Feat/support multimodal embedding (#29115)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

api/core/rag/index_processor/constant/doc_type.py

@@ -0,0 +1,6 @@
+from enum import StrEnum
+
+
+class DocType(StrEnum):
+    TEXT = "text"
+    IMAGE = "image"

api/core/rag/index_processor/constant/index_type.py

@@ -1,7 +1,12 @@
 from enum import StrEnum
 
 
-class IndexType(StrEnum):
+class IndexStructureType(StrEnum):
     PARAGRAPH_INDEX = "text_model"
     QA_INDEX = "qa_model"
     PARENT_CHILD_INDEX = "hierarchical_model"
+
+
+class IndexTechniqueType(StrEnum):
+    ECONOMY = "economy"
+    HIGH_QUALITY = "high_quality"

api/core/rag/index_processor/constant/query_type.py

@@ -0,0 +1,6 @@
+from enum import StrEnum
+
+
+class QueryType(StrEnum):
+    TEXT_QUERY = "text_query"
+    IMAGE_QUERY = "image_query"

api/core/rag/index_processor/index_processor_base.py

@@ -1,20 +1,34 @@
 """Abstract interface for document loader implementations."""
 
+import cgi
+import logging
+import mimetypes
+import os
+import re
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from typing import TYPE_CHECKING, Any, Optional
+from urllib.parse import unquote, urlparse
+
+import httpx
 
 from configs import dify_config
+from core.helper import ssrf_proxy
 from core.rag.extractor.entity.extract_setting import ExtractSetting
-from core.rag.models.document import Document
+from core.rag.index_processor.constant.doc_type import DocType
+from core.rag.models.document import AttachmentDocument, Document
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.rag.splitter.fixed_text_splitter import (
     EnhanceRecursiveCharacterTextSplitter,
     FixedRecursiveCharacterTextSplitter,
 )
 from core.rag.splitter.text_splitter import TextSplitter
+from extensions.ext_database import db
+from extensions.ext_storage import storage
+from models import Account, ToolFile
 from models.dataset import Dataset, DatasetProcessRule
 from models.dataset import Document as DatasetDocument
+from models.model import UploadFile
 
 if TYPE_CHECKING:
     from core.model_manager import ModelInstance
@@ -28,11 +42,18 @@ class BaseIndexProcessor(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def transform(self, documents: list[Document], **kwargs) -> list[Document]:
+    def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         raise NotImplementedError
 
     @abstractmethod
-    def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True, **kwargs):
+    def load(
+        self,
+        dataset: Dataset,
+        documents: list[Document],
+        multimodal_documents: list[AttachmentDocument] | None = None,
+        with_keywords: bool = True,
+        **kwargs,
+    ):
         raise NotImplementedError
 
     @abstractmethod
@@ -96,3 +117,178 @@ class BaseIndexProcessor(ABC):
             )
 
