import os
from langchain_openai import ChatOpenAI
from valthera.aggregator import DataAggregator
from mocks import hubspot, posthog, snowflake # Replace these with your actual connector implementations
from langchain_valthera.tools import ValtheraTool
# Initialize the DataAggregator with your data connectors
data_aggregator = DataAggregator(
connectors={
"hubspot": hubspot(),
"posthog": posthog(),
"app_db": snowflake()
}
)
# Initialize the ValtheraTool with your scoring configurations
valthera_tool = ValtheraTool(
data_aggregator=data_aggregator,
motivation_config=[
{"key": "hubspot_lead_score", "weight": 0.30, "transform": lambda x: min(x, 100) / 100.0},
{"key": "posthog_events_count_past_30days", "weight": 0.30, "transform": lambda x: min(x, 50) / 50.0},
{"key": "hubspot_marketing_emails_opened", "weight": 0.20, "transform": lambda x: min(x / 10.0, 1.0)},
{"key": "posthog_session_count", "weight": 0.20, "transform": lambda x: min(x / 5.0, 1.0)}
],
ability_config=[
{"key": "posthog_onboarding_steps_completed", "weight": 0.30, "transform": lambda x: min(x / 5.0, 1.0)},
{"key": "posthog_session_count", "weight": 0.30, "transform": lambda x: min(x / 10.0, 1.0)},
{"key": "behavior_complexity", "weight": 0.40, "transform": lambda x: 1 - (min(x, 5) / 5.0)}
]
)
print("✅ ValtheraTool successfully initialized for LangChain integration!")
