feat: enhance metadata handling and document processing

- Add metadata field to Document type for frontend
- Add type field to MetadataFieldUpdateRequest
- Update KB API with URL decode support for Chinese filenames
- Enhance metadata auto inference service
- Improve metadata field definition service
- Update .gitignore to exclude logs and snapshots

.gitignore

@@ -163,7 +163,10 @@ cython_debug/
 # Project specific
 ai-service/uploads/
 ai-service/config/
+ai-service/logs/
 *.local
+qdrant_snapshots/
+*.snapshot
 
 /.trae/
 /.claude/

ai-service-admin/src/types/knowledge-base.ts

@@ -34,6 +34,7 @@ export interface Document {
   kbId: string
   fileName: string
   status: string
+  metadata?: Record<string, any>
   jobId?: string
   createdAt: string
   updatedAt: string

ai-service-admin/src/types/metadata.ts

@@ -39,6 +39,7 @@ export interface MetadataFieldCreateRequest {
 
 export interface MetadataFieldUpdateRequest {
   label?: string
+  type?: MetadataFieldType
   required?: boolean
   options?: string[]
   default?: string | number | boolean

ai-service-admin/src/views/admin/knowledge-base/components/DocumentList.vue

@@ -16,7 +16,7 @@
       <input
         ref="fileInputRef"
         type="file"
-        accept=".txt,.md,.pdf,.doc,.docx,.xls,.xlsx"
+        accept=".txt,.md,.markdown,.pdf,.doc,.docx,.xls,.xlsx,.jpg,.jpeg,.png,.gif,.webp,.bmp,.tiff,.tif"
         style="display: none"
         @change="handleFileSelect"
       />
@@ -298,7 +298,10 @@ const handleFileSelect = (event: Event) => {
   const file = target.files?.[0]
   if (!file) return
 
-  const allowedExtensions = ['.txt', '.md', '.pdf', '.doc', '.docx', '.xls', '.xlsx']
+  const allowedExtensions = [
+    '.txt', '.md', '.markdown', '.pdf', '.doc', '.docx', '.xls', '.xlsx',
+    '.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp', '.tiff', '.tif'
+  ]
   const ext = '.' + file.name.split('.').pop()?.toLowerCase()
 
   if (!allowedExtensions.includes(ext)) {

ai-service/app/api/admin/kb.py

@@ -11,6 +11,7 @@ import hashlib
 from dataclasses import dataclass
 from typing import Annotated, Any, Optional
 from logging.handlers import RotatingFileHandler
+from urllib.parse import unquote
 
 import tiktoken
 from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, Query, UploadFile
@@ -64,6 +65,7 @@ class TextChunk:
     end_token: int
     page: int | None = None
     source: str | None = None
+    metadata: dict | None = None
 
 
 def chunk_text_by_lines(
@@ -602,10 +604,14 @@ async def upload_document(
 
     doc_kb_service = KBService(session)
     file_content = await file.read()
+    
+    # URL decode filename to handle Chinese characters
+    decoded_filename = unquote(file.filename or "unknown")
+    
     document, job = await doc_kb_service.upload_document(
         tenant_id=tenant_id,
         kb_id=kb_id,
-        file_name=file.filename or "unknown",
+        file_name=decoded_filename,
         file_content=file_content,
         file_type=file.content_type,
         metadata=metadata_dict,
@@ -615,7 +621,7 @@ async def upload_document(
     await session.commit()
 
     background_tasks.add_task(
-        _index_document, tenant_id, kb_id, str(job.id), str(document.id), file_content, file.filename, metadata_dict
+        _index_document, tenant_id, kb_id, str(job.id), str(document.id), file_content, decoded_filename, metadata_dict
     )
 
     return JSONResponse(
@@ -676,6 +682,7 @@ async def _index_document(
             logger.info(f"[INDEX] File extension: {file_ext}, content size: {len(content)} bytes")
 
             text_extensions = {".txt", ".md", ".markdown", ".rst", ".log", ".json", ".xml", ".yaml", ".yml"}
+            markdown_extensions = {".md", ".markdown"}
             image_extensions = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".tif"}
 
