Merge branch 'feat/onboarding' of github.com:langflow-ai/openrag into feat/onboarding
This commit is contained in:
Mike Fortman
commit
4ec10c9b48
4 changed files with 269 additions and 4 deletions
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@ -233,7 +233,7 @@ async def update_settings(request, session_manager):
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)
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async def onboarding(request, session_manager):
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async def onboarding(request, flows_service):
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"""Handle onboarding configuration setup"""
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try:
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# Get current configuration
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@ -323,6 +323,23 @@ async def onboarding(request, session_manager):
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if config_manager.save_config_file(current_config):
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updated_fields = [k for k in body.keys() if k != "sample_data"] # Exclude sample_data from log
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logger.info("Onboarding configuration updated successfully", updated_fields=updated_fields)
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# If model_provider was updated, assign the new provider to flows
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if "model_provider" in body:
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provider = body["model_provider"].strip().lower()
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try:
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flow_result = await flows_service.assign_model_provider(provider)
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if flow_result.get("success"):
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logger.info(f"Successfully assigned {provider} to flows", flow_result=flow_result)
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else:
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logger.warning(f"Failed to assign {provider} to flows", flow_result=flow_result)
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# Continue even if flow assignment fails - configuration was still saved
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except Exception as e:
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logger.error(f"Error assigning model provider to flows", provider=provider, error=str(e))
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# Continue even if flow assignment fails - configuration was still saved
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return JSONResponse({
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"message": "Onboarding configuration updated successfully",
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"edited": True # Confirm that config is now marked as edited
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@ -399,6 +399,21 @@ class AppClients:
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)
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# Component template paths
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WATSONX_LLM_COMPONENT_PATH = os.getenv("WATSONX_LLM_COMPONENT_PATH", "flows/components/watsonx_llm.json")
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WATSONX_EMBEDDING_COMPONENT_PATH = os.getenv("WATSONX_EMBEDDING_COMPONENT_PATH", "flows/components/watsonx_embedding.json")
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OLLAMA_LLM_COMPONENT_PATH = os.getenv("OLLAMA_LLM_COMPONENT_PATH", "flows/components/ollama_llm.json")
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OLLAMA_EMBEDDING_COMPONENT_PATH = os.getenv("OLLAMA_EMBEDDING_COMPONENT_PATH", "flows/components/ollama_embedding.json")
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# Component IDs in flows
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NUDGES_EMBEDDING_COMPONENT_ID = os.getenv("NUDGES_EMBEDDING_COMPONENT_ID", "EmbeddingModel-eZ6bT")
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NUDGES_LLM_COMPONENT_ID = os.getenv("NUDGES_LLM_COMPONENT_ID", "LanguageModelComponent-0YME7")
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AGENT_EMBEDDING_COMPONENT_ID = os.getenv("AGENT_EMBEDDING_COMPONENT_ID", "EmbeddingModel-eZ6bT")
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AGENT_LLM_COMPONENT_ID = os.getenv("AGENT_LLM_COMPONENT_ID", "LanguageModelComponent-0YME7")
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INGESTION_EMBEDDING_COMPONENT_ID = os.getenv("INGESTION_EMBEDDING_COMPONENT_ID", "OpenAIEmbeddings-joRJ6")
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# Global clients instance
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clients = AppClients()
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@ -914,7 +914,8 @@ async def create_app():
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"/onboarding",
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require_auth(services["session_manager"])(
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partial(
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settings.onboarding, session_manager=services["session_manager"]
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settings.onboarding,
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flows_service=services["flows_service"]
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)
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),
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methods=["POST"],
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@ -1,6 +1,14 @@
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from config.settings import NUDGES_FLOW_ID, LANGFLOW_URL, LANGFLOW_CHAT_FLOW_ID, LANGFLOW_INGEST_FLOW_ID, clients
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from config.settings import (
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NUDGES_FLOW_ID, LANGFLOW_URL, LANGFLOW_CHAT_FLOW_ID, LANGFLOW_INGEST_FLOW_ID, clients,
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WATSONX_LLM_COMPONENT_PATH, WATSONX_EMBEDDING_COMPONENT_PATH,
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OLLAMA_LLM_COMPONENT_PATH, OLLAMA_EMBEDDING_COMPONENT_PATH,
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NUDGES_EMBEDDING_COMPONENT_ID, NUDGES_LLM_COMPONENT_ID,
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AGENT_EMBEDDING_COMPONENT_ID, AGENT_LLM_COMPONENT_ID,
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INGESTION_EMBEDDING_COMPONENT_ID
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)
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import json
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import os
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import re
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from utils.logging_config import get_logger
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logger = get_logger(__name__)
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@ -25,7 +33,7 @@ class FlowsService:
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flow_file = "flows/openrag_nudges.json"
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flow_id = NUDGES_FLOW_ID
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elif flow_type == "retrieval":
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flow_file = "flows/openrag_agent.json"
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flow_file = "flows/openrag_agent.json"
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flow_id = LANGFLOW_CHAT_FLOW_ID
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elif flow_type == "ingest":
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flow_file = "flows/ingestion_flow.json"
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@ -108,3 +116,227 @@ class FlowsService:
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"success": False,
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"error": f"Error: {str(e)}"
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}
