This commit is contained in:
parent
6624427528
commit
f289955974
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@ -294,10 +294,12 @@ def use_guidance() -> rx.Component:
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font_weight="var(--prismui-font-weight-3)",
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),
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rx.hstack(
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# 预定航班按钮
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# 工作流按钮：预定航班
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workflow_button(WorkflowType.BOOK_FLIGHT),
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# 银行客服按钮
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# 工作流按钮：银行客户服务
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workflow_button(WorkflowType.BANK_CUSTOMER_SERVICE),
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# 工作流按钮：研究
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workflow_button(WorkflowType.RESEARCH),
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flex_wrap="wrap",
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align_items="center",
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gap="12px",
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@ -408,7 +410,6 @@ def thinking(item: MessageHistoryItem) -> rx.Component:
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rx.markdown(
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item.content,
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width="100%",
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margin="4px 0",
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line_height="1.6",
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font_size="var(--prismui-font-size-1)",
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color="var(--prismui-color-3)",
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@ -109,10 +109,11 @@ class ConversationState(rx.State):
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# 当前会话
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conversation = self.conversations[self.conversation_id]
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# 最后一个消息
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message = next(reversed(conversation.messages.values()))
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message = next(iter(reversed(conversation.messages.values())), None)
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# 若最后一个消息包含审批且审批结果为 None 则将会话正在运行设置为是、消息正在运行设置为是
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if (
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isinstance(approval := message.approval, Approval)
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message is not None
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and isinstance(approval := message.approval, Approval)
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and approval.result is None
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):
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conversation.is_running = True
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@ -396,8 +397,21 @@ class ConversationState(rx.State):
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usage=usage,
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)
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# 消息列表
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messages: list[Message] = []
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# 研究
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case WorkflowType.RESEARCH:
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from application.workshop.research import (
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run_stream_events,
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)
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# 运行并流式输出事件
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stream_events = run_stream_events(
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user_prompt=user_prompt,
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message_history=message_history,
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usage=usage,
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)
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messages: list[Message] = [] # 消息列表
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previous_provider_details = {} # 上一个供应商详情
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# 获取运行流式输出事件
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async for event in stream_events:
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# 若等待流式输出则将等待流式输出设置为否
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@ -459,26 +473,47 @@ class ConversationState(rx.State):
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# ========== 增量事件 ==========
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case PartDeltaEvent(index=index, delta=delta):
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# 获取消息
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match delta:
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# 思考分片增量
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case ThinkingPartDelta(
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content_delta=content_delta,
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provider_details=provider_details,
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):
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# 若内容增量不为空则增量更新
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if content_delta:
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conversation.messages[
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message = conversation.messages[
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index_map_to_message_id[index]
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].content += content_delta
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]
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# 若内容增量非空则将内容追加至消息内容，否则若供应商详情可调用更新消息内容
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if content_delta:
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message.content += content_delta
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yield # 通知前端更新渲染
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else:
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if callable(provider_details):
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# 获取原始内容
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raw_content = (
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previous_provider_details := provider_details(
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previous_provider_details
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)
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).get("raw_content")
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if isinstance(raw_content, str):
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...