             if file_ext in text_extensions or not file_ext:
@@ -783,16 +790,6 @@ async def _index_document(
             
             image_base64_for_inference = None
             mime_type_for_inference = None
-            if file_ext in image_extensions:
-                import base64
-                image_base64_for_inference = base64.b64encode(content).decode("utf-8")
-                mime_type_map = {
-                    ".jpg": "image/jpeg", ".jpeg": "image/jpeg",
-                    ".png": "image/png", ".gif": "image/gif",
-                    ".webp": "image/webp", ".bmp": "image/bmp",
-                    ".tiff": "image/tiff", ".tif": "image/tiff",
-                }
-                mime_type_for_inference = mime_type_map.get(file_ext, "image/jpeg")
 
             logger.info("[INDEX] Starting metadata auto-inference...")
             inference_result = await inference_service.infer_metadata(
@@ -811,6 +808,11 @@ async def _index_document(
                     f"inferred_fields={list(inference_result.inferred_metadata.keys())}, "
                     f"confidence_scores={inference_result.confidence_scores}"
                 )
+                
+                document = await kb_service.get_document(tenant_id, doc_id)
+                if document:
+                    document.doc_metadata = metadata
+                    logger.info(f"[INDEX] Updated document metadata in database: {metadata}")
             else:
                 logger.warning(
                     f"[INDEX] Metadata inference FAILED: {inference_result.error_message}, "
@@ -846,6 +848,31 @@ async def _index_document(
                         pc.page = page.page
                     all_chunks.extend(page_chunks)
                 logger.info(f"[INDEX] Total chunks from PDF: {len(all_chunks)}")
+            elif file_ext in markdown_extensions:
+                logger.info("[INDEX] Markdown file detected, using intelligent chunking")
+                from app.services.document.markdown_chunker import MarkdownChunker, MarkdownElementType
+                chunker = MarkdownChunker(max_chunk_size=1000, min_chunk_size=50)
+                md_chunks = chunker.chunk(text, doc_id=doc_id)
+                
+                for i, md_chunk in enumerate(md_chunks):
+                    chunk_metadata = {
+                        "element_type": md_chunk.element_type.value,
+                        "header_context": md_chunk.header_context,
+                        "language": md_chunk.language,
+                    }
+                    chunk_metadata.update(md_chunk.metadata)
+                    
+                    all_chunks.append(TextChunk(
+                        text=md_chunk.content,
+                        start_token=i,
+                        end_token=i + 1,
+                        page=None,
+                        source=filename,
+                    ))
+                    if all_chunks:
+                        all_chunks[-1].metadata = chunk_metadata
+                
+                logger.info(f"[INDEX] Total chunks from Markdown: {len(all_chunks)}, element types: {[c.element_type.value for c in md_chunks[:5]]}...")
             else:
                 logger.info("[INDEX] Using line-based chunking")
                 all_chunks = chunk_text_by_lines(
@@ -913,21 +940,13 @@ async def _index_document(
 
             if points:
                 if settings.kb_vector_log_enabled:
-                    vector_payloads = []
+                    payloads_only = []
                     for point in points:
                         if use_multi_vector:
-                            payload = {
+                            payload = point.get("payload")
-                                "id": point.get("id"),
-                                "vector": point.get("vector"),
-                                "payload": point.get("payload"),
-                            }
                         else:
-                            payload = {
+                            payload = point.payload
-                                "id": point.id,
+                        payloads_only.append(payload)
-                                "vector": point.vector,
-                                "payload": point.payload,
-                            }
-                        vector_payloads.append(payload)
 
                     kb_vector_logger.info(json.dumps({
                         "tenant_id": tenant_id,
@@ -938,7 +957,8 @@ async def _index_document(
                         "file_ext": file_ext,
                         "is_image": file_ext in image_extensions,
                         "metadata": doc_metadata,
-                        "vectors": vector_payloads,
+                        "chunk_count": len(payloads_only),
+                        "payloads": payloads_only,
                     }, ensure_ascii=False))
 
                 logger.info(f"[INDEX] Upserting {len(points)} vectors to Qdrant for kb_id={kb_id}...")

ai-service/app/models/entities.py

@@ -7,10 +7,18 @@ import uuid
 from datetime import datetime
 from enum import Enum
 from typing import Any
+from zoneinfo import ZoneInfo
 
 from sqlalchemy import JSON, Column
 from sqlmodel import Field, Index, SQLModel
 
+SHANGHAI_TZ = ZoneInfo("Asia/Shanghai")
+
+
+def beijing_now() -> datetime:
+    """Get current time in Shanghai/Beijing timezone (Asia/Shanghai)"""
+    return datetime.now(SHANGHAI_TZ).replace(tzinfo=None)
+
 
 class ChatSession(SQLModel, table=True):
     """
@@ -32,8 +40,8 @@ class ChatSession(SQLModel, table=True):
         sa_column=Column("metadata", JSON, nullable=True),
         description="Session metadata"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Session creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Session creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class ChatMessage(SQLModel, table=True):
@@ -65,7 +73,7 @@ class ChatMessage(SQLModel, table=True):
     first_token_ms: int | None = Field(default=None, description="Time to first token in milliseconds (for streaming)")
     is_error: bool = Field(default=False, description="Whether this message is an error response")
     error_message: str | None = Field(default=None, description="Error message if any")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Message creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Message creation time")
 
     prompt_template_id: uuid.UUID | None = Field(
         default=None,
@@ -150,8 +158,8 @@ class UserMemory(SQLModel, table=True):
     summary_version: int = Field(default=1, description="Summary version / update round")
     last_turn_id: str | None = Field(default=None, description="Last turn identifier (optional)")
     expires_at: datetime | None = Field(default=None, description="Expiration time (optional)")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class SharedSession(SQLModel, table=True):
@@ -177,8 +185,8 @@ class SharedSession(SQLModel, table=True):
     is_active: bool = Field(default=True, description="Whether share is active")
     max_concurrent_users: int = Field(default=10, description="Maximum concurrent users allowed")
     current_users: int = Field(default=0, description="Current number of online users")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class DocumentStatus(str, Enum):
@@ -213,8 +221,8 @@ class Tenant(SQLModel, table=True):
     tenant_id: str = Field(..., description="Full tenant ID (format: name@ash@year)", unique=True, index=True)
     name: str = Field(..., description="Tenant display name (first part of tenant_id)")
     year: str = Field(..., description="Year part from tenant_id")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class KBType(str, Enum):
@@ -247,8 +255,8 @@ class KnowledgeBase(SQLModel, table=True):
     priority: int = Field(default=0, ge=0, description="Priority weight, higher value means higher priority")
     is_enabled: bool = Field(default=True, description="Whether the knowledge base is enabled")
     doc_count: int = Field(default=0, ge=0, description="Document count (cached statistic)")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class Document(SQLModel, table=True):
@@ -272,8 +280,8 @@ class Document(SQLModel, table=True):
     status: str = Field(default=DocumentStatus.PENDING.value, description="Document status")
     error_msg: str | None = Field(default=None, description="Error message if failed")
     doc_metadata: dict | None = Field(default=None, sa_type=JSON, description="Document metadata as JSON")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Upload time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Upload time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class IndexJob(SQLModel, table=True):
@@ -293,8 +301,8 @@ class IndexJob(SQLModel, table=True):
     status: str = Field(default=IndexJobStatus.PENDING.value, description="Job status")
     progress: int = Field(default=0, ge=0, le=100, description="Progress percentage")
     error_msg: str | None = Field(default=None, description="Error message if failed")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Job creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Job creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class KnowledgeBaseCreate(SQLModel):
@@ -346,8 +354,8 @@ class ApiKey(SQLModel, table=True):
         description="Optional IP allowlist for this key",
     )
     rate_limit_qpm: int | None = Field(default=60, description="Per-minute quota for this key")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class ApiKeyCreate(SQLModel):
@@ -390,8 +398,8 @@ class PromptTemplate(SQLModel, table=True):
         sa_column=Column("metadata", JSON, nullable=True),
         description="[AC-IDSMETA-16] Structured metadata for the prompt template"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class PromptTemplateVersion(SQLModel, table=True):
@@ -426,7 +434,7 @@ class PromptTemplateVersion(SQLModel, table=True):
         sa_column=Column("variables", JSON, nullable=True),
         description="Variable definitions, e.g., [{'name': 'persona_name', 'default': '小N'}]"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
 
