完善模型测试与参考文件历史能力

backend/config.py

@@ -33,7 +33,7 @@ class Settings(BaseSettings):
 
     # 文件上传限制
     MAX_UPLOAD_SIZE: int = 50 * 1024 * 1024  # 50MB
-    ALLOWED_EXTENSIONS: list = [".docx", ".xlsx", ".xls", ".csv", ".pdf", ".txt", ".md"]
+    ALLOWED_EXTENSIONS: list = [".docx", ".doc", ".xlsx", ".xls", ".xlsm", ".csv", ".pdf", ".txt", ".md", ".json"]
 
     # 加密（用于 API Key 加密）
     ENCRYPTION_KEY: str = "change-this-to-a-32-byte-key-in-production!!"

backend/database.py

@@ -1,4 +1,5 @@
 from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession
+from sqlalchemy import inspect, text
 from sqlalchemy.orm import DeclarativeBase
 from config import settings
 
@@ -24,5 +25,18 @@ async def init_db():
     from models.ai_model import AiModel
     from models.document import Document
     from models.generation_log import GenerationLog
+    from models.reference_file import ReferenceFile
     async with engine.begin() as conn:
         await conn.run_sync(Base.metadata.create_all)
+        dialect_name = conn.dialect.name
+        columns = await conn.run_sync(lambda sync_conn: [column["name"] for column in inspect(sync_conn).get_columns("ai_models")])
+        if "supports_streaming" not in columns:
+            if dialect_name == "sqlite":
+                await conn.execute(text("ALTER TABLE ai_models ADD COLUMN supports_streaming BOOLEAN DEFAULT 0"))
+            else:
+                await conn.execute(text("ALTER TABLE ai_models ADD COLUMN supports_streaming TINYINT(1) DEFAULT 0"))
+        if "enable_reasoning" not in columns:
+            if dialect_name == "sqlite":
+                await conn.execute(text("ALTER TABLE ai_models ADD COLUMN enable_reasoning BOOLEAN DEFAULT 0"))
+            else:
+                await conn.execute(text("ALTER TABLE ai_models ADD COLUMN enable_reasoning TINYINT(1) DEFAULT 0"))

backend/models/ai_model.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Integer, String, Text, DateTime, func
+from sqlalchemy import Boolean, Column, Integer, String, Text, DateTime, func
 from database import Base
 
 class AiModel(Base):
@@ -9,6 +9,8 @@ class AiModel(Base):
     api_format = Column(String(20), default="openai", comment="anthropic/openai")
     api_endpoint = Column(String(500), default="", comment="API接口地址")
     api_key_encrypted = Column(Text, default="", comment="加密后的API Key")
+    supports_streaming = Column(Boolean, default=False, comment="是否支持流式传输")
+    enable_reasoning = Column(Boolean, default=False, comment="是否开启思考模式")
     status = Column(String(20), default="enabled", comment="enabled/disabled")
     created_at = Column(DateTime, server_default=func.now())
     updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())

backend/models/reference_file.py

@@ -0,0 +1,17 @@
+from datetime import datetime
+
+from sqlalchemy import DateTime, Integer, String, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from database import Base
+
+
+class ReferenceFile(Base):
+    __tablename__ = "reference_files"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    file_name: Mapped[str] = mapped_column(String(255), default="", comment="原始文件名")
+    file_path: Mapped[str] = mapped_column(String(500), default="", comment="MinIO 对象路径")
+    file_size: Mapped[int] = mapped_column(Integer, default=0, comment="文件大小")
+    content_type: Mapped[str] = mapped_column(String(120), default="", comment="文件类型")
+    created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())

backend/routers/generate.py

@@ -15,9 +15,10 @@ from models.ai_model import AiModel
 from models.document import Document
 from models.generation_log import GenerationLog
 from models.paragraph import Paragraph
+from models.reference_file import ReferenceFile
 from models.template import Template
 from schemas.schemas import GenerateFullRequest, GenerateTestRequest, Response
-from services.ai_service import call_ai
+from services.ai_service import call_ai, stream_ai_preview
 from services.file_summary import summarize_minio_files
 from services.generation_runtime import (
     build_mock_content,
@@ -45,6 +46,17 @@ def _serialize_document(document: Document) -> dict:
         "updated_at": document.updated_at,
     }
 
+
+def _serialize_reference_file(file: ReferenceFile) -> dict:
+    return {
+        "id": file.id,
+        "file_name": file.file_name,
+        "file_path": file.file_path,
+        "file_size": file.file_size,
+        "content_type": file.content_type,
+        "created_at": file.created_at,
+    }
+
 @router.post("/test")
 async def generate_test(body: GenerateTestRequest, db: AsyncSession = Depends(get_db)):
     paragraph = await db.get(Paragraph, body.paragraph_id)
@@ -64,6 +76,7 @@ async def generate_test(body: GenerateTestRequest, db: AsyncSession = Depends(ge
         content = build_mock_content(paragraph)
         message = "当前未找到可用模型，返回本地模拟生成结果。"
     else:
+        setattr(paragraph, "enable_reasoning", bool(model.enable_reasoning))
         result = await call_ai(paragraph, model, file_summaries)
         content = result.content
         message = f"已通过模型 {result.used_model} 生成。"
@@ -77,14 +90,101 @@ async def generate_test(body: GenerateTestRequest, db: AsyncSession = Depends(ge
     )
 