         return character_splitter  # type: ignore
+
+    def _get_content_files(self, document: Document, current_user: Account | None = None) -> list[AttachmentDocument]:
+        """
+        Get the content files from the document.
+        """
+        multi_model_documents: list[AttachmentDocument] = []
+        text = document.page_content
+        images = self._extract_markdown_images(text)
+        if not images:
+            return multi_model_documents
+        upload_file_id_list = []
+
+        for image in images:
+            # Collect all upload_file_ids including duplicates to preserve occurrence count
+
+            # For data before v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/image-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For data after v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/file-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For tools directory - direct file formats (e.g., .png, .jpg, etc.)
+            # Match URL including any query parameters up to common URL boundaries (space, parenthesis, quotes)
+            pattern = r"/files/tools/([a-f0-9\-]+)\.([a-zA-Z0-9]+)(?:\?[^\s\)\"\']*)?"
+            match = re.search(pattern, image)
+            if match:
+                if current_user:
+                    tool_file_id = match.group(1)
+                    upload_file_id = self._download_tool_file(tool_file_id, current_user)
+                    if upload_file_id:
+                        upload_file_id_list.append(upload_file_id)
+                continue
+            if current_user:
+                upload_file_id = self._download_image(image.split(" ")[0], current_user)
+                if upload_file_id:
+                    upload_file_id_list.append(upload_file_id)
+
+        if not upload_file_id_list:
+            return multi_model_documents
+
+        # Get unique IDs for database query
+        unique_upload_file_ids = list(set(upload_file_id_list))
+        upload_files = db.session.query(UploadFile).where(UploadFile.id.in_(unique_upload_file_ids)).all()
+
+        # Create a mapping from ID to UploadFile for quick lookup
+        upload_file_map = {upload_file.id: upload_file for upload_file in upload_files}
+
+        # Create a Document for each occurrence (including duplicates)
+        for upload_file_id in upload_file_id_list:
+            upload_file = upload_file_map.get(upload_file_id)
+            if upload_file:
+                multi_model_documents.append(
+                    AttachmentDocument(
+                        page_content=upload_file.name,
+                        metadata={
+                            "doc_id": upload_file.id,
+                            "doc_hash": "",
+                            "document_id": document.metadata.get("document_id"),
+                            "dataset_id": document.metadata.get("dataset_id"),
+                            "doc_type": DocType.IMAGE,
+                        },
+                    )
+                )
+        return multi_model_documents
+
+    def _extract_markdown_images(self, text: str) -> list[str]:
+        """
+        Extract the markdown images from the text.
+        """
+        pattern = r"!\[.*?\]\((.*?)\)"
+        return re.findall(pattern, text)
+
+    def _download_image(self, image_url: str, current_user: Account) -> str | None:
+        """
+        Download the image from the URL.
+        Image size must not exceed 2MB.
+        """
+        from services.file_service import FileService
+
+        MAX_IMAGE_SIZE = dify_config.ATTACHMENT_IMAGE_FILE_SIZE_LIMIT * 1024 * 1024
+        DOWNLOAD_TIMEOUT = dify_config.ATTACHMENT_IMAGE_DOWNLOAD_TIMEOUT
+
+        try:
+            # Download with timeout
+            response = ssrf_proxy.get(image_url, timeout=DOWNLOAD_TIMEOUT)
+            response.raise_for_status()
+
+            # Check Content-Length header if available
+            content_length = response.headers.get("Content-Length")
+            if content_length and int(content_length) > MAX_IMAGE_SIZE:
+                logging.warning("Image from %s exceeds 2MB limit (size: %s bytes)", image_url, content_length)
+                return None
+
+            filename = None
+
+            content_disposition = response.headers.get("content-disposition")
+            if content_disposition:
+                _, params = cgi.parse_header(content_disposition)
+                if "filename" in params:
+                    filename = params["filename"]
+                    filename = unquote(filename)
+
+            if not filename:
+                parsed_url = urlparse(image_url)
+                # unquote 处理 URL 中的中文
+                path = unquote(parsed_url.path)
+                filename = os.path.basename(path)
+
+            if not filename:
+                filename = "downloaded_image_file"
+
+            name, current_ext = os.path.splitext(filename)
+
+            content_type = response.headers.get("content-type", "").split(";")[0].strip()
+
+            real_ext = mimetypes.guess_extension(content_type)
+
+            if not current_ext and real_ext or current_ext in [".php", ".jsp", ".asp", ".html"] and real_ext:
+                filename = f"{name}{real_ext}"
+            # Download content with size limit
+            blob = b""
+            for chunk in response.iter_bytes(chunk_size=8192):
+                blob += chunk
+                if len(blob) > MAX_IMAGE_SIZE:
+                    logging.warning("Image from %s exceeds 2MB limit during download", image_url)
+                    return None
+
+            if not blob:
+                logging.warning("Image from %s is empty", image_url)
+                return None
+
+            upload_file = FileService(db.engine).upload_file(
+                filename=filename,
+                content=blob,
+                mimetype=content_type,
+                user=current_user,
+            )
+            return upload_file.id
+        except httpx.TimeoutException:
+            logging.warning("Timeout downloading image from %s after %s seconds", image_url, DOWNLOAD_TIMEOUT)
+            return None
+        except httpx.RequestError as e:
+            logging.warning("Error downloading image from %s: %s", image_url, str(e))
+            return None
+        except Exception:
+            logging.exception("Unexpected error downloading image from %s", image_url)
+            return None
+
+    def _download_tool_file(self, tool_file_id: str, current_user: Account) -> str | None:
+        """
+        Download the tool file from the ID.
+        """
+        from services.file_service import FileService
+
+        tool_file = db.session.query(ToolFile).where(ToolFile.id == tool_file_id).first()
+        if not tool_file:
+            return None
+        blob = storage.load_once(tool_file.file_key)
+        upload_file = FileService(db.engine).upload_file(
+            filename=tool_file.name,
+            content=blob,
+            mimetype=tool_file.mimetype,
+            user=current_user,
+        )
+        return upload_file.id

api/core/rag/index_processor/index_processor_factory.py

@@ -1,6 +1,6 @@
 """Abstract interface for document loader implementations."""
 