 
 class PromptTemplateCreate(SQLModel):
@@ -514,8 +522,8 @@ class IntentRule(SQLModel, table=True):
         sa_column=Column("semantic_examples", JSON, nullable=True),
         description="[v0.8.0] Semantic example sentences for dynamic vector computation"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class IntentRuleCreate(SQLModel):
@@ -614,8 +622,8 @@ class ForbiddenWord(SQLModel, table=True):
     fallback_reply: str | None = Field(default=None, description="Fallback reply for 'block' strategy")
     is_enabled: bool = Field(default=True, description="Whether the word is enabled")
     hit_count: int = Field(default=0, ge=0, description="Hit count for statistics")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class ForbiddenWordCreate(SQLModel):
@@ -666,8 +674,8 @@ class BehaviorRule(SQLModel, table=True):
     )
     category: str = Field(..., description="Category: compliance/tone/boundary/custom")
     is_enabled: bool = Field(default=True, description="Whether the rule is enabled")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class BehaviorRuleCreate(SQLModel):
@@ -779,8 +787,8 @@ class ScriptFlow(SQLModel, table=True):
         sa_column=Column("metadata", JSON, nullable=True),
         description="[AC-IDSMETA-16] Structured metadata for the script flow"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Creation time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
 
 
 class FlowInstance(SQLModel, table=True):
@@ -814,8 +822,8 @@ class FlowInstance(SQLModel, table=True):
         sa_column=Column("context", JSON, nullable=True),
         description="Flow execution context, stores user inputs"
     )
-    started_at: datetime = Field(default_factory=datetime.utcnow, description="Instance start time")
+    started_at: datetime = Field(default_factory=beijing_now, description="Instance start time")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
+    updated_at: datetime = Field(default_factory=beijing_now, description="Last update time")
     completed_at: datetime | None = Field(default=None, description="Completion time (nullable)")
 
 
@@ -981,7 +989,7 @@ class FlowTestRecord(SQLModel, table=True):
         description="Final ChatResponse with reply, confidence, should_transfer"
     )
     total_duration_ms: int | None = Field(default=None, description="Total execution time in milliseconds")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Record creation time", index=True)
+    created_at: datetime = Field(default_factory=beijing_now, description="Record creation time", index=True)
 
 
 class FlowTestStepResult(SQLModel):
@@ -1034,7 +1042,7 @@ class ExportTask(SQLModel, table=True):
     )
     error_message: str | None = Field(default=None, description="Error message if failed")
     expires_at: datetime | None = Field(default=None, description="File expiration time (for cleanup)")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="Task creation time")
+    created_at: datetime = Field(default_factory=beijing_now, description="Task creation time")
     completed_at: datetime | None = Field(default=None, description="Completion time")
 
 
@@ -1167,8 +1175,8 @@ class MetadataFieldDefinition(SQLModel, table=True):
         description="字段状态: draft/active/deprecated"
     )
     version: int = Field(default=1, description="版本号")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class MetadataFieldDefinitionCreate(SQLModel):
@@ -1299,8 +1307,8 @@ class SlotDefinition(SQLModel, table=True):
         description="关联的元数据字段 ID",
         foreign_key="metadata_field_definitions.id",
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
     def get_effective_strategies(self) -> list[str]:
         """
@@ -1423,7 +1431,7 @@ class SlotValue(SQLModel):
         description="置信度 0.0~1.0"
     )
     updated_at: datetime = Field(
-        default_factory=datetime.utcnow,
+        default_factory=beijing_now,
         description="最后更新时间"
     )
 