 
+@router.post("/test-stream")
+async def generate_test_stream(body: GenerateTestRequest, db: AsyncSession = Depends(get_db)):
+    paragraph = await db.get(Paragraph, body.paragraph_id)
+    if paragraph is None or paragraph.template_id != body.template_id:
+        raise HTTPException(status_code=404, detail="段落不存在")
+    if body.prompt_text:
+        paragraph.prompt_text = body.prompt_text
+
+    model = None
+    if body.model_id:
+        model = await db.get(AiModel, body.model_id)
+    elif paragraph.model_id:
+        model = await db.get(AiModel, paragraph.model_id)
+
+    if model is None or model.status != "enabled":
+        raise HTTPException(status_code=400, detail="当前段落未配置可用的流式模型")
+    if not model.supports_streaming:
+        raise HTTPException(status_code=400, detail="当前模型未开启流式传输")
+
+    file_summaries = await asyncio.to_thread(summarize_minio_files, body.file_paths or []) if body.file_paths else []
+    setattr(paragraph, "enable_reasoning", bool(model.enable_reasoning))
+
+    async def event_stream():
+        yield {
+            "event": "message",
+            "data": json.dumps(
+                {
+                    "type": "meta",
+                    "message": f"正在通过模型 {model.name} 流式生成...",
+                    "file_summaries": file_summaries,
+                },
+                ensure_ascii=False,
+            ),
+        }
+        try:
+            async for chunk in stream_ai_preview(paragraph, model, file_summaries):
+                yield {
+                    "event": "message",
+                    "data": json.dumps({"type": "delta", "content": chunk}, ensure_ascii=False),
+                }
+            yield {
+                "event": "message",
+                "data": json.dumps({"type": "done"}, ensure_ascii=False),
+            }
+        except Exception as error:
+            fallback_message = str(error)
+            if "503" in fallback_message or "temporarily unavailable" in fallback_message.lower():
+                try:
+                    result = await call_ai(paragraph, model, file_summaries)
+                    yield {
+                        "event": "message",
+                        "data": json.dumps(
+                            {
+                                "type": "meta",
+                                "message": "流式通道暂时不可用，已自动回退为普通返回。",
+                                "file_summaries": file_summaries,
+                            },
+                            ensure_ascii=False,
+                        ),
+                    }
+                    yield {
+                        "event": "message",
+                        "data": json.dumps({"type": "delta", "content": result.raw_text}, ensure_ascii=False),
+                    }
+                    yield {
+                        "event": "message",
+                        "data": json.dumps({"type": "done"}, ensure_ascii=False),
+                    }
+                    return
+                except Exception as fallback_error:
+                    fallback_message = f"{fallback_message}；普通调用回退也失败：{fallback_error}"
+            yield {
+                "event": "message",
+                "data": json.dumps({"type": "error", "message": fallback_message}, ensure_ascii=False),
+            }
+
+    return EventSourceResponse(
+        event_stream(),
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
 @router.post("/upload")
-async def upload_reference_file(file: UploadFile = File(...)):
+async def upload_reference_file(file: UploadFile = File(...), db: AsyncSession = Depends(get_db)):
     if not file.filename:
         raise HTTPException(status_code=400, detail="文件名不能为空")
 
     ext = os.path.splitext(file.filename)[1].lower()
     if ext not in settings.ALLOWED_EXTENSIONS:
-        raise HTTPException(status_code=400, detail="文件类型不支持")
+        allowed = " / ".join(settings.ALLOWED_EXTENSIONS)
+        raise HTTPException(status_code=400, detail=f"文件类型不支持：{ext or '无扩展名'}。当前支持：{allowed}")
 
     content = await file.read()
     if not content:
@@ -100,12 +200,40 @@ async def upload_reference_file(file: UploadFile = File(...)):
         content,
         file.content_type or "application/octet-stream",
     )
-    return Response(
-        data={
-            "file_name": file.filename,
-            "file_path": f"{settings.MINIO_BUCKET_UPLOADS}/{object_name}",
-        }
+    record = ReferenceFile(
+        file_name=file.filename,
+        file_path=f"{settings.MINIO_BUCKET_UPLOADS}/{object_name}",
+        file_size=len(content),
+        content_type=file.content_type or "application/octet-stream",
     )
+    db.add(record)
+    await db.commit()
+    await db.refresh(record)
+    return Response(
+        data=_serialize_reference_file(record)
+    )
+
+
+@router.get("/reference-files")
+async def list_reference_files(
+    page: int = Query(1, ge=1),
+    page_size: int = Query(20, ge=1, le=100),
+    keyword: str = Query("", description="按文件名搜索"),
+    db: AsyncSession = Depends(get_db),
+):
+    stmt = select(ReferenceFile)
+    count_stmt = select(func.count(ReferenceFile.id))
+    if keyword:
+        like_keyword = f"%{keyword.strip()}%"
+        stmt = stmt.where(ReferenceFile.file_name.like(like_keyword))
+        count_stmt = count_stmt.where(ReferenceFile.file_name.like(like_keyword))
+
+    total = (await db.execute(count_stmt)).scalar_one()
+    result = await db.execute(
+        stmt.order_by(ReferenceFile.id.desc()).offset((page - 1) * page_size).limit(page_size)
+    )
+    items = [_serialize_reference_file(item) for item in result.scalars().all()]
+    return Response(data={"items": items, "total": total, "page": page, "page_size": page_size})
 
 
 @router.post("/full")

backend/routers/models.py

@@ -20,6 +20,8 @@ def _serialize_model(model: AiModel) -> dict:
         "api_format": model.api_format,
         "api_endpoint": model.api_endpoint,
         "api_key_preview": mask_secret(api_key),
+        "supports_streaming": bool(model.supports_streaming),
+        "enable_reasoning": bool(model.enable_reasoning),
         "status": model.status,
         "created_at": model.created_at,
     }
@@ -40,6 +42,8 @@ async def create_model(body: AiModelCreate, db: AsyncSession = Depends(get_db)):
         api_format=body.api_format,
         api_endpoint=body.api_endpoint,
         api_key_encrypted=encrypt_text(body.api_key),
+        supports_streaming=body.supports_streaming,
+        enable_reasoning=body.enable_reasoning,
         status=body.status,
     )
     db.add(model)
@@ -62,6 +66,10 @@ async def update_model(model_id: int, body: AiModelUpdate, db: AsyncSession = De
         model.api_format = body.api_format
     if body.api_endpoint is not None:
         model.api_endpoint = body.api_endpoint
+    if body.supports_streaming is not None:
+        model.supports_streaming = body.supports_streaming
+    if body.enable_reasoning is not None:
+        model.enable_reasoning = body.enable_reasoning
     if body.status is not None:
         model.status = body.status
     if body.api_key:
@@ -95,6 +103,7 @@ async def test_model(model_id: int, db: AsyncSession = Depends(get_db)):
         need_prompt = False
         prompt_text = ""
         output_format = "text"
+        enable_reasoning = bool(model.enable_reasoning)
 