-from core.rag.index_processor.constant.index_type import IndexType
+from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
 from core.rag.index_processor.processor.parent_child_index_processor import ParentChildIndexProcessor
@@ -19,11 +19,11 @@ class IndexProcessorFactory:
         if not self._index_type:
             raise ValueError("Index type must be specified.")
 
-        if self._index_type == IndexType.PARAGRAPH_INDEX:
+        if self._index_type == IndexStructureType.PARAGRAPH_INDEX:
             return ParagraphIndexProcessor()
-        elif self._index_type == IndexType.QA_INDEX:
+        elif self._index_type == IndexStructureType.QA_INDEX:
             return QAIndexProcessor()
-        elif self._index_type == IndexType.PARENT_CHILD_INDEX:
+        elif self._index_type == IndexStructureType.PARENT_CHILD_INDEX:
             return ParentChildIndexProcessor()
         else:
             raise ValueError(f"Index type {self._index_type} is not supported.")

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -11,14 +11,17 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.docstore.dataset_docstore import DatasetDocumentStore
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
-from core.rag.index_processor.constant.index_type import IndexType
+from core.rag.index_processor.constant.doc_type import DocType
+from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
-from core.rag.models.document import Document
+from core.rag.models.document import AttachmentDocument, Document, MultimodalGeneralStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
+from models.account import Account
 from models.dataset import Dataset, DatasetProcessRule
 from models.dataset import Document as DatasetDocument
+from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import Rule
 
 
@@ -33,7 +36,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
 
         return text_docs
 
-    def transform(self, documents: list[Document], **kwargs) -> list[Document]:
+    def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         process_rule = kwargs.get("process_rule")
         if not process_rule:
             raise ValueError("No process rule found.")
@@ -69,6 +72,11 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                     if document_node.metadata is not None:
                         document_node.metadata["doc_id"] = doc_id
                         document_node.metadata["doc_hash"] = hash
+                    multimodal_documents = (
+                        self._get_content_files(document_node, current_user) if document_node.metadata else None
+                    )
+                    if multimodal_documents:
+                        document_node.attachments = multimodal_documents
                     # delete Splitter character
                     page_content = remove_leading_symbols(document_node.page_content).strip()
                     if len(page_content) > 0:
@@ -77,10 +85,19 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             all_documents.extend(split_documents)
         return all_documents
 
-    def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True, **kwargs):
+    def load(
+        self,
+        dataset: Dataset,
+        documents: list[Document],
+        multimodal_documents: list[AttachmentDocument] | None = None,
+        with_keywords: bool = True,
+        **kwargs,
+    ):
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             vector.create(documents)
+            if multimodal_documents and dataset.is_multimodal:
+                vector.create_multimodal(multimodal_documents)
             with_keywords = False
         if with_keywords:
             keywords_list = kwargs.get("keywords_list")
@@ -134,8 +151,9 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
         return docs
 
     def index(self, dataset: Dataset, document: DatasetDocument, chunks: Any):
+        documents: list[Any] = []
+        all_multimodal_documents: list[Any] = []
         if isinstance(chunks, list):
-            documents = []
             for content in chunks:
                 metadata = {
                     "dataset_id": dataset.id,
@@ -144,26 +162,68 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                     "doc_hash": helper.generate_text_hash(content),
                 }
                 doc = Document(page_content=content, metadata=metadata)
+                attachments = self._get_content_files(doc)
+                if attachments:
+                    doc.attachments = attachments
+                    all_multimodal_documents.extend(attachments)
                 documents.append(doc)
-            if documents:
-                # save node to document segment
-                doc_store = DatasetDocumentStore(dataset=dataset, user_id=document.created_by, document_id=document.id)
-                # add document segments
-                doc_store.add_documents(docs=documents, save_child=False)
-                if dataset.indexing_technique == "high_quality":
-                    vector = Vector(dataset)
-                    vector.create(documents)
-                elif dataset.indexing_technique == "economy":
-                    keyword = Keyword(dataset)
-                    keyword.add_texts(documents)
         else:
-            raise ValueError("Chunks is not a list")
+            multimodal_general_structure = MultimodalGeneralStructureChunk.model_validate(chunks)
+            for general_chunk in multimodal_general_structure.general_chunks:
+                metadata = {
+                    "dataset_id": dataset.id,
+                    "document_id": document.id,
+                    "doc_id": str(uuid.uuid4()),
+                    "doc_hash": helper.generate_text_hash(general_chunk.content),
+                }
+                doc = Document(page_content=general_chunk.content, metadata=metadata)
+                if general_chunk.files:
+                    attachments = []
+                    for file in general_chunk.files:
+                        file_metadata = {
+                            "doc_id": file.id,
+                            "doc_hash": "",
+                            "document_id": document.id,
+                            "dataset_id": dataset.id,
+                            "doc_type": DocType.IMAGE,
+                        }
+                        file_document = AttachmentDocument(
+                            page_content=file.filename or "image_file", metadata=file_metadata
+                        )
+                        attachments.append(file_document)
+                        all_multimodal_documents.append(file_document)
+                    doc.attachments = attachments
+                else:
+                    account = AccountService.load_user(document.created_by)
+                    if not account:
+                        raise ValueError("Invalid account")
+                    doc.attachments = self._get_content_files(doc, current_user=account)
+                    if doc.attachments:
+                        all_multimodal_documents.extend(doc.attachments)
+                documents.append(doc)
+        if documents:
+            # save node to document segment
+            doc_store = DatasetDocumentStore(dataset=dataset, user_id=document.created_by, document_id=document.id)
+            # add document segments
+            doc_store.add_documents(docs=documents, save_child=False)
+            if dataset.indexing_technique == "high_quality":
+                vector = Vector(dataset)
+                vector.create(documents)
+                if all_multimodal_documents:
+                    vector.create_multimodal(all_multimodal_documents)
+            elif dataset.indexing_technique == "economy":
+                keyword = Keyword(dataset)
+                keyword.add_texts(documents)
 
