fix typing

backend/alembic/versions/8e1ac4f39a9f_enable_contextual_retrieval.py

@@ -1,50 +0,0 @@
-"""enable contextual retrieval
-
-Revision ID: 8e1ac4f39a9f
-Revises: 3781a5eb12cb
-Create Date: 2024-12-20 13:29:09.918661
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "8e1ac4f39a9f"
-down_revision = "3781a5eb12cb"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "search_settings",
-        sa.Column(
-            "enable_contextual_rag",
-            sa.Boolean(),
-            nullable=False,
-            server_default="false",
-        ),
-    )
-    op.add_column(
-        "search_settings",
-        sa.Column(
-            "contextual_rag_llm_name",
-            sa.String(),
-            nullable=True,
-        ),
-    )
-    op.add_column(
-        "search_settings",
-        sa.Column(
-            "contextual_rag_llm_provider",
-            sa.String(),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("search_settings", "enable_contextual_rag")
-    op.drop_column("search_settings", "contextual_rag_llm_name")
-    op.drop_column("search_settings", "contextual_rag_llm_provider")

backend/ee/onyx/server/tenants/provisioning.py

@@ -94,7 +94,6 @@ async def get_or_provision_tenant(
         # Notify control plane if we have created / assigned a new tenant
         if not DEV_MODE:
             await notify_control_plane(tenant_id, email, referral_source)
-
         return tenant_id
 
     except Exception as e:

backend/onyx/auth/users.py

@@ -56,6 +56,7 @@ from httpx_oauth.oauth2 import OAuth2Token
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from ee.onyx.configs.app_configs import ANONYMOUS_USER_COOKIE_NAME
 from onyx.auth.api_key import get_hashed_api_key_from_request
 from onyx.auth.email_utils import send_forgot_password_email
 from onyx.auth.email_utils import send_user_verification_email
@@ -360,8 +361,18 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 reason="Password must contain at least one special character from the following set: "
                 f"{PASSWORD_SPECIAL_CHARS}."
             )
+
         return
 
+    async def on_after_login(
+        self,
+        user: User,
+        request: Optional[Request] = None,
+        response: Optional[Response] = None,
+    ) -> None:
+        if response:
+            response.delete_cookie(ANONYMOUS_USER_COOKIE_NAME)
+
     async def oauth_callback(
         self,
         oauth_name: str,

backend/onyx/chat/prune_and_merge.py

@@ -153,8 +153,6 @@ def _apply_pruning(
     # remove docs that are explicitly marked as not for QA
     sections = _remove_sections_to_ignore(sections=sections)
 
-    section_idx_token_count: dict[int, int] = {}
-
     final_section_ind = None
     total_tokens = 0
     for ind, section in enumerate(sections):
@@ -204,20 +202,10 @@ def _apply_pruning(
             section_token_count = DOC_EMBEDDING_CONTEXT_SIZE
 
         total_tokens += section_token_count
-        section_idx_token_count[ind] = section_token_count
-
         if total_tokens > token_limit:
             final_section_ind = ind
             break
 
-    try:
-        logger.debug(f"Number of documents after pruning: {ind + 1}")
-        logger.debug("Number of tokens per document (pruned):")
-        for x, y in section_idx_token_count.items():
-            logger.debug(f"{x + 1}: {y}")
-    except Exception as e:
-        logger.error(f"Error logging prune statistics: {e}")
-
     if final_section_ind is not None:
         if is_manually_selected_docs or use_sections:
             if final_section_ind != len(sections) - 1:
@@ -374,26 +362,6 @@ def _merge_sections(sections: list[InferenceSection]) -> list[InferenceSection]:
         reverse=True,
     )
 
-    try:
-        num_original_sections = len(sections)
-        num_original_document_ids = len(
-            set([section.center_chunk.document_id for section in sections])
-        )
-        num_merged_sections = len(new_sections)
-        num_merged_document_ids = len(
-            set([section.center_chunk.document_id for section in new_sections])
-        )
-        logger.debug(
-            f"Merged {num_original_sections} sections from {num_original_document_ids} documents "
-            f"into {num_merged_sections} new sections in {num_merged_document_ids} documents"
-        )
-
-        logger.debug("Number of chunks per document (new ranking):")
-        for x, y in enumerate(new_sections):
-            logger.debug(f"{x + 1}: {len(y.chunks)}")
-    except Exception as e:
-        logger.error(f"Error logging merge statistics: {e}")
-
     return new_sections
 
 

backend/onyx/configs/app_configs.py

@@ -495,11 +495,6 @@ NUM_SECONDARY_INDEXING_WORKERS = int(
 ENABLE_MULTIPASS_INDEXING = (
     os.environ.get("ENABLE_MULTIPASS_INDEXING", "").lower() == "true"
 )
-# Enable contextual retrieval
-ENABLE_CONTEXTUAL_RAG = os.environ.get("ENABLE_CONTEXTUAL_RAG", "").lower() == "true"
-
-DEFAULT_CONTEXTUAL_RAG_LLM_NAME = "gpt-4o-mini"
-DEFAULT_CONTEXTUAL_RAG_LLM_PROVIDER = "DevEnvPresetOpenAI"
 # Finer grained chunking for more detail retention
 # Slightly larger since the sentence aware split is a max cutoff so most minichunks will be under MINI_CHUNK_SIZE
 # tokens. But we need it to be at least as big as 1/4th chunk size to avoid having a tiny mini-chunk at the end
@@ -541,17 +536,6 @@ MAX_FILE_SIZE_BYTES = int(
     os.environ.get("MAX_FILE_SIZE_BYTES") or 2 * 1024 * 1024 * 1024
 )  # 2GB in bytes
 
-# Use document summary for contextual rag
-USE_DOCUMENT_SUMMARY = os.environ.get("USE_DOCUMENT_SUMMARY", "true").lower() == "true"
-# Use chunk summary for contextual rag
-USE_CHUNK_SUMMARY = os.environ.get("USE_CHUNK_SUMMARY", "true").lower() == "true"
-# Average summary embeddings for contextual rag (not yet implemented)
-AVERAGE_SUMMARY_EMBEDDINGS = (
-    os.environ.get("AVERAGE_SUMMARY_EMBEDDINGS", "false").lower() == "true"
-)
-
-MAX_TOKENS_FOR_FULL_INCLUSION = 4096
-
 #####
 # Miscellaneous
 #####

backend/onyx/connectors/google_drive/doc_conversion.py

@@ -30,7 +30,6 @@ from onyx.file_processing.file_validation import is_valid_image_type
 from onyx.file_processing.image_summarization import summarize_image_with_error_handling
 from onyx.file_processing.image_utils import store_image_and_create_section
 from onyx.llm.interfaces import LLM
-from onyx.utils.lazy import lazy_eval
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -77,26 +76,6 @@ def is_gdrive_image_mime_type(mime_type: str) -> bool:
     return is_valid_image_type(mime_type)
 
 
-def download_request(service: GoogleDriveService, file_id: str) -> bytes:
-    """
-    Download the file from Google Drive.
-    """
-    # For other file types, download the file
-    # Use the correct API call for downloading files
-    request = service.files().get_media(fileId=file_id)
-    response_bytes = io.BytesIO()
-    downloader = MediaIoBaseDownload(response_bytes, request)
-    done = False
-    while not done:
-        _, done = downloader.next_chunk()
-
-    response = response_bytes.getvalue()
-    if not response:
-        logger.warning(f"Failed to download {file_id}")
-        return bytes()
-    return response
-
-
 def _download_and_extract_sections_basic(
     file: dict[str, str],
     service: GoogleDriveService,
@@ -135,31 +114,41 @@ def _download_and_extract_sections_basic(
 
     # For other file types, download the file
     # Use the correct API call for downloading files
-    response_call = lazy_eval(lambda: download_request(service, file_id))
+    request = service.files().get_media(fileId=file_id)
+    response_bytes = io.BytesIO()
+    downloader = MediaIoBaseDownload(response_bytes, request)
+    done = False
+    while not done:
+        _, done = downloader.next_chunk()
+
+    response = response_bytes.getvalue()
+    if not response:
+        logger.warning(f"Failed to download {file_name}")
+        return []
 
     # Process based on mime type
     if mime_type == "text/plain":
-        text = response_call().decode("utf-8")
+        text = response.decode("utf-8")
         return [TextSection(link=link, text=text)]
 
     elif (
         mime_type
         == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
     ):
-        text, _ = docx_to_text_and_images(io.BytesIO(response_call()))
+        text, _ = docx_to_text_and_images(io.BytesIO(response))
         return [TextSection(link=link, text=text)]
 
     elif (
         mime_type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
     ):
-        text = xlsx_to_text(io.BytesIO(response_call()))
+        text = xlsx_to_text(io.BytesIO(response))
         return [TextSection(link=link, text=text)]
 
     elif (
         mime_type
         == "application/vnd.openxmlformats-officedocument.presentationml.presentation"
     ):
-        text = pptx_to_text(io.BytesIO(response_call()))
+        text = pptx_to_text(io.BytesIO(response))
         return [TextSection(link=link, text=text)]
 
     elif is_gdrive_image_mime_type(mime_type):
@@ -169,7 +158,7 @@ def _download_and_extract_sections_basic(
             with get_session_with_current_tenant() as db_session:
                 section, embedded_id = store_image_and_create_section(
                     db_session=db_session,
-                    image_data=response_call(),
+                    image_data=response,
                     file_name=file_id,
                     display_name=file_name,
                     media_type=mime_type,
@@ -182,7 +171,7 @@ def _download_and_extract_sections_basic(
         return sections
 
     elif mime_type == "application/pdf":
-        text, _pdf_meta, images = read_pdf_file(io.BytesIO(response_call()))
+        text, _pdf_meta, images = read_pdf_file(io.BytesIO(response))
         pdf_sections: list[TextSection | ImageSection] = [
             TextSection(link=link, text=text)
         ]
@@ -205,15 +194,8 @@ def _download_and_extract_sections_basic(
 
     else:
         # For unsupported file types, try to extract text
-        if mime_type in [
-            "application/vnd.google-apps.video",
-            "application/vnd.google-apps.audio",
-            "application/zip",
-        ]:
-            return []
-        # For unsupported file types, try to extract text
         try:
-            text = extract_file_text(io.BytesIO(response_call()), file_name)
+            text = extract_file_text(io.BytesIO(response), file_name)
             return [TextSection(link=link, text=text)]
         except Exception as e:
             logger.warning(f"Failed to extract text from {file_name}: {e}")

backend/onyx/connectors/models.py

@@ -163,9 +163,6 @@ class DocumentBase(BaseModel):
                 attributes.append(k + INDEX_SEPARATOR + v)
         return attributes
 
-    def get_text_content(self) -> str:
-        return " ".join([section.text for section in self.sections if section.text])
-
 
 class Document(DocumentBase):
     """Used for Onyx ingestion api, the ID is required"""

backend/onyx/context/search/models.py

@@ -60,7 +60,7 @@ class SearchSettingsCreationRequest(InferenceSettings, IndexingSetting):
         inference_settings = InferenceSettings.from_db_model(search_settings)
         indexing_setting = IndexingSetting.from_db_model(search_settings)
 
-        return cls(**inference_settings.model_dump(), **indexing_setting.model_dump())
+        return cls(**inference_settings.dict(), **indexing_setting.dict())
 
 
 class SavedSearchSettings(InferenceSettings, IndexingSetting):
@@ -80,9 +80,6 @@ class SavedSearchSettings(InferenceSettings, IndexingSetting):
             reduced_dimension=search_settings.reduced_dimension,
             # Whether switching to this model requires re-indexing
             background_reindex_enabled=search_settings.background_reindex_enabled,
-            enable_contextual_rag=search_settings.enable_contextual_rag,
-            contextual_rag_llm_name=search_settings.contextual_rag_llm_name,
-            contextual_rag_llm_provider=search_settings.contextual_rag_llm_provider,
             # Reranking Details
             rerank_model_name=search_settings.rerank_model_name,
             rerank_provider_type=search_settings.rerank_provider_type,
@@ -221,8 +218,6 @@ class InferenceChunk(BaseChunk):
     # to specify that a set of words should be highlighted. For example:
     # ["<hi>the</hi> <hi>answer</hi> is 42", "he couldn't find an <hi>answer</hi>"]
     match_highlights: list[str]
-    doc_summary: str
-    chunk_context: str
 