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async def assign_model_provider(self, provider: str):
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"""
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Replace OpenAI components with the specified provider components in all flows
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Args:
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provider: "watsonx", "ollama", or "openai"
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Returns:
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dict: Success/error response with details for each flow
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"""
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if provider not in ["watsonx", "ollama", "openai"]:
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raise ValueError("provider must be 'watsonx', 'ollama', or 'openai'")
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if provider == "openai":
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logger.info("Provider is already OpenAI, no changes needed")
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return {"success": True, "message": "Provider is already OpenAI, no changes needed"}
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try:
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# Load component templates based on provider
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llm_template, embedding_template = self._load_component_templates(provider)
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logger.info(f"Assigning {provider} components")
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# Define flow configurations
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flow_configs = [
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{
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"name": "nudges",
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"file": "flows/openrag_nudges.json",
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"flow_id": NUDGES_FLOW_ID,
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"embedding_id": NUDGES_EMBEDDING_COMPONENT_ID,
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"llm_id": NUDGES_LLM_COMPONENT_ID
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},
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{
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"name": "retrieval",
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"file": "flows/openrag_agent.json",
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"flow_id": LANGFLOW_CHAT_FLOW_ID,
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"embedding_id": AGENT_EMBEDDING_COMPONENT_ID,
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"llm_id": AGENT_LLM_COMPONENT_ID
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},
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{
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"name": "ingest",
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"file": "flows/ingestion_flow.json",
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"flow_id": LANGFLOW_INGEST_FLOW_ID,
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"embedding_id": INGESTION_EMBEDDING_COMPONENT_ID,
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"llm_id": None # Ingestion flow might not have LLM
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}
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]
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results = []
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# Process each flow sequentially
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for config in flow_configs:
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try:
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result = await self._update_flow_components(
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config, llm_template, embedding_template
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)
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results.append(result)
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logger.info(f"Successfully updated {config['name']} flow")
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except Exception as e:
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error_msg = f"Failed to update {config['name']} flow: {str(e)}"
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logger.error(error_msg)
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results.append({
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"flow": config['name'],
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"success": False,
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"error": error_msg
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})
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# Continue with other flows even if one fails
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# Check if all flows were successful
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all_success = all(r.get("success", False) for r in results)
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return {
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"success": all_success,
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"message": f"Model provider assignment to {provider} {'completed' if all_success else 'completed with errors'}",
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"provider": provider,
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"results": results
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}
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except Exception as e:
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logger.error(f"Error assigning model provider {provider}", error=str(e))
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return {
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"success": False,
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"error": f"Failed to assign model provider: {str(e)}"
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}
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def _load_component_templates(self, provider: str):
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"""Load component templates for the specified provider"""
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if provider == "watsonx":
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llm_path = WATSONX_LLM_COMPONENT_PATH
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embedding_path = WATSONX_EMBEDDING_COMPONENT_PATH
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elif provider == "ollama":
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llm_path = OLLAMA_LLM_COMPONENT_PATH
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embedding_path = OLLAMA_EMBEDDING_COMPONENT_PATH
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else:
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raise ValueError(f"Unsupported provider: {provider}")
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# Get the project root directory (same logic as reset_langflow_flow)
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current_file_dir = os.path.dirname(os.path.abspath(__file__)) # src/services/
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src_dir = os.path.dirname(current_file_dir) # src/
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project_root = os.path.dirname(src_dir) # project root
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# Load LLM template
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llm_full_path = os.path.join(project_root, llm_path)
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if not os.path.exists(llm_full_path):
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raise FileNotFoundError(f"LLM component template not found at: {llm_full_path}")
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with open(llm_full_path, 'r') as f:
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llm_template = json.load(f)
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# Load embedding template