     def format_preview(self, chunks: Any) -> Mapping[str, Any]:
         if isinstance(chunks, list):
             preview = []
             for content in chunks:
                 preview.append({"content": content})
-            return {"chunk_structure": IndexType.PARAGRAPH_INDEX, "preview": preview, "total_segments": len(chunks)}
+            return {
+                "chunk_structure": IndexStructureType.PARAGRAPH_INDEX,
+                "preview": preview,
+                "total_segments": len(chunks),
+            }
         else:
             raise ValueError("Chunks is not a list")

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -13,14 +13,17 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.docstore.dataset_docstore import DatasetDocumentStore
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
-from core.rag.index_processor.constant.index_type import IndexType
+from core.rag.index_processor.constant.doc_type import DocType
+from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
-from core.rag.models.document import ChildDocument, Document, ParentChildStructureChunk
+from core.rag.models.document import AttachmentDocument, ChildDocument, Document, ParentChildStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from libs import helper
+from models import Account
 from models.dataset import ChildChunk, Dataset, DatasetProcessRule, DocumentSegment
 from models.dataset import Document as DatasetDocument
+from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import ParentMode, Rule
 
 
@@ -35,7 +38,7 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
 
         return text_docs
 
-    def transform(self, documents: list[Document], **kwargs) -> list[Document]:
+    def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         process_rule = kwargs.get("process_rule")
         if not process_rule:
             raise ValueError("No process rule found.")
@@ -77,6 +80,9 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
                             page_content = page_content
                         if len(page_content) > 0:
                             document_node.page_content = page_content
+                            multimodel_documents = self._get_content_files(document_node, current_user)
+                            if multimodel_documents:
+                                document_node.attachments = multimodel_documents
                             # parse document to child nodes
                             child_nodes = self._split_child_nodes(
                                 document_node, rules, process_rule.get("mode"), kwargs.get("embedding_model_instance")
@@ -87,6 +93,9 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
         elif rules.parent_mode == ParentMode.FULL_DOC:
             page_content = "\n".join([document.page_content for document in documents])
             document = Document(page_content=page_content, metadata=documents[0].metadata)
+            multimodel_documents = self._get_content_files(document)
+            if multimodel_documents:
+                document.attachments = multimodel_documents
             # parse document to child nodes
             child_nodes = self._split_child_nodes(
                 document, rules, process_rule.get("mode"), kwargs.get("embedding_model_instance")
@@ -104,7 +113,14 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
 