@@ -1450,8 +1458,8 @@ class MetadataSchema(SQLModel, table=True):
     )
     is_default: bool = Field(default=False, description="是否为租户默认模式")
     is_enabled: bool = Field(default=True, description="是否启用")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class MetadataSchemaCreate(SQLModel):
@@ -1530,8 +1538,8 @@ class DecompositionTemplate(SQLModel, table=True):
         sa_column=Column("example_output", JSON, nullable=True),
         description="示例输出 JSON"
     )
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class DecompositionTemplateCreate(SQLModel):
@@ -1625,8 +1633,8 @@ class HighRiskPolicy(SQLModel, table=True):
     )
     priority: int = Field(default=0, description="优先级 (值越高优先级越高)")
     is_enabled: bool = Field(default=True, description="是否启用")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class HighRiskPolicyCreate(SQLModel):
@@ -1673,8 +1681,8 @@ class SessionModeRecord(SQLModel, table=True):
     )
     reason: str | None = Field(default=None, description="模式切换原因")
     switched_at: datetime | None = Field(default=None, description="模式切换时间")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class MidAuditLog(SQLModel, table=True):
@@ -1708,7 +1716,7 @@ class MidAuditLog(SQLModel, table=True):
     react_iterations: int | None = Field(default=None, description="ReAct循环次数")
     high_risk_scenario: str | None = Field(default=None, description="触发的高风险场景")
     latency_ms: int | None = Field(default=None, description="总耗时(ms)")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间", index=True)
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间", index=True)
 
 
 class SceneSlotBundleStatus(str, Enum):
@@ -1784,8 +1792,8 @@ class SceneSlotBundle(SQLModel, table=True):
         description="状态: draft/active/deprecated"
     )
     version: int = Field(default=1, description="版本号")
-    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    created_at: datetime = Field(default_factory=beijing_now, description="创建时间")
-    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+    updated_at: datetime = Field(default_factory=beijing_now, description="更新时间")
 
 
 class SceneSlotBundleCreate(SQLModel):

ai-service/app/schemas/metadata.py

@@ -107,12 +107,14 @@ class MetadataFieldDefinitionUpdateRequest(BaseModel):
     """[AC-MRS-01] 更新元数据字段定义请求"""
 
     label: str | None = Field(default=None, min_length=1, max_length=64)
+    type: str | None = Field(default=None, description="字段类型")
     required: bool | None = None
     options: list[str] | None = None
     default_value: Any | None = None
     scope: list[str] | None = None
     is_filterable: bool | None = None
     is_rank_feature: bool | None = None
+    usage_description: str | None = Field(default=None, description="用途说明")
     field_roles: list[str] | None = Field(
         default=None,
         description="[AC-MRS-01] 字段角色列表"

ai-service/app/services/llm/openai_client.py

@@ -295,7 +295,7 @@ class OpenAIClient(LLMClient):
             role = msg.get("role", "unknown")
             content = msg.get("content", "")
             logger.info(f"[AC-AISVC-06] [{i}] role={role}, content_length={len(content)}")
-            logger.info(f"[AC-AISVC-06] [{i}] content:\n{content}")
+        
         logger.info("[AC-AISVC-06] ======================================")
 
         try:

ai-service/app/services/metadata_auto_inference_service.py

@@ -42,10 +42,10 @@ class AutoInferenceResult:
     error_message: str | None = None
 
 
-METADATA_INFERENCE_SYSTEM_PROMPT = """你是一个专业的文档元数据分析助手。你的任务是根据文档内容，自动推断并填写元数据字段。
+METADATA_INFERENCE_SYSTEM_PROMPT = """你是一个专业的文档元数据分析助手。你的任务是根据文档内容，自动推断并填写元数据字段，并以 JSON 格式输出结果。
 
 ## 输出要求
-请严格按照以下 JSON 格式输出，不要添加任何其他内容：
+你必须严格按照以下 JSON 格式输出，不要添加任何其他内容：
 
 ```json
 {
@@ -60,6 +60,29 @@ METADATA_INFERENCE_SYSTEM_PROMPT = """你是一个专业的文档元数据分析
 }
 ```
 
