Feat/support multimodal embedding (#29115)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Jyong
@@ -1,6 +1,15 @@
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from core.model_manager import ModelInstance
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import base64
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from core.model_manager import ModelInstance, ModelManager
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.entities.rerank_entities import RerankResult
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from core.rag.index_processor.constant.doc_type import DocType
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from core.rag.index_processor.constant.query_type import QueryType
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from core.rag.models.document import Document
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from core.rag.rerank.rerank_base import BaseRerankRunner
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from extensions.ext_database import db
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from extensions.ext_storage import storage
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from models.model import UploadFile
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class RerankModelRunner(BaseRerankRunner):
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@@ -14,6 +23,7 @@ class RerankModelRunner(BaseRerankRunner):
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score_threshold: float | None = None,
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top_n: int | None = None,
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user: str | None = None,
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query_type: QueryType = QueryType.TEXT_QUERY,
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) -> list[Document]:
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"""
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Run rerank model
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@@ -24,6 +34,56 @@ class RerankModelRunner(BaseRerankRunner):
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:param user: unique user id if needed
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:return:
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"""
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model_manager = ModelManager()
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is_support_vision = model_manager.check_model_support_vision(
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tenant_id=self.rerank_model_instance.provider_model_bundle.configuration.tenant_id,
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provider=self.rerank_model_instance.provider,
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model=self.rerank_model_instance.model,
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model_type=ModelType.RERANK,
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)
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if not is_support_vision:
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if query_type == QueryType.TEXT_QUERY:
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rerank_result, unique_documents = self.fetch_text_rerank(query, documents, score_threshold, top_n, user)
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else:
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return documents
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else:
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rerank_result, unique_documents = self.fetch_multimodal_rerank(
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query, documents, score_threshold, top_n, user, query_type
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)
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rerank_documents = []
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for result in rerank_result.docs:
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if score_threshold is None or result.score >= score_threshold:
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# format document
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rerank_document = Document(
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page_content=result.text,
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metadata=unique_documents[result.index].metadata,
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provider=unique_documents[result.index].provider,
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)
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if rerank_document.metadata is not None:
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rerank_document.metadata["score"] = result.score
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rerank_documents.append(rerank_document)
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rerank_documents.sort(key=lambda x: x.metadata.get("score", 0.0), reverse=True)
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return rerank_documents[:top_n] if top_n else rerank_documents
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def fetch_text_rerank(
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self,
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query: str,
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documents: list[Document],
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score_threshold: float | None = None,
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top_n: int | None = None,
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user: str | None = None,
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) -> tuple[RerankResult, list[Document]]:
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"""
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Fetch text rerank
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:param query: search query
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:param documents: documents for reranking
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:param score_threshold: score threshold
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:param top_n: top n
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:param user: unique user id if needed
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:return:
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"""
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docs = []
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doc_ids = set()
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unique_documents = []
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@@ -33,33 +93,99 @@ class RerankModelRunner(BaseRerankRunner):
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and document.metadata is not None
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and document.metadata["doc_id"] not in doc_ids
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):
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doc_ids.add(document.metadata["doc_id"])
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docs.append(document.page_content)
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unique_documents.append(document)
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if not document.metadata.get("doc_type") or document.metadata.get("doc_type") == DocType.TEXT:
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doc_ids.add(document.metadata["doc_id"])
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docs.append(document.page_content)
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unique_documents.append(document)
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elif document.provider == "external":
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if document not in unique_documents:
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docs.append(document.page_content)
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unique_documents.append(document)
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documents = unique_documents
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rerank_result = self.rerank_model_instance.invoke_rerank(
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query=query, docs=docs, score_threshold=score_threshold, top_n=top_n, user=user
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)
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return rerank_result, unique_documents
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rerank_documents = []
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def fetch_multimodal_rerank(
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self,
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query: str,
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documents: list[Document],
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score_threshold: float | None = None,
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top_n: int | None = None,
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user: str | None = None,
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query_type: QueryType = QueryType.TEXT_QUERY,
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) -> tuple[RerankResult, list[Document]]:
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"""
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Fetch multimodal rerank
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:param query: search query
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:param documents: documents for reranking
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:param score_threshold: score threshold
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:param top_n: top n
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:param user: unique user id if needed
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:param query_type: query type
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:return: rerank result
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"""
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docs = []
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doc_ids = set()
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unique_documents = []
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for document in documents:
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if (
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document.provider == "dify"
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and document.metadata is not None
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and document.metadata["doc_id"] not in doc_ids
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):
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if document.metadata.get("doc_type") == DocType.IMAGE:
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# Query file info within db.session context to ensure thread-safe access
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upload_file = (
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db.session.query(UploadFile).where(UploadFile.id == document.metadata["doc_id"]).first()
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)
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if upload_file:
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blob = storage.load_once(upload_file.key)
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document_file_base64 = base64.b64encode(blob).decode()
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document_file_dict = {
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"content": document_file_base64,
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"content_type": document.metadata["doc_type"],
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}
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docs.append(document_file_dict)
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else:
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document_text_dict = {
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"content": document.page_content,
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"content_type": document.metadata.get("doc_type") or DocType.TEXT,
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}
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docs.append(document_text_dict)
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doc_ids.add(document.metadata["doc_id"])
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unique_documents.append(document)
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elif document.provider == "external":
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if document not in unique_documents:
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docs.append(
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{
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"content": document.page_content,
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"content_type": document.metadata.get("doc_type") or DocType.TEXT,
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}
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)
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unique_documents.append(document)
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for result in rerank_result.docs:
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if score_threshold is None or result.score >= score_threshold:
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# format document
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rerank_document = Document(
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page_content=result.text,
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metadata=documents[result.index].metadata,
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provider=documents[result.index].provider,
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documents = unique_documents
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if query_type == QueryType.TEXT_QUERY:
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rerank_result, unique_documents = self.fetch_text_rerank(query, documents, score_threshold, top_n, user)
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return rerank_result, unique_documents
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elif query_type == QueryType.IMAGE_QUERY:
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# Query file info within db.session context to ensure thread-safe access
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upload_file = db.session.query(UploadFile).where(UploadFile.id == query).first()
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if upload_file:
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blob = storage.load_once(upload_file.key)
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file_query = base64.b64encode(blob).decode()
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file_query_dict = {
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"content": file_query,
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"content_type": DocType.IMAGE,
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}
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rerank_result = self.rerank_model_instance.invoke_multimodal_rerank(
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query=file_query_dict, docs=docs, score_threshold=score_threshold, top_n=top_n, user=user
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)
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if rerank_document.metadata is not None:
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rerank_document.metadata["score"] = result.score
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rerank_documents.append(rerank_document)
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return rerank_result, unique_documents
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else:
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raise ValueError(f"Upload file not found for query: {query}")
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rerank_documents.sort(key=lambda x: x.metadata.get("score", 0.0), reverse=True)
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return rerank_documents[:top_n] if top_n else rerank_documents
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else:
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raise ValueError(f"Query type {query_type} is not supported")
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