gmakstutis/openrag
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

added replacing the model with the provider model when resetting

Browse source
This commit is contained in:
Lucas Oliveira 2025-09-25 17:49:36 -03:00
parent ac08320227
commit 15def39f3a
1 changed files with 41 additions and 20 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

61
src/services/flows_service.py
View file

@ -1,3 +1,4 @@
import asyncio
from config.settings import (
NUDGES_FLOW_ID,
LANGFLOW_URL,
@ -162,12 +163,30 @@ class FlowsService:
if config.edited:
logger.info(f"Updating {flow_type} flow with current configuration settings")
# Update the flow with the current embedding and LLM models
provider = config.provider.model_provider.lower()
# Step 1: Assign model provider (replace components) if not OpenAI
if provider != "openai":
logger.info(f"Assigning {provider} components to {flow_type} flow")
provider_result = await self.assign_model_provider(provider)
if not provider_result.get("success"):
logger.warning(f"Failed to assign {provider} components: {provider_result.get('error', 'Unknown error')}")
# Continue anyway, maybe just value updates will work
# Step 2: Update model values for the specific flow being reset
single_flow_config = [{
"name": flow_type,
"flow_id": flow_id,
}]
logger.info(f"Updating {flow_type} flow model values")
update_result = await self.change_langflow_model_value(
provider=config.provider.model_provider.lower(),
provider=provider,
embedding_model=config.knowledge.embedding_model,
llm_model=config.agent.llm_model,
endpoint=getattr(config.provider, 'endpoint', None)
endpoint=config.provider.endpoint if config.provider.endpoint else None,
flow_configs=single_flow_config
)
if update_result.get("success"):
@ -593,16 +612,17 @@ class FlowsService:
return False
async def change_langflow_model_value(
self, provider: str, embedding_model: str, llm_model: str, endpoint: str = None
self, provider: str, embedding_model: str, llm_model: str, endpoint: str = None, flow_configs: list = None
):
"""
Change dropdown values for provider-specific components across all flows
Change dropdown values for provider-specific components across flows
Args:
provider: The provider ("watsonx", "ollama", "openai")
embedding_model: The embedding model name to set
llm_model: The LLM model name to set
endpoint: The endpoint URL (required for watsonx/ibm provider)
flow_configs: Optional list of specific flow configs to update. If None, updates all flows.
Returns:
dict: Success/error response with details for each flow
@ -618,21 +638,22 @@ class FlowsService:
f"Changing dropdown values for provider {provider}, embedding: {embedding_model}, llm: {llm_model}, endpoint: {endpoint}"
)
# Define flow configurations with provider-specific component IDs (removed hardcoded file paths)
flow_configs = [
{
"name": "nudges",
"flow_id": NUDGES_FLOW_ID,
},
{
"name": "retrieval",
"flow_id": LANGFLOW_CHAT_FLOW_ID,
},
{
"name": "ingest",
"flow_id": LANGFLOW_INGEST_FLOW_ID,
},
]
# Use provided flow_configs or default to all flows
if flow_configs is None:
flow_configs = [
{
"name": "nudges",
"flow_id": NUDGES_FLOW_ID,
},
{
"name": "retrieval",
"flow_id": LANGFLOW_CHAT_FLOW_ID,
},
{
"name": "ingest",
"flow_id": LANGFLOW_INGEST_FLOW_ID,
},
]
# Determine target component IDs based on provider
target_embedding_id, target_llm_id, target_llm_text_id = self._get_provider_component_ids(