Layerup Security

该Layerup Security集成允许您保护您对任何 LangChain LLM、LLM 链或 LLM 代理的调用。LLM 对象包装在任何现有的 LLM 对象周围，允许在您的用户和您的 LLM 之间建立一个安全的层。

虽然 Layerup Security 对象被设计为 LLM，但它本身并不是 LLM，它只是包装在 LLM 周围，使其能够适应与底层 LLM 相同的功能。

设置

首先，您需要从 Layerup 网站获得 Layerup Security 帐户。

接下来，通过 仪表板创建项目，并复制您的 API 密钥。建议将您的 API 密钥放在项目的环境中。

安装 Layerup Security SDK

pip install LayerupSecurity

并安装 LangChain Community

pip install langchain-community

现在，您已准备好开始使用 Layerup Security 保护您的 LLM 调用！

from langchain_community.llms.layerup_security import LayerupSecurity
from langchain_openai import OpenAI

# Create an instance of your favorite LLM
openai = OpenAI(
    model_name="gpt-3.5-turbo",
    openai_api_key="OPENAI_API_KEY",
)

# Configure Layerup Security
layerup_security = LayerupSecurity(
    # Specify a LLM that Layerup Security will wrap around
    llm=openai,

    # Layerup API key, from the Layerup dashboard
    layerup_api_key="LAYERUP_API_KEY",

    # Custom base URL, if self hosting
    layerup_api_base_url="https://api.uselayerup.com/v1",

    # List of guardrails to run on prompts before the LLM is invoked
    prompt_guardrails=[],

    # List of guardrails to run on responses from the LLM
    response_guardrails=["layerup.hallucination"],

    # Whether or not to mask the prompt for PII & sensitive data before it is sent to the LLM
    mask=False,

    # Metadata for abuse tracking, customer tracking, and scope tracking.
    metadata={"customer": "[email protected]"},

    # Handler for guardrail violations on the prompt guardrails
    handle_prompt_guardrail_violation=(
        lambda violation: {
            "role": "assistant",
            "content": (
                "There was sensitive data! I cannot respond. "
                "Here's a dynamic canned response. Current date: {}"
            ).format(datetime.now())
        }
        if violation["offending_guardrail"] == "layerup.sensitive_data"
        else None
    ),

    # Handler for guardrail violations on the response guardrails
    handle_response_guardrail_violation=(
        lambda violation: {
            "role": "assistant",
            "content": (
                "Custom canned response with dynamic data! "
                "The violation rule was {}."
            ).format(violation["offending_guardrail"])
        }
    ),
)

response = layerup_security.invoke(
    "Summarize this message: my name is Bob Dylan. My SSN is 123-45-6789."
)

API 参考：LayerupSecurity | OpenAI

相关

• LLM 概念指南
• LLM 操作指南