1

agent/application/pages/conversation.py

@@ -294,10 +294,12 @@ def use_guidance() -> rx.Component:
                 font_weight="var(--prismui-font-weight-3)",
             ),
             rx.hstack(
-                # 预定航班按钮
+                # 工作流按钮：预定航班
                 workflow_button(WorkflowType.BOOK_FLIGHT),
-                # 银行客服按钮
+                # 工作流按钮：银行客户服务
                 workflow_button(WorkflowType.BANK_CUSTOMER_SERVICE),
+                # 工作流按钮：研究
+                workflow_button(WorkflowType.RESEARCH),
                 flex_wrap="wrap",
                 align_items="center",
                 gap="12px",
@@ -408,7 +410,6 @@ def thinking(item: MessageHistoryItem) -> rx.Component:
                     rx.markdown(
                         item.content,
                         width="100%",
-                        margin="4px 0",
                         line_height="1.6",
                         font_size="var(--prismui-font-size-1)",
                         color="var(--prismui-color-3)",

agent/application/states/conversation.py

@@ -109,10 +109,11 @@ class ConversationState(rx.State):
         # 当前会话
         conversation = self.conversations[self.conversation_id]
         # 最后一个消息
-        message = next(reversed(conversation.messages.values()))
+        message = next(iter(reversed(conversation.messages.values())), None)
         # 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
         if (
-            isinstance(approval := message.approval, Approval)
+            message is not None
+            and isinstance(approval := message.approval, Approval)
             and approval.result is None
         ):
             conversation.is_running = True
@@ -396,8 +397,21 @@ class ConversationState(rx.State):
                     usage=usage,
                 )
 
-        # 消息列表
+            # 研究
-        messages: list[Message] = []
+            case WorkflowType.RESEARCH:
+                from application.workshop.research import (
+                    run_stream_events,
+                )
+
+                # 运行并流式输出事件
+                stream_events = run_stream_events(
+                    user_prompt=user_prompt,
+                    message_history=message_history,
+                    usage=usage,
+                )
+
+        messages: list[Message] = []  # 消息列表
+        previous_provider_details = {}  # 上一个供应商详情
         # 获取运行流式输出事件
         async for event in stream_events:
             # 若等待流式输出则将等待流式输出设置为否
@@ -459,26 +473,47 @@ class ConversationState(rx.State):
 
                 # ========== 增量事件 ==========
                 case PartDeltaEvent(index=index, delta=delta):
+                    # 获取消息
                     match delta:
                         # 思考分片增量
                         case ThinkingPartDelta(
                             content_delta=content_delta,
+                            provider_details=provider_details,
                         ):
-                            # 若内容增量不为空则增量更新
+                            message = conversation.messages[
+                                index_map_to_message_id[index]
+                            ]
+                            # 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
                             if content_delta:
-                                conversation.messages[
+                                message.content += content_delta
-                                    index_map_to_message_id[index]
-                                ].content += content_delta
                                 yield  # 通知前端更新渲染
 
+                            else:
+                                if callable(provider_details):
+                                    # 获取原始内容
+                                    raw_content = (
+                                        previous_provider_details := provider_details(
+                                            previous_provider_details
+                                        )
+                                    ).get("raw_content")
+                                    if isinstance(raw_content, str):
+                                        ...
+                                    elif isinstance(raw_content, list):
+                                        raw_content = "".join(raw_content)
+                                    else:
+                                        continue
+                                    message.content = raw_content
+                                    yield  # 通知前端更新渲染
+
                         # 文本分片增量
                         case TextPartDelta(
                             content_delta=content_delta,
                         ):
-                            # 增量更新
+                            message = conversation.messages[
-                            conversation.messages[
                                 index_map_to_message_id[index]
-                            ].content += content_delta
+                            ]
+                            # 增量更新
+                            message.content += content_delta
                             yield  # 通知前端更新渲染
 
                 # ========== 结束事件 ==========
@@ -677,6 +712,12 @@ class ConversationState(rx.State):
                 # 设置用户提示词
                 self.set_user_prompt(f"帮我查询账户余额")
 
