@@ -197,7 +201,9 @@ function OnboardingPage() {
{!isComplete && (
- Please fill in all required fields
+ {!!settings.llm_model && !!settings.embedding_model && !isDoclingHealthy
+ ? "docling-serve must be running to continue"
+ : "Please fill in all required fields"}
)}
diff --git a/src/api/settings.py b/src/api/settings.py
index 879988d5..17c57a60 100644
--- a/src/api/settings.py
+++ b/src/api/settings.py
@@ -536,7 +536,142 @@ async def onboarding(request, flows_service):
{"error": "No valid fields provided for update"}, status_code=400
)
+ # Initialize the OpenSearch index now that we have the embedding model configured
+ try:
+ # Import here to avoid circular imports
+ from main import init_index
+
+ logger.info(
+ "Initializing OpenSearch index after onboarding configuration"
+ )
+ await init_index()
+ logger.info("OpenSearch index initialization completed successfully")
+ except Exception as e:
+ if isinstance(e, ValueError):
+ logger.error(
+ "Failed to initialize OpenSearch index after onboarding",
+ error=str(e),
+ )
+ return JSONResponse(
+ {
+ "error": str(e),
+ "edited": True,
+ },
+ status_code=400,
+ )
+ logger.error(
+ "Failed to initialize OpenSearch index after onboarding",
+ error=str(e),
+ )
+ # Don't fail the entire onboarding process if index creation fails
+ # The application can still work, but document operations may fail
+
# Save the updated configuration (this will mark it as edited)
+
+ # If model_provider was updated, assign the new provider to flows
+ if "model_provider" in body:
+ provider = body["model_provider"].strip().lower()
+ try:
+ flow_result = await flows_service.assign_model_provider(provider)
+
+ if flow_result.get("success"):
+ logger.info(
+ f"Successfully assigned {provider} to flows",
+ flow_result=flow_result,
+ )
+ else:
+ logger.warning(
+ f"Failed to assign {provider} to flows",
+ flow_result=flow_result,
+ )
+ # Continue even if flow assignment fails - configuration was still saved
+
+ except Exception as e:
+ logger.error(
+ "Error assigning model provider to flows",
+ provider=provider,
+ error=str(e),
+ )
+ raise
+
+ # Set Langflow global variables based on provider
+ try:
+ # Set API key for IBM/Watson providers
+ if (provider == "watsonx") and "api_key" in body:
+ api_key = body["api_key"]
+ await clients._create_langflow_global_variable(
+ "WATSONX_API_KEY", api_key, modify=True
+ )
+ logger.info("Set WATSONX_API_KEY global variable in Langflow")
+
+ # Set project ID for IBM/Watson providers
+ if (provider == "watsonx") and "project_id" in body:
+ project_id = body["project_id"]
+ await clients._create_langflow_global_variable(
+ "WATSONX_PROJECT_ID", project_id, modify=True
+ )
+ logger.info(
+ "Set WATSONX_PROJECT_ID global variable in Langflow"
+ )
+
+ # Set API key for OpenAI provider
+ if provider == "openai" and "api_key" in body:
+ api_key = body["api_key"]
+ await clients._create_langflow_global_variable(
+ "OPENAI_API_KEY", api_key, modify=True
+ )
+ logger.info("Set OPENAI_API_KEY global variable in Langflow")
+
+ # Set base URL for Ollama provider
+ if provider == "ollama" and "endpoint" in body:
+ endpoint = transform_localhost_url(body["endpoint"])
+
+ await clients._create_langflow_global_variable(
+ "OLLAMA_BASE_URL", endpoint, modify=True
+ )
+ logger.info("Set OLLAMA_BASE_URL global variable in Langflow")
+
+ await flows_service.change_langflow_model_value(
+ provider,
+ body["embedding_model"],
+ body["llm_model"],
+ body["endpoint"],
+ )
+
+ except Exception as e:
+ logger.error(
+ "Failed to set Langflow global variables",
+ provider=provider,
+ error=str(e),
+ )
+ raise
+
+ # Handle sample data ingestion if requested
+ if should_ingest_sample_data:
+ try:
+ # Import the function here to avoid circular imports
