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Merge pull request #564 from langflow-ai/fix-restore-embeddings

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fix: Respect selected embedding model when restoring flow
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pushkala-datastax 2025-12-02 09:55:58 -08:00 • committed by GitHub
commit 3fbf19aaec
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1 changed files with 63 additions and 9 deletions
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72
src/services/flows_service.py
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@ -424,15 +424,69 @@ class FlowsService:
]
logger.info(f"Updating {flow_type} flow model values")
# Use LLM provider for most flows, embedding provider for ingest flows
provider_to_use = embedding_provider if flow_type in ["ingest", "url_ingest"] else llm_provider
update_result = await self.change_langflow_model_value(
provider=provider_to_use,
embedding_model=config.knowledge.embedding_model if flow_type in ["ingest", "url_ingest"] else None,
llm_model=config.agent.llm_model if flow_type not in ["ingest", "url_ingest"] else None,
endpoint=endpoint,
flow_configs=single_flow_config,
)
# For retrieval flow: need to update both LLM and embedding (potentially different providers)
# For ingest flows: only update embedding
# For other flows: only update LLM
if flow_type == "retrieval":
# Retrieval flow uses both LLM and embedding models
# Update LLM first
llm_endpoint = getattr(llm_provider_config, "endpoint", None)
llm_result = await self.change_langflow_model_value(
provider=llm_provider,
embedding_model=None,
llm_model=config.agent.llm_model,
endpoint=llm_endpoint,
flow_configs=single_flow_config,
)
if not llm_result.get("success"):
logger.warning(
f"Failed to update LLM in {flow_type} flow: {llm_result.get('error', 'Unknown error')}"
)
# Update embedding model
embedding_provider_config = config.get_embedding_provider_config()
embedding_endpoint = getattr(embedding_provider_config, "endpoint", None)
embedding_result = await self.change_langflow_model_value(
provider=embedding_provider,
embedding_model=config.knowledge.embedding_model,
llm_model=None,
endpoint=embedding_endpoint,
flow_configs=single_flow_config,
)
if not embedding_result.get("success"):
logger.warning(
f"Failed to update embedding in {flow_type} flow: {embedding_result.get('error', 'Unknown error')}"
)
# Consider it successful if either update succeeded
update_result = {
"success": llm_result.get("success") or embedding_result.get("success"),
"llm_result": llm_result,
"embedding_result": embedding_result,
}
elif flow_type in ["ingest", "url_ingest"]:
# Ingest flows only need embedding model
embedding_provider_config = config.get_embedding_provider_config()
embedding_endpoint = getattr(embedding_provider_config, "endpoint", None)
update_result = await self.change_langflow_model_value(
provider=embedding_provider,
embedding_model=config.knowledge.embedding_model,
llm_model=None,
endpoint=embedding_endpoint,
flow_configs=single_flow_config,
)
else:
# Other flows (nudges) only need LLM model
llm_endpoint = getattr(llm_provider_config, "endpoint", None)
update_result = await self.change_langflow_model_value(
provider=llm_provider,
embedding_model=None,
llm_model=config.agent.llm_model,
endpoint=llm_endpoint,
flow_configs=single_flow_config,
)
if update_result.get("success"):
logger.info(