community[minor]: feat: Layerup Security integration

docs/core_docs/docs/integrations/llms/layerup_security.mdx

@@ -0,0 +1,31 @@
+import CodeBlock from "@theme/CodeBlock";
+
+# Layerup Security
+
+The [Layerup Security](https://uselayerup.com) integration allows you to secure your calls to any LangChain LLM, LLM chain or LLM agent. The LLM object wraps around any existing LLM object, allowing for a secure layer between your users and your LLMs.
+
+While the Layerup Security object is designed as an LLM, it is not actually an LLM itself, it simply wraps around an LLM, allowing it to adapt the same functionality as the underlying LLM.
+
+## Setup
+
+First, you'll need a Layerup Security account from the Layerup [website](https://uselayerup.com).
+
+Next, create a project via the [dashboard](https://dashboard.uselayerup.com), and copy your API key. We recommend putting your API key in your project's environment.
+
+Install the Layerup Security SDK:
+
+```bash npm2yarn
+npm install @layerup/layerup-security
+```
+
+And install LangChain Community:
+
+```bash npm2yarn
+npm install @langchain/community
+```
+
+And now you're ready to start protecting your LLM calls with Layerup Security!
+
+import LayerupSecurityExampleCode from "@examples/llms/layerup_security.ts";
+
+<CodeBlock language="typescript">{LayerupSecurityExampleCode}</CodeBlock>

examples/package.json

@@ -54,6 +54,7 @@
  "@langchain/textsplitters": "workspace:*",
  "@langchain/weaviate": "workspace:*",
  "@langchain/yandex": "workspace:*",
+ "@layerup/layerup-security": "^1.5.12",
  "@opensearch-project/opensearch": "^2.2.0",
  "@pinecone-database/pinecone": "^2.2.0",
  "@planetscale/database": "^1.8.0",

examples/src/llms/layerup_security.ts

@@ -0,0 +1,59 @@
+import {
+ LayerupSecurity,
+ LayerupSecurityOptions,
+} from "@langchain/community/llms/layerup_security";
+import { GuardrailResponse } from "@layerup/layerup-security";
+import { OpenAI } from "@langchain/openai";
+
+// Create an instance of your favorite LLM
+const openai = new OpenAI({
+ modelName: "gpt-3.5-turbo",
+ openAIApiKey: process.env.OPENAI_API_KEY,
+});
+
+// Configure Layerup Security
+const layerupSecurityOptions: LayerupSecurityOptions = {
+ // Specify a LLM that Layerup Security will wrap around
+ llm: openai,
+
+ // Layerup API key, from the Layerup dashboard
+ layerupApiKey: process.env.LAYERUP_API_KEY,
+
+ // Custom base URL, if self hosting
+ layerupApiBaseUrl: "https://api.uselayerup.com/v1",
+
+ // List of guardrails to run on prompts before the LLM is invoked
+ promptGuardrails: [],
+
+ // List of guardrails to run on responses from the LLM
+ responseGuardrails: ["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 response guardrails
+ handlePromptGuardrailViolation: (violation: GuardrailResponse) => {
+ if (violation.offending_guardrail === "layerup.sensitive_data") {
+ // Custom logic goes here
+ }
+
+ return {
+ role: "assistant",
+ content: `There was sensitive data! I cannot respond. Here's a dynamic canned response. Current date: ${Date.now()}`,
+ };
+ },
+
+ // Handler for guardrail violations on the response guardrails
+ handleResponseGuardrailViolation: (violation: GuardrailResponse) => ({
+ role: "assistant",
+ content: `Custom canned response with dynamic data! The violation rule was ${violation.offending_guardrail}.`,
+ }),
+};
+
+const layerupSecurity = new LayerupSecurity(layerupSecurityOptions);
+const response = await layerupSecurity.invoke(
+ "Summarize this message: my name is Bob Dylan. My SSN is 123-45-6789."
+);

libs/langchain-community/langchain.config.js

@@ -106,6 +106,7 @@ export const config = {
  "llms/watsonx_ai": "llms/watsonx_ai",
  "llms/writer": "llms/writer",
  "llms/yandex": "llms/yandex",
+ "llms/layerup_security": "llms/layerup_security",
  // vectorstores
  "vectorstores/analyticdb": "vectorstores/analyticdb",
  "vectorstores/astradb": "vectorstores/astradb",
@@ -340,6 +341,7 @@ export const config = {
  "llms/llama_cpp",
  "llms/writer",
  "llms/portkey",
+ "llms/layerup_security",
  "vectorstores/analyticdb",
  "vectorstores/astradb",
  "vectorstores/azure_aisearch",