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embedding_full_path = os.path.join(project_root, embedding_path)
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if not os.path.exists(embedding_full_path):
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raise FileNotFoundError(f"Embedding component template not found at: {embedding_full_path}")
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with open(embedding_full_path, 'r') as f:
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embedding_template = json.load(f)
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logger.info(f"Loaded component templates for {provider}")
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return llm_template, embedding_template
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async def _update_flow_components(self, config, llm_template, embedding_template):
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"""Update components in a specific flow"""
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flow_name = config["name"]
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flow_file = config["file"]
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flow_id = config["flow_id"]
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old_embedding_id = config["embedding_id"]
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old_llm_id = config["llm_id"]
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# Extract IDs from templates
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new_llm_id = llm_template["data"]["id"]
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new_embedding_id = embedding_template["data"]["id"]
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# Get the project root directory
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current_file_dir = os.path.dirname(os.path.abspath(__file__))
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src_dir = os.path.dirname(current_file_dir)
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project_root = os.path.dirname(src_dir)
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flow_path = os.path.join(project_root, flow_file)
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if not os.path.exists(flow_path):
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raise FileNotFoundError(f"Flow file not found at: {flow_path}")
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# Load flow JSON
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with open(flow_path, 'r') as f:
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flow_data = json.load(f)
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# Find and replace components
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components_updated = []
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# Replace embedding component
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embedding_node = self._find_node_by_id(flow_data, old_embedding_id)
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if embedding_node:
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# Preserve position
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original_position = embedding_node.get("position", {})
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# Replace with new template
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new_embedding_node = embedding_template.copy()
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new_embedding_node["position"] = original_position
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# Replace in flow
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self._replace_node_in_flow(flow_data, old_embedding_id, new_embedding_node)
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components_updated.append(f"embedding: {old_embedding_id} -> {new_embedding_id}")
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# Replace LLM component (if exists in this flow)
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if old_llm_id:
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llm_node = self._find_node_by_id(flow_data, old_llm_id)
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if llm_node:
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# Preserve position
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original_position = llm_node.get("position", {})
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# Replace with new template
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new_llm_node = llm_template.copy()
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new_llm_node["position"] = original_position
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# Replace in flow
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self._replace_node_in_flow(flow_data, old_llm_id, new_llm_node)
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components_updated.append(f"llm: {old_llm_id} -> {new_llm_id}")
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# Update all edge references using regex replacement
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flow_json_str = json.dumps(flow_data)
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# Replace embedding ID references
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flow_json_str = re.sub(r'\b' + re.escape(old_embedding_id) + r'\b', new_embedding_id, flow_json_str)
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# Replace LLM ID references (if applicable)
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if old_llm_id:
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flow_json_str = re.sub(r'\b' + re.escape(old_llm_id) + r'\b', new_llm_id, flow_json_str)
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# Convert back to JSON
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flow_data = json.loads(flow_json_str)
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# PATCH the updated flow
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response = await clients.langflow_request(
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"PATCH",
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f"/api/v1/flows/{flow_id}",
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json=flow_data
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)
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if response.status_code != 200:
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raise Exception(f"Failed to update flow: HTTP {response.status_code} - {response.text}")
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return {
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"flow": flow_name,
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"success": True,
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"components_updated": components_updated,
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"flow_id": flow_id
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}
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def _find_node_by_id(self, flow_data, node_id):
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"""Find a node by ID in the flow data"""
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nodes = flow_data.get("data", {}).get("nodes", [])
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for node in nodes:
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if node.get("id") == node_id:
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return node
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return None
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def _replace_node_in_flow(self, flow_data, old_id, new_node):
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"""Replace a node in the flow data"""
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nodes = flow_data.get("data", {}).get("nodes", [])
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for i, node in enumerate(nodes):
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if node.get("id") == old_id:
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nodes[i] = new_node
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return True
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return False
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