base test
This commit is contained in:
parent
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@ -560,12 +560,19 @@ class HuoshanAPI:
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max_tokens=16384, # 16K
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timeout=600,
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stream=True,
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thinking={
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"type": "disabled", # 不使用深度思考能力
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# "type": "enabled", # 使用深度思考能力
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# "type": "auto", # 模型自行判断是否使用深度思考能力
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},
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tools=tools # 传入 tools 参数
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)
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for chunk in completion:
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if chunk.choices and chunk.choices[0].delta.content is not None: # type: ignore
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yield chunk.choices[0].delta.content # type: ignore
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except Exception as e:
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import traceback
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traceback.print_exc()
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raise ValueError(f"火山引擎API流式调用失败: {str(e)}")
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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]:
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46
graph/test/t1.py
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46
graph/test/t1.py
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@ -0,0 +1,46 @@
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import os
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import asyncio
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from typing import Annotated
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from langchain.chat_models import init_chat_model
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langgraph.graph.state import RunnableConfig
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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graph_builder = StateGraph(State)
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llm = init_chat_model(
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model="openai:doubao-seed-1.6-250615",
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temperature=0,
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base_url="https://ark.cn-beijing.volces.com/api/v3/",
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api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
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)
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def chatbot(state: State):
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return {"messages": [llm.invoke(state["messages"])]}
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graph_builder.add_node("chatbot", chatbot)
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graph_builder.add_edge(START, "chatbot")
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graph_builder.add_edge("chatbot", END)
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graph = graph_builder.compile()
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# 单对话节点测试
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async def run():
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config:RunnableConfig = {"configurable": {"thread_id": "1"}}
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inputs:State = {"messages": [HumanMessage(content="你好")]}
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result = await graph.ainvoke(inputs, config=config)
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print(result)
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if __name__ == "__main__":
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asyncio.run(run())
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56
graph/test/t2.py
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56
graph/test/t2.py
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@ -0,0 +1,56 @@
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import os
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import asyncio
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from typing import Annotated
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from langchain.chat_models import init_chat_model
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from langgraph.prebuilt import create_react_agent
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from langchain_core.messages import HumanMessage
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from langgraph.graph.state import RunnableConfig
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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graph_builder = StateGraph(State)
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llm = init_chat_model(
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model="openai:doubao-seed-1.6-250615",
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temperature=0,
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base_url="https://ark.cn-beijing.volces.com/api/v3/",
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api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
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)
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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。")
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graph_builder.add_node("chatbot", chatAgent)
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graph_builder.add_edge(START, "chatbot")
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graph_builder.add_edge("chatbot", END)
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graph = graph_builder.compile()
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# 单智能体节点测试
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async def run():
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config:RunnableConfig = {"configurable": {"thread_id": "1"}}
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inputs:State = {"messages": [HumanMessage(content="你好")]}
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# # 非流式处理查询
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# result = await graph.ainvoke(inputs, config=config)
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# print(result)
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# 流式处理查询
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async for message_chunk, metadata in graph.astream(
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input=inputs,
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config=config,
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stream_mode="messages"
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):
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# 输出最终结果
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if message_chunk.content: # type: ignore
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print(message_chunk.content, end="|", flush=True) # type: ignore
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if __name__ == "__main__":
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asyncio.run(run())
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68
graph/test/t3.py
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68
graph/test/t3.py
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@ -0,0 +1,68 @@
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import os
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import asyncio
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from typing import Annotated
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from langchain.chat_models import init_chat_model
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from langgraph.prebuilt import create_react_agent
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from langchain_core.messages import HumanMessage
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from langgraph.graph.state import RunnableConfig
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from typing_extensions import TypedDict
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from langchain.tools import tool
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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from tools.llm.huoshan_langchain import HuoshanChatModel
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@tool
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def QueryLocation():
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"""
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查询当前所在位置
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Returns:
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Dict: 返回一个包含以下字段的字典：
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location (str): 当前所在位置。
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"""
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return {"location": f"杭州"}
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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graph_builder = StateGraph(State)
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llm = init_chat_model(
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model="openai:doubao-seed-1.6-250615",
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temperature=0,
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base_url="https://ark.cn-beijing.volces.com/api/v3/",
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api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
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)
