Update Langflow tweaks and add provider credentials to headers

Replaces all references to 'OpenSearchHybrid-Ve6bS' with 'OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4' in main.py, processors, and file service. Adds a utility for injecting provider credentials into Langflow request headers and integrates it into chat and file services for improved credential handling.

src/main.py

@@ -370,7 +370,7 @@ async def _ingest_default_documents_langflow(services, file_paths):
 
     # Prepare tweaks for default documents with anonymous user metadata
     default_tweaks = {
-        "OpenSearchHybrid-Ve6bS": {
+        "OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4": {
             "docs_metadata": [
                 {"key": "owner", "value": None},
                 {"key": "owner_name", "value": anonymous_user.name},

src/models/processors.py

@@ -743,9 +743,9 @@ class LangflowFileProcessor(TaskProcessor):
 
             if metadata_tweaks:
                 # Initialize the OpenSearch component tweaks if not already present
-                if "OpenSearchHybrid-Ve6bS" not in final_tweaks:
+                if "OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4" not in final_tweaks:
-                    final_tweaks["OpenSearchHybrid-Ve6bS"] = {}
+                    final_tweaks["OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4"] = {}
-                final_tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
+                final_tweaks["OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4"]["docs_metadata"] = metadata_tweaks
 
             # Process file using langflow service
             result = await self.langflow_file_service.upload_and_ingest_file(

src/services/chat_service.py

@@ -67,10 +67,15 @@ class ChatService:
 
         # Pass the selected embedding model as a global variable
         from config.settings import get_openrag_config
+        from utils.langflow_headers import add_provider_credentials_to_headers
+        
         config = get_openrag_config()
         embedding_model = config.knowledge.embedding_model
         extra_headers["X-LANGFLOW-GLOBAL-VAR-SELECTED_EMBEDDING_MODEL"] = embedding_model
         
+        # Add provider credentials to headers
+        add_provider_credentials_to_headers(extra_headers, config)
+        logger.debug(f"[LF] Extra headers {extra_headers}")
         # Get context variables for filters, limit, and threshold
         from auth_context import (
             get_score_threshold,
@@ -182,10 +187,15 @@ class ChatService:
 
         # Pass the selected embedding model as a global variable
         from config.settings import get_openrag_config
+        from utils.langflow_headers import add_provider_credentials_to_headers
+        
         config = get_openrag_config()
         embedding_model = config.knowledge.embedding_model
         extra_headers["X-LANGFLOW-GLOBAL-VAR-SELECTED_EMBEDDING_MODEL"] = embedding_model
         
+        # Add provider credentials to headers
+        add_provider_credentials_to_headers(extra_headers, config)
+
         # Build the complete filter expression like the chat service does
         filter_expression = {}
         has_user_filters = False
@@ -306,9 +316,15 @@ class ChatService:
 
             # Pass the selected embedding model as a global variable
             from config.settings import get_openrag_config
+            from utils.langflow_headers import add_provider_credentials_to_headers
+            
             config = get_openrag_config()
             embedding_model = config.knowledge.embedding_model
             extra_headers["X-LANGFLOW-GLOBAL-VAR-SELECTED_EMBEDDING_MODEL"] = embedding_model
+            
+            # Add provider credentials to headers
+            add_provider_credentials_to_headers(extra_headers, config)
+            
             # Ensure the Langflow client exists; try lazy init if needed
             langflow_client = await clients.ensure_langflow_client()
             if not langflow_client:

src/services/langflow_file_service.py

@@ -94,7 +94,7 @@ class LangflowFileService:
         # Pass JWT token via tweaks using the x-langflow-global-var- pattern
         if jwt_token:
             # Using the global variable pattern that Langflow expects for OpenSearch components
-            tweaks["OpenSearchHybrid-Ve6bS"] = {"jwt_token": jwt_token}
+            tweaks["OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4"] = {"jwt_token": jwt_token}
             logger.debug("[LF] Added JWT token to tweaks for OpenSearch components")
         else:
             logger.warning("[LF] No JWT token provided")
@@ -112,9 +112,9 @@ class LangflowFileService:
         logger.info(f"[LF] Metadata tweaks {metadata_tweaks}")
         # if metadata_tweaks:
         #     # Initialize the OpenSearch component tweaks if not already present
-        #     if "OpenSearchHybrid-Ve6bS" not in tweaks:
+        #     if "OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4" not in tweaks:
-        #         tweaks["OpenSearchHybrid-Ve6bS"] = {}
+        #         tweaks["OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4"] = {}
-        #     tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
+        #     tweaks["OpenSearchVectorStoreComponentMultimodalMultiEmbedding-By9U4"]["docs_metadata"] = metadata_tweaks
         #     logger.debug(
         #         "[LF] Added metadata to tweaks", metadata_count=len(metadata_tweaks)
         #     )
@@ -140,8 +140,10 @@ class LangflowFileService:
         filename = str(file_tuples[0][0]) if file_tuples and len(file_tuples) > 0 else ""
         mimetype = str(file_tuples[0][2]) if file_tuples and len(file_tuples) > 0 and len(file_tuples[0]) > 2 else ""
 
-        # Get the current embedding model from config
+        # Get the current embedding model and provider credentials from config
         from config.settings import get_openrag_config
+        from utils.langflow_headers import add_provider_credentials_to_headers
+        
         config = get_openrag_config()
         embedding_model = config.knowledge.embedding_model
 
@@ -156,6 +158,9 @@ class LangflowFileService:
                 "X-Langflow-Global-Var-FILESIZE": str(file_size_bytes),
                 "X-Langflow-Global-Var-SELECTED_EMBEDDING_MODEL": str(embedding_model),
             }
+        
+        # Add provider credentials as global variables for ingestion
+        add_provider_credentials_to_headers(headers, config)
         logger.info(f"[LF] Headers {headers}")
         logger.info(f"[LF] Payload {payload}")
         resp = await clients.langflow_request(

src/utils/langflow_headers.py

@@ -0,0 +1,33 @@
+"""Utility functions for building Langflow request headers."""
+
+from typing import Dict
+from utils.container_utils import transform_localhost_url
+
+
+def add_provider_credentials_to_headers(headers: Dict[str, str], config) -> None:
+    """Add provider credentials to headers as Langflow global variables.
+    
+    Args:
+        headers: Dictionary of headers to add credentials to
+        config: OpenRAGConfig object containing provider configurations
+    """
+    # Add OpenAI credentials
+    if config.providers.openai.api_key:
+        headers["X-LANGFLOW-GLOBAL-VAR-OPENAI_API_KEY"] = str(config.providers.openai.api_key)
+    
+    # Add Anthropic credentials
+    if config.providers.anthropic.api_key:
+        headers["X-LANGFLOW-GLOBAL-VAR-ANTHROPIC_API_KEY"] = str(config.providers.anthropic.api_key)
+    
+    # Add WatsonX credentials
+    if config.providers.watsonx.api_key:
+        headers["X-LANGFLOW-GLOBAL-VAR-WATSONX_API_KEY"] = str(config.providers.watsonx.api_key)
+    
+    if config.providers.watsonx.project_id:
+        headers["X-LANGFLOW-GLOBAL-VAR-WATSONX_PROJECT_ID"] = str(config.providers.watsonx.project_id)
+    
+    # Add Ollama endpoint (with localhost transformation)
+    if config.providers.ollama.endpoint:
+        ollama_endpoint = transform_localhost_url(config.providers.ollama.endpoint)
+        headers["X-LANGFLOW-GLOBAL-VAR-OLLAMA_BASE_URL"] = str(ollama_endpoint)
+