     try:
         result = await call_ai(FakeParagraph(), model)

backend/schemas/schemas.py

@@ -50,6 +50,8 @@ class AiModelCreate(BaseModel):
     api_format: str = "openai"
     api_endpoint: str = ""
     api_key: str = ""
+    supports_streaming: bool = False
+    enable_reasoning: bool = False
     status: str = "enabled"
 
 
@@ -59,6 +61,8 @@ class AiModelUpdate(BaseModel):
     api_format: Optional[str] = None
     api_endpoint: Optional[str] = None
     api_key: str = ""
+    supports_streaming: Optional[bool] = None
+    enable_reasoning: Optional[bool] = None
     status: Optional[str] = None
 
 class AiModelOut(BaseModel):
@@ -68,6 +72,8 @@ class AiModelOut(BaseModel):
     api_format: str = "openai"
     api_endpoint: str = ""
     api_key_preview: str = ""
+    supports_streaming: bool = False
+    enable_reasoning: bool = False
     status: str = "enabled"
     created_at: Optional[datetime] = None
 

backend/services/ai_service.py

@@ -43,12 +43,23 @@ def _ensure_json_content(text: str) -> dict:
     return {"content": [{"type": "text", "text": stripped}]}
 
 
+def _format_file_context(file_summaries: list[dict]) -> str:
+    file_blocks: list[str] = []
+    for item in file_summaries:
+        file_name = item.get("file_name") or "未命名文件"
+        summary = item.get("summary") or "文件内容为空。"
+        file_blocks.append(f"文件：{file_name}\n内容：\n{summary}")
+    return "\n\n".join(file_blocks)
+
+
 def _build_prompt(paragraph: Paragraph, file_summaries: list[dict] | None = None) -> tuple[str, str]:
     system_prompt = (
         "你是一个企业文档撰写助手。"
         "请严格输出 JSON，不要输出 JSON 之外的说明。"
         '格式为：{"content":[{"type":"text","text":"..."},{"type":"table","title":"...","headers":["..."],"rows":[["..."]]}]}。'
     )
+    if getattr(paragraph, "enable_reasoning", False):
+        system_prompt += "你可以先进行充分思考，再给出最终答案，但最终只输出要求的结果内容。"
 
     user_parts = [f"段落标题：{paragraph.title}"]
     if paragraph.content:
@@ -56,10 +67,7 @@ def _build_prompt(paragraph: Paragraph, file_summaries: list[dict] | None = None
     if paragraph.need_prompt and paragraph.prompt_text:
         user_parts.append(f"附加要求：{paragraph.prompt_text}")
     if file_summaries:
-        file_blocks = []
-        for item in file_summaries:
-            file_blocks.append(f"文件名：{item['file_name']}\n文件摘要：{item['summary']}")
-        user_parts.append("参考文件内容：\n" + "\n\n".join(file_blocks))
+        user_parts.append("参考文件内容：\n" + _format_file_context(file_summaries))
     user_parts.append(f"输出格式：{paragraph.output_format}")
     return system_prompt, "\n\n".join(user_parts)
 
@@ -136,6 +144,65 @@ async def _call_openai_compatible(model: AiModel, system_prompt: str, user_promp
     return AiCallResult(content=_ensure_json_content(text), raw_text=text, used_model=model.name)
 
 
+async def _stream_openai_compatible(model: AiModel, system_prompt: str, user_prompt: str):
+    api_key = decrypt_text(model.api_key_encrypted)
+    if not api_key:
+        raise RuntimeError("模型 API Key 不可用")
+
+    payload = {
+        "model": model.name,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        "temperature": 0.3,
+        "stream": True,
+    }
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    last_error: Exception | None = None
+    for attempt in range(settings.AI_MAX_RETRIES):
+        try:
+            async with httpx.AsyncClient(timeout=settings.AI_REQUEST_TIMEOUT, trust_env=False) as client:
+                async with client.stream("POST", _normalize_openai_endpoint(model.api_endpoint), headers=headers, json=payload) as response:
+                    if response.status_code in (429, 500, 502, 503, 504):
+                        body = await response.aread()
+                        raise httpx.HTTPStatusError(
+                            f"上游模型流式响应异常: {response.status_code} - {body.decode('utf-8', errors='ignore')[:500]}",
+                            request=response.request,
+                            response=response,
+                        )
+                    response.raise_for_status()
+                    async for line in response.aiter_lines():
+                        if not line or not line.startswith("data:"):
+                            continue
+                        payload_line = line[5:].strip()
+                        if payload_line == "[DONE]":
+                            break
+                        try:
+                            chunk = json.loads(payload_line)
+                        except json.JSONDecodeError:
+                            continue
+                        delta_payload = chunk.get("choices", [{}])[0].get("delta", {})
+                        delta = delta_payload.get("content", "")
+                        reasoning = delta_payload.get("reasoning_content", "")
+                        if isinstance(delta, list):
+                            delta = "".join(
+                                item.get("text", "") if isinstance(item, dict) else str(item)
+                                for item in delta
+                            )
+                        if delta:
+                            yield delta
+                        if reasoning:
+                            yield reasoning
+                    return
+        except (httpx.TimeoutException, httpx.HTTPStatusError, httpx.NetworkError) as error:
+            last_error = error
+            if attempt == settings.AI_MAX_RETRIES - 1:
+                break
+            await asyncio.sleep(2 ** attempt)
+    raise RuntimeError(f"模型流式调用失败：{last_error}")
+
+
 async def _call_anthropic(model: AiModel, system_prompt: str, user_prompt: str) -> AiCallResult:
     api_key = decrypt_text(model.api_key_encrypted)
     if not api_key:
@@ -162,8 +229,79 @@ async def _call_anthropic(model: AiModel, system_prompt: str, user_prompt: str)
     return AiCallResult(content=_ensure_json_content(text), raw_text=text, used_model=model.name)
 