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elif isinstance(raw_content, list):
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raw_content = "".join(raw_content)
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else:
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continue
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message.content = raw_content
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yield # 通知前端更新渲染
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# 文本分片增量
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case TextPartDelta(
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content_delta=content_delta,
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):
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# 增量更新
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conversation.messages[
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message = conversation.messages[
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index_map_to_message_id[index]
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].content += content_delta
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]
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# 增量更新
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message.content += content_delta
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yield # 通知前端更新渲染
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# ========== 结束事件 ==========
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@ -677,6 +712,12 @@ class ConversationState(rx.State):
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# 设置用户提示词
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self.set_user_prompt(f"帮我查询账户余额")
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# 研究
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case WorkflowType.RESEARCH:
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# 初始化研究工作流
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workflow = Workflow(type=type)
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# 其他
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case _:
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# 初始化对话工作流
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workflow = Workflow()
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@ -61,6 +61,7 @@ class WorkflowType(StrEnum):
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TALK = "对话"
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BOOK_FLIGHT = "预定航班"
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BANK_CUSTOMER_SERVICE = "银行客户服务"
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RESEARCH = "研究"
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class Workflow(BaseModel):
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@ -19,8 +19,8 @@ from pydantic_ai.messages import AgentStreamEvent
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from pydantic_ai.run import AgentRunResultEvent
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from application.workshop.models import (
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DEEPSEEK_V4_FLASH_MODEL,
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DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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DEEPSEEK_V4_FLASH_CHAT_MODEL,
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DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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)
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@ -75,8 +75,8 @@ async def query_account_balance(ctx: RunContext[Deps]) -> str:
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# 智能体
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agent = Agent[Deps, str](
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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deps_type=Deps,
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output_type=str, # 若输出类型为 str，则智能体输出结果时不进行输出类型校验。若定义输出类型，需考虑处理相应事件
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instructions="你是我方银行的客服智能体，为客户提供客服应答。", # 优先使用 instructions
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@ -28,8 +28,8 @@ from pydantic_ai.run import AgentRunResultEvent, AgentRunResult
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from application.workshop.events import AgentRuntimeEvent
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from application.workshop.models import (
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DEEPSEEK_V4_FLASH_MODEL,
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DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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DEEPSEEK_V4_FLASH_CHAT_MODEL,
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DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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)
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@ -73,8 +73,8 @@ class Deps(BaseModel):
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# 主智能体
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agent = Agent[Deps, Flight | DeferredToolRequests](
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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deps_type=Deps,
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output_type=Flight | DeferredToolRequests,
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system_prompt=(
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@ -87,8 +87,8 @@ agent = Agent[Deps, Flight | DeferredToolRequests](
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# 提取所有航班信息智能体
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extraction_flights_agent = Agent[Deps, list[Flight]](
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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deps_type=Deps,
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output_type=list[Flight],
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system_prompt=(
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@ -101,8 +101,8 @@ extraction_flights_agent = Agent[Deps, list[Flight]](
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# 提取航班号智能体
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extraction_flight_number_agent = Agent[Deps, str | None](
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
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model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
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model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
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deps_type=Deps,
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output_type=str | None,
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system_prompt=(
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@ -3,10 +3,10 @@
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智能体模型模块
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"""
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from pydantic_ai import ModelSettings
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.models.openai import OpenAIChatModel, OpenAIResponsesModel
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from pydantic_ai.providers.openai import OpenAIProvider
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DEEPSEEK_V4_FLASH_MODEL = OpenAIChatModel(
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DEEPSEEK_V4_FLASH_CHAT_MODEL = OpenAIChatModel(
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model_name="deepseek-v4-flash",
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provider=OpenAIProvider(
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base_url="https://tokenhub.tencentmaas.com/v1",
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@ -14,6 +14,16 @@ DEEPSEEK_V4_FLASH_MODEL = OpenAIChatModel(
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),
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)
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DEEPSEEK_V4_FLASH_MODEL_SETTINGS = ModelSettings(
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DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS = ModelSettings(
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temperature=0, extra_body={"thinking": {"type": "disabled"}} # 温度控制
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) # 不启用思考模式
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DEEPSEEK_V4_FLASH_RESPONSE_MODEL = OpenAIResponsesModel(
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model_name="deepseek/deepseek-flash",
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provider=OpenAIProvider(
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base_url="https://tokenhub.tencentmaas.com/v1",
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api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
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),
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)
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"sk-4a79ec5c0af74680997ef56758604850"
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@ -0,0 +1,128 @@
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# -*- coding: utf-8 -*-
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"""
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研究
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"""
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from typing import AsyncGenerator
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import httpx