     # when the doc was last updated
     updated_at: datetime | None

backend/onyx/context/search/postprocessing/postprocessing.py

@@ -196,21 +196,9 @@ def cleanup_chunks(chunks: list[InferenceChunkUncleaned]) -> list[InferenceChunk
             RETURN_SEPARATOR
         )
 
-    def _remove_contextual_rag(chunk: InferenceChunkUncleaned) -> str:
-        # remove document summary
-        if chunk.content.startswith(chunk.doc_summary):
-            chunk.content = chunk.content[len(chunk.doc_summary) :].lstrip()
-        # remove chunk context
-        if chunk.content.endswith(chunk.chunk_context):
-            chunk.content = chunk.content[
-                : len(chunk.content) - len(chunk.chunk_context)
-            ].rstrip()
-        return chunk.content
-
     for chunk in chunks:
         chunk.content = _remove_title(chunk)
         chunk.content = _remove_metadata_suffix(chunk)
-        chunk.content = _remove_contextual_rag(chunk)
 
     return [chunk.to_inference_chunk() for chunk in chunks]
 

backend/onyx/db/models.py

@@ -791,15 +791,6 @@ class SearchSettings(Base):
     # Mini and Large Chunks (large chunk also checks for model max context)
     multipass_indexing: Mapped[bool] = mapped_column(Boolean, default=True)
 
-    # Contextual RAG
-    enable_contextual_rag: Mapped[bool] = mapped_column(Boolean, default=False)
-
-    # Contextual RAG LLM
-    contextual_rag_llm_name: Mapped[str | None] = mapped_column(String, nullable=True)
-    contextual_rag_llm_provider: Mapped[str | None] = mapped_column(
-        String, nullable=True
-    )
-
     multilingual_expansion: Mapped[list[str]] = mapped_column(
         postgresql.ARRAY(String), default=[]
     )

backend/onyx/db/search_settings.py

@@ -62,9 +62,6 @@ def create_search_settings(
         multipass_indexing=search_settings.multipass_indexing,
         embedding_precision=search_settings.embedding_precision,
         reduced_dimension=search_settings.reduced_dimension,
-        enable_contextual_rag=search_settings.enable_contextual_rag,
-        contextual_rag_llm_name=search_settings.contextual_rag_llm_name,
-        contextual_rag_llm_provider=search_settings.contextual_rag_llm_provider,
         multilingual_expansion=search_settings.multilingual_expansion,
         disable_rerank_for_streaming=search_settings.disable_rerank_for_streaming,
         rerank_model_name=search_settings.rerank_model_name,
@@ -322,7 +319,6 @@ def get_old_default_embedding_model() -> IndexingSetting:
         passage_prefix=(ASYM_PASSAGE_PREFIX if is_overridden else ""),
         index_name="danswer_chunk",
         multipass_indexing=False,
-        enable_contextual_rag=False,
         api_url=None,
     )
 
@@ -337,6 +333,5 @@ def get_new_default_embedding_model() -> IndexingSetting:
         passage_prefix=ASYM_PASSAGE_PREFIX,
         index_name=f"danswer_chunk_{clean_model_name(DOCUMENT_ENCODER_MODEL)}",
         multipass_indexing=False,
-        enable_contextual_rag=False,
         api_url=None,
     )

backend/onyx/document_index/vespa/app_config/schemas/danswer_chunk.sd

@@ -98,12 +98,6 @@ schema DANSWER_CHUNK_NAME {
         field metadata type string {
             indexing: summary | attribute
         }
-        field chunk_context type string {
-            indexing: summary | attribute
-        }
-        field doc_summary type string {
-            indexing: summary | attribute
-        }
         field metadata_suffix type string {
             indexing: summary | attribute
         }

backend/onyx/document_index/vespa/chunk_retrieval.py

@@ -24,11 +24,9 @@ from onyx.document_index.vespa.shared_utils.vespa_request_builders import (
 from onyx.document_index.vespa_constants import ACCESS_CONTROL_LIST
 from onyx.document_index.vespa_constants import BLURB
 from onyx.document_index.vespa_constants import BOOST
-from onyx.document_index.vespa_constants import CHUNK_CONTEXT
 from onyx.document_index.vespa_constants import CHUNK_ID
 from onyx.document_index.vespa_constants import CONTENT
 from onyx.document_index.vespa_constants import CONTENT_SUMMARY
-from onyx.document_index.vespa_constants import DOC_SUMMARY
 from onyx.document_index.vespa_constants import DOC_UPDATED_AT
 from onyx.document_index.vespa_constants import DOCUMENT_ID
 from onyx.document_index.vespa_constants import DOCUMENT_ID_ENDPOINT
@@ -128,8 +126,7 @@ def _vespa_hit_to_inference_chunk(
     return InferenceChunkUncleaned(
         chunk_id=fields[CHUNK_ID],
         blurb=fields.get(BLURB, ""),  # Unused
-        content=fields[CONTENT],  # Includes extra title prefix and metadata suffix;
-        # also sometimes context for contextual rag
+        content=fields[CONTENT],  # Includes extra title prefix and metadata suffix
         source_links=source_links_dict or {0: ""},
         section_continuation=fields[SECTION_CONTINUATION],
         document_id=fields[DOCUMENT_ID],
@@ -146,8 +143,6 @@ def _vespa_hit_to_inference_chunk(
         large_chunk_reference_ids=fields.get(LARGE_CHUNK_REFERENCE_IDS, []),
         metadata=metadata,
         metadata_suffix=fields.get(METADATA_SUFFIX),
-        doc_summary=fields.get(DOC_SUMMARY, ""),
-        chunk_context=fields.get(CHUNK_CONTEXT, ""),
         match_highlights=match_highlights,
         updated_at=updated_at,
     )
@@ -350,19 +345,6 @@ def query_vespa(
     filtered_hits = [hit for hit in hits if hit["fields"].get(CONTENT) is not None]
 
     inference_chunks = [_vespa_hit_to_inference_chunk(hit) for hit in filtered_hits]
-
-    try:
-        num_retrieved_inference_chunks = len(inference_chunks)
-        num_retrieved_document_ids = len(
-            set([chunk.document_id for chunk in inference_chunks])
-        )
-        logger.debug(
-            f"Retrieved {num_retrieved_inference_chunks} inference chunks for {num_retrieved_document_ids} documents"
-        )
-    except Exception as e:
-        # Debug logging only, should not fail the retrieval
-        logger.error(f"Error logging retrieval statistics: {e}")
-
     # Good Debugging Spot
     return inference_chunks
 

backend/onyx/document_index/vespa/index.py

@@ -187,7 +187,7 @@ class VespaIndex(DocumentIndex):
     ) -> None:
         if MULTI_TENANT:
             logger.info(
-                "Skipping Vespa index setup for multitenant (would wipe all indices)"
+                "Skipping Vespa index seup for multitenant (would wipe all indices)"
             )
             return None
 

backend/onyx/document_index/vespa/indexing_utils.py

@@ -25,11 +25,9 @@ from onyx.document_index.vespa_constants import ACCESS_CONTROL_LIST
 from onyx.document_index.vespa_constants import AGGREGATED_CHUNK_BOOST_FACTOR
 from onyx.document_index.vespa_constants import BLURB
 from onyx.document_index.vespa_constants import BOOST
-from onyx.document_index.vespa_constants import CHUNK_CONTEXT
 from onyx.document_index.vespa_constants import CHUNK_ID
 from onyx.document_index.vespa_constants import CONTENT
 from onyx.document_index.vespa_constants import CONTENT_SUMMARY
-from onyx.document_index.vespa_constants import DOC_SUMMARY
 from onyx.document_index.vespa_constants import DOC_UPDATED_AT
 from onyx.document_index.vespa_constants import DOCUMENT_ID
 from onyx.document_index.vespa_constants import DOCUMENT_ID_ENDPOINT
@@ -176,7 +174,7 @@ def _index_vespa_chunk(
         # For the BM25 index, the keyword suffix is used, the vector is already generated with the more
         # natural language representation of the metadata section
         CONTENT: remove_invalid_unicode_chars(
-            f"{chunk.title_prefix}{chunk.doc_summary}{chunk.content}{chunk.chunk_context}{chunk.metadata_suffix_keyword}"
+            f"{chunk.title_prefix}{chunk.content}{chunk.metadata_suffix_keyword}"
         ),
         # This duplication of `content` is needed for keyword highlighting
         # Note that it's not exactly the same as the actual content
@@ -191,8 +189,6 @@ def _index_vespa_chunk(
         # Save as a list for efficient extraction as an Attribute
         METADATA_LIST: metadata_list,
         METADATA_SUFFIX: remove_invalid_unicode_chars(chunk.metadata_suffix_keyword),
-        CHUNK_CONTEXT: chunk.chunk_context,
-        DOC_SUMMARY: chunk.doc_summary,
         EMBEDDINGS: embeddings_name_vector_map,
         TITLE_EMBEDDING: chunk.title_embedding,
         DOC_UPDATED_AT: _vespa_get_updated_at_attribute(document.doc_updated_at),

backend/onyx/document_index/vespa_constants.py

@@ -71,8 +71,6 @@ LARGE_CHUNK_REFERENCE_IDS = "large_chunk_reference_ids"
 METADATA = "metadata"
 METADATA_LIST = "metadata_list"
 METADATA_SUFFIX = "metadata_suffix"
-DOC_SUMMARY = "doc_summary"
-CHUNK_CONTEXT = "chunk_context"
 BOOST = "boost"
 AGGREGATED_CHUNK_BOOST_FACTOR = "aggregated_chunk_boost_factor"
 DOC_UPDATED_AT = "doc_updated_at"  # Indexed as seconds since epoch
@@ -108,8 +106,6 @@ YQL_BASE = (
     f"{LARGE_CHUNK_REFERENCE_IDS}, "
     f"{METADATA}, "
     f"{METADATA_SUFFIX}, "
-    f"{DOC_SUMMARY}, "
-    f"{CHUNK_CONTEXT}, "
     f"{CONTENT_SUMMARY} "
     f"from {{index_name}} where "
 )

backend/onyx/indexing/chunker.py

@@ -1,10 +1,7 @@
-from onyx.configs.app_configs import AVERAGE_SUMMARY_EMBEDDINGS
 from onyx.configs.app_configs import BLURB_SIZE
 from onyx.configs.app_configs import LARGE_CHUNK_RATIO
 from onyx.configs.app_configs import MINI_CHUNK_SIZE
 from onyx.configs.app_configs import SKIP_METADATA_IN_CHUNK
-from onyx.configs.app_configs import USE_CHUNK_SUMMARY
-from onyx.configs.app_configs import USE_DOCUMENT_SUMMARY
 from onyx.configs.constants import DocumentSource
 from onyx.configs.constants import RETURN_SEPARATOR
 from onyx.configs.constants import SECTION_SEPARATOR
@@ -16,7 +13,6 @@ from onyx.connectors.models import IndexingDocument
 from onyx.connectors.models import Section
 from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.indexing.models import DocAwareChunk
-from onyx.llm.utils import MAX_CONTEXT_TOKENS
 from onyx.natural_language_processing.utils import BaseTokenizer
 from onyx.utils.logger import setup_logger
 from onyx.utils.text_processing import clean_text
@@ -86,9 +82,6 @@ def _combine_chunks(chunks: list[DocAwareChunk], large_chunk_id: int) -> DocAwar
         large_chunk_reference_ids=[chunk.chunk_id for chunk in chunks],
         mini_chunk_texts=None,
         large_chunk_id=large_chunk_id,
-        chunk_context="",
-        doc_summary="",
-        contextual_rag_reserved_tokens=0,
     )
 
     offset = 0
@@ -127,7 +120,6 @@ class Chunker:
         tokenizer: BaseTokenizer,
         enable_multipass: bool = False,
         enable_large_chunks: bool = False,
-        enable_contextual_rag: bool = False,
         blurb_size: int = BLURB_SIZE,
         include_metadata: bool = not SKIP_METADATA_IN_CHUNK,
         chunk_token_limit: int = DOC_EMBEDDING_CONTEXT_SIZE,
@@ -141,20 +133,9 @@ class Chunker:
         self.chunk_token_limit = chunk_token_limit
         self.enable_multipass = enable_multipass
         self.enable_large_chunks = enable_large_chunks
-        self.enable_contextual_rag = enable_contextual_rag
-        if enable_contextual_rag:
-            assert (
-                USE_CHUNK_SUMMARY or USE_DOCUMENT_SUMMARY
-            ), "Contextual RAG requires at least one of chunk summary and document summary enabled"
-        self.default_contextual_rag_reserved_tokens = MAX_CONTEXT_TOKENS * (
-            int(USE_CHUNK_SUMMARY) + int(USE_DOCUMENT_SUMMARY)
-        )
         self.tokenizer = tokenizer
         self.callback = callback
 