 
+async def _stream_anthropic(model: AiModel, system_prompt: str, user_prompt: str):
+    api_key = decrypt_text(model.api_key_encrypted)
+    if not api_key:
+        raise RuntimeError("模型 API Key 不可用")
+
+    payload = {
+        "model": model.name,
+        "max_tokens": 2048,
+        "system": system_prompt,
+        "messages": [{"role": "user", "content": user_prompt}],
+        "stream": True,
+    }
+    headers = {
+        "x-api-key": api_key,
+        "anthropic-version": "2023-06-01",
+        "content-type": "application/json",
+    }
+    last_error: Exception | None = None
+    for attempt in range(settings.AI_MAX_RETRIES):
+        try:
+            async with httpx.AsyncClient(timeout=settings.AI_REQUEST_TIMEOUT, trust_env=False) as client:
+                async with client.stream("POST", _normalize_anthropic_endpoint(model.api_endpoint), headers=headers, json=payload) as response:
+                    if response.status_code in (429, 500, 502, 503, 504):
+                        body = await response.aread()
+                        raise httpx.HTTPStatusError(
+                            f"上游模型流式响应异常: {response.status_code} - {body.decode('utf-8', errors='ignore')[:500]}",
+                            request=response.request,
+                            response=response,
+                        )
+                    response.raise_for_status()
+                    async for line in response.aiter_lines():
+                        if not line or not line.startswith("data:"):
+                            continue
+                        payload_line = line[5:].strip()
+                        if payload_line == "[DONE]":
+                            break
+                        try:
+                            chunk = json.loads(payload_line)
+                        except json.JSONDecodeError:
+                            continue
+                        if chunk.get("type") == "content_block_delta":
+                            delta = chunk.get("delta", {}).get("text", "")
+                            if delta:
+                                yield delta
+                    return
+        except (httpx.TimeoutException, httpx.HTTPStatusError, httpx.NetworkError) as error:
+            last_error = error
+            if attempt == settings.AI_MAX_RETRIES - 1:
+                break
+            await asyncio.sleep(2 ** attempt)
+    raise RuntimeError(f"模型流式调用失败：{last_error}")
+
+
 async def call_ai(paragraph: Paragraph, model: AiModel, file_summaries: list[dict] | None = None) -> AiCallResult:
     system_prompt, user_prompt = _build_prompt(paragraph, file_summaries)
     if model.api_format == "anthropic":
         return await _call_anthropic(model, system_prompt, user_prompt)
     return await _call_openai_compatible(model, system_prompt, user_prompt)
+
+
+def build_test_stream_prompt(paragraph: Paragraph, file_summaries: list[dict] | None = None) -> tuple[str, str]:
+    system_prompt = "你是一个企业文档撰写助手。请直接输出适合预览的正文内容或 Markdown 表格，不要输出 JSON。"
+    if getattr(paragraph, "enable_reasoning", False):
+        system_prompt += "你可以先进行充分思考，再持续输出最终可展示的内容。"
+    _, user_prompt = _build_prompt(paragraph, file_summaries or [])
+    return system_prompt, user_prompt
+
+
+async def stream_ai_preview(paragraph: Paragraph, model: AiModel, file_summaries: list[dict] | None = None):
+    system_prompt, user_prompt = build_test_stream_prompt(paragraph, file_summaries)
+    if model.api_format == "anthropic":
+        async for chunk in _stream_anthropic(model, system_prompt, user_prompt):
+            yield chunk
+        return
+    async for chunk in _stream_openai_compatible(model, system_prompt, user_prompt):
+        yield chunk

backend/services/file_summary.py

@@ -63,10 +63,12 @@ def summarize_file_bytes(file_name: str, content: bytes) -> str:
         return _decode_text(content)[:4000]
     if suffix == ".csv":
         return _summarize_csv(content)
-    if suffix in {".xlsx", ".xls"}:
+    if suffix in {".xlsx", ".xls", ".xlsm"}:
         return _summarize_excel(content, suffix)
     if suffix == ".docx":
         return _summarize_docx(content)
+    if suffix == ".doc":
+        return "当前暂不支持直接解析 .doc 旧版 Word 文件正文，建议先另存为 .docx 后再上传。"
     if suffix == ".pdf":
         return _summarize_pdf(content)
     return f"暂不支持解析该文件内容：{file_name}"

backend/services/generation_runtime.py

@@ -117,6 +117,7 @@ async def run_generation(document_id: int, template_id: int):
                         if model is None:
                             content = build_mock_content(paragraph)
                         else:
+                            setattr(paragraph, "enable_reasoning", bool(model.enable_reasoning))
                             result_data = await call_ai(paragraph, model)
                             content = result_data.content
                         status = "success"