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# llm = HuoshanChatModel()
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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。")
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graph_builder.add_node("chatbot", chatAgent)
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graph_builder.add_edge(START, "chatbot")
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graph_builder.add_edge("chatbot", END)
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graph = graph_builder.compile()
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# 单智能体节点测试 工具使用 function calling
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async def run():
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config:RunnableConfig = {"configurable": {"thread_id": "1"}}
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inputs:State = {"messages": [HumanMessage(content="我现在在哪啊？")]}
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# # 非流式处理查询
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# result = await graph.ainvoke(inputs, config=config)
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# print(result)
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# 流式处理查询
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async for message_chunk, metadata in graph.astream(
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input=inputs,
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config=config,
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stream_mode="messages"
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):
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# 输出最终结果
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if message_chunk.content: # type: ignore
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print(f"type:{message_chunk.type} \n msg:{message_chunk.content}", end="\n", flush=True) # type: ignore
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if __name__ == "__main__":
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asyncio.run(run())
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161
graph/test/t4.py
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161
graph/test/t4.py
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import os
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import asyncio
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import json
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from typing import Annotated
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from typing_extensions import NotRequired, TypedDict
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from langchain.chat_models import init_chat_model
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from langgraph.prebuilt import create_react_agent
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from langchain_core.messages import HumanMessage, human
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from langgraph.graph.state import RunnableConfig
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from typing_extensions import TypedDict
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from langchain.tools import tool
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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from tools.llm.huoshan_langchain import HuoshanChatModel
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@tool
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def QueryLocation():
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"""
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查询当前所在位置
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Returns:
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Dict: 返回一个包含以下字段的字典：
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location (str): 当前所在位置。
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"""
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return {"location": f"杭州"}
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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agent: NotRequired[str]
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graph_builder = StateGraph(State)
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llm = init_chat_model(
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model="openai:doubao-seed-1.6-250615",
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temperature=0,
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base_url="https://ark.cn-beijing.volces.com/api/v3/",
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api_key="0d5189b3-9a03-4393-81be-8c1ba1e97cbb",
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)
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# llm = HuoshanChatModel()
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routerAgent = create_react_agent(llm, tools=[],
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prompt=f"""
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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
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下面是智能体名称与任务描述:
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taskA：用于回答位置相关的问题
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taskB：用于给用户讲一个笑话
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taskC：用于回复用户的其他问题
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# 输出规范
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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：
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{{
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"agent": "taskA", # 智能体名称
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}}
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""")
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taskAgentA = create_react_agent(llm, tools=[QueryLocation],
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prompt=f"""
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你的任务是回答位置相关的问题
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""")
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taskAgentB = create_react_agent(llm, tools=[],
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prompt=f"""
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你的任务是给用户讲一个笑话
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""")
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taskAgentC = create_react_agent(llm, tools=[],
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prompt=f"""
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你的任务是礼貌的回复用户
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""")
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def node_router(state: State):
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"""
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路由函数，根据输入的状态判断下一个节点
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"""
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last_msg = state["messages"][-1].content
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# print(f"last_msg:{last_msg}")
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try:
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data = json.loads(last_msg)
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nextAgent = data.get("agent", "taskC")
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except json.JSONDecodeError:
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nextAgent = "taskC"
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return nextAgent
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def taskAgentB_node(state: State):
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"""
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taskAgentB节点，用于给用户讲一个笑话
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"""
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human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
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# print(f"taskAgentB_node state:{state}")
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res = taskAgentB.invoke({"messages": human_msgs})
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state["messages"].append(res["messages"][-1])
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return state
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def taskAgentA_node(state: State):
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"""
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taskAgentA节点，用于回答位置相关的问题
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"""
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human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
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# print(f"taskAgentA_node state:{state}")
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res = taskAgentA.invoke({"messages": human_msgs})
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state["messages"].append(res["messages"][-1])
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return state
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def taskAgentC_node(state: State):
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"""
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taskAgentC节点，用于回复用户的其他问题
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"""
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human_msgs = [msg for msg in state["messages"] if msg.type == "human"]
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# print(f"taskAgentC_node state:{state}")
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res = taskAgentC.invoke({"messages": human_msgs})
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state["messages"].append(res["messages"][-1])
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return state
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graph_builder.add_node("router", routerAgent)
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graph_builder.add_node("taskA", taskAgentA_node)
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graph_builder.add_node("taskB", taskAgentB_node)
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graph_builder.add_node("taskC", taskAgentC_node)
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graph_builder.add_edge(START, "router")
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graph_builder.add_conditional_edges(
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"router",
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node_router,
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{
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"taskA": "taskA",
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"taskB": "taskB",
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"taskC": "taskC",
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}
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)
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graph_builder.add_edge("taskA", END)
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graph_builder.add_edge("taskB", END)
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graph_builder.add_edge("taskC", END)
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graph = graph_builder.compile()
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# 多智能体节点测试 工具使用 function calling
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#
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async def run():
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config:RunnableConfig = {"configurable": {"thread_id": "1"}}
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# inputs:State = {"messages": [HumanMessage(content="我现在在哪啊？")]}
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# inputs:State = {"messages": [HumanMessage(content="给我讲个笑话吧")]}
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inputs:State = {"messages": [HumanMessage(content="现在是几点？")]}
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# # 非流式处理查询
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# result = await graph.ainvoke(inputs, config=config)
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# print(result)
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# 流式处理查询
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async for message_chunk, metadata in graph.astream(
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input=inputs,
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config=config,
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stream_mode="messages"
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):
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# 输出最终结果
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if message_chunk.content: # type: ignore
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print(f"type:{message_chunk.type} \n msg:{message_chunk.content}", end="\n", flush=True) # type: ignore
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if __name__ == "__main__":
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asyncio.run(run())
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@ -19,5 +19,6 @@ PyMySQL
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DBUtils
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PyMuPDF
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langchain-community
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langchain-openai
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langgraph
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langgraph-checkpoint-mongodb
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@ -7,7 +7,7 @@ from tools.database.mongo import mainDB
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from langchain.tools import tool
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import json
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@tool
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@tool(return_direct=True)
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def QueryOriginalScript(session_id: str):
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"""
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查询原始剧本内容是否存在
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@ -52,7 +52,7 @@ def QueryOriginalScriptContent(session_id: str):
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}
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@tool
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@tool(return_direct=True)
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def QueryDiagnosisAndAssessment(session_id: str):
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"""
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查询诊断与资产评估报告是否存在
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@ -81,7 +81,7 @@ def QueryDiagnosisAndAssessmentContent(session_id: str):
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"content": script["diagnosis_and_assessment"] if script else "",
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}
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@tool
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@tool(return_direct=True)
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def QueryAdaptationIdeas(session_id: str):
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"""
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查询改编思路是否存在
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@ -110,7 +110,7 @@ def QueryAdaptationIdeasContent(session_id: str):
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"content": script["adaptation_ideas"] if script else "",
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}
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@tool
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@tool(return_direct=True)
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def QueryScriptBible(session_id: str):
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"""
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查询剧本圣经是否存在
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@ -139,7 +139,7 @@ def QueryScriptBibleContent(session_id: str):
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"content": script["script_bible"] if script else {},
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}
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@tool
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@tool(return_direct=True)
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def QueryCoreOutline(session_id: str):
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"""
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查询剧本圣经中的核心大纲是否存在
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@ -154,7 +154,7 @@ def QueryCoreOutline(session_id: str):
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"exist": script is not None,
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}
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@tool
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@tool(return_direct=True)
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def QueryCharacterProfile(session_id: str):
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"""
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查询剧本圣经中的核心人物小传是否存在
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@ -169,7 +169,7 @@ def QueryCharacterProfile(session_id: str):
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"exist": script is not None,
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}
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@tool
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@tool(return_direct=True)
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def QueryCoreEventTimeline(session_id: str):
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"""
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查询剧本圣经中的重大事件时间线是否存在
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@ -184,7 +184,7 @@ def QueryCoreEventTimeline(session_id: str):
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"exist": script is not None,
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}
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@tool
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@tool(return_direct=True)
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def QueryCharacterList(session_id: str):
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"""
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查询剧本圣经中的总人物表是否存在
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@ -199,7 +199,7 @@ def QueryCharacterList(session_id: str):
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"exist": script is not None,
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}
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@tool
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@tool(return_direct=True)
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def QueryEpisodeCount(session_id: str):
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"""
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查询剧集创作情况
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@ -2,7 +2,7 @@ from bson import ObjectId
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from tools.database.mongo import mainDB
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from langchain.tools import tool
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@tool
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@tool(return_direct=True)
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def UpdateDiagnosisAndAssessmentTool(session_id: str, diagnosis_and_assessment: str):
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"""
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更新诊断与资产评估报告
|
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@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user