-        self.max_context = 0
-        self.prompt_tokens = 0
-
         self.blurb_splitter = SentenceSplitter(
             tokenizer=tokenizer.tokenize,
             chunk_size=blurb_size,
@@ -240,12 +221,30 @@ class Chunker:
             metadata_suffix_keyword=metadata_suffix_keyword,
             mini_chunk_texts=self._get_mini_chunk_texts(text),
             large_chunk_id=None,
-            doc_summary="",
-            chunk_context="",
-            contextual_rag_reserved_tokens=0,  # set per-document in _handle_single_document
         )
         chunks_list.append(new_chunk)
 
+    def _chunk_document(
+        self,
+        document: IndexingDocument,
+        title_prefix: str,
+        metadata_suffix_semantic: str,
+        metadata_suffix_keyword: str,
+        content_token_limit: int,
+    ) -> list[DocAwareChunk]:
+        """
+        Legacy method for backward compatibility.
+        Calls _chunk_document_with_sections with document.sections.
+        """
+        return self._chunk_document_with_sections(
+            document,
+            document.processed_sections,
+            title_prefix,
+            metadata_suffix_semantic,
+            metadata_suffix_keyword,
+            content_token_limit,
+        )
+
     def _chunk_document_with_sections(
         self,
         document: IndexingDocument,
@@ -265,7 +264,7 @@ class Chunker:
 
         for section_idx, section in enumerate(sections):
             # Get section text and other attributes
-            section_text = clean_text(str(section.text or ""))
+            section_text = clean_text(section.text or "")
             section_link_text = section.link or ""
             image_url = section.image_file_name
 
@@ -310,7 +309,7 @@ class Chunker:
                 continue
 
             # CASE 2: Normal text section
-            section_token_count = len(self.tokenizer.encode(section_text))
+            section_token_count = len(self.tokenizer.tokenize(section_text))
 
             # If the section is large on its own, split it separately
             if section_token_count > content_token_limit:
@@ -333,7 +332,8 @@ class Chunker:
                     # If even the split_text is bigger than strict limit, further split
                     if (
                         STRICT_CHUNK_TOKEN_LIMIT
-                        and len(self.tokenizer.encode(split_text)) > content_token_limit
+                        and len(self.tokenizer.tokenize(split_text))
+                        > content_token_limit
                     ):
                         smaller_chunks = self._split_oversized_chunk(
                             split_text, content_token_limit
@@ -363,10 +363,10 @@ class Chunker:
                 continue
 
             # If we can still fit this section into the current chunk, do so
-            current_token_count = len(self.tokenizer.encode(chunk_text))
+            current_token_count = len(self.tokenizer.tokenize(chunk_text))
             current_offset = len(shared_precompare_cleanup(chunk_text))
             next_section_tokens = (
-                len(self.tokenizer.encode(SECTION_SEPARATOR)) + section_token_count
+                len(self.tokenizer.tokenize(SECTION_SEPARATOR)) + section_token_count
             )
 
             if next_section_tokens + current_token_count <= content_token_limit:
@@ -414,7 +414,7 @@ class Chunker:
         # Title prep
         title = self._extract_blurb(document.get_title_for_document_index() or "")
         title_prefix = title + RETURN_SEPARATOR if title else ""
-        title_tokens = len(self.tokenizer.encode(title_prefix))
+        title_tokens = len(self.tokenizer.tokenize(title_prefix))
 
         # Metadata prep
         metadata_suffix_semantic = ""
@@ -427,50 +427,15 @@ class Chunker:
             ) = _get_metadata_suffix_for_document_index(
                 document.metadata, include_separator=True
             )
-            metadata_tokens = len(self.tokenizer.encode(metadata_suffix_semantic))
+            metadata_tokens = len(self.tokenizer.tokenize(metadata_suffix_semantic))
 
         # If metadata is too large, skip it in the semantic content
         if metadata_tokens >= self.chunk_token_limit * MAX_METADATA_PERCENTAGE:
             metadata_suffix_semantic = ""
             metadata_tokens = 0
 
-        single_chunk_fits = True
-        doc_token_count = 0
-        if self.enable_contextual_rag:
-            doc_content = document.get_text_content()
-            tokenized_doc = self.tokenizer.tokenize(doc_content)
-            doc_token_count = len(tokenized_doc)
-
-            # check if doc + title + metadata fits in a single chunk. If so, no need for contextual RAG
-            single_chunk_fits = (
-                doc_token_count + title_tokens + metadata_tokens
-                <= self.chunk_token_limit
-            )
-
-        # expand the size of the context used for contextual rag based on whether chunk context and doc summary are used
-        context_size = 0
-        if (
-            self.enable_contextual_rag
-            and not single_chunk_fits
-            and not AVERAGE_SUMMARY_EMBEDDINGS
-        ):
-            context_size += self.default_contextual_rag_reserved_tokens
-
         # Adjust content token limit to accommodate title + metadata
-        content_token_limit = (
-            self.chunk_token_limit - title_tokens - metadata_tokens - context_size
-        )
-
-        # first check: if there is not enough actual chunk content when including contextual rag,
-        # then don't do contextual rag
-        if content_token_limit <= CHUNK_MIN_CONTENT:
-            context_size = 0  # Don't do contextual RAG
-            # revert to previous content token limit
-            content_token_limit = (
-                self.chunk_token_limit - title_tokens - metadata_tokens
-            )
-
-        # If there is not enough context remaining then just index the chunk with no prefix/suffix
+        content_token_limit = self.chunk_token_limit - title_tokens - metadata_tokens
         if content_token_limit <= CHUNK_MIN_CONTENT:
             # Not enough space left, so revert to full chunk without the prefix
             content_token_limit = self.chunk_token_limit
@@ -494,9 +459,6 @@ class Chunker:
             large_chunks = generate_large_chunks(normal_chunks)
             normal_chunks.extend(large_chunks)
 
-        for chunk in normal_chunks:
-            chunk.contextual_rag_reserved_tokens = context_size
-
         return normal_chunks
 
     def chunk(self, documents: list[IndexingDocument]) -> list[DocAwareChunk]:

backend/onyx/indexing/embedder.py

@@ -121,7 +121,7 @@ class DefaultIndexingEmbedder(IndexingEmbedder):
             if chunk.large_chunk_reference_ids:
                 large_chunks_present = True
             chunk_text = (
-                f"{chunk.title_prefix}{chunk.doc_summary}{chunk.content}{chunk.chunk_context}{chunk.metadata_suffix_semantic}"
+                f"{chunk.title_prefix}{chunk.content}{chunk.metadata_suffix_semantic}"
             ) or chunk.source_document.get_title_for_document_index()
 
             if not chunk_text:

backend/onyx/indexing/indexing_pipeline.py

@@ -1,4 +1,3 @@
-from collections import defaultdict
 from collections.abc import Callable
 from functools import partial
 from typing import Protocol
@@ -9,13 +8,7 @@ from sqlalchemy.orm import Session
 
 from onyx.access.access import get_access_for_documents
 from onyx.access.models import DocumentAccess
-from onyx.configs.app_configs import DEFAULT_CONTEXTUAL_RAG_LLM_NAME
-from onyx.configs.app_configs import DEFAULT_CONTEXTUAL_RAG_LLM_PROVIDER
-from onyx.configs.app_configs import ENABLE_CONTEXTUAL_RAG
 from onyx.configs.app_configs import MAX_DOCUMENT_CHARS
-from onyx.configs.app_configs import MAX_TOKENS_FOR_FULL_INCLUSION
-from onyx.configs.app_configs import USE_CHUNK_SUMMARY
-from onyx.configs.app_configs import USE_DOCUMENT_SUMMARY
 from onyx.configs.constants import DEFAULT_BOOST
 from onyx.configs.llm_configs import get_image_extraction_and_analysis_enabled
 from onyx.configs.model_configs import USE_INFORMATION_CONTENT_CLASSIFICATION
@@ -43,10 +36,9 @@ from onyx.db.document import upsert_documents
 from onyx.db.document_set import fetch_document_sets_for_documents
 from onyx.db.engine import get_session_with_current_tenant
 from onyx.db.models import Document as DBDocument
-from onyx.db.models import IndexModelStatus
 from onyx.db.pg_file_store import get_pgfilestore_by_file_name
 from onyx.db.pg_file_store import read_lobj
-from onyx.db.search_settings import get_active_search_settings
+from onyx.db.search_settings import get_current_search_settings
 from onyx.db.tag import create_or_add_document_tag
 from onyx.db.tag import create_or_add_document_tag_list
 from onyx.document_index.document_index_utils import (
@@ -65,24 +57,11 @@ from onyx.indexing.models import DocMetadataAwareIndexChunk
 from onyx.indexing.models import IndexChunk
 from onyx.indexing.models import UpdatableChunkData
 from onyx.indexing.vector_db_insertion import write_chunks_to_vector_db_with_backoff
-from onyx.llm.chat_llm import LLMRateLimitError
 from onyx.llm.factory import get_default_llm_with_vision
-from onyx.llm.factory import get_llm_for_contextual_rag
-from onyx.llm.interfaces import LLM
-from onyx.llm.utils import get_max_input_tokens
-from onyx.llm.utils import MAX_CONTEXT_TOKENS
-from onyx.llm.utils import message_to_string
 from onyx.natural_language_processing.search_nlp_models import (
     InformationContentClassificationModel,
 )
-from onyx.natural_language_processing.utils import BaseTokenizer
-from onyx.natural_language_processing.utils import get_tokenizer
-from onyx.natural_language_processing.utils import tokenizer_trim_middle
-from onyx.prompts.chat_prompts import CONTEXTUAL_RAG_PROMPT1
-from onyx.prompts.chat_prompts import CONTEXTUAL_RAG_PROMPT2
-from onyx.prompts.chat_prompts import DOCUMENT_SUMMARY_PROMPT
 from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_functions_tuples_in_parallel
 from onyx.utils.timing import log_function_time
 from shared_configs.configs import (
     INDEXING_INFORMATION_CONTENT_CLASSIFICATION_CUTOFF_LENGTH,
@@ -270,8 +249,6 @@ def index_doc_batch_with_handler(
     db_session: Session,
     tenant_id: str,
     ignore_time_skip: bool = False,
-    enable_contextual_rag: bool = False,
-    llm: LLM | None = None,
 ) -> IndexingPipelineResult:
     try:
         index_pipeline_result = index_doc_batch(
@@ -284,8 +261,6 @@ def index_doc_batch_with_handler(
             db_session=db_session,
             ignore_time_skip=ignore_time_skip,
             tenant_id=tenant_id,
-            enable_contextual_rag=enable_contextual_rag,
-            llm=llm,
         )
     except Exception as e:
         # don't log the batch directly, it's too much text
@@ -464,7 +439,7 @@ def process_image_sections(documents: list[Document]) -> list[IndexingDocument]:
                 **document.dict(),
                 processed_sections=[
                     Section(
-                        text=section.text if isinstance(section, TextSection) else "",
+                        text=section.text if isinstance(section, TextSection) else None,
                         link=section.link,
                         image_file_name=section.image_file_name
                         if isinstance(section, ImageSection)
@@ -484,11 +459,11 @@ def process_image_sections(documents: list[Document]) -> list[IndexingDocument]:
         for section in document.sections:
             # For ImageSection, process and create base Section with both text and image_file_name
             if isinstance(section, ImageSection):
-                # Default section with image path preserved - ensure text is always a string
+                # Default section with image path preserved
                 processed_section = Section(
                     link=section.link,
                     image_file_name=section.image_file_name,
-                    text="",  # Initialize with empty string
+                    text=None,  # Will be populated if summarization succeeds
                 )
 