docs/tasks/task_detail_2026_07_02.md

@@ -106,3 +106,53 @@
     5. 重写模板编辑页，使其更接近原型的三栏编辑结构，并实现“立即测试”三步弹窗、多文件上传、文件摘要展示和测试结果预览。
     6. 增补 PDF 与老式 Excel 解析依赖，并执行后端语法检查与前端类型检查。
 - **执行结果**: 模板编辑页已更接近原型编辑态，“立即测试”现支持多文件上传、文件内容解析、带提示词调用 AI 模型并返回结果。
+
+## 会话 ID: local-20260702163801
+- [2026-07-02 16:38:01]
+- **执行原因**: 用户要求先提交代码后继续完善，并新增模型“是否支持流式传输”配置及模板测试流式返回能力。
+- **执行过程**:
+    1. 先提交“模板测试弹窗与多文件解析链路”改动，保持工作区清晰。
+    2. 为模型表新增 `supports_streaming` 字段，并在数据库初始化时兼容旧表自动补列。
+    3. 更新模型创建、编辑、列表返回结构，以及模型管理页表单和展示，支持配置是否开启流式传输。
+    4. 扩展 AI 服务，新增 OpenAI / Anthropic 的流式输出能力。
+    5. 为段落测试新增流式接口；当模型开启流式能力时，模板编辑页测试弹窗在处理中阶段实时显示模型返回内容。
+    6. 执行后端语法检查与前端类型检查，确认本轮流式能力改动稳定。
+- **执行结果**: 当前系统已支持在模型配置中开启流式传输，并在模板编辑页对开启流式的模型进行实时测试返回。
+
+## 会话 ID: local-20260702165813
+- [2026-07-02 16:58:13]
+- **执行原因**: 用户希望在模型设置中增加“是否开启思考”能力，并确认是否能接入现有测试与生成流程。
+- **执行过程**:
+    1. 为模型表、初始化 SQL、后端 Schema 和模型管理页补齐 `enable_reasoning` 字段，支持创建、编辑和展示思考模式开关。
+    2. 修正段落测试、流式测试、模型连接测试和整份文档生成链路，将模型上的思考模式显式传递给 AI 提示词构建逻辑。
+    3. 执行后端语法检查与前端类型检查，确认本轮改动稳定可用。
+- **执行结果**: 当前系统已支持在模型配置中开启或关闭思考模式，且会同时作用于模型连接测试、模板编辑页测试和正式生成流程。
+
+## 会话 ID: local-20260702170209
+- [2026-07-02 17:02:09]
+- **执行原因**: 用户希望在提交给 AI 的参考文件内容中显式带上文件名，便于模型理解每份内容对应的来源文件。
+- **执行过程**:
+    1. 调整 AI 提示词中的参考文件拼接格式，将多文件上下文改为“文件：xxx”加“内容：...”的结构化文本。
+    2. 修正流式预览调用，确保测试流式场景也复用同一套系统提示词与文件上下文格式。
+    3. 执行后端语法检查与前端类型检查，确认本轮改动稳定。
+- **执行结果**: 当前无论普通测试还是流式测试，AI 在接收参考文件时都能明确看到每个文件的文件名与对应内容摘要。
+
+## 会话 ID: local-20260702170627
+- [2026-07-02 17:06:27]
+- **执行原因**: 用户希望参考文件支持一次选择多个，并将已上传文件保存为系统历史，后续可直接复用或重新上传。
+- **执行过程**:
+    1. 新增 `reference_files` 数据表与后端模型，在参考文件上传成功后持久化保存文件名、对象路径、大小、类型和创建时间。
+    2. 新增历史文件列表接口，支持按文件名搜索和分页读取已上传的参考文件记录。
+    3. 重写模板编辑页测试弹窗的文件区，保留多文件本地上传，同时新增历史文件库选择区，允许“历史文件 + 新上传文件”混合提交给 AI。
+    4. 执行后端语法检查与前端类型检查，确认本轮改动稳定。
+- **执行结果**: 当前段落测试已支持多文件一起提交，且上传过的参考文件会进入系统历史库，后续可以直接勾选历史文件或重新上传新文件。
+
+## 会话 ID: local-20260702171007
+- [2026-07-02 17:10:07]
+- **执行原因**: 用户反馈上传参考文件时报“文件类型不支持”，需要补齐支持范围并提升报错可读性。
+- **执行过程**:
+    1. 扩展后端参考文件白名单，补充 `.doc`、`.xlsm`、`.json` 等常见文件后缀。
+    2. 调整文件摘要服务，支持解析 `.xlsm`，并对 `.doc` 返回明确的转换建议说明。
+    3. 优化上传接口错误提示，返回实际不支持的扩展名和当前支持列表。
+    4. 同步更新模板测试弹窗中的上传提示文案，并执行后端语法检查与前端类型检查。
+- **执行结果**: 当前参考文件上传支持范围更完整，遇到不支持的文件类型时也会直接显示具体后缀和支持列表，便于快速判断问题。

init.sql

@@ -19,6 +19,8 @@ CREATE TABLE IF NOT EXISTS ai_models (
     api_format VARCHAR(20) DEFAULT 'openai' COMMENT 'anthropic/openai',
     api_endpoint VARCHAR(500) DEFAULT '' COMMENT 'API 地址',
     api_key_encrypted TEXT DEFAULT '' COMMENT '加密后的 API Key',
+    supports_streaming TINYINT(1) DEFAULT 0 COMMENT '是否支持流式传输',
+    enable_reasoning TINYINT(1) DEFAULT 0 COMMENT '是否开启思考模式',
     status VARCHAR(20) DEFAULT 'enabled' COMMENT 'enabled/disabled',
     created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
     updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
@@ -74,3 +76,12 @@ CREATE TABLE IF NOT EXISTS generation_logs (
     FOREIGN KEY (paragraph_id) REFERENCES paragraphs(id) ON DELETE CASCADE,
     FOREIGN KEY (model_id) REFERENCES ai_models(id) ON DELETE SET NULL
 ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+
+CREATE TABLE IF NOT EXISTS reference_files (
+    id INT AUTO_INCREMENT PRIMARY KEY,
+    file_name VARCHAR(255) DEFAULT '' COMMENT '原始文件名',
+    file_path VARCHAR(500) DEFAULT '' COMMENT 'MinIO 对象路径',
+    file_size INT DEFAULT 0 COMMENT '文件大小',
+    content_type VARCHAR(120) DEFAULT '' COMMENT '文件类型',
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

