+
+graph_builder.add_conditional_edges(
+    "router",
+    node_router,
+    {
+        "taskA": "taskA",
+        "taskB": "taskB",
+        "taskC": "taskC",
+    }
+)
+
+graph_builder.add_edge("taskA", END)
+graph_builder.add_edge("taskB", END)
+graph_builder.add_edge("taskC", END)
+
+graph = graph_builder.compile()
+
+# 多智能体节点测试 工具使用 function calling
+# 
+async def run():
+    config:RunnableConfig = {"configurable": {"thread_id": "1"}}
+    # inputs:State = {"messages": [HumanMessage(content="我现在在哪啊？")]}
+    # inputs:State = {"messages": [HumanMessage(content="给我讲个笑话吧")]}
+    inputs:State = {"messages": [HumanMessage(content="现在是几点？")]}
+    
+    # # 非流式处理查询
+    # result = await graph.ainvoke(inputs, config=config)
+    # print(result)
+
+    # 流式处理查询
+    async for message_chunk, metadata in graph.astream(
+            input=inputs,
+            config=config,
+            stream_mode="messages"
+    ):
+        # 输出最终结果
+        if message_chunk.content: # type: ignore
+            print(f"type:{message_chunk.type} \n msg:{message_chunk.content}", end="\n", flush=True) # type: ignore
+
+
+if __name__ == "__main__":    
+    asyncio.run(run())

requirements.txt

@@ -19,5 +19,6 @@ PyMySQL
 DBUtils
 PyMuPDF
 langchain-community
+langchain-openai
 langgraph
 langgraph-checkpoint-mongodb

tools/agent/queryDB.py

@@ -7,7 +7,7 @@ from tools.database.mongo import mainDB
 from langchain.tools import tool
 import json
 
-@tool
+@tool(return_direct=True)
 def QueryOriginalScript(session_id: str):
     """
     查询原始剧本内容是否存在
@@ -52,7 +52,7 @@ def QueryOriginalScriptContent(session_id: str):
     }
 
 
-@tool
+@tool(return_direct=True)
 def QueryDiagnosisAndAssessment(session_id: str):
     """
     查询诊断与资产评估报告是否存在
@@ -81,7 +81,7 @@ def QueryDiagnosisAndAssessmentContent(session_id: str):
         "content": script["diagnosis_and_assessment"] if script else "",
     }
 
-@tool
+@tool(return_direct=True)
 def QueryAdaptationIdeas(session_id: str):
     """
     查询改编思路是否存在
@@ -110,7 +110,7 @@ def QueryAdaptationIdeasContent(session_id: str):
         "content": script["adaptation_ideas"] if script else "",
     }
 
-@tool
+@tool(return_direct=True)
 def QueryScriptBible(session_id: str):
     """
     查询剧本圣经是否存在
@@ -139,7 +139,7 @@ def QueryScriptBibleContent(session_id: str):
         "content": script["script_bible"] if script else {},
     }
 
-@tool
+@tool(return_direct=True)
 def QueryCoreOutline(session_id: str):
     """
     查询剧本圣经中的核心大纲是否存在
@@ -154,7 +154,7 @@ def QueryCoreOutline(session_id: str):
         "exist": script is not None,
     }
 
-@tool
+@tool(return_direct=True)
 def QueryCharacterProfile(session_id: str):
     """
     查询剧本圣经中的核心人物小传是否存在
@@ -169,7 +169,7 @@ def QueryCharacterProfile(session_id: str):
         "exist": script is not None,
     }
 
-@tool
+@tool(return_direct=True)
 def QueryCoreEventTimeline(session_id: str):
     """
     查询剧本圣经中的重大事件时间线是否存在
@@ -184,7 +184,7 @@ def QueryCoreEventTimeline(session_id: str):
         "exist": script is not None,
     }
 
-@tool
+@tool(return_direct=True)
 def QueryCharacterList(session_id: str):
     """
     查询剧本圣经中的总人物表是否存在
@@ -199,7 +199,7 @@ def QueryCharacterList(session_id: str):
         "exist": script is not None,
     }
 
-@tool
+@tool(return_direct=True)
 def QueryEpisodeCount(session_id: str):
     """
     查询剧集创作情况

tools/agent/updateDB.py

@@ -2,7 +2,7 @@ from bson import ObjectId
 from tools.database.mongo import mainDB
 from langchain.tools import tool
 
-@tool
+@tool(return_direct=True)
 def UpdateDiagnosisAndAssessmentTool(session_id: str, diagnosis_and_assessment: str):
     """
     更新诊断与资产评估报告
@@ -30,7 +30,7 @@ def UpdateDiagnosisAndAssessment(session_id: str, diagnosis_and_assessment: str)
         "success": script.modified_count > 0,
     }
 
-@tool
+@tool(return_direct=True)
 def UpdateAdaptationIdeasTool(session_id: str, adaptation_ideas: str):
     """
     更新改编思路
@@ -58,7 +58,7 @@ def UpdateAdaptationIdeas(session_id: str, adaptation_ideas: str):
         "success": script.modified_count > 0,
     }
 
-@tool
+@tool(return_direct=True)
 def UpdateScriptBibleTool(
     session_id: str, 
     core_outline:str|None = None,
@@ -117,7 +117,7 @@ def UpdateScriptBible(
         "success": script.modified_count > 0,
     }
 
-@tool
+@tool(return_direct=True)
 def SetTotalEpisodeNumTool(session_id: str, total_episode_num: int):
     """
     设置总集数
@@ -145,7 +145,7 @@ def SetTotalEpisodeNum(session_id: str, total_episode_num: int):
         "success": script.modified_count > 0,
     }
 
-@tool
+@tool(return_direct=True)
 def UpdateOneEpisodeTool(session_id: str, episode_num:int, content: str):
     """
     更新单集内容

tools/llm/huoshan_langchain.py

@@ -237,7 +237,6 @@ class HuoshanChatModel(BaseChatModel):
             for chunk in self._api.get_chat_response_stream(
                 messages=api_messages,
                 tools=tools,
-                **kwargs
             ):
                 if chunk:
                     # 创建增量消息