+ from main import ingest_default_documents_when_ready
+
+ # Get services from the current app state
+ # We need to access the app instance to get services
+ app = request.scope.get("app")
+ if app and hasattr(app.state, "services"):
+ services = app.state.services
+ logger.info(
+ "Starting sample data ingestion as requested in onboarding"
+ )
+ await ingest_default_documents_when_ready(services)
+ logger.info("Sample data ingestion completed successfully")
+ else:
+ logger.error(
+ "Could not access services for sample data ingestion"
+ )
+
+ except Exception as e:
+ logger.error(
+ "Failed to complete sample data ingestion", error=str(e)
+ )
+ # Don't fail the entire onboarding process if sample data fails
if config_manager.save_config_file(current_config):
updated_fields = [
k for k in body.keys() if k != "sample_data"
@@ -546,144 +681,19 @@ async def onboarding(request, flows_service):
updated_fields=updated_fields,
)
- # If model_provider was updated, assign the new provider to flows
- if "model_provider" in body:
- provider = body["model_provider"].strip().lower()
- try:
- flow_result = await flows_service.assign_model_provider(provider)
-
- if flow_result.get("success"):
- logger.info(
- f"Successfully assigned {provider} to flows",
- flow_result=flow_result,
- )
- else:
- logger.warning(
- f"Failed to assign {provider} to flows",
- flow_result=flow_result,
- )
- # Continue even if flow assignment fails - configuration was still saved
-
- except Exception as e:
- logger.error(
- "Error assigning model provider to flows",
- provider=provider,
- error=str(e),
- )
- # Continue even if flow assignment fails - configuration was still saved
-
- # Set Langflow global variables based on provider
- if "model_provider" in body:
- provider = body["model_provider"].strip().lower()
-
- try:
- # Set API key for IBM/Watson providers
- if (provider == "watsonx") and "api_key" in body:
- api_key = body["api_key"]
- await clients._create_langflow_global_variable(
- "WATSONX_API_KEY", api_key, modify=True
- )
- logger.info("Set WATSONX_API_KEY global variable in Langflow")
-
- # Set project ID for IBM/Watson providers
- if (provider == "watsonx") and "project_id" in body:
- project_id = body["project_id"]
- await clients._create_langflow_global_variable(
- "WATSONX_PROJECT_ID", project_id, modify=True
- )
- logger.info(
- "Set WATSONX_PROJECT_ID global variable in Langflow"
- )
-
- # Set API key for OpenAI provider
- if provider == "openai" and "api_key" in body:
- api_key = body["api_key"]
- await clients._create_langflow_global_variable(
- "OPENAI_API_KEY", api_key, modify=True
- )
- logger.info("Set OPENAI_API_KEY global variable in Langflow")
-
- # Set base URL for Ollama provider
- if provider == "ollama" and "endpoint" in body:
- endpoint = transform_localhost_url(body["endpoint"])
-
- await clients._create_langflow_global_variable(
- "OLLAMA_BASE_URL", endpoint, modify=True
- )
- logger.info("Set OLLAMA_BASE_URL global variable in Langflow")
-
- await flows_service.change_langflow_model_value(
- provider,
- body["embedding_model"],
- body["llm_model"],
- body["endpoint"],
- )
-
- except Exception as e:
- logger.error(
- "Failed to set Langflow global variables",
- provider=provider,
- error=str(e),
- )
- # Continue even if setting global variables fails
-
- # Initialize the OpenSearch index now that we have the embedding model configured
- try:
- # Import here to avoid circular imports
- from main import init_index
-
- logger.info(
- "Initializing OpenSearch index after onboarding configuration"
- )
- await init_index()
- logger.info("OpenSearch index initialization completed successfully")
- except Exception as e:
- logger.error(
- "Failed to initialize OpenSearch index after onboarding",
