Valthera

Valthera 是一个开源框架，旨在赋能 LLM Agent 驱动有意义的、上下文感知的用户互动。它实时评估用户的动机和能力，确保仅在用户最容易接受时触发通知和操作。

langchain-valthera 将 Valthera 与 LangChain 集成，使开发人员能够构建更智能、行为驱动的互动系统，从而提供个性化的交互。

安装与设置

安装 langchain-valthera

通过 pip 安装 LangChain Valthera 包

pip install -U langchain-valthera

导入 ValtheraTool

from langchain_valthera.tools import ValtheraTool

示例：初始化 LangChain 的 ValtheraTool

此示例展示了如何使用 DataAggregator 和动机与能力评分的配置来初始化 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 参考：ChatOpenAI

langchain-valthera 集成允许您评估用户行为，并决定最佳的互动方案，确保在您的 LangChain 应用程序中，互动既及时又相关。