+            # 研究
+            case WorkflowType.RESEARCH:
+                # 初始化研究工作流
+                workflow = Workflow(type=type)
+
+            # 其他
             case _:
                 # 初始化对话工作流
                 workflow = Workflow()

agent/application/states/models.py

@@ -61,6 +61,7 @@ class WorkflowType(StrEnum):
     TALK = "对话"
     BOOK_FLIGHT = "预定航班"
     BANK_CUSTOMER_SERVICE = "银行客户服务"
+    RESEARCH = "研究"
 
 
 class Workflow(BaseModel):

agent/application/workshop/bank_customer_service.py

@@ -19,8 +19,8 @@ from pydantic_ai.messages import AgentStreamEvent
 from pydantic_ai.run import AgentRunResultEvent
 
 from application.workshop.models import (
-    DEEPSEEK_V4_FLASH_MODEL,
+    DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
 )
 
 
@@ -75,8 +75,8 @@ async def query_account_balance(ctx: RunContext[Deps]) -> str:
 
 # 智能体
 agent = Agent[Deps, str](
-    model=DEEPSEEK_V4_FLASH_MODEL,
+    model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
     deps_type=Deps,
     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
     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

agent/application/workshop/book_flight.py

@@ -28,8 +28,8 @@ from pydantic_ai.run import AgentRunResultEvent, AgentRunResult
 
 from application.workshop.events import AgentRuntimeEvent
 from application.workshop.models import (
-    DEEPSEEK_V4_FLASH_MODEL,
+    DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
 )
 
 
@@ -73,8 +73,8 @@ class Deps(BaseModel):
 
 # 主智能体
 agent = Agent[Deps, Flight | DeferredToolRequests](
-    model=DEEPSEEK_V4_FLASH_MODEL,
+    model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
     deps_type=Deps,
     output_type=Flight | DeferredToolRequests,
     system_prompt=(
@@ -87,8 +87,8 @@ agent = Agent[Deps, Flight | DeferredToolRequests](
 
 # 提取所有航班信息智能体
 extraction_flights_agent = Agent[Deps, list[Flight]](
-    model=DEEPSEEK_V4_FLASH_MODEL,
+    model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
     deps_type=Deps,
     output_type=list[Flight],
     system_prompt=(
@@ -101,8 +101,8 @@ extraction_flights_agent = Agent[Deps, list[Flight]](
 
 # 提取航班号智能体
 extraction_flight_number_agent = Agent[Deps, str | None](
-    model=DEEPSEEK_V4_FLASH_MODEL,
+    model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
-    model_settings=DEEPSEEK_V4_FLASH_MODEL_SETTINGS,
+    model_settings=DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS,
     deps_type=Deps,
     output_type=str | None,
     system_prompt=(

agent/application/workshop/models.py

@@ -3,10 +3,10 @@
 智能体模型模块
 """
 from pydantic_ai import ModelSettings
-from pydantic_ai.models.openai import OpenAIChatModel
+from pydantic_ai.models.openai import OpenAIChatModel, OpenAIResponsesModel
 from pydantic_ai.providers.openai import OpenAIProvider
 
-DEEPSEEK_V4_FLASH_MODEL = OpenAIChatModel(
+DEEPSEEK_V4_FLASH_CHAT_MODEL = OpenAIChatModel(
     model_name="deepseek-v4-flash",
     provider=OpenAIProvider(
         base_url="https://tokenhub.tencentmaas.com/v1",
@@ -14,6 +14,16 @@ DEEPSEEK_V4_FLASH_MODEL = OpenAIChatModel(
     ),
 )
 
-DEEPSEEK_V4_FLASH_MODEL_SETTINGS = ModelSettings(
+DEEPSEEK_V4_FLASH_CHAT_MODEL_SETTINGS = ModelSettings(
     temperature=0, extra_body={"thinking": {"type": "disabled"}}  # 温度控制
 )  # 不启用思考模式
+
+DEEPSEEK_V4_FLASH_RESPONSE_MODEL = OpenAIResponsesModel(
+    model_name="deepseek/deepseek-flash",
+    provider=OpenAIProvider(
+        base_url="https://tokenhub.tencentmaas.com/v1",
+        api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
+    ),
+)
+
+"sk-4a79ec5c0af74680997ef56758604850"