- error=str(e),
- )
- # Don't fail the entire onboarding process if index creation fails
- # The application can still work, but document operations may fail
-
- # Handle sample data ingestion if requested
- if should_ingest_sample_data:
- try:
- # Import the function here to avoid circular imports
- from main import ingest_default_documents_when_ready
-
- # Get services from the current app state
- # We need to access the app instance to get services
- app = request.scope.get("app")
- if app and hasattr(app.state, "services"):
- services = app.state.services
- logger.info(
- "Starting sample data ingestion as requested in onboarding"
- )
- await ingest_default_documents_when_ready(services)
- logger.info("Sample data ingestion completed successfully")
- else:
- logger.error(
- "Could not access services for sample data ingestion"
- )
-
- except Exception as e:
- logger.error(
- "Failed to complete sample data ingestion", error=str(e)
- )
- # Don't fail the entire onboarding process if sample data fails
-
- return JSONResponse(
- {
- "message": "Onboarding configuration updated successfully",
- "edited": True, # Confirm that config is now marked as edited
- "sample_data_ingested": should_ingest_sample_data,
- }
- )
else:
return JSONResponse(
{"error": "Failed to save configuration"}, status_code=500
)
+ return JSONResponse(
+ {
+ "message": "Onboarding configuration updated successfully",
+ "edited": True, # Confirm that config is now marked as edited
+ "sample_data_ingested": should_ingest_sample_data,
+ }
+ )
+
except Exception as e:
logger.error("Failed to update onboarding settings", error=str(e))
return JSONResponse(
diff --git a/src/config/settings.py b/src/config/settings.py
index b1ffba79..2edb0d69 100644
--- a/src/config/settings.py
+++ b/src/config/settings.py
@@ -81,12 +81,6 @@ OPENAI_EMBEDDING_DIMENSIONS = {
"text-embedding-ada-002": 1536,
}
-OLLAMA_EMBEDDING_DIMENSIONS = {
- "nomic-embed-text": 768,
- "all-minilm": 384,
- "mxbai-embed-large": 1024,
-}
-
WATSONX_EMBEDDING_DIMENSIONS = {
# IBM Models
"ibm/granite-embedding-107m-multilingual": 384,
diff --git a/src/main.py b/src/main.py
index 1094f8b5..b16a50d5 100644
--- a/src/main.py
+++ b/src/main.py
@@ -168,7 +168,12 @@ async def init_index():
embedding_model = config.knowledge.embedding_model
# Create dynamic index body based on the configured embedding model
- dynamic_index_body = create_dynamic_index_body(embedding_model)
+ # Pass provider and endpoint for dynamic dimension resolution (Ollama probing)
+ dynamic_index_body = await create_dynamic_index_body(
+ embedding_model,
+ provider=config.provider.model_provider,
+ endpoint=config.provider.endpoint
+ )
# Create documents index
if not await clients.opensearch.indices.exists(index=INDEX_NAME):
diff --git a/src/utils/embeddings.py b/src/utils/embeddings.py
index 26029a7f..20acda9e 100644
--- a/src/utils/embeddings.py
+++ b/src/utils/embeddings.py
@@ -1,14 +1,128 @@
-from config.settings import OLLAMA_EMBEDDING_DIMENSIONS, OPENAI_EMBEDDING_DIMENSIONS, VECTOR_DIM, WATSONX_EMBEDDING_DIMENSIONS
+import httpx
+from config.settings import OPENAI_EMBEDDING_DIMENSIONS, VECTOR_DIM, WATSONX_EMBEDDING_DIMENSIONS
+from utils.container_utils import transform_localhost_url
from utils.logging_config import get_logger
logger = get_logger(__name__)
-def get_embedding_dimensions(model_name: str) -> int:
+
+async def _probe_ollama_embedding_dimension(endpoint: str, model_name: str) -> int:
+ """Probe Ollama server to get embedding dimension for a model.
+
+ Args:
+ endpoint: Ollama server endpoint (e.g., "http://localhost:11434")
+ model_name: Name of the embedding model
+
+ Returns:
+ The embedding dimension.
+
+ Raises:
+ ValueError: If the dimension cannot be determined.
