diff --git a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
index b382016473..8d6e821f4c 100644
--- a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
+++ b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
@@ -3,11 +3,13 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from core.rag.datasource.retrieval_service import RetrievalService
+from core.rag.entities.context_entities import DocumentContext
 from core.rag.models.document import Document as RetrievalDocument
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
 from extensions.ext_database import db
-from models.dataset import Dataset, Document, DocumentSegment
+from models.dataset import Dataset
+from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model = {
@@ -54,7 +56,6 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
 
         if not dataset:
             return ""
-
         for hit_callback in self.hit_callbacks:
             hit_callback.on_query(query, dataset.id)
         if dataset.provider == "external":
@@ -125,7 +126,6 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     )
                 else:
                     documents = []
-
                 for hit_callback in self.hit_callbacks:
                     hit_callback.on_tool_end(documents)
                 document_score_list = {}
@@ -134,50 +134,46 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                         if item.metadata is not None and item.metadata.get("score"):
                             document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
                 document_context_list = []
-                index_node_ids = [document.metadata["doc_id"] for document in documents]
-                segments = DocumentSegment.query.filter(
-                    DocumentSegment.dataset_id == self.dataset_id,
-                    DocumentSegment.completed_at.isnot(None),
-                    DocumentSegment.status == "completed",
-                    DocumentSegment.enabled == True,
-                    DocumentSegment.index_node_id.in_(index_node_ids),
-                ).all()
-
-                if segments:
-                    index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-                    sorted_segments = sorted(
-                        segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
-                    )
-                    for segment in sorted_segments:
+                records = RetrievalService.format_retrieval_documents(documents)
+                if records:
+                    for record in records:
+                        segment = record.segment
                         if segment.answer:
                             document_context_list.append(
-                                f"question:{segment.get_sign_content()} answer:{segment.answer}"
+                                DocumentContext(
+                                    content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
+                                    score=record.score,
+                                )
                             )
                         else:
-                            document_context_list.append(segment.get_sign_content())
+                            document_context_list.append(
+                                DocumentContext(
+                                    content=segment.get_sign_content(),
+                                    score=record.score,
+                                )
+                            )
+                    retrieval_resource_list = []
                     if self.return_resource:
-                        context_list = []
-                        resource_number = 1
-                        for segment in sorted_segments:
-                            document_segment = Document.query.filter(
-                                Document.id == segment.document_id,
-                                Document.enabled == True,
-                                Document.archived == False,
+                        for record in records:
+                            segment = record.segment
+                            dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
+                            document = DatasetDocument.query.filter(
+                                DatasetDocument.id == segment.document_id,
+                                DatasetDocument.enabled == True,
+                                DatasetDocument.archived == False,
                             ).first()
-                            if not document_segment:
-                                continue
-                            if dataset and document_segment:
+                            if dataset and document:
                                 source = {
-                                    "position": resource_number,
                                     "dataset_id": dataset.id,
                                     "dataset_name": dataset.name,
-                                    "document_id": document_segment.id,
-                                    "document_name": document_segment.name,
-                                    "data_source_type": document_segment.data_source_type,
+                                    "document_id": document.id,  # type: ignore
+                                    "document_name": document.name,  # type: ignore
+                                    "data_source_type": document.data_source_type,  # type: ignore
                                     "segment_id": segment.id,
                                     "retriever_from": self.retriever_from,
-                                    "score": document_score_list.get(segment.index_node_id, None),
+                                    "score": record.score or 0.0,
                                 }
+
                                 if self.retriever_from == "dev":
                                     source["hit_count"] = segment.hit_count
                                     source["word_count"] = segment.word_count
@@ -187,10 +183,19 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                                     source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
                                 else:
                                     source["content"] = segment.content
-                                context_list.append(source)
-                            resource_number += 1
+                                retrieval_resource_list.append(source)
 
-                        for hit_callback in self.hit_callbacks:
-                            hit_callback.return_retriever_resource_info(context_list)
-
-                return str("\n".join(document_context_list))
+            if self.return_resource and retrieval_resource_list:
+                retrieval_resource_list = sorted(
+                    retrieval_resource_list,
+                    key=lambda x: x.get("score") or 0.0,
+                    reverse=True,
+                )
+                for position, item in enumerate(retrieval_resource_list, start=1):  # type: ignore
+                    item["position"] = position  # type: ignore
+                for hit_callback in self.hit_callbacks:
+                    hit_callback.return_retriever_resource_info(retrieval_resource_list)
+            if document_context_list:
+                document_context_list = sorted(document_context_list, key=lambda x: x.score or 0.0, reverse=True)
+                return str("\n".join([document_context.content for document_context in document_context_list]))
+            return ""