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>

.gitignore

@@ -19,6 +19,7 @@ build/
 node_modules/
 .next/
 out/
+*.tsbuildinfo
 
 # 运行时数据
 runtime/
@@ -33,6 +34,7 @@ generated_images/
 # 数据目录中的运行时文件（保留源文件如 .tex）
 data/database/
 data/knowledge_base/
+data/images/
 
 # LaTeX 中间文件
 *.aux

backend/main.py

@@ -197,6 +197,11 @@ if os.path.exists(settings.upload_dir):
 if os.path.exists(settings.generated_images_dir):
     app.mount("/generated_images", StaticFiles(directory=settings.generated_images_dir), name="generated_images")
 
+# 挂载PDF图片提取目录
+IMAGES_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "images")
+os.makedirs(IMAGES_DIR, exist_ok=True)
+app.mount("/images", StaticFiles(directory=IMAGES_DIR), name="images")
+
 # 根路径
 @app.get("/")
 async def root():

backend/pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "duckduckgo-search>=6.0.0",
     "docx2txt>=0.9",
     "pypdf>=6.12.0",
+    "pymupdf>=1.27.2.3",
 ]
 
 [project.optional-dependencies]

backend/src/api/document.py

@@ -227,10 +227,11 @@ async def delete_document(
 @router.post("/{document_id}/process")
 async def process_document(
     document_id: int,
+    force: bool = False,
     current_user: str = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
-    """处理文档（向量化）"""
+    """处理文档（向量化），force=true 强制重新处理"""
     try:
         # 获取用户ID
         from ..models.user import User
@@ -253,12 +254,17 @@ async def process_document(
                 detail="文档不存在"
             )
 
-        if document.is_processed:
+        if document.is_processed and not force:
-            return {"message": "文档已经处理过了"}
+            return {"message": "文档已经处理过了，使用 force=true 强制重新处理"}
 
-        # 处理文档
+        # 强制重新处理时，先删除已有的向量数据
+        if force and document.is_processed:
             document_service = DocumentService(db)
-        success = await asyncio.to_thread(document_service.process_document, document.id)
+            document_service.delete_document_chunks(document.id)
+
+        # 处理文档（直接 await 异步方法）
+        document_service = DocumentService(db)
+        success = await document_service.process_document(document.id)
 
         if success:
             return {"message": "文档处理成功"}

backend/src/llm/siliconflow.py

@@ -1,7 +1,8 @@
 """
-大模型API集成 — 支持 SiliconFlow 和 DeepSeek 官方
+大模型API集成 — 支持 SiliconFlow 和 DeepSeek 官方 + 视觉模型
 """
 import os
+import base64
 from typing import List, Dict, Any, Optional, AsyncGenerator, Tuple
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
@@ -13,6 +14,17 @@ from ..core.config import get_settings
 
 settings = get_settings()
 
+# 图片描述提示词
+IMAGE_DESCRIPTION_PROMPT = """你是一个国土空间规划专家。请详细描述这张PDF文档中的图片内容。
+
+图片周围文字上下文（来自PDF页面）：{context_text}
+
+要求：
+1. 说明图片类型（地图/规划图/图表/流程图/示意图/照片等）
+2. 描述图片中的关键信息、数据和空间关系
+3. 提取图中所有文字标注
+4. 描述控制在200-300字"""
+
 # DeepSeek 官方模型 ID 前缀（用于自动路由）
 DEEPSEEK_OFFICIAL_MODELS = {
     "deepseek-chat",
@@ -150,6 +162,61 @@ class SiliconFlowLLM:
         return messages
 
 
+    async def describe_image(self, image_path: str, context_text: str = "") -> str:
+        """使用VLM模型描述图片内容
+
+        Args:
+            image_path: 图片文件路径
+            context_text: 图片周围的PDF文本上下文
+
+        Returns:
+            图片的文字描述
+        """
+        import asyncio
+        import time
+
+        # 读取图片并编码为base64
+        with open(image_path, "rb") as f:
+            image_data = base64.b64encode(f.read()).decode("utf-8")
+
+        # 检测图片格式
+        ext = os.path.splitext(image_path)[1].lower()
+        mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".gif": "image/gif", ".webp": "image/webp"}
+        mime_type = mime_map.get(ext, "image/png")
+
+        prompt = IMAGE_DESCRIPTION_PROMPT.format(context_text=context_text[:600])
+
+        vision_model = "Qwen/Qwen3-VL-8B-Instruct"
+        api_key, base_url, _ = _resolve_provider(vision_model)
+
+        client = openai.AsyncOpenAI(api_key=api_key, base_url=base_url)
+
+        max_retries = 3
+        for attempt in range(max_retries):
+            try:
+                response = await client.chat.completions.create(
+                    model=vision_model,
+                    messages=[{
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": prompt},
+                            {"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{image_data}"}},
+                        ],
+                    }],
+                    max_tokens=600,
+                    temperature=0.3,
+                    timeout=90.0,
+                )
+                return response.choices[0].message.content or ""
+
+            except Exception as e:
+                print(f"[VLM] 描述失败 attempt={attempt+1}: {e}")
+                if attempt < max_retries - 1:
+                    await asyncio.sleep(2 ** attempt)
+
+        return ""
+
+
 # 全局LLM实例（使用默认模型）
 llm_client = SiliconFlowLLM()
 