libs/langchain-community/package.json

@@ -82,6 +82,7 @@
  "@huggingface/inference": "^2.6.4",
  "@jest/globals": "^29.5.0",
  "@langchain/scripts": "~0.0",
+ "@layerup/layerup-security": "^1.5.12",
  "@mendable/firecrawl-js": "^0.0.13",
  "@mlc-ai/web-llm": "^0.2.35",
  "@mozilla/readability": "^0.4.4",
@@ -236,6 +237,7 @@
  "@google-cloud/storage": "^6.10.1 || ^7.7.0",
  "@gradientai/nodejs-sdk": "^1.2.0",
  "@huggingface/inference": "^2.6.4",
+ "@layerup/layerup-security": "^1.5.12",
  "@mendable/firecrawl-js": "^0.0.13",
  "@mlc-ai/web-llm": "^0.2.35",
  "@mozilla/readability": "*",
@@ -404,6 +406,9 @@
  "@huggingface/inference": {
  "optional": true
  },
+ "@layerup/layerup-security": {
+ "optional": true
+ },
  "@mendable/firecrawl-js": {
  "optional": true
  },

libs/langchain-community/src/llms/layerup_security.ts

@@ -0,0 +1,169 @@
+import {
+ LLM,
+ BaseLLM,
+ type BaseLLMParams,
+} from "@langchain/core/language_models/llms";
+import {
+ GuardrailResponse,
+ LayerupSecurity as LayerupSecuritySDK,
+ LLMMessage,
+} from "@layerup/layerup-security";
+
+export interface LayerupSecurityOptions extends BaseLLMParams {
+ llm: BaseLLM;
+ layerupApiKey?: string;
+ layerupApiBaseUrl?: string;
+ promptGuardrails?: string[];
+ responseGuardrails?: string[];
+ mask?: boolean;
+ metadata?: Record<string, unknown>;
+ handlePromptGuardrailViolation?: (violation: GuardrailResponse) => LLMMessage;
+ handleResponseGuardrailViolation?: (
+ violation: GuardrailResponse
+ ) => LLMMessage;
+}
+
+function defaultGuardrailViolationHandler(
+ violation: GuardrailResponse
+): LLMMessage {
+ if (violation.canned_response) return violation.canned_response;
+
+ const guardrailName = violation.offending_guardrail
+ ? `Guardrail ${violation.offending_guardrail}`
+ : "A guardrail";
+ throw new Error(
+ `${guardrailName} was violated without a proper guardrail violation handler.`
+ );
+}
+
+export class LayerupSecurity extends LLM {
+ static lc_name() {
+ return "LayerupSecurity";
+ }
+
+ lc_serializable = true;
+
+ llm: BaseLLM;
+
+ layerupApiKey: string;
+
+ layerupApiBaseUrl = "https://api.uselayerup.com/v1";
+
+ promptGuardrails: string[] = [];
+
+ responseGuardrails: string[] = [];
+
+ mask = false;
+
+ metadata: Record<string, unknown> = {};
+
+ handlePromptGuardrailViolation: (violation: GuardrailResponse) => LLMMessage =
+ defaultGuardrailViolationHandler;
+
+ handleResponseGuardrailViolation: (
+ violation: GuardrailResponse
+ ) => LLMMessage = defaultGuardrailViolationHandler;
+
+ private layerup: LayerupSecuritySDK;
+
+ constructor(options: LayerupSecurityOptions) {
+ super(options);
+
+ if (!options.llm) {
+ throw new Error("Layerup Security requires an LLM to be provided.");
+ } else if (!options.layerupApiKey) {
+ throw new Error("Layerup Security requires an API key to be provided.");
+ }
+
+ this.llm = options.llm;
+ this.layerupApiKey = options.layerupApiKey;
+ this.layerupApiBaseUrl =
+ options.layerupApiBaseUrl || this.layerupApiBaseUrl;
+ this.promptGuardrails = options.promptGuardrails || this.promptGuardrails;
+ this.responseGuardrails =
+ options.responseGuardrails || this.responseGuardrails;
+ this.mask = options.mask || this.mask;
+ this.metadata = options.metadata || this.metadata;
+ this.handlePromptGuardrailViolation =
+ options.handlePromptGuardrailViolation ||
+ this.handlePromptGuardrailViolation;
+ this.handleResponseGuardrailViolation =
+ options.handleResponseGuardrailViolation ||
+ this.handleResponseGuardrailViolation;
+
+ this.layerup = new LayerupSecuritySDK({
+ apiKey: this.layerupApiKey,
+ baseURL: this.layerupApiBaseUrl,
+ });
+ }
+
+ _llmType() {
+ return "layerup_security";
+ }
+
+ async _call(input: string, options?: BaseLLMParams): Promise<string> {
+ // Since LangChain LLMs only support string inputs, we will wrap each call to Layerup in a single-message
+ // array of messages, then extract the string element when we need to access it.
+ let messages: LLMMessage[] = [
+ {
+ role: "user",
+ content: input,
+ },
+ ];
+ let unmaskResponse;
+
+ if (this.mask) {
+ [messages, unmaskResponse] = await this.layerup.maskPrompt(
+ messages,
+ this.metadata
+ );
+ }
+
+ if (this.promptGuardrails.length > 0) {
+ const securityResponse = await this.layerup.executeGuardrails(
+ this.promptGuardrails,
+ messages,
+ input,
+ this.metadata
+ );
+
+ // If there is a guardrail violation, extract the canned response and reply with that instead
+ if (!securityResponse.all_safe) {
+ const replacedResponse: LLMMessage =
+ this.handlePromptGuardrailViolation(securityResponse);
+ return replacedResponse.content as string;
+ }
+ }
+
+ // Invoke the underlying LLM with the prompt and options
+ let result = await this.llm.invoke(messages[0].content as string, options);
+
+ if (this.mask && unmaskResponse) {
+ result = unmaskResponse(result);
+ }
+
+ // Add to messages array for response guardrail handler
+ messages.push({
+ role: "assistant",
+ content: result,
+ });
+
+ if (this.responseGuardrails.length > 0) {
+ const securityResponse = await this.layerup.executeGuardrails(
+ this.responseGuardrails,
+ messages,
+ result,
+ this.metadata
+ );
+
+ // If there is a guardrail violation, extract the canned response and reply with that instead
+ if (!securityResponse.all_safe) {
+ const replacedResponse: LLMMessage =
+ this.handleResponseGuardrailViolation(securityResponse);
+ return replacedResponse.content as string;
+ }
+ }
+
+ return result;
+ }
+}

libs/langchain-community/src/llms/tests/layerup_security.test.ts

@@ -0,0 +1,48 @@
+import { test } from "@jest/globals";
+import { LLM, type BaseLLMParams } from "@langchain/core/language_models/llms";
+import { GuardrailResponse } from "@layerup/layerup-security/types.js";
+import {
+ LayerupSecurity,
+ LayerupSecurityOptions,
+} from "../layerup_security.js";
+
+// Mock LLM for testing purposes
+export class MockLLM extends LLM {
+ static lc_name() {
+ return "MockLLM";
+ }
+
+ lc_serializable = true;
+
+ _llmType() {
+ return "mock_llm";
+ }
+
+ async _call(_input: string, _options?: BaseLLMParams): Promise<string> {
+ return "Hi Bob! How are you?";
+ }
+}
+
+test("Test LayerupSecurity with invalid API key", async () => {
+ const mockLLM = new MockLLM({});
+ const layerupSecurityOptions: LayerupSecurityOptions = {
+ llm: mockLLM,
+ layerupApiKey: "-- invalid API key --",
+ layerupApiBaseUrl: "https://api.uselayerup.com/v1",
+ promptGuardrails: [],
+ responseGuardrails: ["layerup.hallucination"],
+ mask: false,
+ metadata: { customer: "[email protected]" },
+ handleResponseGuardrailViolation: (violation: GuardrailResponse) => ({
+ role: "assistant",
+ content: `Custom canned response with dynamic data! The violation rule was ${violation.offending_guardrail}.`,
+ }),
+ };
+
+ await expect(async () => {
+ const layerupSecurity = new LayerupSecurity(layerupSecurityOptions);
+ await layerupSecurity.invoke(
+ "My name is Bob Dylan. My SSN is 123-45-6789."
+ );
+ }).rejects.toThrowError();
+}, 50000);

libs/langchain-community/src/load/import_constants.ts

@@ -35,6 +35,7 @@ export const optionalImportEntrypoints: string[] = [
  "langchain_community/llms/sagemaker_endpoint",
  "langchain_community/llms/watsonx_ai",
  "langchain_community/llms/writer",
+ "langchain_community/llms/layerup_security",
  "langchain_community/vectorstores/analyticdb",
  "langchain_community/vectorstores/astradb",
  "langchain_community/vectorstores/azure_aisearch",