fix: add fail safe mode for embeddings model

src/lfx/src/lfx/components/elastic/opensearch_multimodal.py

@@ -641,8 +641,14 @@
     @check_cached_vector_store
     def build_vector_store(self) -> OpenSearch:
         # Return raw OpenSearch client as our "vector store."
-        self.log(self.ingest_data)
         client = self.build_client()
+
+        # Check if we're in ingestion-only mode (no search query)
+        has_search_query = bool((self.search_query or "").strip())
+        if not has_search_query:
+            logger.debug("🔄 Ingestion-only mode activated: search operations will be skipped")
+            logger.debug("Starting ingestion mode...")
+
         logger.warning(f"Embedding: {self.embedding}")
         self._add_documents_to_vector_store(client=client)
         return client
@@ -660,25 +666,39 @@
         Args:
             client: OpenSearch client for performing operations
         """
+        logger.debug("[INGESTION] _add_documents_to_vector_store called")
         # Convert DataFrame to Data if needed using parent's method
         self.ingest_data = self._prepare_ingest_data()
 
+        logger.debug(
+            f"[INGESTION] ingest_data type: {type(self.ingest_data)}, length: {len(self.ingest_data) if self.ingest_data else 0}"
+        )
+        logger.debug(
+            f"[INGESTION] ingest_data content: {self.ingest_data[:2] if self.ingest_data and len(self.ingest_data) > 0 else 'empty'}"
+        )
+
         docs = self.ingest_data or []
         if not docs:
-            self.log("No documents to ingest.")
+            logger.debug("✓ Ingestion complete: No documents provided")
             return
 
         if not self.embedding:
             msg = "Embedding handle is required to embed documents."
             raise ValueError(msg)
 
-        # Normalize embedding to list
+        # Normalize embedding to list first
         embeddings_list = self.embedding if isinstance(self.embedding, list) else [self.embedding]
 
+        # Filter out None values (fail-safe mode) - do this BEFORE checking if empty
+        embeddings_list = [e for e in embeddings_list if e is not None]
+
+        # NOW check if we have any valid embeddings left after filtering
         if not embeddings_list:
-            msg = "At least one embedding is required to embed documents."
-            raise ValueError(msg)
+            logger.warning("All embeddings returned None (fail-safe mode enabled). Skipping document ingestion.")
+            self.log("Embedding returned None (fail-safe mode enabled). Skipping document ingestion.")
+            return
 
+        logger.debug(f"[INGESTION] Valid embeddings after filtering: {len(embeddings_list)}")
         self.log(f"Available embedding models: {len(embeddings_list)}")
 
         # Select the embedding to use for ingestion
@@ -790,6 +810,7 @@
 
         dynamic_field_name = get_embedding_field_name(embedding_model)
 
+        logger.info(f"✓ Selected embedding model for ingestion: '{embedding_model}'")
         self.log(f"Using embedding model for ingestion: {embedding_model}")
         self.log(f"Dynamic vector field: {dynamic_field_name}")
 
@@ -814,6 +835,7 @@
         metadatas = []
         # Process docs_metadata table input into a dict
         additional_metadata = {}
+        logger.debug(f"[LF] Docs metadata {self.docs_metadata}")
         if hasattr(self, "docs_metadata") and self.docs_metadata:
             logger.info(f"[LF] Docs metadata {self.docs_metadata}")
             if isinstance(self.docs_metadata[-1], Data):
@@ -956,6 +978,9 @@
         )
         self.log(metadatas)
 
+        logger.info(
+            f"✓ Ingestion complete: Successfully indexed {len(return_ids)} documents with model '{embedding_model}'"
+        )
         self.log(f"Successfully indexed {len(return_ids)} documents with model {embedding_model}.")
 
     # ---------- helpers for filters ----------
@@ -1172,6 +1197,11 @@
             msg = "Embedding is required to run hybrid search (KNN + keyword)."
             raise ValueError(msg)
 
+        # Check if embedding is None (fail-safe mode)
+        if self.embedding is None or (isinstance(self.embedding, list) and all(e is None for e in self.embedding)):
+            logger.error("Embedding returned None (fail-safe mode enabled). Cannot perform search.")
+            return []
+
         # Build filter clauses first so we can use them in model detection
         filter_clauses = self._coerce_filter_clauses(filter_obj)
 
@@ -1187,6 +1217,14 @@
 
         # Normalize embedding to list
         embeddings_list = self.embedding if isinstance(self.embedding, list) else [self.embedding]
+        # Filter out None values (fail-safe mode)
+        embeddings_list = [e for e in embeddings_list if e is not None]
+
+        if not embeddings_list:
+            logger.error(
+                "No valid embeddings available after filtering None values (fail-safe mode). Cannot perform search."
+            )
+            return []
 
         # Create a comprehensive map of model names to embedding objects
         # Check all possible identifiers (deployment, model, model_id, model_name)
@@ -1518,16 +1556,29 @@
         This is the main interface method that performs the multi-model search using the
         configured search_query and returns results in Langflow's Data format.
 
+        Always builds the vector store (triggering ingestion if needed), then performs
+        search only if a query is provided.
+
         Returns:
             List of Data objects containing search results with text and metadata
 
         Raises:
             Exception: If search operation fails
         """
         try:
-            raw = self.search(self.search_query or "")
+            # Always build/cache the vector store to ensure ingestion happens
+            if self._cached_vector_store is None:
+                self.build_vector_store()
+
+            # Only perform search if query is provided
+            search_query = (self.search_query or "").strip()
+            if not search_query:
+                self.log("No search query provided - ingestion completed, returning empty results")
+                return []
+
+            # Perform search with the provided query
+            raw = self.search(search_query)
             return [Data(text=hit["page_content"], **hit["metadata"]) for hit in raw]
-            self.log(self.ingest_data)
         except Exception as e:
             self.log(f"search_documents error: {e}")
             raise