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from pydantic import BaseModel, Field
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from pydantic_ai import Agent, ModelMessage, RunContext, RunUsage
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from pydantic_ai.messages import AgentStreamEvent
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from pydantic_ai.run import AgentRunResultEvent
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from pydantic_ai.toolsets import FunctionToolset
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from application.workshop.models import DEEPSEEK_V4_FLASH_RESPONSE_MODEL
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class Args(BaseModel):
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query: str = Field(description="查询语句")
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# 实例化工具集
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bocha_web_search = FunctionToolset()
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@bocha_web_search.tool_plain() # 普通工具：不使用运行上下文
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async def web_search(args: Args) -> str:
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"""
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联网搜索工具：
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用于查询互联网实时信息、新闻、政策、行业报告、企业公告、最新数据。
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适合查询模型静态知识库不存在的外部网页信息。
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回答时必须标注引用来源URL，禁止编造链接。
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不要用于简单常识、数学计算、代码编写等无需联网的任务。
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"""
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try:
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async with httpx.AsyncClient(timeout=60) as client:
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response = await client.post(
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"https://api.bocha.cn/v1/web-search",
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headers={
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"Authorization": f"Bearer sk-a9ed73d8b26745799cf970f84d70f512",
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"Content-Type": "application/json",
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},
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json={
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"query": args.query,
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"summary": True, # 显示文本摘要
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},
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)
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response.raise_for_status()
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# 解析响应载荷
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response_payload = response.json()
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except httpx.HTTPStatusError as exception:
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return f"请求错误 {exception.response.status_code}: {exception.response.text}"
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except Exception as exception:
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return f"请求异常: {str(exception)}"
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if (code := response_payload.get("code")) != 200:
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return f"业务错误 {code}: {response_payload.get("msg", "未知业务错误")}"
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web_pages = response_payload.get("data", {}).get("webPages", {}).get("value", [])
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if not web_pages:
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return "未搜索到相关网页"
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result = [] # 搜索结果
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for idx, web_page in enumerate(web_pages, 1):
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# 解析网页的标题
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name = web_page.get("name", "")
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# 解析网页的URL
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url = web_page.get("url", "")
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# 解析网页的网站名称
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site_name = web_page.get("siteName", "")
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# 解析网页的发布时间
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date_published = web_page.get("datePublished") or web_page.get(
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"dateLastCrawled", ""
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)
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# 优先解析网页内容的文本摘要，若文本摘要为空则解析简短描述
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summary = web_page.get("summary") or web_page.get("snippet", "")
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result.append(
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f"### 搜索结果 {idx}\n"
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f"- 标题：{name}\n"
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f"- URL：{url}\n"
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f"- 来源网站：{site_name}\n"
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f"- 发布时间：{date_published}\n"
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f"- 摘要：{summary}"
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)
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return "\n\n".join(result)
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instruction = """
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你是研究助手，可调用联网搜索工具。
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1. 事实、时效、外部资料类问题必须搜索；静态常识可直接回答，支持多次检索、改写query。
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2. 优先采用一手资料、官方权威信源，慎用自媒体与转载内容。
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3. 所有事实描述必须标注来源链接，引用格式：`[参考{序号}] {URL}`，序号匹配搜索结果编号。
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4. 严格区分：【事实】来自网页原文；【推论】基于事实推导；资料不足时标注【推测】，不可当作事实。
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5. 严禁编造链接和事实；证据不足直接说明，不要强行下结论。
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6. 组织回答：先陈列证据，再总结结论，结论不能超出资料支撑范围。
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"""
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# 智能体
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agent = Agent(
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model=DEEPSEEK_V4_FLASH_RESPONSE_MODEL,
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output_type=str, # 若输出类型为 str，则智能体输出结果时不进行输出类型校验。若定义输出类型，需考虑处理相应事件
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instructions=instruction,
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toolsets=[bocha_web_search],
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)
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async def run_stream_events(
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user_prompt: str | None,
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message_history: list[ModelMessage],
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usage: RunUsage,
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) -> AsyncGenerator[AgentStreamEvent | AgentRunResultEvent, None]:
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"""
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运行并流式输出事件
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:param user_prompt: 用户提示词
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:param message_history: 消息历史
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:param usage: 使用量
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:return: AsyncGenerator
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"""
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async with agent.run_stream_events(
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user_prompt=user_prompt,
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message_history=message_history,
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usage=usage,
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) as events:
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async for event in events:
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print(event)
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yield event
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@ -8,7 +8,7 @@ from pydantic_ai import Agent, ModelMessage, RunUsage
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from pydantic_ai.messages import AgentStreamEvent
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from pydantic_ai.run import AgentRunResultEvent
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from application.workshop.models import DEEPSEEK_V4_FLASH_MODEL
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from application.workshop.models import DEEPSEEK_V4_FLASH_CHAT_MODEL
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instruction = """
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@ -28,7 +28,7 @@ instruction = """
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"""
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agent = Agent(
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model=DEEPSEEK_V4_FLASH_MODEL,
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model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
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instructions=instruction,
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)
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