agent/application/workshop/research.py

@@ -0,0 +1,128 @@
+# -*- coding: utf-8 -*-
+"""
+研究
+"""
+from typing import AsyncGenerator
+
+import httpx
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent, ModelMessage, RunContext, RunUsage
+from pydantic_ai.messages import AgentStreamEvent
+from pydantic_ai.run import AgentRunResultEvent
+from pydantic_ai.toolsets import FunctionToolset
+
+from application.workshop.models import DEEPSEEK_V4_FLASH_RESPONSE_MODEL
+
+
+class Args(BaseModel):
+    query: str = Field(description="查询语句")
+
+
+# 实例化工具集
+bocha_web_search = FunctionToolset()
+
+
+@bocha_web_search.tool_plain()  # 普通工具：不使用运行上下文
+async def web_search(args: Args) -> str:
+    """
+    联网搜索工具：
+    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。
+    适合查询模型静态知识库不存在的外部网页信息。
+    回答时必须标注引用来源URL，禁止编造链接。
+    不要用于简单常识、数学计算、代码编写等无需联网的任务。
+    """
+    try:
+        async with httpx.AsyncClient(timeout=60) as client:
+            response = await client.post(
+                "https://api.bocha.cn/v1/web-search",
+                headers={
+                    "Authorization": f"Bearer sk-a9ed73d8b26745799cf970f84d70f512",
+                    "Content-Type": "application/json",
+                },
+                json={
+                    "query": args.query,
+                    "summary": True,  # 显示文本摘要
+                },
+            )
+            response.raise_for_status()
+            # 解析响应载荷
+            response_payload = response.json()
+
+    except httpx.HTTPStatusError as exception:
+        return f"请求错误 {exception.response.status_code}: {exception.response.text}"
+
+    except Exception as exception:
+        return f"请求异常: {str(exception)}"
+
+    if (code := response_payload.get("code")) != 200:
+        return f"业务错误 {code}: {response_payload.get("msg", "未知业务错误")}"
+
+    web_pages = response_payload.get("data", {}).get("webPages", {}).get("value", [])
+    if not web_pages:
+        return "未搜索到相关网页"
+
+    result = []  # 搜索结果
+    for idx, web_page in enumerate(web_pages, 1):
+        # 解析网页的标题
+        name = web_page.get("name", "")
+        # 解析网页的URL
+        url = web_page.get("url", "")
+        # 解析网页的网站名称
+        site_name = web_page.get("siteName", "")
+        # 解析网页的发布时间
+        date_published = web_page.get("datePublished") or web_page.get(
+            "dateLastCrawled", ""
+        )
+        # 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
+        summary = web_page.get("summary") or web_page.get("snippet", "")
+
+        result.append(
+            f"### 搜索结果 {idx}\n"
+            f"- 标题：{name}\n"
+            f"- URL：{url}\n"
+            f"- 来源网站：{site_name}\n"
+            f"- 发布时间：{date_published}\n"
+            f"- 摘要：{summary}"
+        )
+    return "\n\n".join(result)
+
+
+instruction = """
+你是研究助手，可调用联网搜索工具。
+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。
+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。
+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。
+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。
+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。
+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。
+"""
+
+# 智能体
+agent = Agent(
+    model=DEEPSEEK_V4_FLASH_RESPONSE_MODEL,
+    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
+    instructions=instruction,
+    toolsets=[bocha_web_search],
+)
+
+
+async def run_stream_events(
+    user_prompt: str | None,
+    message_history: list[ModelMessage],
+    usage: RunUsage,
+) -> AsyncGenerator[AgentStreamEvent | AgentRunResultEvent, None]:
+    """
+    运行并流式输出事件
+    :param user_prompt: 用户提示词
+    :param message_history: 消息历史
+    :param usage: 使用量
+    :return: AsyncGenerator
+    """
+    async with agent.run_stream_events(
+        user_prompt=user_prompt,
+        message_history=message_history,
+        usage=usage,
+    ) as events:
+        async for event in events:
+            print(event)
+            yield event

agent/application/workshop/talk.py

@@ -8,7 +8,7 @@ from pydantic_ai import Agent, ModelMessage, RunUsage
 from pydantic_ai.messages import AgentStreamEvent
 from pydantic_ai.run import AgentRunResultEvent
 
-from application.workshop.models import DEEPSEEK_V4_FLASH_MODEL
+from application.workshop.models import DEEPSEEK_V4_FLASH_CHAT_MODEL
 
 
 instruction = """
@@ -28,7 +28,7 @@ instruction = """
 """
 
 agent = Agent(
-    model=DEEPSEEK_V4_FLASH_MODEL,
+    model=DEEPSEEK_V4_FLASH_CHAT_MODEL,
     instructions=instruction,
 )