                 # Try to get image summary
@@ -531,21 +506,13 @@ def process_image_sections(documents: list[Document]) -> list[IndexingDocument]:
             # For TextSection, create a base Section with text and link
             elif isinstance(section, TextSection):
                 processed_section = Section(
-                    text=section.text or "",  # Ensure text is always a string, not None
-                    link=section.link,
-                    image_file_name=None,
+                    text=section.text, link=section.link, image_file_name=None
                 )
                 processed_sections.append(processed_section)
 
-            # If it's already a base Section (unlikely), just append it with text validation
+            # If it's already a base Section (unlikely), just append it
             else:
-                # Ensure text is always a string
-                processed_section = Section(
-                    text=section.text if section.text is not None else "",
-                    link=section.link,
-                    image_file_name=section.image_file_name,
-                )
-                processed_sections.append(processed_section)
+                processed_sections.append(section)
 
         # Create IndexingDocument with original sections and processed_sections
         indexed_document = IndexingDocument(
@@ -556,145 +523,6 @@ def process_image_sections(documents: list[Document]) -> list[IndexingDocument]:
     return indexed_documents
 
 
-def add_document_summaries(
-    chunks_by_doc: list[DocAwareChunk],
-    llm: LLM,
-    tokenizer: BaseTokenizer,
-    trunc_doc_tokens: int,
-) -> list[int] | None:
-    """
-    Adds a document summary to a list of chunks from the same document.
-    Returns the number of tokens in the document.
-    """
-
-    doc_tokens = []
-    # this is value is the same for each chunk in the document; 0 indicates
-    # There is not enough space for contextual RAG (the chunk content
-    # and possibly metadata took up too much space)
-    if chunks_by_doc[0].contextual_rag_reserved_tokens == 0:
-        return None
-
-    doc_tokens = tokenizer.encode(chunks_by_doc[0].source_document.get_text_content())
-    doc_content = tokenizer_trim_middle(doc_tokens, trunc_doc_tokens, tokenizer)
-    summary_prompt = DOCUMENT_SUMMARY_PROMPT.format(document=doc_content)
-    doc_summary = message_to_string(
-        llm.invoke(summary_prompt, max_tokens=MAX_CONTEXT_TOKENS)
-    )
-
-    for chunk in chunks_by_doc:
-        chunk.doc_summary = doc_summary
-
-    return doc_tokens
-
-
-def add_chunk_summaries(
-    chunks_by_doc: list[DocAwareChunk],
-    llm: LLM,
-    tokenizer: BaseTokenizer,
-    trunc_doc_chunk_tokens: int,
-    doc_tokens: list[int] | None,
-) -> None:
-    """
-    Adds chunk summaries to the chunks grouped by document id.
-    Chunk summaries look at the chunk as well as the entire document (or a summary,
-    if the document is too long) and describe how the chunk relates to the document.
-    """
-    # all chunks within a document have the same contextual_rag_reserved_tokens
-    if chunks_by_doc[0].contextual_rag_reserved_tokens == 0:
-        return
-
-    # use values computed in above doc summary section if available
-    doc_tokens = doc_tokens or tokenizer.encode(
-        chunks_by_doc[0].source_document.get_text_content()
-    )
-    doc_content = tokenizer_trim_middle(doc_tokens, trunc_doc_chunk_tokens, tokenizer)
-
-    # only compute doc summary if needed
-    doc_info = (
-        doc_content
-        if len(doc_tokens) <= MAX_TOKENS_FOR_FULL_INCLUSION
-        else chunks_by_doc[0].doc_summary
-    )
-    if not doc_info:
-        # This happens if the document is too long AND document summaries are turned off
-        # In this case we compute a doc summary using the LLM
-        doc_info = message_to_string(
-            llm.invoke(
-                DOCUMENT_SUMMARY_PROMPT.format(document=doc_content),
-                max_tokens=MAX_CONTEXT_TOKENS,
-            )
-        )
-
-    context_prompt1 = CONTEXTUAL_RAG_PROMPT1.format(document=doc_info)
-
-    def assign_context(chunk: DocAwareChunk) -> None:
-        context_prompt2 = CONTEXTUAL_RAG_PROMPT2.format(chunk=chunk.content)
-        try:
-            chunk.chunk_context = message_to_string(
-                llm.invoke(
-                    context_prompt1 + context_prompt2,
-                    max_tokens=MAX_CONTEXT_TOKENS,
-                )
-            )
-        except LLMRateLimitError as e:
-            # Erroring during chunker is undesirable, so we log the error and continue
-            # TODO: for v2, add robust retry logic
-            logger.exception(f"Rate limit adding chunk summary: {e}", exc_info=e)
-            chunk.chunk_context = ""
-        except Exception as e:
-            logger.exception(f"Error adding chunk summary: {e}", exc_info=e)
-            chunk.chunk_context = ""
-
-    run_functions_tuples_in_parallel(
-        [(assign_context, (chunk,)) for chunk in chunks_by_doc]
-    )
-
-
-def add_contextual_summaries(
-    chunks: list[DocAwareChunk],
-    llm: LLM,
-    tokenizer: BaseTokenizer,
-    chunk_token_limit: int,
-) -> list[DocAwareChunk]:
-    """
-    Adds Document summary and chunk-within-document context to the chunks
-    based on which environment variables are set.
-    """
-    max_context = get_max_input_tokens(
-        model_name=llm.config.model_name,
-        model_provider=llm.config.model_provider,
-        output_tokens=MAX_CONTEXT_TOKENS,
-    )
-    doc2chunks = defaultdict(list)
-    for chunk in chunks:
-        doc2chunks[chunk.source_document.id].append(chunk)
-
-    # The number of tokens allowed for the document when computing a document summary
-    trunc_doc_summary_tokens = max_context - len(
-        tokenizer.encode(DOCUMENT_SUMMARY_PROMPT)
-    )
-
-    prompt_tokens = len(
-        tokenizer.encode(CONTEXTUAL_RAG_PROMPT1 + CONTEXTUAL_RAG_PROMPT2)
-    )
-    # The number of tokens allowed for the document when computing a
-    # "chunk in context of document" summary
-    trunc_doc_chunk_tokens = max_context - prompt_tokens - chunk_token_limit
-    for chunks_by_doc in doc2chunks.values():
-        doc_tokens = None
-        if USE_DOCUMENT_SUMMARY:
-            doc_tokens = add_document_summaries(
-                chunks_by_doc, llm, tokenizer, trunc_doc_summary_tokens
-            )
-
-        if USE_CHUNK_SUMMARY:
-            add_chunk_summaries(
-                chunks_by_doc, llm, tokenizer, trunc_doc_chunk_tokens, doc_tokens
-            )
-
-    return chunks
-
-
 @log_function_time(debug_only=True)
 def index_doc_batch(
     *,
@@ -706,8 +534,6 @@ def index_doc_batch(
     index_attempt_metadata: IndexAttemptMetadata,
     db_session: Session,
     tenant_id: str,
-    enable_contextual_rag: bool = False,
-    llm: LLM | None = None,
     ignore_time_skip: bool = False,
     filter_fnc: Callable[[list[Document]], list[Document]] = filter_documents,
 ) -> IndexingPipelineResult:
@@ -770,20 +596,6 @@ def index_doc_batch(
     # a common source of failure for the indexing pipeline
     chunks: list[DocAwareChunk] = chunker.chunk(ctx.indexable_docs)
 
-    # contextual RAG
-    if enable_contextual_rag:
-        assert llm is not None, "must provide an LLM for contextual RAG"
-        llm_tokenizer = get_tokenizer(
-            model_name=llm.config.model_name,
-            provider_type=llm.config.model_provider,
-        )
-
-        # Because the chunker's tokens are different from the LLM's tokens,
-        # We add a fudge factor to ensure we truncate prompts to the LLM's token limit
-        chunks = add_contextual_summaries(
-            chunks, llm, llm_tokenizer, chunker.chunk_token_limit * 2
-        )
-
     logger.debug("Starting embedding")
     chunks_with_embeddings, embedding_failures = (
         embed_chunks_with_failure_handling(
@@ -979,33 +791,13 @@ def build_indexing_pipeline(
     callback: IndexingHeartbeatInterface | None = None,
 ) -> IndexingPipelineProtocol:
     """Builds a pipeline which takes in a list (batch) of docs and indexes them."""
-    all_search_settings = get_active_search_settings(db_session)
-    if (
-        all_search_settings.secondary
-        and all_search_settings.secondary.status == IndexModelStatus.FUTURE
-    ):
-        search_settings = all_search_settings.secondary
-    else:
-        search_settings = all_search_settings.primary
-
+    search_settings = get_current_search_settings(db_session)
     multipass_config = get_multipass_config(search_settings)
 
-    enable_contextual_rag = (
-        search_settings.enable_contextual_rag or ENABLE_CONTEXTUAL_RAG
-    )
-    llm = None
-    if enable_contextual_rag:
-        llm = get_llm_for_contextual_rag(
-            search_settings.contextual_rag_llm_name or DEFAULT_CONTEXTUAL_RAG_LLM_NAME,
-            search_settings.contextual_rag_llm_provider
-            or DEFAULT_CONTEXTUAL_RAG_LLM_PROVIDER,
-        )
-
     chunker = chunker or Chunker(
         tokenizer=embedder.embedding_model.tokenizer,
         enable_multipass=multipass_config.multipass_indexing,
         enable_large_chunks=multipass_config.enable_large_chunks,
-        enable_contextual_rag=enable_contextual_rag,
         # after every doc, update status in case there are a bunch of really long docs
         callback=callback,
     )
@@ -1019,6 +811,4 @@ def build_indexing_pipeline(
         ignore_time_skip=ignore_time_skip,
         db_session=db_session,
         tenant_id=tenant_id,
-        enable_contextual_rag=enable_contextual_rag,
-        llm=llm,
     )

backend/onyx/indexing/models.py

@@ -49,15 +49,6 @@ class DocAwareChunk(BaseChunk):
     metadata_suffix_semantic: str
     metadata_suffix_keyword: str
 
-    # This is the number of tokens reserved for contextual RAG
-    # in the chunk. doc_summary and chunk_context conbined should
-    # contain at most this many tokens.
-    contextual_rag_reserved_tokens: int
-    # This is the summary for the document generated for contextual RAG
-    doc_summary: str
-    # This is the context for this chunk generated for contextual RAG
-    chunk_context: str
-
     mini_chunk_texts: list[str] | None
 
     large_chunk_id: int | None
@@ -163,9 +154,6 @@ class IndexingSetting(EmbeddingModelDetail):
     reduced_dimension: int | None = None
 
     background_reindex_enabled: bool = True
-    enable_contextual_rag: bool
-    contextual_rag_llm_name: str | None = None
-    contextual_rag_llm_provider: str | None = None
 
     # This disables the "model_" protected namespace for pydantic
     model_config = {"protected_namespaces": ()}
@@ -190,7 +178,6 @@ class IndexingSetting(EmbeddingModelDetail):
             embedding_precision=search_settings.embedding_precision,
             reduced_dimension=search_settings.reduced_dimension,
             background_reindex_enabled=search_settings.background_reindex_enabled,
-            enable_contextual_rag=search_settings.enable_contextual_rag,
         )
 
 

backend/onyx/llm/chat_llm.py

@@ -425,12 +425,12 @@ class DefaultMultiLLM(LLM):
                 messages=processed_prompt,
                 tools=tools,
                 tool_choice=tool_choice if tools else None,
-                max_tokens=max_tokens,
                 # streaming choice
                 stream=stream,
                 # model params
                 temperature=0,
                 timeout=timeout_override or self._timeout,
+                max_tokens=max_tokens,
                 # For now, we don't support parallel tool calls
                 # NOTE: we can't pass this in if tools are not specified
                 # or else OpenAI throws an error
@@ -531,7 +531,6 @@ class DefaultMultiLLM(LLM):
                 tool_choice,
                 structured_response_format,
                 timeout_override,
-                max_tokens,
             )
             return
 

backend/onyx/llm/factory.py

@@ -16,7 +16,6 @@ from onyx.llm.exceptions import GenAIDisabledException
 from onyx.llm.interfaces import LLM
 from onyx.llm.override_models import LLMOverride
 from onyx.llm.utils import model_supports_image_input
-from onyx.server.manage.llm.models import LLMProvider
 from onyx.server.manage.llm.models import LLMProviderView
 from onyx.utils.headers import build_llm_extra_headers
 from onyx.utils.logger import setup_logger
@@ -155,40 +154,6 @@ def get_default_llm_with_vision(
     return None
 
 
-def llm_from_provider(
-    model_name: str,
-    llm_provider: LLMProvider,
-    timeout: int | None = None,
-    temperature: float | None = None,
-    additional_headers: dict[str, str] | None = None,
-    long_term_logger: LongTermLogger | None = None,
-) -> LLM:
-    return get_llm(
-        provider=llm_provider.provider,
-        model=model_name,
-        deployment_name=llm_provider.deployment_name,
-        api_key=llm_provider.api_key,
-        api_base=llm_provider.api_base,
-        api_version=llm_provider.api_version,
-        custom_config=llm_provider.custom_config,
-        timeout=timeout,
-        temperature=temperature,
-        additional_headers=additional_headers,
-        long_term_logger=long_term_logger,
-    )
-
-
-def get_llm_for_contextual_rag(model_name: str, model_provider: str) -> LLM:
-    with get_session_context_manager() as db_session:
-        llm_provider = fetch_llm_provider_view(db_session, model_provider)
-    if not llm_provider:
-        raise ValueError("No LLM provider with name {} found".format(model_provider))
-    return llm_from_provider(
-        model_name=model_name,
-        llm_provider=llm_provider,
-    )
-
-
 def get_default_llms(
     timeout: int | None = None,
     temperature: float | None = None,
@@ -214,9 +179,14 @@ def get_default_llms(
         raise ValueError("No fast default model name found")
 
     def _create_llm(model: str) -> LLM:
-        return llm_from_provider(
-            model_name=model,
-            llm_provider=llm_provider,
+        return get_llm(
+            provider=llm_provider.provider,
+            model=model,
+            deployment_name=llm_provider.deployment_name,
+            api_key=llm_provider.api_key,
+            api_base=llm_provider.api_base,
+            api_version=llm_provider.api_version,
+            custom_config=llm_provider.custom_config,
             timeout=timeout,
             temperature=temperature,
             additional_headers=additional_headers,

backend/onyx/llm/utils.py

@@ -29,19 +29,13 @@ from litellm.exceptions import Timeout  # type: ignore
 from litellm.exceptions import UnprocessableEntityError  # type: ignore
 
 from onyx.configs.app_configs import LITELLM_CUSTOM_ERROR_MESSAGE_MAPPINGS
-from onyx.configs.app_configs import MAX_TOKENS_FOR_FULL_INCLUSION
-from onyx.configs.app_configs import USE_CHUNK_SUMMARY
-from onyx.configs.app_configs import USE_DOCUMENT_SUMMARY
 from onyx.configs.constants import MessageType
-from onyx.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
 from onyx.configs.model_configs import GEN_AI_MAX_TOKENS
 from onyx.configs.model_configs import GEN_AI_MODEL_FALLBACK_MAX_TOKENS
 from onyx.configs.model_configs import GEN_AI_NUM_RESERVED_OUTPUT_TOKENS
 from onyx.file_store.models import ChatFileType
 from onyx.file_store.models import InMemoryChatFile
 from onyx.llm.interfaces import LLM
-from onyx.prompts.chat_prompts import CONTEXTUAL_RAG_TOKEN_ESTIMATE
-from onyx.prompts.chat_prompts import DOCUMENT_SUMMARY_TOKEN_ESTIMATE
 from onyx.prompts.constants import CODE_BLOCK_PAT
 from onyx.utils.b64 import get_image_type
 from onyx.utils.b64 import get_image_type_from_bytes
@@ -50,10 +44,6 @@ from shared_configs.configs import LOG_LEVEL
 
 logger = setup_logger()
 
-MAX_CONTEXT_TOKENS = 100
-ONE_MILLION = 1_000_000
-CHUNKS_PER_DOC_ESTIMATE = 5
-
 
 def litellm_exception_to_error_msg(
     e: Exception,
@@ -426,72 +416,6 @@ def _find_model_obj(model_map: dict, provider: str, model_name: str) -> dict | N
     return None
 
 
-def get_llm_contextual_cost(
-    llm: LLM,
-) -> float:
-    """
-    Approximate the cost of using the given LLM for indexing with Contextual RAG.
-
-    We use a precomputed estimate for the number of tokens in the contextualizing prompts,
-    and we assume that every chunk is maximized in terms of content and context.
-    We also assume that every document is maximized in terms of content, as currently if
-    a document is longer than a certain length, its summary is used instead of the full content.
-
-    We expect that the first assumption will overestimate more than the second one
-    underestimates, so this should be a fairly conservative price estimate. Also,
-    this does not account for the cost of documents that fit within a single chunk
-    which do not get contextualized.
-    """
-
-    # calculate input costs
-    num_tokens = ONE_MILLION
-    num_input_chunks = num_tokens // DOC_EMBEDDING_CONTEXT_SIZE
-
-    # We assume that the documents are MAX_TOKENS_FOR_FULL_INCLUSION tokens long
-    # on average.
-    num_docs = num_tokens // MAX_TOKENS_FOR_FULL_INCLUSION
-
-    num_input_tokens = 0
-    num_output_tokens = 0
-
-    if not USE_CHUNK_SUMMARY and not USE_DOCUMENT_SUMMARY:
-        return 0
-
-    if USE_CHUNK_SUMMARY:
-        # Each per-chunk prompt includes:
-        # - The prompt tokens
-        # - the document tokens
-        # - the chunk tokens
-
-        # for each chunk, we prompt the LLM with the contextual RAG prompt
-        # and the full document content (or the doc summary, so this is an overestimate)
-        num_input_tokens += num_input_chunks * (
-            CONTEXTUAL_RAG_TOKEN_ESTIMATE + MAX_TOKENS_FOR_FULL_INCLUSION
-        )
-
-        # in aggregate, each chunk content is used as a prompt input once
-        # so the full input size is covered
-        num_input_tokens += num_tokens
-
-        # A single MAX_CONTEXT_TOKENS worth of output is generated per chunk
-        num_output_tokens += num_input_chunks * MAX_CONTEXT_TOKENS
-
-    # going over each doc once means all the tokens, plus the prompt tokens for
-    # the summary prompt. This CAN happen even when USE_DOCUMENT_SUMMARY is false,
-    # since doc summaries are used for longer documents when USE_CHUNK_SUMMARY is true.
-    # So, we include this unconditionally to overestimate.
-    num_input_tokens += num_tokens + num_docs * DOCUMENT_SUMMARY_TOKEN_ESTIMATE
-    num_output_tokens += num_docs * MAX_CONTEXT_TOKENS
-
-    usd_per_prompt, usd_per_completion = litellm.cost_per_token(
-        model=llm.config.model_name,
-        prompt_tokens=num_input_tokens,
-        completion_tokens=num_output_tokens,
-    )
-    # Costs are in USD dollars per million tokens
-    return usd_per_prompt + usd_per_completion
-
-
 def get_llm_max_tokens(
     model_map: dict,
     model_name: str,

backend/onyx/main.py

@@ -391,11 +391,6 @@ def get_application() -> FastAPI:
             prefix="/auth",
         )
 
-    if (
-        AUTH_TYPE == AuthType.CLOUD
-        or AUTH_TYPE == AuthType.BASIC
-        or AUTH_TYPE == AuthType.GOOGLE_OAUTH
-    ):
         # Add refresh token endpoint for OAuth as well
         include_auth_router_with_prefix(
             application,

backend/onyx/natural_language_processing/utils.py

@@ -3,8 +3,6 @@ from abc import ABC
 from abc import abstractmethod
 from copy import copy
 
-from tokenizers import Encoding  # type: ignore
-from tokenizers import Tokenizer  # type: ignore
 from transformers import logging as transformer_logging  # type:ignore
 
 from onyx.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
@@ -13,8 +11,6 @@ from onyx.context.search.models import InferenceChunk
 from onyx.utils.logger import setup_logger
 from shared_configs.enums import EmbeddingProvider
 
-TRIM_SEP_PAT = "\n... {n} tokens removed...\n"
-
 logger = setup_logger()
 transformer_logging.set_verbosity_error()
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -71,27 +67,16 @@ class TiktokenTokenizer(BaseTokenizer):
 
 class HuggingFaceTokenizer(BaseTokenizer):
     def __init__(self, model_name: str):
-        self.encoder: Tokenizer = Tokenizer.from_pretrained(model_name)
+        from tokenizers import Tokenizer  # type: ignore
 
-    def _safer_encode(self, string: str) -> Encoding:
-        """
-        Encode a string using the HuggingFaceTokenizer, but if it fails,
-        encode the string as ASCII and decode it back to a string. This helps
-        in cases where the string has weird characters like \udeb4.
-        """
-        try:
-            return self.encoder.encode(string, add_special_tokens=False)
-        except Exception:
-            return self.encoder.encode(
-                string.encode("ascii", "ignore").decode(), add_special_tokens=False
-            )
+        self.encoder = Tokenizer.from_pretrained(model_name)
 
     def encode(self, string: str) -> list[int]:
         # this returns no special tokens
-        return self._safer_encode(string).ids
+        return self.encoder.encode(string, add_special_tokens=False).ids
 
     def tokenize(self, string: str) -> list[str]:
-        return self._safer_encode(string).tokens
+        return self.encoder.encode(string, add_special_tokens=False).tokens
 
     def decode(self, tokens: list[int]) -> str:
         return self.encoder.decode(tokens)
@@ -174,26 +159,9 @@ def tokenizer_trim_content(
     content: str, desired_length: int, tokenizer: BaseTokenizer
 ) -> str:
     tokens = tokenizer.encode(content)
-    if len(tokens) <= desired_length:
-        return content
-
-    return tokenizer.decode(tokens[:desired_length])
-
-
-def tokenizer_trim_middle(
-    tokens: list[int], desired_length: int, tokenizer: BaseTokenizer
-) -> str:
-    if len(tokens) <= desired_length:
-        return tokenizer.decode(tokens)
-    sep_str = TRIM_SEP_PAT.format(n=len(tokens) - desired_length)
-    sep_tokens = tokenizer.encode(sep_str)
-    slice_size = (desired_length - len(sep_tokens)) // 2
-    assert slice_size > 0, "Slice size is not positive, desired length is too short"
-    return (
-        tokenizer.decode(tokens[:slice_size])
-        + sep_str
-        + tokenizer.decode(tokens[-slice_size:])
-    )
+    if len(tokens) > desired_length:
+        content = tokenizer.decode(tokens[:desired_length])
+    return content
 
 
 def tokenizer_trim_chunks(

backend/onyx/prompts/chat_prompts.py

@@ -220,29 +220,3 @@ Chat History:
 
