> Valthera 是一个开源框架，使 LLM 代理能够推动有意义的、上下文感知的用户互动。它实时评估用户的动机和能力，确保仅在用户最愿意接受时触发通知和操作。 > > **langchain-valthera** 将 Valthera 与 LangChain 集成，使开发者能够构建更智能的、由行为驱动的互动系统，提供个性化互动体验。

安装和设置

安装 langchain-valthera

通过 pip 安装 LangChain Valthera 包：

pip install -U langchain-valthera

uv add langchain-valthera

导入 ValtheraTool：

from langchain_valthera.tools import ValtheraTool

示例：为 LangChain 初始化 ValtheraTool

此示例展示如何使用 DataAggregator 以及动机和能力评分配置来初始化 ValtheraTool。

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!")

langchain-valthera 集成允许您评估用户行为并决定最佳互动方案，确保在您的 LangChain 应用中互动及时且相关。