backend/src/rag/chains.py

@@ -198,7 +198,9 @@ class RAGChain:
                 "filename": metadata.get("filename", "未知文件"),
                 "page": metadata.get("chunk_index", 0),
                 "score": metadata.get("score"),
-                "preview": doc.page_content
+                "preview": doc.page_content,
+                "source_type": metadata.get("source_type", "rag"),
+                "image_url": metadata.get("image_url"),
             })
         return sources
 

backend/src/rag/document_loaders.py

@@ -1,8 +1,9 @@
 """
-LangChain 1.0 文档加载器封装
+LangChain 1.0 文档加载器封装 + PDF图片提取
 """
-from typing import List, Optional
+from typing import List, Optional, Dict
 from pathlib import Path
+import fitz  # pymupdf
 from langchain_community.document_loaders import (
     PyPDFLoader,
     Docx2txtLoader,
@@ -11,6 +12,60 @@ from langchain_community.document_loaders import (
 )
 from langchain_core.documents import Document
 
+
+class PDFImageExtractor:
+    """使用pymupdf从PDF中提取内嵌图片"""
+
+    @staticmethod
+    def extract_images(file_path: str, output_dir: str) -> List[dict]:
+        """提取PDF中所有图片，返回图片元数据列表"""
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+        images = []
+        doc = fitz.open(file_path)
+
+        for page_num in range(len(doc)):
+            page = doc[page_num]
+            # 获取页面文本作为图片上下文
+            page_text = page.get_text("text")
+            # 提取页面内嵌图片
+            image_list = page.get_images(full=True)
+
+            for img_idx, img_info in enumerate(image_list):
+                xref = img_info[0]
+                try:
+                    base_image = doc.extract_image(xref)
+                    image_bytes = base_image["image"]
+                    ext = base_image["ext"]
+
+                    # 过滤太小的图片（图标、装饰元素等）
+                    if len(image_bytes) < 2048:
+                        continue
+
+                    # 图片周围文本（取该页文字前后各300字作为上下文）
+                    context_start = max(0, page_text.find(
+                        page_text[:len(page_text)//2]) if len(page_text) > 600
+                        else 0)
+                    context_text = page_text[context_start:context_start+600].strip()
+
+                    filename = f"page{page_num+1}_img{img_idx+1}.{ext}"
+                    output_path = Path(output_dir) / filename
+                    output_path.write_bytes(image_bytes)
+
+                    images.append({
+                        "path": str(output_path),
+                        "filename": filename,
+                        "page": page_num + 1,
+                        "context_text": context_text,
+                        "size": len(image_bytes),
+                    })
+                except Exception as e:
+                    print(f"[ImageExtractor] 提取图片失败 page={page_num+1} img={img_idx}: {e}")
+                    continue
+
+        doc.close()
+        return images
+
+
 class DocumentLoaderFactory:
     """文档加载器工厂"""
     

backend/src/rag/prompts.py

@@ -13,12 +13,16 @@ RAG_SYSTEM_PROMPT = """你是一个专业的国土空间规划知识问答助手
 要求：
 1. 综合评估上下文信息的相关性，优先采用与问题最相关的内容，不强行引用不相关的来源
 2. 回答中必须使用 [来源N] 格式（N为上下文中的来源编号）标注所引用的具体来源，例如"根据[来源3]，国土空间规划..."
-3. 回答要准确、专业、详细，结构清晰，逻辑性强
+3. 如果上下文中包含[图片描述]内容且与问题相关，**必须**在回答中展示该图片。上下文中已有"图片URL: /images/..."字段，请直接复制该URL，使用Markdown语法引用：
-4. 如果上下文中没有相关信息，请诚实说明
+   ![图片描述](图片URL)
-5. 在回答末尾，列出所有实际引用的参考来源，格式为：
+   例如：上下文中某来源包含"图片URL: /images/1/page3_img1.png"，则插入：
+   ![该图为XX规划图](/images/1/page3_img1.png)
+4. 回答要准确、专业、详细，结构清晰，逻辑性强
+5. 如果上下文中没有相关信息，请诚实说明
+6. 在回答末尾，列出所有实际引用的参考来源，格式为：
    **参考来源：**
    - [来源N] 文档标题
-6. 回答长度控制在500-1000字之间
+7. 回答长度控制在500-1000字之间
 
