feat(multimodal): support document image extraction and inline vision processing

Added document image extraction capability for PDF and DOCX files, including page/index metadata and storage integration. Extended `process_files` with `document_image_recognition` flag to conditionally enable vision-based image processing when model supports it. Updated knowledge repository and workflow node logic to enforce status=1 checks. Added PyMuPDF dependency.

api/app/core/workflow/nodes/document_extractor/node.py

@@ -1,12 +1,15 @@
 import logging
+import uuid
 from typing import Any
 
+from app.core.config import settings
 from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.document_extractor.config import DocExtractorNodeConfig
 from app.core.workflow.variable.base_variable import VariableType, FileObject
 from app.db import get_db_read
+from app.models.file_metadata_model import FileMetadata
 from app.schemas.app_schema import FileInput, FileType, TransferMethod
 
 logger = logging.getLogger(__name__)
@@ -15,7 +18,6 @@ logger = logging.getLogger(__name__)
 def _file_object_to_file_input(f: FileObject) -> FileInput:
     """Convert workflow FileObject to multimodal FileInput."""
     file_type = f.origin_file_type or ""
-    # Prefer mime_type for more accurate type detection
     if not file_type and f.mime_type:
         file_type = f.mime_type
     resolved_type = FileType.trans(f.type) if isinstance(f.type, str) else f.type
@@ -51,21 +53,68 @@ def _normalise_files(val: Any) -> list[FileObject]:
     return []
 
 
+async def _save_image_to_storage(
+    img_bytes: bytes,
+    ext: str,
+    tenant_id: uuid.UUID,
+    workspace_id: uuid.UUID,
+) -> tuple[uuid.UUID, str]:
+    """
+    将图片字节保存到存储后端，写入 FileMetadata，返回 (file_id, url)。
+    """
+    from app.services.file_storage_service import FileStorageService, generate_file_key
+
+    file_id = uuid.uuid4()
+    file_ext = f".{ext}" if not ext.startswith(".") else ext
+    content_type = f"image/{ext}"
+
+    file_key = generate_file_key(
+        tenant_id=tenant_id,
+        workspace_id=workspace_id,
+        file_id=file_id,
+        file_ext=file_ext,
+    )
+
+    storage_svc = FileStorageService()
+    await storage_svc.storage.upload(file_key, img_bytes, content_type)
+
+    with get_db_read() as db:
+        meta = FileMetadata(
+            id=file_id,
+            tenant_id=tenant_id,
+            workspace_id=workspace_id,
+            file_key=file_key,
+            file_name=f"doc_image_{file_id}{file_ext}",
+            file_ext=file_ext,
+            file_size=len(img_bytes),
+            content_type=content_type,
+            status="completed",
+        )
+        db.add(meta)
+        db.commit()
+
+    url = f"{settings.FILE_LOCAL_SERVER_URL}/storage/permanent/{file_id}"
+    return file_id, url
+
+
 class DocExtractorNode(BaseNode):
     """Document Extractor Node.
 
     Reads one or more file variables and extracts their text content
-    by delegating to MultimodalService._extract_document_text.
+    and embedded images.
 
     Outputs:
-        text   (string)        – full concatenated text of all input files
-        chunks (array[string]) – per-file extracted text
+        text   (string)        – full text with image placeholders like [图片 第N页 第M张]
+        chunks (array[string]) – per-file extracted text (with placeholders)
+        images (array[file])   – extracted images as FileObject list, each with
+                                 name encoding position: "p{page}_i{index}"
     """
 
     def _output_types(self) -> dict[str, VariableType]:
         return {
             "text": VariableType.STRING,
             "chunks": VariableType.ARRAY_STRING,
+            "images": VariableType.ARRAY_FILE,
         }
 
     def _extract_output(self, business_result: Any) -> Any:
@@ -80,13 +129,18 @@ class DocExtractorNode(BaseNode):
         raw_val = self.get_variable(config.file_selector, variable_pool, strict=False)
         if raw_val is None:
             logger.warning(f"Node {self.node_id}: file variable '{config.file_selector}' is empty")
-            return {"text": "", "chunks": []}
+            return {"text": "", "chunks": [], "images": []}
 