 Based on the above, what is a short name to convey the topic of the conversation?
 """.strip()
-
-# NOTE: the prompt separation is partially done for efficiency; previously I tried
-# to do it all in one prompt with sequential format() calls but this will cause a backend
-# error when the document contains any {} as python will expect the {} to be filled by
-# format() arguments
-CONTEXTUAL_RAG_PROMPT1 = """<document>
-{document}
-</document>
-Here is the chunk we want to situate within the whole document"""
-
-CONTEXTUAL_RAG_PROMPT2 = """<chunk>
-{chunk}
-</chunk>
-Please give a short succinct context to situate this chunk within the overall document
-for the purposes of improving search retrieval of the chunk. Answer only with the succinct
-context and nothing else. """
-
-CONTEXTUAL_RAG_TOKEN_ESTIMATE = 64  # 19 + 45
-
-DOCUMENT_SUMMARY_PROMPT = """<document>
-{document}
-</document>
-Please give a short succinct summary of the entire document. Answer only with the succinct
-summary and nothing else. """
-
-DOCUMENT_SUMMARY_TOKEN_ESTIMATE = 29

backend/onyx/seeding/load_docs.py

@@ -87,9 +87,6 @@ def _create_indexable_chunks(
             metadata_suffix_keyword="",
             mini_chunk_texts=None,
             large_chunk_reference_ids=[],
-            doc_summary="",
-            chunk_context="",
-            contextual_rag_reserved_tokens=0,
             embeddings=ChunkEmbedding(
                 full_embedding=preprocessed_doc["content_embedding"],
                 mini_chunk_embeddings=[],

backend/onyx/server/manage/llm/api.py

@@ -21,11 +21,9 @@ from onyx.llm.factory import get_default_llms
 from onyx.llm.factory import get_llm
 from onyx.llm.llm_provider_options import fetch_available_well_known_llms
 from onyx.llm.llm_provider_options import WellKnownLLMProviderDescriptor
-from onyx.llm.utils import get_llm_contextual_cost
 from onyx.llm.utils import litellm_exception_to_error_msg
 from onyx.llm.utils import model_supports_image_input
 from onyx.llm.utils import test_llm
-from onyx.server.manage.llm.models import LLMCost
 from onyx.server.manage.llm.models import LLMProviderDescriptor
 from onyx.server.manage.llm.models import LLMProviderUpsertRequest
 from onyx.server.manage.llm.models import LLMProviderView
@@ -288,38 +286,3 @@ def list_llm_provider_basics(
             db_session, user
         )
     ]
-
-
-@admin_router.get("/provider-contextual-cost")
-def get_provider_contextual_cost(
-    _: User | None = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> list[LLMCost]:
-    """
-    Get the cost of Re-indexing all documents for contextual retrieval.
-
-    See https://docs.litellm.ai/docs/completion/token_usage#5-cost_per_token
-    This includes:
-    - The cost of invoking the LLM on each chunk-document pair to get
-      - the doc_summary
-      - the chunk_context
-    - The per-token cost of the LLM used to generate the doc_summary and chunk_context
-    """
-    providers = fetch_existing_llm_providers(db_session)
-    costs = []
-    for provider in providers:
-        for model_name in provider.display_model_names or provider.model_names or []:
-            llm = get_llm(
-                provider=provider.provider,
-                model=model_name,
-                deployment_name=provider.deployment_name,
-                api_key=provider.api_key,
-                api_base=provider.api_base,
-                api_version=provider.api_version,
-                custom_config=provider.custom_config,
-            )
-            cost = get_llm_contextual_cost(llm)
-            costs.append(
-                LLMCost(provider=provider.name, model_name=model_name, cost=cost)
-            )
-    return costs

backend/onyx/server/manage/llm/models.py

@@ -119,9 +119,3 @@ class VisionProviderResponse(LLMProviderView):
     """Response model for vision providers endpoint, including vision-specific fields."""
 
     vision_models: list[str]
-
-
-class LLMCost(BaseModel):
-    provider: str
-    model_name: str
-    cost: float

backend/scripts/query_time_check/seed_dummy_docs.py

@@ -63,10 +63,7 @@ def generate_dummy_chunk(
         title_prefix=f"Title prefix for doc {doc_id}",
         metadata_suffix_semantic="",
         metadata_suffix_keyword="",
-        doc_summary="",
-        chunk_context="",
         mini_chunk_texts=None,
-        contextual_rag_reserved_tokens=0,
         embeddings=ChunkEmbedding(
             full_embedding=generate_random_embedding(embedding_dim),
             mini_chunk_embeddings=[],

backend/shared_configs/configs.py

@@ -99,7 +99,6 @@ PRESERVED_SEARCH_FIELDS = [
     "api_url",
     "index_name",
     "multipass_indexing",
-    "enable_contextual_rag",
     "model_dim",
     "normalize",
     "passage_prefix",

backend/tests/unit/ee/onyx/external_permissions/salesforce/test_postprocessing.py

@@ -32,8 +32,6 @@ def create_test_chunk(
         match_highlights=[],
         updated_at=datetime.now(),
         image_file_name=None,
-        doc_summary="",
-        chunk_context="",
     )
 
 

backend/tests/unit/onyx/chat/conftest.py

@@ -78,8 +78,6 @@ def mock_inference_sections() -> list[InferenceSection]:
                 source_links={0: "https://example.com/doc1"},
                 match_highlights=[],
                 image_file_name=None,
-                doc_summary="",
-                chunk_context="",
             ),
             chunks=MagicMock(),
         ),
@@ -103,8 +101,6 @@ def mock_inference_sections() -> list[InferenceSection]:
                 source_links={0: "https://example.com/doc2"},
                 match_highlights=[],
                 image_file_name=None,
-                doc_summary="",
-                chunk_context="",
             ),
             chunks=MagicMock(),
         ),

backend/tests/unit/onyx/chat/stream_processing/test_quotes_processing.py

@@ -151,8 +151,6 @@ def test_fuzzy_match_quotes_to_docs() -> None:
         match_highlights=[],
         updated_at=None,
         image_file_name=None,
-        doc_summary="",
-        chunk_context="",
     )
     test_chunk_1 = InferenceChunk(
         document_id="test doc 1",
@@ -172,8 +170,6 @@ def test_fuzzy_match_quotes_to_docs() -> None:
         match_highlights=[],
         updated_at=None,
         image_file_name=None,
-        doc_summary="",
-        chunk_context="",
     )
 
     test_quotes = [

backend/tests/unit/onyx/chat/test_prune_and_merge.py

@@ -38,8 +38,6 @@ def create_inference_chunk(
         match_highlights=[],
         updated_at=None,
         image_file_name=None,
-        doc_summary="",
-        chunk_context="",
     )
 
 

backend/tests/unit/onyx/indexing/conftest.py

@@ -1,6 +1,5 @@
 import pytest
 
-from onyx.indexing.embedder import DefaultIndexingEmbedder
 from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 
 
@@ -18,13 +17,3 @@ class MockHeartbeat(IndexingHeartbeatInterface):
 @pytest.fixture
 def mock_heartbeat() -> MockHeartbeat:
     return MockHeartbeat()
-
-
-@pytest.fixture
-def embedder() -> DefaultIndexingEmbedder:
-    return DefaultIndexingEmbedder(
-        model_name="intfloat/e5-base-v2",
-        normalize=True,
-        query_prefix=None,
-        passage_prefix=None,
-    )

backend/tests/unit/onyx/indexing/test_chunker.py

@@ -1,24 +1,25 @@
-from typing import Any
-from unittest.mock import Mock
-
 import pytest
 
-from onyx.configs.app_configs import USE_CHUNK_SUMMARY
-from onyx.configs.app_configs import USE_DOCUMENT_SUMMARY
 from onyx.configs.constants import DocumentSource
 from onyx.connectors.models import Document
 from onyx.connectors.models import TextSection
 from onyx.indexing.chunker import Chunker
 from onyx.indexing.embedder import DefaultIndexingEmbedder
 from onyx.indexing.indexing_pipeline import process_image_sections
-from onyx.llm.utils import MAX_CONTEXT_TOKENS
 from tests.unit.onyx.indexing.conftest import MockHeartbeat
 
 
-@pytest.mark.parametrize("enable_contextual_rag", [True, False])
-def test_chunk_document(
-    embedder: DefaultIndexingEmbedder, enable_contextual_rag: bool
-) -> None:
+@pytest.fixture
+def embedder() -> DefaultIndexingEmbedder:
+    return DefaultIndexingEmbedder(
+        model_name="intfloat/e5-base-v2",
+        normalize=True,
+        query_prefix=None,
+        passage_prefix=None,
+    )
+
+
+def test_chunk_document(embedder: DefaultIndexingEmbedder) -> None:
     short_section_1 = "This is a short section."
     long_section = (
         "This is a long section that should be split into multiple chunks. " * 100
@@ -44,22 +45,9 @@ def test_chunk_document(
     )
     indexing_documents = process_image_sections([document])
 
-    mock_llm_invoke_count = 0
-
-    def mock_llm_invoke(self: Any, *args: Any, **kwargs: Any) -> Mock:
-        nonlocal mock_llm_invoke_count
-        mock_llm_invoke_count += 1
-        m = Mock()
-        m.content = f"Test{mock_llm_invoke_count}"
-        return m
-
-    mock_llm = Mock()
-    mock_llm.invoke = mock_llm_invoke
-
     chunker = Chunker(
         tokenizer=embedder.embedding_model.tokenizer,
         enable_multipass=False,
-        enable_contextual_rag=enable_contextual_rag,
     )
     chunks = chunker.chunk(indexing_documents)
 
@@ -70,14 +58,6 @@ def test_chunk_document(
     assert "tag1" in chunks[0].metadata_suffix_keyword
     assert "tag2" in chunks[0].metadata_suffix_semantic
 
-    rag_tokens = MAX_CONTEXT_TOKENS * (
-        int(USE_DOCUMENT_SUMMARY) + int(USE_CHUNK_SUMMARY)
-    )
-    for chunk in chunks:
-        assert chunk.contextual_rag_reserved_tokens == (
-            rag_tokens if enable_contextual_rag else 0
-        )
-
 
 def test_chunker_heartbeat(
     embedder: DefaultIndexingEmbedder, mock_heartbeat: MockHeartbeat
@@ -98,7 +78,6 @@ def test_chunker_heartbeat(
         tokenizer=embedder.embedding_model.tokenizer,
         enable_multipass=False,
         callback=mock_heartbeat,
-        enable_contextual_rag=False,
     )
 
     chunks = chunker.chunk(indexing_documents)

backend/tests/unit/onyx/indexing/test_embedder.py

@@ -21,13 +21,7 @@ def mock_embedding_model() -> Generator[Mock, None, None]:
         yield mock
 
 
-@pytest.mark.parametrize(
-    "chunk_context, doc_summary",
-    [("Test chunk context", "Test document summary"), ("", "")],
-)
-def test_default_indexing_embedder_embed_chunks(
-    mock_embedding_model: Mock, chunk_context: str, doc_summary: str
-) -> None:
+def test_default_indexing_embedder_embed_chunks(mock_embedding_model: Mock) -> None:
     # Setup
     embedder = DefaultIndexingEmbedder(
         model_name="test-model",
@@ -69,9 +63,6 @@ def test_default_indexing_embedder_embed_chunks(
             large_chunk_reference_ids=[],
             large_chunk_id=None,
             image_file_name=None,
-            chunk_context=chunk_context,
-            doc_summary=doc_summary,
-            contextual_rag_reserved_tokens=200,
         )
     ]
 