web/src/api/generate.ts

@@ -2,7 +2,9 @@ import http from './index'
 
 export const generateApi = {
   test: (data: any) => http.post('/generate/test', data),
+  testStream: () => '/api/v1/generate/test-stream',
   upload: (formData: FormData) => http.post('/generate/upload', formData, { headers: { 'Content-Type': 'multipart/form-data' } }),
+  referenceFiles: (params?: any) => http.get('/generate/reference-files', { params }),
   full: (data: any) => http.post('/generate/full', data),
   progress: (id: number) => `/api/v1/generate/progress/${id}`,
   cancel: (id: number) => http.post(`/generate/cancel/${id}`),

web/src/types/index.ts

@@ -13,7 +13,7 @@ export interface Paragraph {
 
 export interface AiModel {
   id: number; name: string; provider: string; api_format: 'anthropic' | 'openai'
-  api_endpoint: string; api_key_preview: string; status: 'enabled' | 'disabled'
+  api_endpoint: string; api_key_preview: string; supports_streaming: boolean; enable_reasoning: boolean; status: 'enabled' | 'disabled'
 }
 
 export interface Document {
@@ -23,5 +23,9 @@ export interface Document {
   file_path: string; error: string
 }
 
+export interface ReferenceFile {
+  id: number; file_name: string; file_path: string; file_size: number; content_type: string; created_at: string
+}
+
 export interface ApiResponse<T = any> { code: number; data: T; message: string }
 export interface PageData<T = any> { items: T[]; total: number; page: number; page_size: number }

web/src/views/ModelManage.vue

@@ -18,6 +18,8 @@
             <div class="mc-name">{{ item.name }}</div>
             <div class="mc-provider">{{ item.provider }} · {{ item.api_endpoint }}</div>
             <div class="mc-provider">密钥：{{ item.api_key_preview || '未设置' }}</div>
+            <div class="mc-provider">流式传输：{{ item.supports_streaming ? '支持' : '关闭' }}</div>
+            <div class="mc-provider">思考模式：{{ item.enable_reasoning ? '开启' : '关闭' }}</div>
           </div>
           <div :class="['mc-status', item.status === 'enabled' ? 'on' : 'off']">
             {{ item.status === 'enabled' ? '已启用' : '已禁用' }}
@@ -48,6 +50,12 @@
         <a-form-item label="API 地址">
           <a-input v-model:value="form.api_endpoint" />
         </a-form-item>
+        <a-form-item label="支持流式传输">
+          <a-switch v-model:checked="form.supports_streaming" />
+        </a-form-item>
+        <a-form-item label="开启思考模式">
+          <a-switch v-model:checked="form.enable_reasoning" />
+        </a-form-item>
         <a-form-item label="API Key">
           <a-input-password v-model:value="form.api_key" />
         </a-form-item>
@@ -66,7 +74,7 @@ const models = ref<any[]>([])
 const modalOpen = ref(false)
 const isEdit = ref(false)
 const editId = ref(0)
-const form = ref({ name: '', provider: '', api_format: 'openai', api_endpoint: '', api_key: '' })
+const form = ref({ name: '', provider: '', api_format: 'openai', api_endpoint: '', api_key: '', supports_streaming: false, enable_reasoning: false })
 
 async function refreshList() {
   await store.fetchList()
@@ -79,7 +87,7 @@ onMounted(async () => {
 
 function openAdd() {
   isEdit.value = false
-  form.value = { name: '', provider: '', api_format: 'openai', api_endpoint: '', api_key: '' }
+  form.value = { name: '', provider: '', api_format: 'openai', api_endpoint: '', api_key: '', supports_streaming: false, enable_reasoning: false }
   modalOpen.value = true
 }
 
@@ -92,6 +100,8 @@ function openEdit(item: any) {
     api_format: item.api_format,
     api_endpoint: item.api_endpoint,
     api_key: '',
+    supports_streaming: !!item.supports_streaming,
+    enable_reasoning: !!item.enable_reasoning,
   }
   modalOpen.value = true
 }

web/src/views/TemplateEditor.vue

@@ -140,7 +140,7 @@
           <a-upload-dragger :multiple="true" :beforeUpload="beforeTestUpload" :showUploadList="false">
             <p class="ant-upload-drag-icon"><upload-outlined /></p>
             <p class="ant-upload-text">点击或拖拽上传参考文件</p>
-            <p class="ant-upload-hint">支持多文件：docx / xlsx / xls / csv / pdf / txt / md</p>
+            <p class="ant-upload-hint">支持多文件：docx / doc / xlsx / xls / xlsm / csv / pdf / txt / md / json</p>
           </a-upload-dragger>
         </div>
 