 请基于上述上下文信息回答用户的问题。"""
 

backend/src/rag/vector_store.py

@@ -65,6 +65,17 @@ class VectorStore:
         print(f"[DEBUG-VectorStore] 返回结果数量: {len(result)}")
         return result
     
+    def delete_by_document_id(self, document_id: int) -> bool:
+        """删除指定文档的所有向量数据"""
+        try:
+            self.vectorstore._collection.delete(
+                where={"document_id": document_id}
+            )
+            return True
+        except Exception as e:
+            print(f"删除向量数据失败: {str(e)}")
+            return False
+
     def max_marginal_relevance_search(
         self,
         query: str,

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
@@ -10,8 +10,12 @@ 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"""
@@ -21,13 +25,13 @@ class DocumentService:
         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,
@@ -43,12 +47,26 @@ class DocumentService:
             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
+            # 3. PDF图片提取和描述（仅PDF文件）
-            success = self.vector_store.add_documents(splits)
+            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
@@ -58,6 +76,77 @@ class DocumentService:
             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）"""
         try:
@@ -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()

backend/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -443,6 +443,7 @@ dependencies = [
     { name = "psycopg", extra = ["binary"] },
     { name = "pydantic", extra = ["email"] },
     { name = "pydantic-settings" },
+    { name = "pymupdf" },
     { name = "pypdf" },
     { name = "pypdf2" },
     { name = "python-docx" },
@@ -492,6 +493,7 @@ requires-dist = [
     { name = "psycopg", extras = ["binary"], specifier = ">=3.1.0" },
     { name = "pydantic", extras = ["email"], specifier = ">=2.5.0" },
     { name = "pydantic-settings", specifier = ">=2.1.0" },
+    { name = "pymupdf", specifier = ">=1.27.2.3" },
     { name = "pypdf", specifier = ">=6.12.0" },
     { name = "pypdf2", specifier = ">=3.0.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.4.0" },
@@ -2893,6 +2895,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
 ]
 
+[[package]]
+name = "pymupdf"
+version = "1.27.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/22/32/708bedc9dde7b328d45abbc076091769d44f2f24ad151ad92d56a6ec142b/pymupdf-1.27.2.3.tar.gz", hash = "sha256:7a92faa25129e8bbec5e50eeb9214f187665428c31b05c4ef6e36c58c0b1c6d2", size = 85759618, upload-time = "2026-04-24T14:13:14.42Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/09/ddbdfa7ee91fbabd6f63d7d744884cbdfe3e7ff9b8604749fb38bddf5c5d/pymupdf-1.27.2.3-cp310-abi3-macosx_10_9_x86_64.whl", hash = "sha256:fc1bc3cae6e9e150b0dbb0a9221bdfd411d65f0db2fe359eaa22467d7cc2a05f", size = 24002636, upload-time = "2026-04-24T14:09:17.459Z" },
+    { url = "https://files.pythonhosted.org/packages/01/89/3f8edd6c4f50ca370e2a2f2a3011face36f3760728ffe76dffec91c0fca0/pymupdf-1.27.2.3-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:660d93cb6da5bbddf11d3982ae27745dd3a9902d9f24cdb69adab83962294b5a", size = 23278238, upload-time = "2026-04-24T14:09:32.882Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/26/b7e5a70eb83bd189f8b5df87ec442746b992f2f632662839b288170d357d/pymupdf-1.27.2.3-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1dd460a3ae4597a755f00a3bd9771f5ebf1531dc111f6a36bf05dd00a6b84425", size = 24333923, upload-time = "2026-04-24T14:09:47.341Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/a0/aa1ee2240f29481a04a827c313333b4ecd8a14d6ac3e15d3f41a30574781/pymupdf-1.27.2.3-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:857842b4888827bd6155a1131341b2822a7ebe9a8c15a975fd7d490d7a64a30c", size = 24963198, upload-time = "2026-04-24T14:10:07.408Z" },
+    { url = "https://files.pythonhosted.org/packages/69/49/4f742451f980840829fc00ba158bebb25d389c846d8f4f8c65936ee55de8/pymupdf-1.27.2.3-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:580983849c64a08d08344ca3d1580e87c01f046a8392421797bc850efd72a5b6", size = 25184609, upload-time = "2026-04-24T14:10:22.911Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/3f/3853d6608f394faf6eec2bd4e8ea9f6a00beea329b071abdb29f4164cc3d/pymupdf-1.27.2.3-cp310-abi3-win32.whl", hash = "sha256:a5c1088a87189891a4946ab314a14b7934ac4c5b6077f7e74ebee956f8906d0e", size = 18019286, upload-time = "2026-04-24T14:10:34.239Z" },
+    { url = "https://files.pythonhosted.org/packages/44/47/5fb10fe73f96b31253a41647c362ea9e0380920bddf16028414a051247fc/pymupdf-1.27.2.3-cp310-abi3-win_amd64.whl", hash = "sha256:d20f68ef15195e073071dbc4ae7455257c7889af7584e39df490c0a92728526e", size = 19249102, upload-time = "2026-04-24T14:10:46.72Z" },
+    { url = "https://files.pythonhosted.org/packages/53/a4/b9e91aac82293f9c954654c85581ee8212b5b05efadc534b581141241e6f/pymupdf-1.27.2.3-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:77691604c5d1d0233827139bbcdea61fd57879c84712b8e49b1f45520f7ab9c2", size = 25000393, upload-time = "2026-04-24T14:11:01.669Z" },
+]
+
 [[package]]
 name = "pypdf"
 version = "6.12.0"