@@ -90,7 +81,7 @@ def test_default_indexing_embedder_embed_chunks(
 
     # Verify the embedding model was called correctly
     mock_embedding_model.return_value.encode.assert_any_call(
-        texts=[f"Title: {doc_summary}Test chunk{chunk_context}"],
+        texts=["Title: Test chunk"],
         text_type=EmbedTextType.PASSAGE,
         large_chunks_present=False,
     )

backend/tests/unit/onyx/indexing/test_indexing_pipeline.py

@@ -1,8 +1,6 @@
-from typing import Any
 from typing import cast
 from typing import List
 from unittest.mock import Mock
-from unittest.mock import patch
 
 import pytest
 
@@ -11,12 +9,8 @@ from onyx.connectors.models import Document
 from onyx.connectors.models import DocumentSource
 from onyx.connectors.models import ImageSection
 from onyx.connectors.models import TextSection
-from onyx.indexing.chunker import Chunker
-from onyx.indexing.embedder import DefaultIndexingEmbedder
 from onyx.indexing.indexing_pipeline import _get_aggregated_chunk_boost_factor
-from onyx.indexing.indexing_pipeline import add_contextual_summaries
 from onyx.indexing.indexing_pipeline import filter_documents
-from onyx.indexing.indexing_pipeline import process_image_sections
 from onyx.indexing.models import ChunkEmbedding
 from onyx.indexing.models import IndexChunk
 from onyx.natural_language_processing.search_nlp_models import (
@@ -172,9 +166,6 @@ def create_test_chunk(
         embeddings=ChunkEmbedding(full_embedding=[], mini_chunk_embeddings=[]),
         title_embedding=None,
         image_file_name=None,
-        chunk_context="",
-        doc_summary="",
-        contextual_rag_reserved_tokens=200,
     )
 
 
@@ -258,76 +249,3 @@ def test_get_aggregated_boost_factor_individual_failure() -> None:
         )
 
     assert "Failed to predict content classification for chunk" in str(exc_info.value)
-
-
-@patch("onyx.llm.utils.GEN_AI_MAX_TOKENS", 4096)
-@pytest.mark.parametrize("enable_contextual_rag", [True, False])
-def test_contextual_rag(
-    embedder: DefaultIndexingEmbedder, enable_contextual_rag: bool
-) -> None:
-    short_section_1 = "This is a short section."
-    long_section = (
-        "This is a long section that should be split into multiple chunks. " * 100
-    )
-    short_section_2 = "This is another short section."
-    short_section_3 = "This is another short section again."
-    short_section_4 = "Final short section."
-    semantic_identifier = "Test Document"
-
-    document = Document(
-        id="test_doc",
-        source=DocumentSource.WEB,
-        semantic_identifier=semantic_identifier,
-        metadata={"tags": ["tag1", "tag2"]},
-        doc_updated_at=None,
-        sections=[
-            TextSection(text=short_section_1, link="link1"),
-            TextSection(text=short_section_2, link="link2"),
-            TextSection(text=long_section, link="link3"),
-            TextSection(text=short_section_3, link="link4"),
-            TextSection(text=short_section_4, link="link5"),
-        ],
-    )
-    indexing_documents = process_image_sections([document])
-
-    mock_llm_invoke_count = 0
-
-    def mock_llm_invoke(self: Any, *args: Any, **kwargs: Any) -> Mock:
-        nonlocal mock_llm_invoke_count
-        mock_llm_invoke_count += 1
-        m = Mock()
-        m.content = f"Test{mock_llm_invoke_count}"
-        return m
-
-    llm_tokenizer = embedder.embedding_model.tokenizer
-
-    mock_llm = Mock()
-    mock_llm.invoke = mock_llm_invoke
-
-    chunker = Chunker(
-        tokenizer=embedder.embedding_model.tokenizer,
-        enable_multipass=False,
-        enable_contextual_rag=enable_contextual_rag,
-    )
-    chunks = chunker.chunk(indexing_documents)
-
-    chunks = add_contextual_summaries(
-        chunks, mock_llm, llm_tokenizer, chunker.chunk_token_limit * 2
-    )
-
-    assert len(chunks) == 5
-    assert short_section_1 in chunks[0].content
-    assert short_section_3 in chunks[-1].content
-    assert short_section_4 in chunks[-1].content
-    assert "tag1" in chunks[0].metadata_suffix_keyword
-    assert "tag2" in chunks[0].metadata_suffix_semantic
-
-    doc_summary = "Test1" if enable_contextual_rag else ""
-    chunk_context = ""
-    count = 2
-    for chunk in chunks:
-        if enable_contextual_rag:
-            chunk_context = f"Test{count}"
-            count += 1
-        assert chunk.doc_summary == doc_summary
-        assert chunk.chunk_context == chunk_context

backend/tests/unit/onyx/llm/test_chat_llm.py

@@ -140,12 +140,12 @@ def test_multiple_tool_calls(default_multi_llm: DefaultMultiLLM) -> None:
             ],
             tools=tools,
             tool_choice=None,
-            max_tokens=None,
             stream=False,
             temperature=0.0,  # Default value from GEN_AI_TEMPERATURE
             timeout=30,
             parallel_tool_calls=False,
             mock_response=MOCK_LLM_RESPONSE,
+            max_tokens=None,
         )
 
 
@@ -286,10 +286,10 @@ def test_multiple_tool_calls_streaming(default_multi_llm: DefaultMultiLLM) -> No
             ],
             tools=tools,
             tool_choice=None,
-            max_tokens=None,
             stream=True,
             temperature=0.0,  # Default value from GEN_AI_TEMPERATURE
             timeout=30,
             parallel_tool_calls=False,
             mock_response=MOCK_LLM_RESPONSE,
+            max_tokens=None,
         )

web/src/app/admin/configuration/llm/constants.ts

@@ -1,8 +1,5 @@
 export const LLM_PROVIDERS_ADMIN_URL = "/api/admin/llm/provider";
 
-export const LLM_CONTEXTUAL_COST_ADMIN_URL =
-  "/api/admin/llm/provider-contextual-cost";
-
 export const EMBEDDING_PROVIDERS_ADMIN_URL =
   "/api/admin/embedding/embedding-provider";
 

web/src/app/admin/configuration/search/page.tsx

@@ -143,15 +143,6 @@ function Main() {
                       </Text>
                     </div>
 
-                    <div>
-                      <Text className="font-semibold">Contextual RAG</Text>
-                      <Text className="text-text-700">
-                        {searchSettings.enable_contextual_rag
-                          ? "Enabled"
-                          : "Disabled"}
-                      </Text>
-                    </div>
-
                     <div>
                       <Text className="font-semibold">
                         Disable Reranking for Streaming

web/src/app/admin/embeddings/interfaces.ts

@@ -26,18 +26,9 @@ export enum EmbeddingPrecision {
   BFLOAT16 = "bfloat16",
 }
 
-export interface LLMContextualCost {
-  provider: string;
-  model_name: string;
-  cost: number;
-}
-
 export interface AdvancedSearchConfiguration {
   index_name: string | null;
   multipass_indexing: boolean;
-  enable_contextual_rag: boolean;
-  contextual_rag_llm_name: string | null;
-  contextual_rag_llm_provider: string | null;
   multilingual_expansion: string[];
   disable_rerank_for_streaming: boolean;
   api_url: string | null;

web/src/app/admin/embeddings/pages/AdvancedEmbeddingFormPage.tsx

@@ -3,11 +3,7 @@ import { Formik, Form, FormikProps, FieldArray, Field } from "formik";
 import * as Yup from "yup";
 import { TrashIcon } from "@/components/icons/icons";
 import { FaPlus } from "react-icons/fa";
-import {
-  AdvancedSearchConfiguration,
-  EmbeddingPrecision,
-  LLMContextualCost,
-} from "../interfaces";
+import { AdvancedSearchConfiguration, EmbeddingPrecision } from "../interfaces";
 import {
   BooleanFormField,
   Label,
@@ -16,13 +12,6 @@ import {
 } from "@/components/admin/connectors/Field";
 import NumberInput from "../../connectors/[connector]/pages/ConnectorInput/NumberInput";
 import { StringOrNumberOption } from "@/components/Dropdown";
-import useSWR from "swr";
-import { LLM_CONTEXTUAL_COST_ADMIN_URL } from "../../configuration/llm/constants";
-import { getDisplayNameForModel } from "@/lib/hooks";
-import { errorHandlingFetcher } from "@/lib/fetcher";
-
-// Number of tokens to show cost calculation for
-const COST_CALCULATION_TOKENS = 1_000_000;
 
 interface AdvancedEmbeddingFormPageProps {
   updateAdvancedEmbeddingDetails: (
@@ -56,66 +45,14 @@ const AdvancedEmbeddingFormPage = forwardRef<
     },
     ref
   ) => {
-    // Fetch contextual costs
-    const { data: contextualCosts, error: costError } = useSWR<
-      LLMContextualCost[]
-    >(LLM_CONTEXTUAL_COST_ADMIN_URL, errorHandlingFetcher);
-
-    const llmOptions: StringOrNumberOption[] = React.useMemo(
-      () =>
-        (contextualCosts || []).map((cost) => {
-          return {
-            name: getDisplayNameForModel(cost.model_name),
-            value: cost.model_name,
-          };
-        }),
-      [contextualCosts]
-    );
-
-    // Helper function to format cost as USD
-    const formatCost = (cost: number) => {
-      return new Intl.NumberFormat("en-US", {
-        style: "currency",
-        currency: "USD",
-      }).format(cost);
-    };
-
-    // Get cost info for selected model
-    const getSelectedModelCost = (modelName: string | null) => {
-      if (!contextualCosts || !modelName) return null;
-      return contextualCosts.find((cost) => cost.model_name === modelName);
-    };
-
-    // Get the current value for the selector based on the parent state
-    const getCurrentLLMValue = React.useMemo(() => {
-      if (!advancedEmbeddingDetails.contextual_rag_llm_name) return null;
-      return advancedEmbeddingDetails.contextual_rag_llm_name;
-    }, [advancedEmbeddingDetails.contextual_rag_llm_name]);
-
     return (
       <div className="py-4 rounded-lg max-w-4xl px-4 mx-auto">
         <Formik
           innerRef={ref}
-          initialValues={{
-            ...advancedEmbeddingDetails,
-            contextual_rag_llm: getCurrentLLMValue,
-          }}
+          initialValues={advancedEmbeddingDetails}
           validationSchema={Yup.object().shape({
             multilingual_expansion: Yup.array().of(Yup.string()),
             multipass_indexing: Yup.boolean(),
-            enable_contextual_rag: Yup.boolean(),
-            contextual_rag_llm: Yup.string()
-              .nullable()
-              .test(
-                "required-if-contextual-rag",
-                "LLM must be selected when Contextual RAG is enabled",
-                function (value) {
-                  const enableContextualRag = this.parent.enable_contextual_rag;
-                  console.log("enableContextualRag", enableContextualRag);
-                  console.log("value", value);
-                  return !enableContextualRag || value !== null;
-                }
-              ),
             disable_rerank_for_streaming: Yup.boolean(),
             num_rerank: Yup.number()
               .required("Number of results to rerank is required")
@@ -142,26 +79,10 @@ const AdvancedEmbeddingFormPage = forwardRef<
           validate={(values) => {
             // Call updateAdvancedEmbeddingDetails for each changed field
             Object.entries(values).forEach(([key, value]) => {
-              if (key === "contextual_rag_llm") {
-                const selectedModel = (contextualCosts || []).find(
-                  (cost) => cost.model_name === value
-                );
-                if (selectedModel) {
-                  updateAdvancedEmbeddingDetails(
-                    "contextual_rag_llm_provider",
-                    selectedModel.provider
-                  );
-                  updateAdvancedEmbeddingDetails(
-                    "contextual_rag_llm_name",
-                    selectedModel.model_name
-                  );
-                }
-              } else {
-                updateAdvancedEmbeddingDetails(
-                  key as keyof AdvancedSearchConfiguration,
-                  value
-                );
-              }
+              updateAdvancedEmbeddingDetails(
+                key as keyof AdvancedSearchConfiguration,
+                value
+              );
             });
 