@@ -151,12 +151,62 @@
           </div>
         </div>
 
-        <a-button type="primary" block :loading="testing" @click="startTest">开始测试</a-button>
+        <div class="history-card">
+          <div class="history-head">
+            <div>
+              <div class="history-title">历史文件</div>
+              <div class="history-desc">已上传过的文件会保存在系统里，下次可直接选择复用。</div>
+            </div>
+            <a-button size="small" @click="fetchReferenceHistory">刷新</a-button>
+          </div>
+
+          <div class="history-search">
+            <a-input-search
+              v-model:value="historyKeyword"
+              placeholder="按文件名搜索历史文件"
+              allow-clear
+              @search="fetchReferenceHistory"
+            />
+          </div>
+
+          <a-spin :spinning="historyLoading">
+            <div v-if="referenceHistory.length" class="history-list">
+              <label v-for="item in referenceHistory" :key="item.id" class="history-item">
+                <input
+                  type="checkbox"
+                  :checked="selectedHistoryPaths.includes(item.file_path)"
+                  @change="toggleHistoryFile(item.file_path)"
+                />
+                <div class="history-item-main">
+                  <div class="history-item-name">{{ item.file_name }}</div>
+                  <div class="history-item-meta">
+                    <span>{{ formatFileSize(item.file_size) }}</span>
+                    <span>{{ formatDateTime(item.created_at) }}</span>
+                  </div>
+                </div>
+              </label>
+            </div>
+            <a-empty v-else description="暂无历史文件" />
+          </a-spin>
+        </div>
+
+        <div v-if="selectedHistoryItems.length" class="uploaded-list">
+          <div class="uploaded-item" v-for="item in selectedHistoryItems" :key="item.file_path">
+            <span class="uploaded-name">{{ item.file_name }}</span>
+            <a-button type="link" size="small" danger @click="toggleHistoryFile(item.file_path)">取消选择</a-button>
+          </div>
+        </div>
+
+        <a-button type="primary" block :loading="testing" @click="startTest">开始测试（支持多文件）</a-button>
       </div>
 
       <div v-else-if="testStep === 1" class="test-processing">
         <a-spin size="large" />
         <p class="processing-text">{{ testStatusText }}</p>
+        <div v-if="streamedText" class="streaming-card">
+          <div class="streaming-title">模型实时返回内容</div>
+          <pre class="streaming-pre">{{ streamedText }}</pre>
+        </div>
       </div>
 
       <div v-else>
@@ -194,6 +244,7 @@ import {
 import { useTemplateStore } from '@/stores/template'
 import { useModelStore } from '@/stores/model'
 import { generateApi } from '@/api/generate'
+import type { ReferenceFile } from '@/types'
 
 interface LocalUploadFile {
   uid: string
@@ -219,8 +270,16 @@ const testFiles = ref<LocalUploadFile[]>([])
 const testResultHtml = ref('')
 const testResultMessage = ref('')
 const testFileSummaries = ref<any[]>([])
+const streamedText = ref('')
+const referenceHistory = ref<ReferenceFile[]>([])
+const selectedHistoryPaths = ref<string[]>([])
+const historyKeyword = ref('')
+const historyLoading = ref(false)
 
 const selectedPara = computed(() => paragraphs.value.find((item) => item.id === selectedId.value))
+const selectedHistoryItems = computed(() =>
+  referenceHistory.value.filter((item) => selectedHistoryPaths.value.includes(item.file_path))
+)
 
 function selectPara(id: number) {
   selectedId.value = id
@@ -240,6 +299,8 @@ function openTestModal() {
   testResultHtml.value = ''
   testResultMessage.value = ''
   testFileSummaries.value = []
+  selectedHistoryPaths.value = []
+  fetchReferenceHistory()
 }
 
 function resetTestModal() {
@@ -250,6 +311,8 @@ function resetTestModal() {
   testResultHtml.value = ''
   testResultMessage.value = ''
   testFileSummaries.value = []
+  streamedText.value = ''
+  selectedHistoryPaths.value = []
 }
 
 function beforeTestUpload(file: File) {
@@ -265,6 +328,40 @@ function removeTestFile(uid: string) {
   testFiles.value = testFiles.value.filter((item) => item.uid !== uid)
 }
 
+function toggleHistoryFile(filePath: string) {
+  if (selectedHistoryPaths.value.includes(filePath)) {
+    selectedHistoryPaths.value = selectedHistoryPaths.value.filter((item) => item !== filePath)
+    return
+  }
+  selectedHistoryPaths.value = [...selectedHistoryPaths.value, filePath]
+}
+
+function formatFileSize(fileSize: number) {
+  if (fileSize < 1024) return `${fileSize} B`
+  if (fileSize < 1024 * 1024) return `${(fileSize / 1024).toFixed(1)} KB`
+  return `${(fileSize / (1024 * 1024)).toFixed(1)} MB`
+}
+
+function formatDateTime(value: string) {
+  return value ? value.replace('T', ' ').slice(0, 19) : ''
+}
+
+async function fetchReferenceHistory() {
+  historyLoading.value = true
+  try {
+    const response: any = await generateApi.referenceFiles({
+      page: 1,
+      page_size: 30,
+      keyword: historyKeyword.value.trim(),
+    })
+    referenceHistory.value = response.data?.items || []
+  } catch (error: any) {
+    message.error(error.message || '加载历史文件失败')
+  } finally {
+    historyLoading.value = false
+  }
+}
+
 function renderTestResult(content: any) {
   const blocks = content?.content || []
   return blocks
@@ -291,7 +388,7 @@ function renderTestResult(content: any) {
 
 async function startTest() {
   if (!selectedPara.value) return
-  if (selectedPara.value.need_file && !testFiles.value.length) {
+  if (selectedPara.value.need_file && !testFiles.value.length && !selectedHistoryPaths.value.length) {
     message.warning('请先上传至少一个参考文件')
     return
   }
@@ -308,21 +405,34 @@ async function startTest() {
       const uploadRes: any = await generateApi.upload(formData)
       filePaths.push(uploadRes.data.file_path)
     }
+    for (const filePath of selectedHistoryPaths.value) {
+      if (!filePaths.includes(filePath)) {
+        filePaths.push(filePath)
+      }
+    }
+    await fetchReferenceHistory()
 
     testStatusText.value = '正在解析文件内容并请求 AI 模型...'
     const templateId = Number(route.params.id)
-    const response: any = await generateApi.test({
-      paragraph_id: selectedPara.value.id,
-      template_id: templateId,
-      prompt_text: selectedPara.value.prompt_text || '',
-      model_id: selectedPara.value.model_id || 0,
-      file_paths: filePaths,
-    })
+    const currentModel = models.value.find((item) => item.id === selectedPara.value.model_id)
+    if (currentModel?.supports_streaming) {
+      testStatusText.value = '正在流式接收模型返回内容...'
+      streamedText.value = ''
+      await runStreamingTest(templateId, filePaths)
+    } else {
+      const response: any = await generateApi.test({
+        paragraph_id: selectedPara.value.id,
+        template_id: templateId,
+        prompt_text: selectedPara.value.prompt_text || '',
+        model_id: selectedPara.value.model_id || 0,
+        file_paths: filePaths,
+      })
 
