diff --git a/backend/danswer/search/models.py b/backend/danswer/search/models.py
index d895df098b..ada16ef06a 100644
--- a/backend/danswer/search/models.py
+++ b/backend/danswer/search/models.py
@@ -249,6 +249,13 @@ class SavedSearchDoc(SearchDoc):
         return self.score < other.score
 
 
+class SavedSearchDocWithContent(SavedSearchDoc):
+    """Used for endpoints that need to return the actual contents of the retrieved
+    section in addition to the match_highlights."""
+
+    content: str
+
+
 class RetrievalDocs(BaseModel):
     top_documents: list[SavedSearchDoc]
 
diff --git a/backend/danswer/search/utils.py b/backend/danswer/search/utils.py
index a0b41f9fbe..8b138d2e9b 100644
--- a/backend/danswer/search/utils.py
+++ b/backend/danswer/search/utils.py
@@ -5,10 +5,18 @@ from danswer.db.models import SearchDoc as DBSearchDoc
 from danswer.search.models import InferenceChunk
 from danswer.search.models import InferenceSection
 from danswer.search.models import SavedSearchDoc
+from danswer.search.models import SavedSearchDocWithContent
 from danswer.search.models import SearchDoc
 
 
-T = TypeVar("T", InferenceSection, InferenceChunk, SearchDoc)
+T = TypeVar(
+    "T",
+    InferenceSection,
+    InferenceChunk,
+    SearchDoc,
+    SavedSearchDoc,
+    SavedSearchDocWithContent,
+)
 
 
 def dedupe_documents(items: list[T]) -> tuple[list[T], list[int]]:
diff --git a/backend/ee/danswer/server/query_and_chat/query_backend.py b/backend/ee/danswer/server/query_and_chat/query_backend.py
index b0a9732374..f6cf7297a4 100644
--- a/backend/ee/danswer/server/query_and_chat/query_backend.py
+++ b/backend/ee/danswer/server/query_and_chat/query_backend.py
@@ -1,5 +1,6 @@
 from fastapi import APIRouter
 from fastapi import Depends
+from pydantic import BaseModel
 from sqlalchemy.orm import Session
 
 from danswer.auth.users import current_user
@@ -17,11 +18,9 @@ from danswer.llm.utils import get_max_input_tokens
 from danswer.one_shot_answer.answer_question import get_search_answer
 from danswer.one_shot_answer.models import DirectQARequest
 from danswer.one_shot_answer.models import OneShotQAResponse
-from danswer.search.models import SavedSearchDoc
+from danswer.search.models import SavedSearchDocWithContent
 from danswer.search.models import SearchRequest
-from danswer.search.models import SearchResponse
 from danswer.search.pipeline import SearchPipeline
-from danswer.search.utils import chunks_or_sections_to_search_docs
 from danswer.search.utils import dedupe_documents
 from danswer.search.utils import drop_llm_indices
 from danswer.utils.logger import setup_logger
@@ -32,12 +31,17 @@ logger = setup_logger()
 basic_router = APIRouter(prefix="/query")
 
 
+class DocumentSearchResponse(BaseModel):
+    top_documents: list[SavedSearchDocWithContent]
+    llm_indices: list[int]
+
+
 @basic_router.post("/document-search")
 def handle_search_request(
     search_request: DocumentSearchRequest,
     user: User | None = Depends(current_user),
     db_session: Session = Depends(get_session),
-) -> SearchResponse:
+) -> DocumentSearchResponse:
     """Simple search endpoint, does not create a new message or records in the DB"""
     query = search_request.message
     logger.info(f"Received document search query: {query}")
@@ -67,7 +71,30 @@ def handle_search_request(
     top_sections = search_pipeline.reranked_sections
     # If using surrounding context or full doc, this will be empty
     relevant_section_indices = search_pipeline.relevant_section_indices
-    top_docs = chunks_or_sections_to_search_docs(top_sections)
+    top_docs = [
+        SavedSearchDocWithContent(
+            document_id=section.center_chunk.document_id,
+            chunk_ind=section.center_chunk.chunk_id,
+            content=section.center_chunk.content,
+            semantic_identifier=section.center_chunk.semantic_identifier or "Unknown",
+            link=section.center_chunk.source_links.get(0)
+            if section.center_chunk.source_links
+            else None,
+            blurb=section.center_chunk.blurb,
+            source_type=section.center_chunk.source_type,
+            boost=section.center_chunk.boost,
+            hidden=section.center_chunk.hidden,
+            metadata=section.center_chunk.metadata,
+            score=section.center_chunk.score or 0.0,
+            match_highlights=section.center_chunk.match_highlights,
+            updated_at=section.center_chunk.updated_at,
+            primary_owners=section.center_chunk.primary_owners,
+            secondary_owners=section.center_chunk.secondary_owners,
+            is_internet=False,
+            db_doc_id=0,
+        )
+        for section in top_sections
+    ]
 
     # Deduping happens at the last step to avoid harming quality by dropping content early on
     deduped_docs = top_docs
@@ -75,18 +102,15 @@ def handle_search_request(
     if search_request.retrieval_options.dedupe_docs:
         deduped_docs, dropped_inds = dedupe_documents(top_docs)
 
-    # No need to save the docs for this API
-    fake_saved_docs = [SavedSearchDoc.from_search_doc(doc) for doc in deduped_docs]
-
     if dropped_inds:
         relevant_section_indices = drop_llm_indices(
             llm_indices=relevant_section_indices,
-            search_docs=fake_saved_docs,
+            search_docs=deduped_docs,
             dropped_indices=dropped_inds,
         )
 
-    return SearchResponse(
-        top_documents=fake_saved_docs, llm_indices=relevant_section_indices
+    return DocumentSearchResponse(
+        top_documents=deduped_docs, llm_indices=relevant_section_indices
     )