             // Run validation and report errors
@@ -175,23 +96,6 @@ const AdvancedEmbeddingFormPage = forwardRef<
                   .shape({
                     multilingual_expansion: Yup.array().of(Yup.string()),
                     multipass_indexing: Yup.boolean(),
-                    enable_contextual_rag: Yup.boolean(),
-                    contextual_rag_llm: Yup.string()
-                      .nullable()
-                      .test(
-                        "required-if-contextual-rag",
-                        "LLM must be selected when Contextual RAG is enabled",
-                        function (value) {
-                          const enableContextualRag =
-                            this.parent.enable_contextual_rag;
-                          console.log(
-                            "enableContextualRag2",
-                            enableContextualRag
-                          );
-                          console.log("value2", value);
-                          return !enableContextualRag || value !== null;
-                        }
-                      ),
                     disable_rerank_for_streaming: Yup.boolean(),
                     num_rerank: Yup.number()
                       .required("Number of results to rerank is required")
@@ -286,56 +190,6 @@ const AdvancedEmbeddingFormPage = forwardRef<
                 label="Disable Rerank for Streaming"
                 name="disable_rerank_for_streaming"
               />
-              <BooleanFormField
-                subtext="Enable contextual RAG for all chunk sizes."
-                optional
-                label="Contextual RAG"
-                name="enable_contextual_rag"
-              />
-              <div>
-                <SelectorFormField
-                  name="contextual_rag_llm"
-                  label="Contextual RAG LLM"
-                  subtext={
-                    costError
-                      ? "Error loading LLM models. Please try again later."
-                      : !contextualCosts
-                        ? "Loading available LLM models..."
-                        : values.enable_contextual_rag
-                          ? "Select the LLM model to use for contextual RAG processing."
-                          : "Enable Contextual RAG above to select an LLM model."
-                  }
-                  options={llmOptions}
-                  disabled={
-                    !values.enable_contextual_rag ||
-                    !contextualCosts ||
-                    !!costError
-                  }
-                />
-                {values.enable_contextual_rag &&
-                  values.contextual_rag_llm &&
-                  !costError && (
-                    <div className="mt-2 text-sm text-text-600">
-                      {contextualCosts ? (
-                        <>
-                          Estimated cost for processing{" "}
-                          {COST_CALCULATION_TOKENS.toLocaleString()} tokens:{" "}
-                          <span className="font-medium">
-                            {getSelectedModelCost(values.contextual_rag_llm)
-                              ? formatCost(
-                                  getSelectedModelCost(
-                                    values.contextual_rag_llm
-                                  )!.cost
-                                )
-                              : "Cost information not available"}
-                          </span>
-                        </>
-                      ) : (
-                        "Loading cost information..."
-                      )}
-                    </div>
-                  )}
-              </div>
               <NumberInput
                 description="Number of results to rerank"
                 optional={false}

web/src/app/admin/embeddings/pages/EmbeddingFormPage.tsx

@@ -64,9 +64,6 @@ export default function EmbeddingForm() {
     useState<AdvancedSearchConfiguration>({
       index_name: "",
       multipass_indexing: true,
-      enable_contextual_rag: false,
-      contextual_rag_llm_name: null,
-      contextual_rag_llm_provider: null,
       multilingual_expansion: [],
       disable_rerank_for_streaming: false,
       api_url: null,
@@ -155,9 +152,6 @@ export default function EmbeddingForm() {
       setAdvancedEmbeddingDetails({
         index_name: searchSettings.index_name,
         multipass_indexing: searchSettings.multipass_indexing,
-        enable_contextual_rag: searchSettings.enable_contextual_rag,
-        contextual_rag_llm_name: searchSettings.contextual_rag_llm_name,
-        contextual_rag_llm_provider: searchSettings.contextual_rag_llm_provider,
         multilingual_expansion: searchSettings.multilingual_expansion,
         disable_rerank_for_streaming:
           searchSettings.disable_rerank_for_streaming,
@@ -203,9 +197,7 @@ export default function EmbeddingForm() {
     searchSettings?.embedding_precision !=
       advancedEmbeddingDetails.embedding_precision ||
     searchSettings?.reduced_dimension !=
-      advancedEmbeddingDetails.reduced_dimension ||
-    searchSettings?.enable_contextual_rag !=
-      advancedEmbeddingDetails.enable_contextual_rag;
+      advancedEmbeddingDetails.reduced_dimension;
 
   const updateSearch = useCallback(async () => {
     if (!selectedProvider) {
@@ -392,14 +384,6 @@ export default function EmbeddingForm() {
                     advancedEmbeddingDetails.reduced_dimension && (
                     <li>Reduced dimension modification</li>
                   )}
-                  {(searchSettings?.enable_contextual_rag !=
-                    advancedEmbeddingDetails.enable_contextual_rag ||
-                    searchSettings?.contextual_rag_llm_name !=
-                      advancedEmbeddingDetails.contextual_rag_llm_name ||
-                    searchSettings?.contextual_rag_llm_provider !=
-                      advancedEmbeddingDetails.contextual_rag_llm_provider) && (
-                    <li>Contextual RAG modification</li>
-                  )}
                 </ul>
               </div>
             </div>
@@ -487,11 +471,6 @@ export default function EmbeddingForm() {
   };
 
   const handleReIndex = async () => {
-    console.log("handleReIndex");
-    console.log(selectedProvider);
-    console.log(advancedEmbeddingDetails);
-    console.log(rerankingDetails);
-    console.log(reindexType);
     if (!selectedProvider) {
       return;
     }

web/src/app/layout.tsx

@@ -20,7 +20,7 @@ import {
 import { fetchAssistantData } from "@/lib/chat/fetchAssistantdata";
 import { AppProvider } from "@/components/context/AppProvider";
 import { PHProvider } from "./providers";
-import { getAuthTypeMetadataSS, getCurrentUserSS } from "@/lib/userSS";
+import { getCurrentUserSS } from "@/lib/userSS";
 import { Suspense } from "react";
 import PostHogPageView from "./PostHogPageView";
 import Script from "next/script";
@@ -70,13 +70,11 @@ export default async function RootLayout({
 }: {
   children: React.ReactNode;
 }) {
-  const [combinedSettings, assistantsData, user, authTypeMetadata] =
-    await Promise.all([
-      fetchSettingsSS(),
-      fetchAssistantData(),
-      getCurrentUserSS(),
-      getAuthTypeMetadataSS(),
-    ]);
+  const [combinedSettings, assistantsData, user] = await Promise.all([
+    fetchSettingsSS(),
+    fetchAssistantData(),
+    getCurrentUserSS(),
+  ]);
 
   const productGating =
     combinedSettings?.settings.application_status ?? ApplicationStatus.ACTIVE;
@@ -149,7 +147,6 @@ export default async function RootLayout({
 
   return getPageContent(
     <AppProvider
-      authTypeMetadata={authTypeMetadata}
       user={user}
       settings={combinedSettings}
       assistants={assistants}

web/src/components/admin/connectors/Field.tsx

@@ -676,7 +676,6 @@ interface SelectorFormFieldProps {
   includeReset?: boolean;
   fontSize?: "sm" | "md" | "lg";
   small?: boolean;
-  disabled?: boolean;
 }
 
 export function SelectorFormField({
@@ -692,7 +691,6 @@ export function SelectorFormField({
   includeReset = false,
   fontSize = "md",
   small = false,
-  disabled = false,
 }: SelectorFormFieldProps) {
   const [field] = useField<string>(name);
   const { setFieldValue } = useFormikContext();
@@ -744,9 +742,8 @@ export function SelectorFormField({
                 : setFieldValue(name, selected))
           }
           defaultValue={defaultValue}
-          disabled={disabled}
         >
-          <SelectTrigger className={sizeClass.input} disabled={disabled}>
+          <SelectTrigger className={sizeClass.input}>
             <SelectValue placeholder="Select...">
               {currentlySelected?.name || defaultValue || ""}
             </SelectValue>

web/src/components/context/AppProvider.tsx

@@ -7,7 +7,6 @@ import { AssistantsProvider } from "./AssistantsContext";
 import { Persona } from "@/app/admin/assistants/interfaces";
 import { User } from "@/lib/types";
 import { ModalProvider } from "./ModalContext";
-import { AuthTypeMetadata } from "@/lib/userSS";
 
 interface AppProviderProps {
   children: React.ReactNode;
@@ -16,7 +15,6 @@ interface AppProviderProps {
   assistants: Persona[];
   hasAnyConnectors: boolean;
   hasImageCompatibleModel: boolean;
-  authTypeMetadata: AuthTypeMetadata;
 }
 
 export const AppProvider = ({
@@ -26,15 +24,10 @@ export const AppProvider = ({
   assistants,
   hasAnyConnectors,
   hasImageCompatibleModel,
-  authTypeMetadata,
 }: AppProviderProps) => {
   return (
     <SettingsProvider settings={settings}>
-      <UserProvider
-        settings={settings}
-        user={user}
-        authTypeMetadata={authTypeMetadata}
-      >
+      <UserProvider settings={settings} user={user}>
         <ProviderContextProvider>
           <AssistantsProvider
             initialAssistants={assistants}

web/src/components/user/UserProvider.tsx

@@ -13,7 +13,6 @@ import { usePostHog } from "posthog-js/react";
 import { CombinedSettings } from "@/app/admin/settings/interfaces";
 import { SettingsContext } from "../settings/SettingsProvider";
 import { useTokenRefresh } from "@/hooks/useTokenRefresh";
-import { AuthTypeMetadata } from "@/lib/userSS";
 
 interface UserContextType {
   user: User | null;
@@ -34,12 +33,10 @@ interface UserContextType {
 const UserContext = createContext<UserContextType | undefined>(undefined);
 
 export function UserProvider({
-  authTypeMetadata,
   children,
   user,
   settings,
 }: {
-  authTypeMetadata: AuthTypeMetadata;
   children: React.ReactNode;
   user: User | null;
   settings: CombinedSettings;
@@ -105,7 +102,7 @@ export function UserProvider({
   };
 
   // Use the custom token refresh hook
-  useTokenRefresh(upToDateUser, authTypeMetadata, fetchUser);
+  useTokenRefresh(upToDateUser, fetchUser);
 
   const updateUserTemperatureOverrideEnabled = async (enabled: boolean) => {
     try {

web/src/hooks/useTokenRefresh.ts

@@ -1,7 +1,6 @@
 import { useState, useEffect, useRef } from "react";
 import { User } from "@/lib/types";
 import { NO_AUTH_USER_ID } from "@/lib/extension/constants";
-import { AuthTypeMetadata } from "@/lib/userSS";
 
 // Refresh token every 10 minutes (600000ms)
 // This is shorter than the session expiry time to ensure tokens stay valid
@@ -10,7 +9,6 @@ const REFRESH_INTERVAL = 600000;
 //  Custom hook for handling JWT token refresh for current user
 export function useTokenRefresh(
   user: User | null,
-  authTypeMetadata: AuthTypeMetadata,
   onRefreshFail: () => Promise<void>
 ) {
   // Track last refresh time to avoid unnecessary calls
@@ -20,13 +18,7 @@ export function useTokenRefresh(
   const isFirstLoad = useRef(true);
 
   useEffect(() => {
-    if (
-      !user ||
-      user.id === NO_AUTH_USER_ID ||
-      authTypeMetadata.authType === "oidc" ||
-      authTypeMetadata.authType === "saml"
-    )
-      return;
+    if (!user || user.id === NO_AUTH_USER_ID) return;
 
     const refreshTokenPeriodically = async () => {
       try {