-    testResultMessage.value = response.data?.message || '测试完成'
-    testFileSummaries.value = response.data?.file_summaries || []
-    testResultHtml.value = renderTestResult(response.data?.content)
-    testStep.value = 2
+      testResultMessage.value = response.data?.message || '测试完成'
+      testFileSummaries.value = response.data?.file_summaries || []
+      testResultHtml.value = renderTestResult(response.data?.content)
+      testStep.value = 2
+    }
   } catch (error: any) {
     message.error(error.message || '段落测试失败')
     resetTestModal()
@@ -331,6 +441,68 @@ async function startTest() {
   }
 }
 
+async function runStreamingTest(templateId: number, filePaths: string[]) {
+  const response = await fetch(generateApi.testStream(), {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      paragraph_id: selectedPara.value.id,
+      template_id: templateId,
+      prompt_text: selectedPara.value.prompt_text || '',
+      model_id: selectedPara.value.model_id || 0,
+      file_paths: filePaths,
+    }),
+  })
+  if (!response.ok || !response.body) {
+    throw new Error('流式测试启动失败')
+  }
+
+  const reader = response.body.getReader()
+  const decoder = new TextDecoder('utf-8')
+  let buffer = ''
+
+  const processEventBlock = (block: string) => {
+    const normalizedBlock = block.replace(/\r\n/g, '\n').trim()
+    if (!normalizedBlock) return
+    const dataLines = normalizedBlock
+      .split('\n')
+      .filter((line) => line.startsWith('data: '))
+      .map((line) => line.slice(6))
+    if (!dataLines.length) return
+
+    const payload = JSON.parse(dataLines.join('\n'))
+    if (payload.type === 'meta') {
+      testFileSummaries.value = payload.file_summaries || []
+      testResultMessage.value = payload.message || '流式生成中'
+    } else if (payload.type === 'delta') {
+      streamedText.value += payload.content || ''
+    } else if (payload.type === 'error') {
+      throw new Error(payload.message || '流式测试失败')
+    } else if (payload.type === 'done') {
+      testResultHtml.value = `<pre class="streamed-pre">${streamedText.value}</pre>`
+      testStep.value = 2
+    }
+  }
+
+  while (true) {
+    const { done, value } = await reader.read()
+    if (done) break
+    buffer += decoder.decode(value, { stream: true })
+    const segments = buffer.split(/\r?\n\r?\n/)
+    buffer = segments.pop() || ''
+    for (const segment of segments) {
+      processEventBlock(segment)
+    }
+  }
+  if (buffer.trim()) {
+    processEventBlock(buffer)
+  }
+  if (!testResultHtml.value) {
+    testResultHtml.value = `<pre class="streamed-pre">${streamedText.value}</pre>`
+    testStep.value = 2
+  }
+}
+
 onMounted(async () => {
   const id = Number(route.params.id)
   const template = await store.fetchOne(id)
@@ -654,6 +826,75 @@ onMounted(async () => {
   margin-bottom: 16px;
 }
 
+.history-card {
+  margin-bottom: 16px;
+  padding: 16px;
+  border: 1px solid #e5e7eb;
+  border-radius: 12px;
+  background: #fafbfc;
+}
+
+.history-head {
+  display: flex;
+  justify-content: space-between;
+  align-items: flex-start;
+  gap: 12px;
+}
+
+.history-title {
+  font-size: 14px;
+  font-weight: 600;
+  color: #111827;
+}
+
+.history-desc {
+  margin-top: 4px;
+  font-size: 12px;
+  color: #6b7280;
+}
+
+.history-search {
+  margin: 12px 0;
+}
+
+.history-list {
+  display: grid;
+  gap: 8px;
+  max-height: 240px;
+  overflow-y: auto;
+}
+
+.history-item {
+  display: flex;
+  gap: 10px;
+  align-items: flex-start;
+  padding: 10px 12px;
+  border: 1px solid #e5e7eb;
+  border-radius: 10px;
+  background: #fff;
+  cursor: pointer;
+}
+
+.history-item-main {
+  min-width: 0;
+  flex: 1;
+}
+
+.history-item-name {
+  font-size: 13px;
+  font-weight: 500;
+  color: #111827;
+  word-break: break-all;
+}
+
+.history-item-meta {
+  display: flex;
+  gap: 12px;
+  margin-top: 4px;
+  font-size: 12px;
+  color: #6b7280;
+}
+
 .uploaded-item {
   display: flex;
   justify-content: space-between;
@@ -678,6 +919,29 @@ onMounted(async () => {
   color: #5b626e;
 }
 
+.streaming-card {
+  margin-top: 20px;
+  background: #f0f0ff;
+  border-left: 3px solid #5b5bd6;
+  border-radius: 4px;
+  padding: 16px;
+  text-align: left;
+}
+
+.streaming-title {
+  font-size: 12px;
+  font-weight: 600;
+  color: #5b5bd6;
+  margin-bottom: 8px;
+}
+
+.streaming-pre {
+  white-space: pre-wrap;
+  margin: 0;
+  font-family: inherit;
+  line-height: 1.8;
+}
+
 .result-meta {
   display: flex;
   align-items: center;
@@ -739,6 +1003,13 @@ onMounted(async () => {
   border-radius: 4px;
 }
 
+:deep(.streamed-pre) {
+  white-space: pre-wrap;
+  margin: 0;
+  font-family: inherit;
+  line-height: 1.8;
+}
+
 :deep(.result-text) {
   margin: 0 0 12px;
   line-height: 1.8;