+## JSON 输出样例
+假设有以下字段定义：
+- grade (枚举类型，选项：小学、初中、高中)
+- subject (枚举类型，选项：语文、数学、英语)
+- difficulty (数字类型，1-5)
+
+如果文档内容是关于"高一数学函数知识点"，则输出：
+
+```json
+{
+  "inferred_metadata": {
+    "grade": "高中",
+    "subject": "数学",
+    "difficulty": 3
+  },
+  "confidence_scores": {
+    "grade": 0.95,
+    "subject": 0.95,
+    "difficulty": 0.7
+  }
+}
+```
+
 ## 推断规则
 1. **仔细分析文档内容**：根据文档的主题、关键词、上下文来推断元数据
 2. **遵循字段定义**：
@@ -78,7 +101,10 @@ METADATA_INFERENCE_SYSTEM_PROMPT = """你是一个专业的文档元数据分析
 ## 注意事项
 - 必须严格按照字段定义的类型和选项填写
 - 不要编造不存在的选项值
-- 保持客观，基于文档内容推断"""
+- 保持客观，基于文档内容推断
+- **只输出合法的 JSON 格式，不要输出思考过程、解释或任何额外文本**
+- 确保 JSON 格式完整闭合，所有字符串用双引号包裹
+- 如果无法推断某字段，可省略该字段，但整体 JSON 必须完整闭合"""
 
 
 class MetadataAutoInferenceService:
@@ -98,9 +124,9 @@ class MetadataAutoInferenceService:
     def __init__(
         self,
         session: AsyncSession,
-        model: str | None = None,
+        model: str | None = "glm-4.7",
         max_tokens: int = 1024,
-        timeout_seconds: int = 60,
+        timeout_seconds: int = 180,
     ):
         self._session = session
         self._model = model
@@ -148,9 +174,12 @@ class MetadataAutoInferenceService:
                 error_message="No field definitions configured",
             )
 
+        logger.info(f"[MetadataAutoInference] Found {len(field_definitions)} field definitions: {[f.field_key for f in field_definitions]}")
+
         field_contexts = self._build_field_contexts(field_definitions)
         
         if not field_contexts:
+            logger.warning(f"[MetadataAutoInference] No field contexts built from definitions")
             return AutoInferenceResult(
                 inferred_metadata=existing_metadata or {},
                 confidence_scores={},
@@ -159,15 +188,26 @@ class MetadataAutoInferenceService:
             )
 
         user_prompt = self._build_user_prompt(content, field_contexts, existing_metadata)
+        logger.info(f"[MetadataAutoInference] === SYSTEM PROMPT ===\n{METADATA_INFERENCE_SYSTEM_PROMPT}\n=== END SYSTEM PROMPT ===")
+        logger.info(f"[MetadataAutoInference] === USER PROMPT ===\n{user_prompt}\n=== END USER PROMPT ===")
 
         try:
             if image_base64 and mime_type:
-                raw_response = await self._call_multimodal_llm(
+                logger.info(f"[MetadataAutoInference] Using multimodal LLM for image recognition, mime_type={mime_type}")
-                    user_prompt, image_base64, mime_type
+                image_content = await self._recognize_image(image_base64, mime_type)
-                )
+                logger.info(f"[MetadataAutoInference] Image recognition result (first 200 chars): {image_content[:200] if image_content else 'EMPTY'}")
+                
+                combined_content = f"{content}\n\n[图片识别内容]\n{image_content}" if content else f"[图片识别内容]\n{image_content}"
+                user_prompt = self._build_user_prompt(combined_content, field_contexts, existing_metadata)
+                logger.info(f"[MetadataAutoInference] === USER PROMPT (with image content) ===\n{user_prompt}\n=== END USER PROMPT ===")
+                
+                raw_response = await self._call_text_llm(user_prompt)
             else:
                 raw_response = await self._call_text_llm(user_prompt)
 
+            logger.info(f"[MetadataAutoInference] LLM response length: {len(raw_response) if raw_response else 0}")
+            logger.info(f"[MetadataAutoInference] LLM response (first 300 chars): {raw_response[:300] if raw_response else 'EMPTY'}")
+
             result = self._parse_llm_response(raw_response, field_contexts)
 
             if existing_metadata:
@@ -331,12 +371,15 @@ class MetadataAutoInferenceService:
         return prompt
 
     async def _call_text_llm(self, prompt: str) -> str:
-        """调用文本 LLM"""
+        """调用文本 LLM 进行元数据推断（使用对话模型配置，支持 JSON 格式化输出）"""
         manager = get_llm_config_manager()
-        client = manager.get_kb_processing_client()
+        client = manager.get_chat_client()
         
-        config = manager.kb_processing_config
+        config = manager.chat_config
-        model = self._model or config.get("model", "gpt-4o-mini")
+        model = config.get("model")
+        if not model:
+            logger.warning("[MetadataAutoInference] No model configured in chat config, using default")
+            model = self._model
 
         from app.services.llm.base import LLMConfig
 
@@ -345,8 +388,13 @@ class MetadataAutoInferenceService:
             max_tokens=self._max_tokens,
             temperature=0.3,
             timeout_seconds=self._timeout_seconds,
+            extra_params={
+                "response_format": {"type": "json_object"},
+            },
         )
 
+        logger.info(f"[MetadataAutoInference] Using model: {model} for text LLM")
+
         messages = [
             {"role": "system", "content": METADATA_INFERENCE_SYSTEM_PROMPT},
             {"role": "user", "content": prompt},
@@ -355,34 +403,45 @@ class MetadataAutoInferenceService:
         response = await client.generate(messages=messages, config=llm_config)
         return response.content or ""
 
-    async def _call_multimodal_llm(
+    async def _recognize_image(self, image_base64: str, mime_type: str) -> str:
-        self,
+        """
-        prompt: str,
+        使用多模态模型识别图片内容。
-        image_base64: str,
+        这个方法只负责图片内容识别，不负责 JSON 格式化输出。
-        mime_type: str,
+        使用对话模型配置。
-    ) -> str:
+        """
-        """调用多模态 LLM"""
         manager = get_llm_config_manager()
-        client = manager.get_kb_processing_client()
+        client = manager.get_chat_client()
         
-        config = manager.kb_processing_config
+        config = manager.chat_config
-        model = self._model or config.get("model", "gpt-4o-mini")
+        model = config.get("model")
+        if not model:
+            logger.warning("[MetadataAutoInference] No model configured in chat config for image recognition")
+            model = self._model
 
         from app.services.llm.base import LLMConfig
 
         llm_config = LLMConfig(
             model=model,
-            max_tokens=self._max_tokens,
+            max_tokens=1024,
             temperature=0.3,
             timeout_seconds=self._timeout_seconds,
         )
 
+        logger.info(f"[MetadataAutoInference] Using model: {model} for image recognition")
+
+        recognition_prompt = """请仔细分析这张图片，提取其中的关键信息，包括：
+1. 图片类型（如：文档截图、图表、照片、示意图等）
+2. 图片中的文字内容（如有）
+3. 图片的主要内容和主题
+4. 任何可见的数据、数字或关键信息
+
+请用简洁的中文描述图片内容："""
+
         messages = [
-            {"role": "system", "content": METADATA_INFERENCE_SYSTEM_PROMPT},
             {
                 "role": "user",
                 "content": [
-                    {"type": "text", "text": prompt},
+                    {"type": "text", "text": recognition_prompt},
                     {
                         "type": "image_url",
                         "image_url": {
@@ -396,12 +455,29 @@ class MetadataAutoInferenceService:
         response = await client.generate(messages=messages, config=llm_config)
         return response.content or ""
 
+    async def _call_multimodal_llm(
+        self,
+        prompt: str,
+        image_base64: str,
+        mime_type: str,
+    ) -> str:
+        """
+        调用多模态 LLM（已弃用，保留向后兼容）。
+        推荐使用 _recognize_image + _call_text_llm 组合。
+        """
+        logger.warning("[MetadataAutoInference] _call_multimodal_llm is deprecated, use _recognize_image + _call_text_llm instead")
+        
+        image_content = await self._recognize_image(image_base64, mime_type)
+        combined_prompt = f"{prompt}\n\n[图片识别内容]\n{image_content}"
+        return await self._call_text_llm(combined_prompt)
+
     def _parse_llm_response(
         self,
         response: str,
         field_contexts: list[InferenceFieldContext],
     ) -> AutoInferenceResult:
         """解析 LLM 响应"""
+        json_str = ""
         try:
             json_str = self._extract_json(response)
             data = json.loads(json_str)
@@ -410,19 +486,30 @@ class MetadataAutoInferenceService:
             confidence_scores = data.get("confidence_scores", {})
 
             field_map = {ctx.field_key: ctx for ctx in field_contexts}
+            label_to_key = {ctx.label: ctx.field_key for ctx in field_contexts}
+            
             validated_metadata = {}
             validated_scores = {}
 
