diff --git a/backend/ee/danswer/server/query_and_chat/chat_backend.py b/backend/ee/danswer/server/query_and_chat/chat_backend.py
index 918adeb5d..0c5149a5c 100644
--- a/backend/ee/danswer/server/query_and_chat/chat_backend.py
+++ b/backend/ee/danswer/server/query_and_chat/chat_backend.py
@@ -90,6 +90,9 @@ def handle_simplified_chat_message(
         search_doc_ids=chat_message_req.search_doc_ids,
         retrieval_options=retrieval_options,
         query_override=chat_message_req.query_override,
+        chunks_above=chat_message_req.chunks_above,
+        chunks_below=chat_message_req.chunks_below,
+        full_doc=chat_message_req.full_doc,
     )
 
     packets = stream_chat_message_objects(
diff --git a/backend/ee/danswer/server/query_and_chat/models.py b/backend/ee/danswer/server/query_and_chat/models.py
index 8eb2f7b80..1876f3f46 100644
--- a/backend/ee/danswer/server/query_and_chat/models.py
+++ b/backend/ee/danswer/server/query_and_chat/models.py
@@ -1,10 +1,22 @@
 from pydantic import BaseModel
 
 from danswer.configs.constants import DocumentSource
+from danswer.search.enums import SearchType
+from danswer.search.models import ChunkContext
 from danswer.search.models import RetrievalDetails
 
 
-class BasicCreateChatMessageRequest(BaseModel):
+class DocumentSearchRequest(ChunkContext):
+    message: str
+    search_type: SearchType
+    retrieval_options: RetrievalDetails
+    recency_bias_multiplier: float = 1.0
+    # This is to forcibly skip (or run) the step, if None it uses the system defaults
+    skip_rerank: bool | None = None
+    skip_llm_chunk_filter: bool | None = None
+
+
+class BasicCreateChatMessageRequest(ChunkContext):
     """Before creating messages, be sure to create a chat_session and get an id
     Note, for simplicity this option only allows for a single linear chain of messages
     """
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 9b666a391..b1c992ac7 100644
--- a/backend/ee/danswer/server/query_and_chat/query_backend.py
+++ b/backend/ee/danswer/server/query_and_chat/query_backend.py
@@ -3,7 +3,6 @@ from fastapi import Depends
 from sqlalchemy.orm import Session
 
 from danswer.auth.users import current_user
-from danswer.configs.chat_configs import DISABLE_LLM_CHUNK_FILTER
 from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTAGE
 from danswer.db.chat import get_persona_by_id
 from danswer.db.engine import get_session
@@ -20,9 +19,9 @@ from danswer.search.models import SavedSearchDoc
 from danswer.search.models import SearchRequest
 from danswer.search.models import SearchResponse
 from danswer.search.pipeline import SearchPipeline
-from danswer.search.utils import chunks_to_search_docs
-from danswer.server.query_and_chat.models import DocumentSearchRequest
+from danswer.search.utils import chunks_or_sections_to_search_docs
 from danswer.utils.logger import setup_logger
+from ee.danswer.server.query_and_chat.models import DocumentSearchRequest
 
 
 logger = setup_logger()
@@ -34,8 +33,6 @@ def handle_search_request(
     search_request: DocumentSearchRequest,
     user: User | None = Depends(current_user),
     db_session: Session = Depends(get_session),
-    # Default to running LLM filter unless globally disabled
-    disable_llm_chunk_filter: bool = DISABLE_LLM_CHUNK_FILTER,
 ) -> SearchResponse:
     """Simple search endpoint, does not create a new message or records in the DB"""
     query = search_request.message
@@ -52,14 +49,18 @@ def handle_search_request(
             limit=search_request.retrieval_options.limit,
             skip_rerank=search_request.skip_rerank,
             skip_llm_chunk_filter=search_request.skip_llm_chunk_filter,
+            chunks_above=search_request.chunks_above,
+            chunks_below=search_request.chunks_below,
+            full_doc=search_request.full_doc,
         ),
         user=user,
         db_session=db_session,
         bypass_acl=False,
     )
-    top_chunks = search_pipeline.reranked_docs
-    relevant_chunk_indices = search_pipeline.relevant_chunk_indicies
-    top_docs = chunks_to_search_docs(top_chunks)
+    top_sections = search_pipeline.reranked_sections
+    # If using surrounding context or full doc, this will be empty
+    relevant_chunk_indices = search_pipeline.relevant_chunk_indices
+    top_docs = chunks_or_sections_to_search_docs(top_sections)
 
     # No need to save the docs for this API
     fake_saved_docs = [SavedSearchDoc.from_search_doc(doc) for doc in top_docs]