         return all_documents
 
-    def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True, **kwargs):
+    def load(
+        self,
+        dataset: Dataset,
+        documents: list[Document],
+        multimodal_documents: list[AttachmentDocument] | None = None,
+        with_keywords: bool = True,
+        **kwargs,
+    ):
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             for document in documents:
@@ -114,6 +130,8 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
                         Document.model_validate(child_document.model_dump()) for child_document in child_documents
                     ]
                     vector.create(formatted_child_documents)
+            if multimodal_documents and dataset.is_multimodal:
+                vector.create_multimodal(multimodal_documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
         # node_ids is segment's node_ids
@@ -244,6 +262,24 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
                 }
                 child_documents.append(ChildDocument(page_content=child, metadata=child_metadata))
             doc = Document(page_content=parent_child.parent_content, metadata=metadata, children=child_documents)
+            if parent_child.files and len(parent_child.files) > 0:
+                attachments = []
+                for file in parent_child.files:
+                    file_metadata = {
+                        "doc_id": file.id,
+                        "doc_hash": "",
+                        "document_id": document.id,
+                        "dataset_id": dataset.id,
+                        "doc_type": DocType.IMAGE,
+                    }
+                    file_document = AttachmentDocument(page_content=file.filename or "", metadata=file_metadata)
+                    attachments.append(file_document)
+                doc.attachments = attachments
+            else:
+                account = AccountService.load_user(document.created_by)
+                if not account:
+                    raise ValueError("Invalid account")
+                doc.attachments = self._get_content_files(doc, current_user=account)
             documents.append(doc)
         if documents:
             # update document parent mode
@@ -267,12 +303,17 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
             doc_store.add_documents(docs=documents, save_child=True)
             if dataset.indexing_technique == "high_quality":
                 all_child_documents = []
+                all_multimodal_documents = []
                 for doc in documents:
                     if doc.children:
                         all_child_documents.extend(doc.children)
+                    if doc.attachments:
+                        all_multimodal_documents.extend(doc.attachments)
+                vector = Vector(dataset)
                 if all_child_documents:
-                    vector = Vector(dataset)
                     vector.create(all_child_documents)
+                if all_multimodal_documents:
+                    vector.create_multimodal(all_multimodal_documents)
 
     def format_preview(self, chunks: Any) -> Mapping[str, Any]:
         parent_childs = ParentChildStructureChunk.model_validate(chunks)
@@ -280,7 +321,7 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
         for parent_child in parent_childs.parent_child_chunks:
             preview.append({"content": parent_child.parent_content, "child_chunks": parent_child.child_contents})
         return {
-            "chunk_structure": IndexType.PARENT_CHILD_INDEX,
+            "chunk_structure": IndexStructureType.PARENT_CHILD_INDEX,
             "parent_mode": parent_childs.parent_mode,
             "preview": preview,
             "total_segments": len(parent_childs.parent_child_chunks),

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -18,12 +18,13 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.docstore.dataset_docstore import DatasetDocumentStore
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
-from core.rag.index_processor.constant.index_type import IndexType
+from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
-from core.rag.models.document import Document, QAStructureChunk
+from core.rag.models.document import AttachmentDocument, Document, QAStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
+from models.account import Account
 from models.dataset import Dataset
 from models.dataset import Document as DatasetDocument
 from services.entities.knowledge_entities.knowledge_entities import Rule
@@ -41,7 +42,7 @@ class QAIndexProcessor(BaseIndexProcessor):
         )
         return text_docs
 
-    def transform(self, documents: list[Document], **kwargs) -> list[Document]:
+    def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         preview = kwargs.get("preview")
         process_rule = kwargs.get("process_rule")
         if not process_rule:
@@ -116,7 +117,7 @@ class QAIndexProcessor(BaseIndexProcessor):
 
         try:
             # Skip the first row
-            df = pd.read_csv(file)
+            df = pd.read_csv(file)  # type: ignore
             text_docs = []
             for _, row in df.iterrows():
                 data = Document(page_content=row.iloc[0], metadata={"answer": row.iloc[1]})
@@ -128,10 +129,19 @@ class QAIndexProcessor(BaseIndexProcessor):
             raise ValueError(str(e))
         return text_docs
 
-    def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True, **kwargs):
+    def load(
+        self,
+        dataset: Dataset,
+        documents: list[Document],
+        multimodal_documents: list[AttachmentDocument] | None = None,
+        with_keywords: bool = True,
+        **kwargs,
+    ):
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             vector.create(documents)
+            if multimodal_documents and dataset.is_multimodal:
+                vector.create_multimodal(multimodal_documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
         vector = Vector(dataset)
@@ -197,7 +207,7 @@ class QAIndexProcessor(BaseIndexProcessor):
         for qa_chunk in qa_chunks.qa_chunks:
             preview.append({"question": qa_chunk.question, "answer": qa_chunk.answer})
         return {
-            "chunk_structure": IndexType.QA_INDEX,
+            "chunk_structure": IndexStructureType.QA_INDEX,
             "qa_preview": preview,
             "total_segments": len(qa_chunks.qa_chunks),
         }