-            for field_key, value in inferred_metadata.items():
+            for field_key_or_label, value in inferred_metadata.items():
-                if field_key not in field_map:
+                actual_field_key = field_key_or_label
-                    continue
+                
+                if field_key_or_label not in field_map:
+                    if field_key_or_label in label_to_key:
+                        actual_field_key = label_to_key[field_key_or_label]
+                    else:
+                        logger.warning(f"[MetadataAutoInference] Unknown field: {field_key_or_label}")
+                        continue
 
-                ctx = field_map[field_key]
+                ctx = field_map[actual_field_key]
                 validated_value = self._validate_field_value(ctx, value)
 
                 if validated_value is not None:
-                    validated_metadata[field_key] = validated_value
+                    validated_metadata[actual_field_key] = validated_value
-                    validated_scores[field_key] = confidence_scores.get(field_key, 0.5)
+                    validated_scores[actual_field_key] = confidence_scores.get(field_key_or_label, 0.5)
+
+            logger.info(f"[MetadataAutoInference] Validated metadata: {validated_metadata}")
+            logger.info(f"[MetadataAutoInference] Confidence scores: {validated_scores}")
 
             return AutoInferenceResult(
                 inferred_metadata=validated_metadata,
@@ -433,6 +520,8 @@ class MetadataAutoInferenceService:
 
         except json.JSONDecodeError as e:
             logger.warning(f"[MetadataAutoInference] Failed to parse JSON: {e}")
+            logger.warning(f"[MetadataAutoInference] Raw LLM response (first 500 chars): {response[:500] if response else 'EMPTY'}")
+            logger.warning(f"[MetadataAutoInference] Extracted JSON string: {json_str[:500] if json_str else 'EMPTY'}")
             return AutoInferenceResult(
                 inferred_metadata={},
                 confidence_scores={},
@@ -482,15 +571,32 @@ class MetadataAutoInferenceService:
 
     def _extract_json(self, content: str) -> str:
         """从响应中提取 JSON"""
+        import re
+
         content = content.strip()
 
+        if content.startswith("```"):
+            content = re.sub(r"^```[a-zA-Z0-9_-]*\s*", "", content)
+            content = re.sub(r"\s*```$", "", content).strip()
+
         if content.startswith("{") and content.endswith("}"):
             return content
 
         json_start = content.find("{")
-        json_end = content.rfind("}")
+        if json_start == -1:
+            return content
 
-        if json_start != -1 and json_end != -1 and json_end > json_start:
+        json_str = content[json_start:]
-            return content[json_start:json_end + 1]
 
-        return content
+        open_braces = json_str.count("{")
+        close_braces = json_str.count("}")
+        if close_braces < open_braces:
+            json_str += "}" * (open_braces - close_braces)
+
+        json_str = re.sub(r",\s*([}\]])", r"\1", json_str)
+
+        json_end = json_str.rfind("}")
+        if json_end != -1:
+            json_str = json_str[:json_end + 1]
+
+        return json_str

ai-service/app/services/metadata_field_definition_service.py

@@ -226,6 +226,8 @@ class MetadataFieldDefinitionService:
         
         if field_update.label is not None:
             field.label = field_update.label
+        if field_update.type is not None:
+            field.type = field_update.type
         if field_update.required is not None:
             field.required = field_update.required
         if field_update.options is not None:
@@ -239,6 +241,8 @@ class MetadataFieldDefinitionService:
             field.is_filterable = field_update.is_filterable
         if field_update.is_rank_feature is not None:
             field.is_rank_feature = field_update.is_rank_feature
+        if field_update.usage_description is not None:
+            field.usage_description = field_update.usage_description
         # [AC-MRS-01] 修复：添加 field_roles 更新逻辑
         if field_update.field_roles is not None:
             self._validate_field_roles(field_update.field_roles)