Valthera

Valthera 是一個開放原始碼框架，可讓 LLM 代理程式驅動有意義、感知情境的使用者互動。它會即時評估使用者的動機和能力，確保僅在使用者最容易接受時才觸發通知和動作。

langchain-valthera 將 Valthera 與 LangChain 整合，使開發人員能夠建置更智慧、行為驅動的互動系統，以提供個人化的互動。

安裝與設定

安裝 langchain-valthera

透過 pip 安裝 LangChain Valthera 套件

pip install -U langchain-valthera

匯入 ValtheraTool

from langchain_valthera.tools import ValtheraTool

範例：初始化 LangChain 的 ValtheraTool

此範例示範如何使用 DataAggregator 和動機與能力評分設定來初始化 ValtheraTool。

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!")

API 參考：ChatOpenAI

langchain-valthera 整合讓您可以評估使用者行為，並決定互動的最佳行動方案，確保互動在您的 LangChain 應用程式中既即時又相關。