feat: multimodal RAG with PDF image extraction and display

Extract images from PDFs using pymupdf, generate descriptions via
Qwen3-VL-8B, store in ChromaDB alongside text chunks, and render
images in chat answers. Includes image proxy rewrite, force re-process
endpoint, and VLM API timeout.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/src/services/document_service.py

@@ -1,8 +1,8 @@
 """
-文档处理服务（LangChain 1.0）
+文档处理服务（LangChain 1.0 + 多模态图片处理）
 """
 import os
-import hashlib
+import asyncio
 from pathlib import Path
 from typing import List, Dict, Any, Optional
 from sqlalchemy.orm import Session
@@ -10,24 +10,28 @@ from langchain_core.documents import Document as LangChainDocument
 
 from ..models.document import Document, DocumentChunk
 from ..rag.vector_store import get_vector_store
-from ..rag.document_loaders import DocumentLoaderFactory
+from ..rag.document_loaders import DocumentLoaderFactory, PDFImageExtractor
 from ..rag.text_splitters import get_text_splitter
+from ..llm.siliconflow import get_llm_client
+
+IMAGES_DIR = Path(__file__).parent.parent.parent.parent / "data" / "images"
+
 
 class DocumentService:
     """文档处理服务"""
-    
+
     def __init__(self, db: Session):
         self.db = db
         self.vector_store = get_vector_store()
-    
+
     async def process_document(self, document_id: int) -> bool:
-        """处理文档（使用LangChain 1.0）"""
+        """处理文档（使用LangChain 1.0 + 多模态图片处理）"""
         try:
             document = self.db.query(Document).filter(Document.id == document_id).first()
             if not document:
                 return False
-            
-            # 1. 使用LangChain加载文档
+
+            # 1. 使用LangChain加载文档（文本）
             documents = DocumentLoaderFactory.load_document(
                 file_path=document.file_path,
                 file_type=document.file_type,
@@ -38,25 +42,110 @@ class DocumentService:
                     "filename": document.filename
                 }
             )
-            
+
             # 2. 使用中文优化的文本分割器
             text_splitter = get_text_splitter(chunk_size=1000, chunk_overlap=200)
             splits = text_splitter.split_documents(documents)
-            
-            # 3. 添加到向量存储
-            success = self.vector_store.add_documents(splits)
-            
+
+            # 3. PDF图片提取和描述（仅PDF文件）
+            image_chunks = []
+            if document.file_type == ".pdf":
+                image_chunks = await self._process_pdf_images(
+                    file_path=document.file_path,
+                    document_id=document.id,
+                    knowledge_base_id=document.knowledge_base_id,
+                    title=document.title,
+                    filename=document.filename
+                )
+
+            # 4. 将文本块和图片描述合并添加到向量存储
+            all_splits = splits + image_chunks
+            success = self.vector_store.add_documents(all_splits)
+
             if success:
                 document.is_processed = True
                 self.db.commit()
+                print(f"[DocumentService] 文档 {document.filename} 处理完成: "
+                      f"{len(splits)} 个文本块, {len(image_chunks)} 个图片描述块")
                 return True
-            
+
             return False
-            
+
         except Exception as e:
             print(f"处理文档失败: {str(e)}")
             self.db.rollback()
             return False
+
+    async def _process_pdf_images(
+        self,
+        file_path: str,
+        document_id: int,
+        knowledge_base_id: int,
+        title: str,
+        filename: str
+    ) -> List[LangChainDocument]:
+        """提取PDF图片并用VLM生成描述"""
+        image_chunks = []
+        try:
+            # 创建图片输出目录
+            img_output_dir = IMAGES_DIR / str(document_id)
+
+            # 提取图片
+            images = PDFImageExtractor.extract_images(str(file_path), str(img_output_dir))
+
+            if not images:
+                print(f"[DocumentService] 未发现可提取的图片: {filename}")
+                return []
+
+            print(f"[DocumentService] 提取到 {len(images)} 张图片, 开始VLM描述生成...")
+
+            # 批量调用VLM生成描述
+            llm_client = get_llm_client()
+            for idx, img in enumerate(images):
+                try:
+                    description = await llm_client.describe_image(
+                        img["path"],
+                        img.get("context_text", "")
+                    )
+
+                    if description:
+                        # 相对路径用于URL访问
+                        rel_path = f"{document_id}/{img['filename']}"
+                        image_url = f"/images/{rel_path}"
+
+                        # 构建图片描述文本块（URL写入内容，LLM可直接引用）
+                        chunk_content = (
+                            f"[图片描述 - 第{img['page']}页]\n"
+                            f"图片URL: {image_url}\n"
+                            f"图片内容：{description}"
+                        )
+
+                        chunk = LangChainDocument(
+                            page_content=chunk_content,
+                            metadata={
+                                "document_id": document_id,
+                                "knowledge_base_id": knowledge_base_id,
+                                "title": title,
+                                "filename": filename,
+                                "source_type": "image",
+                                "image_path": str(img["path"]),
+                                "image_url": image_url,
+                                "page": img["page"],
+                            }
+                        )
+                        image_chunks.append(chunk)
+                        print(f"[DocumentService] 图片描述成功 {idx+1}/{len(images)}: {img['filename']}")
+                    else:
+                        print(f"[DocumentService] 图片描述为空 {idx+1}/{len(images)}: {img['filename']}")
+
+                except Exception as e:
+                    print(f"[DocumentService] 图片处理失败 {img['filename']}: {e}")
+                    continue
+
+        except Exception as e:
+            print(f"[DocumentService] PDF图片处理失败: {e}")
+
+        return image_chunks
     
     def search_documents(self, query: str, knowledge_base_ids: Optional[List[int]] = None, limit: int = 5) -> List[Dict[str, Any]]:
         """搜索文档（保留原有接口兼容性）"""
@@ -114,8 +203,11 @@ class DocumentService:
         ).order_by(DocumentChunk.chunk_index).all()
     
     def delete_document_chunks(self, document_id: int) -> bool:
-        """删除文档的所有块"""
+        """删除文档的所有块（数据库 + 向量存储）"""
         try:
+            # 删除向量存储中的文档数据
+            self.vector_store.delete_by_document_id(document_id)
+            # 删除数据库中的chunk记录
             self.db.query(DocumentChunk).filter(
                 DocumentChunk.document_id == document_id
             ).delete()