web/next.config.js

@@ -32,6 +32,10 @@ const nextConfig = {
         source: '/generated_images/:path*',
         destination: `${backendUrl}/generated_images/:path*`,
       },
+      {
+        source: '/images/:path*',
+        destination: `${backendUrl}/images/:path*`,
+      },
     ];
   },
   // 禁用静态生成，避免 SSR 时使用浏览器 API 的错误

web/src/components/chat/message-item.tsx

@@ -252,6 +252,22 @@ export default function MessageItem({ message, selectedModel }: MessageItemProps
                     td: ({ children }) => (
                       <td className="border border-border px-2 py-1.5">{children}</td>
                     ),
+                    img: ({ src, alt }: any) => (
+                      <a href={src} target="_blank" rel="noopener noreferrer" className="block my-3 group">
+                        <img
+                          src={src}
+                          alt={alt || "图片来源"}
+                          loading="lazy"
+                          className="max-w-full max-h-80 rounded-lg border border-border/50 cursor-pointer
+                            hover:border-primary/40 transition-colors object-contain bg-muted/20"
+                        />
+                        {alt && (
+                          <span className="block text-[11px] text-muted-foreground/70 mt-1 text-center">
+                            {alt}
+                          </span>
+                        )}
+                      </a>
+                    ),
                   }}
                 >
                   {preprocessCitations(message.content, String(message.id))}

web/src/components/chat/source-references.tsx

@@ -1,7 +1,7 @@
 "use client";
 
 import { useState, useMemo } from "react";
-import { Database, Globe, ChevronDown, ChevronRight, Star } from "lucide-react";
+import { Database, Globe, ChevronDown, ChevronRight, Star, Image } from "lucide-react";
 import { Button } from "@/components/ui/button";
 import { cn } from "@/lib/utils";
 
@@ -13,7 +13,8 @@ interface SourceReference {
   score?: number;
   preview: string;
   url?: string;
-  source_type?: "web" | "rag";
+  source_type?: "web" | "rag" | "image";
+  image_url?: string;
 }
 
 interface SourceReferencesProps {
@@ -188,7 +189,8 @@ export default function SourceReferences({
   const showExpandButton = sources.length > maxSources;
   const visibleSources = expanded ? sources : displaySources;
 
-  const ragSources = visibleSources.filter((s) => s.source_type !== "web");
+  const ragSources = visibleSources.filter((s) => s.source_type !== "web" && s.source_type !== "image");
+  const imageSources = visibleSources.filter((s) => s.source_type === "image");
   const webSources = visibleSources.filter((s) => s.source_type === "web");
 
   const sourceSection = (
@@ -218,6 +220,31 @@ export default function SourceReferences({
         </div>
       )}
 
+      {imageSources.length > 0 && (
+        <div className="space-y-1">
+          <div className="flex items-center gap-1.5 text-[11px] font-medium text-muted-foreground px-0.5">
+            <Image className="h-3 w-3 text-purple-500" />
+            <span>
+              图片来源 ({imageSources.length}
+              {expanded && showExpandButton
+                ? `/${sources.filter((s) => s.source_type === "image").length}`
+                : ""}
+              )
+            </span>
+          </div>
+          <div className="divide-y divide-border/30">
+            {imageSources.map((source, i) => (
+              <SourceRow
+                key={i}
+                source={source}
+                answerContent={answerContent}
+                messageId={messageId}
+              />
+            ))}
+          </div>
+        </div>
+      )}
+
       {webSources.length > 0 && (
         <div className="space-y-1">
           <div className="flex items-center gap-1.5 text-[11px] font-medium text-muted-foreground px-0.5">

web/src/types/index.ts

@@ -101,7 +101,8 @@ export interface SourceInfo {
   score?: number;
   preview: string;
   url?: string;
-  source_type?: "web" | "rag";
+  source_type?: "web" | "rag" | "image";
+  image_url?: string;
 }
 
 export interface ChatResponse {