Merge pull request #3442 from onyx-dot-app/vespa_seeding_fix

Update initial seeding for latency requirements

backend/danswer/document_index/interfaces.py

@@ -148,6 +148,7 @@ class Indexable(abc.ABC):
     def index(
         self,
         chunks: list[DocMetadataAwareIndexChunk],
+        fresh_index: bool = False,
     ) -> set[DocumentInsertionRecord]:
         """
         Takes a list of document chunks and indexes them in the document index
@@ -165,9 +166,14 @@ class Indexable(abc.ABC):
         only needs to index chunks into the PRIMARY index. Do not update the secondary index here,
         it is done automatically outside of this code.
 
+        NOTE: The fresh_index parameter, when set to True, assumes no documents have been previously
+        indexed for the given index/tenant. This can be used to optimize the indexing process for
+        new or empty indices.
+
         Parameters:
         - chunks: Document chunks with all of the information needed for indexing to the document
                 index.
+        - fresh_index: Boolean indicating whether this is a fresh index with no existing documents.
 
         Returns:
             List of document ids which map to unique documents and are used for deduping chunks

backend/danswer/document_index/vespa/index.py

@@ -306,6 +306,7 @@ class VespaIndex(DocumentIndex):
     def index(
         self,
         chunks: list[DocMetadataAwareIndexChunk],
+        fresh_index: bool = False,
     ) -> set[DocumentInsertionRecord]:
         """Receive a list of chunks from a batch of documents and index the chunks into Vespa along
         with updating the associated permissions. Assumes that a document will not be split into
@@ -322,26 +323,29 @@ class VespaIndex(DocumentIndex):
             concurrent.futures.ThreadPoolExecutor(max_workers=NUM_THREADS) as executor,
             get_vespa_http_client() as http_client,
         ):
-            # Check for existing documents, existing documents need to have all of their chunks deleted
-            # prior to indexing as the document size (num chunks) may have shrunk
-            first_chunks = [chunk for chunk in cleaned_chunks if chunk.chunk_id == 0]
-            for chunk_batch in batch_generator(first_chunks, BATCH_SIZE):
-                existing_docs.update(
-                    get_existing_documents_from_chunks(
-                        chunks=chunk_batch,
+            if not fresh_index:
+                # Check for existing documents, existing documents need to have all of their chunks deleted
+                # prior to indexing as the document size (num chunks) may have shrunk
+                first_chunks = [
+                    chunk for chunk in cleaned_chunks if chunk.chunk_id == 0
+                ]
+                for chunk_batch in batch_generator(first_chunks, BATCH_SIZE):
+                    existing_docs.update(
+                        get_existing_documents_from_chunks(
+                            chunks=chunk_batch,
+                            index_name=self.index_name,
+                            http_client=http_client,
+                            executor=executor,
+                        )
+                    )
+
+                for doc_id_batch in batch_generator(existing_docs, BATCH_SIZE):
+                    delete_vespa_docs(
+                        document_ids=doc_id_batch,
                         index_name=self.index_name,
                         http_client=http_client,
                         executor=executor,
                     )
-                )
-
-            for doc_id_batch in batch_generator(existing_docs, BATCH_SIZE):
-                delete_vespa_docs(
-                    document_ids=doc_id_batch,
-                    index_name=self.index_name,
-                    http_client=http_client,
-                    executor=executor,
-                )
 
             for chunk_batch in batch_generator(cleaned_chunks, BATCH_SIZE):
                 batch_index_vespa_chunks(

backend/danswer/seeding/load_docs.py

@@ -216,7 +216,7 @@ def seed_initial_documents(
     # as we just sent over the Vespa schema and there is a slight delay
 
     index_with_retries = retry_builder()(document_index.index)
-    index_with_retries(chunks=chunks)
+    index_with_retries(chunks=chunks, fresh_index=True)
 
     # Mock a run for the UI even though it did not actually call out to anything
     mock_successful_index_attempt(

backend/danswer/setup.py

@@ -39,6 +39,7 @@ from danswer.key_value_store.interface import KvKeyNotFoundError
 from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
 from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
 from danswer.natural_language_processing.search_nlp_models import warm_up_cross_encoder
+from danswer.seeding.load_docs import seed_initial_documents
 from danswer.seeding.load_yamls import load_chat_yamls
 from danswer.server.manage.llm.models import LLMProviderUpsertRequest
 from danswer.server.settings.store import load_settings
@@ -150,7 +151,7 @@ def setup_danswer(
     # update multipass indexing setting based on GPU availability
     update_default_multipass_indexing(db_session)
 
-    # seed_initial_documents(db_session, tenant_id, cohere_enabled)
+    seed_initial_documents(db_session, tenant_id, cohere_enabled)
 
 
 def translate_saved_search_settings(db_session: Session) -> None: