Layerup 安全

该 Layerup 安全 集成允许您保护对任何 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，为您的用户和 LLM 之间提供安全层。

虽然 Layerup 安全对象被设计为 LLM，但它实际上并不是一个 LLM，它只是包装在一个 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 获取一个 Layerup 安全账户。

接下来，通过 仪表板 创建一个项目，并复制您的 API 密钥。我们建议将您的 API 密钥放在项目的环境中。

安装 Layerup 安全 SDK：

pip install LayerupSecurity

并安装 LangChain 社区：

pip install langchain-community

现在您准备好使用 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": "example@uselayerup.com"},

    # 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."
)