+ """
+ transformed_endpoint = transform_localhost_url(endpoint)
+ url = f"{transformed_endpoint}/api/embeddings"
+ test_input = "test"
+
+ async with httpx.AsyncClient() as client:
+ errors: list[str] = []
+
+ # Try modern API format first (input parameter)
+ modern_payload = {
+ "model": model_name,
+ "input": test_input,
+ "prompt": test_input,
+ }
+
+ try:
+ response = await client.post(url, json=modern_payload, timeout=10.0)
+ response.raise_for_status()
+ data = response.json()
+
+ # Check for embedding in response
+ if "embedding" in data:
+ dimension = len(data["embedding"])
+ if dimension > 0:
+ logger.info(
+ f"Probed Ollama model '{model_name}': dimension={dimension}"
+ )
+ return dimension
+ elif "embeddings" in data and len(data["embeddings"]) > 0:
+ dimension = len(data["embeddings"][0])
+ if dimension > 0:
+ logger.info(
+ f"Probed Ollama model '{model_name}': dimension={dimension}"
+ )
+ return dimension
+
+ errors.append("response did not include non-zero embedding vector")
+ except Exception as modern_error: # noqa: BLE001 - log and fall back to legacy payload
+ logger.debug(
+ "Modern Ollama embeddings API probe failed",
+ model=model_name,
+ endpoint=transformed_endpoint,
+ error=str(modern_error),
+ )
+ errors.append(str(modern_error))
+
+ # Try legacy API format (prompt parameter)
+ legacy_payload = {
+ "model": model_name,
+ "prompt": test_input,
+ }
+
+ try:
+ response = await client.post(url, json=legacy_payload, timeout=10.0)
+ response.raise_for_status()
+ data = response.json()
+
+ if "embedding" in data:
+ dimension = len(data["embedding"])
+ if dimension > 0:
+ logger.info(
+ f"Probed Ollama model '{model_name}' (legacy): dimension={dimension}"
+ )
+ return dimension
+ elif "embeddings" in data and len(data["embeddings"]) > 0:
+ dimension = len(data["embeddings"][0])
+ if dimension > 0:
+ logger.info(
+ f"Probed Ollama model '{model_name}' (legacy): dimension={dimension}"
+ )
+ return dimension
+
+ errors.append("legacy response did not include non-zero embedding vector")
+ except Exception as legacy_error: # noqa: BLE001 - collect and raise a helpful error later
+ logger.warning(
+ "Legacy Ollama embeddings API probe failed",
+ model=model_name,
+ endpoint=transformed_endpoint,
+ error=str(legacy_error),
+ )
+ errors.append(str(legacy_error))
+
+ # remove the first instance of this error to show either it or the actual error from any of the two methods
+ errors.remove("All connection attempts failed")
+
+ raise ValueError(
+ f"Failed to determine embedding dimensions for Ollama model '{model_name}'. "
+ f"Verify the Ollama server at '{endpoint}' is reachable and the model is available. "
+ f"Error: {errors[0]}"
+ )
+
+
+async def get_embedding_dimensions(model_name: str, provider: str = None, endpoint: str = None) -> int:
"""Get the embedding dimensions for a given model name."""
+ if provider and provider.lower() == "ollama":
+ if not endpoint:
+ raise ValueError(
+ "Ollama endpoint is required to determine embedding dimensions. Please provide a valid endpoint."
+ )
+ return await _probe_ollama_embedding_dimension(endpoint, model_name)
+
# Check all model dictionaries
- all_models = {**OPENAI_EMBEDDING_DIMENSIONS, **OLLAMA_EMBEDDING_DIMENSIONS, **WATSONX_EMBEDDING_DIMENSIONS}
+ all_models = {**OPENAI_EMBEDDING_DIMENSIONS, **WATSONX_EMBEDDING_DIMENSIONS}
model_name = model_name.lower().strip().split(":")[0]
@@ -23,9 +137,22 @@ def get_embedding_dimensions(model_name: str) -> int:
return VECTOR_DIM
-def create_dynamic_index_body(embedding_model: str) -> dict:
- """Create a dynamic index body configuration based on the embedding model."""
- dimensions = get_embedding_dimensions(embedding_model)
+async def create_dynamic_index_body(
+ embedding_model: str,
+ provider: str = None,
+ endpoint: str = None
+) -> dict:
+ """Create a dynamic index body configuration based on the embedding model.
+
+ Args:
+ embedding_model: Name of the embedding model
+ provider: Provider name (e.g., "ollama", "openai", "watsonx")
+ endpoint: Endpoint URL for the provider (used for Ollama probing)
+
+ Returns:
+ OpenSearch index body configuration
+ """
+ dimensions = await get_embedding_dimensions(embedding_model, provider, endpoint)
return {
"settings": {
+ 
- 
-
- 
-OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with Starlette, Next.js, OpenSearch, and Langflow integration.
+OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with [Starlette](https://github.com/Kludex/starlette) and [Next.js](https://github.com/vercel/next.js). Powered by [OpenSearch](https://github.com/opensearch-project/OpenSearch), [Langflow](https://github.com/langflow-ai/langflow), and [Docling](https://github.com/docling-project/docling).
+
+