         files = _normalise_files(raw_val)
         if not files:
-            return {"text": "", "chunks": []}
+            return {"text": "", "chunks": [], "images": []}
+
+        tenant_id = uuid.UUID(self.get_variable("sys.tenant_id", variable_pool, strict=False) or str(uuid.uuid4()))
+        workspace_id = uuid.UUID(self.get_variable("sys.workspace_id", variable_pool))
 
         chunks: list[str] = []
+        image_file_objects: list[dict] = []
+
         with get_db_read() as db:
             from app.services.multimodal_service import MultimodalService
             svc = MultimodalService(db)
@@ -94,13 +148,44 @@ class DocExtractorNode(BaseNode):
                 label = f.name or f.url or f.file_id
                 try:
                     file_input = _file_object_to_file_input(f)
-                    # Ensure URL is populated for local files
                     if not file_input.url:
                         file_input.url = await svc.get_file_url(file_input)
-                    # Reuse cached bytes if already fetched
                     if f.get_content():
                         file_input.set_content(f.get_content())
+
                     text = await svc.extract_document_text(file_input)
+
+                    # 从工作流 features 读取 document_image_recognition 开关
+                    fu_config = self.workflow_config.get("features", {}).get("file_upload", {})
+                    image_recognition = isinstance(fu_config, dict) and fu_config.get("document_image_recognition", False)
+                    if image_recognition:
+                        img_infos = await svc.extract_document_images(file_input)
+                        for img_info in img_infos:
+                            page = img_info["page"]
+                            index = img_info["index"]
+                            ext = img_info.get("ext", "png")
+                            placeholder = f"[图片 第{page}页 第{index + 1}张]" if page > 0 else f"[图片 第{index + 1}张]"
+                            try:
+                                file_id, url = await _save_image_to_storage(
+                                    img_bytes=img_info["bytes"],
+                                    ext=ext,
+                                    tenant_id=tenant_id,
+                                    workspace_id=workspace_id,
+                                )
+                                image_file_objects.append(FileObject(
+                                    type=FileType.IMAGE,
+                                    url=url,
+                                    transfer_method=TransferMethod.REMOTE_URL,
+                                    origin_file_type=f"image/{ext}",
+                                    file_id=str(file_id),
+                                    name=f"p{page}_i{index}",
+                                    mime_type=f"image/{ext}",
+                                    is_file=True,
+                                ).model_dump())
+                                text = text + f"\n{placeholder}: {url}"
+                            except Exception as e:
+                                logger.error(f"Node {self.node_id}: failed to save image {placeholder}: {e}")
+
                     chunks.append(text)
                 except Exception as e:
                     logger.error(
@@ -110,5 +195,8 @@ class DocExtractorNode(BaseNode):
                     chunks.append("")
 
         full_text = "\n\n".join(c for c in chunks if c)
-        logger.info(f"Node {self.node_id}: extracted {len(files)} file(s), total chars={len(full_text)}")
-        return {"text": full_text, "chunks": chunks}
+        logger.info(
+            f"Node {self.node_id}: extracted {len(files)} file(s), "
+            f"total chars={len(full_text)}, images={len(image_file_objects)}"
+        )
+        return {"text": full_text, "chunks": chunks, "images": image_file_objects}

api/app/core/workflow/nodes/knowledge/node.py

@@ -333,7 +333,7 @@ class KnowledgeRetrievalNode(BaseNode):
             tasks = []
             for kb_config in knowledge_bases:
                 db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_config.kb_id)
-                if not db_knowledge:
+                if not (db_knowledge and db_knowledge.chunk_num > 0 and db_knowledge.status == 1):
                     raise RuntimeError("The knowledge base does not exist or access is denied.")
                 tasks.append(self.knowledge_retrieval(db, query, db_knowledge, kb_config))
             if tasks: