Added AzureML LLM

docs/api_refs/typedoc.json

@@ -89,6 +89,7 @@
     "./langchain/src/llms/portkey.ts",
     "./langchain/src/llms/yandex.ts",
     "./langchain/src/llms/fake.ts",
+    "./langchain/src/llms/azure_ml.ts",
     "./langchain/src/prompts/index.ts",
     "./langchain/src/prompts/load.ts",
     "./langchain/src/vectorstores/analyticdb.ts",

docs/core_docs/docs/integrations/llms/azure_ml.mdx

@@ -0,0 +1,17 @@
+# Azure Machine Learning
+
+You can deploy models on Azure and add the endpointUrl, apiKey and deploymentName
+when creating the AzureMLModel to call upon later.
+
+```typescript
+import { AzureMLModel } from "langchain/llms/azure_ml";
+
+const model = new AzureMLModel({
+    endpointUrl: "YOUR_ENDPOINT_URL", 
+    endpointApiKey: "YOUR_ENDPOINT_API_KEY", 
+    deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME",
+});
+
+const res = model.call("Foo");
+console.log({ res });
+```

examples/src/models/llm/azure_ml.ts

@@ -0,0 +1,11 @@
+import { AzureMLModel } from "langchain/llms/azure_ml";
+
+const model = new AzureMLModel({
+    endpointUrl: "YOUR_ENDPOINT_URL", // Or set as process.env.AZURE_ML_ENDPOINTURL
+    endpointApiKey: "YOUR_ENDPOINT_API_KEY", // Or set as process.env.AZURE_ML_APIKEY
+    deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME", // Or set as process.env.AZURE_ML_NAME
+});
+
+const res = model.call("Foo");
+
+console.log({ res });

langchain/.gitignore

@@ -211,6 +211,9 @@ llms/yandex.d.ts
 llms/fake.cjs
 llms/fake.js
 llms/fake.d.ts
+llms/azure_ml.cjs
+llms/azure_ml.js
+llms/azure_ml.d.ts
 prompts.cjs
 prompts.js
 prompts.d.ts

langchain/package.json

@@ -223,6 +223,9 @@
     "llms/fake.cjs",
     "llms/fake.js",
     "llms/fake.d.ts",
+    "llms/azure_ml.cjs",
+    "llms/azure_ml.js",
+    "llms/azure_ml.d.ts",
     "prompts.cjs",
     "prompts.js",
     "prompts.d.ts",
@@ -1763,6 +1766,11 @@
       "import": "./llms/fake.js",
       "require": "./llms/fake.cjs"
     },
+    "./llms/azure_ml": {
+      "types": "./llms/azure_ml.d.ts",
+      "import": "./llms/azure_ml.js",
+      "require": "./llms/azure_ml.cjs"
+    },
     "./prompts": {
       "types": "./prompts.d.ts",
       "import": "./prompts.js",

langchain/scripts/create-entrypoints.js

@@ -368,6 +368,7 @@ const requiresOptionalDependency = [
   "llms/llama_cpp",
   "llms/writer",
   "llms/portkey",
+  "llms/azure_ml",
   "prompts/load",
   "vectorstores/analyticdb",
   "vectorstores/cassandra",

langchain/src/llms/azure_ml.ts

@@ -0,0 +1,158 @@
+import { BaseLLMParams, LLM } from "./base.js";
+
+/**
+ * Interface for the AzureML API response.
+ */
+interface AzureMLResponse {
+  id: string;
+  version: string;
+  created: string;
+  inputs: {
+    input_string: string[];
+  };
+  parameters: {
+    [key: string]: string;
+  };
+  global_parameters: {
+    [key: string]: string;
+  };
+  output: string;
+}
+
+export interface AzureMLInput {
+  endpointUrl?: string;
+  endpointApiKey?: string;
+  deploymentName?: string;
+}
+
+/**
+ * Class that represents an AzureML model. It extends the LLM base class
+ * and provides methods for calling the AzureML endpoint and formatting
+ * the request and response payloads.
+ */
+export class AzureMLModel extends LLM implements AzureMLInput {
+  _llmType() {
+    return "azure_ml";
+  }
+
+  static lc_name() {
+    return "AzureMLModel";
+  }
+
+  static lc_description() {
+    return "A class for interacting with AzureML models.";
+  }
+
+  static lc_fields() {
+    return {
+      endpointUrl: {
+        lc_description: "The URL of the AzureML endpoint.",
+        lc_env: "AZUREML_ENDPOINT_URL",
+      },
+      endpointApiKey: {
+        lc_description: "The API key for the AzureML endpoint.",
+        lc_env: "AZUREML_ENDPOINT_API_KEY",
+      },
+      deploymentName: {
+        lc_description: "The name of the AzureML deployment.",
+        lc_env: "AZUREML_DEPLOYMENT_NAME",
+      },
+    };
+  }
+
+  endpointUrl: string;
+  endpointApiKey: string;
+  deploymentName: string;
+
+  constructor(fields: AzureMLInput & BaseLLMParams) {
+    super(fields ?? {});
+
+    if (fields?.endpointUrl === undefined) {
+      throw new Error("No Azure ML endpointUrl found.");
+    }
+
+    if (fields?.endpointApiKey === undefined) {
+      throw new Error("No Azure ML endpointApiKey found.");
+    }
+
+    if (fields?.deploymentName === undefined) {
+      throw new Error("No Azure ML deploymentName found.");
+    }
+
+    this.endpointUrl = fields.endpointUrl;
+    this.endpointApiKey = fields.endpointApiKey;
+    this.deploymentName = fields.deploymentName;
+  }
+
+  /**
+   * Formats the request payload for the AzureML endpoint. It takes a
+   * prompt and a dictionary of model arguments as input and returns a
+   * string representing the formatted request payload.
+   * @param prompt The prompt for the AzureML model.
+   * @param modelArgs A dictionary of model arguments.
+   * @returns A string representing the formatted request payload.
+   */
+  formatRequestPayload(prompt: string, modelArgs: Record<string, unknown>) {
+    return JSON.stringify({
+      inputs: {
+        input_string: [prompt],
+      },
+      parameters: modelArgs,
+    });
+  }
+
+  /**
+   * Formats the response payload from the AzureML endpoint. It takes a
+   * response payload as input and returns a string representing the
+   * formatted response.
+   * @param responsePayload The response payload from the AzureML endpoint.
+   * @returns A string representing the formatted response.
+   */
+  formatResponsePayload(responsePayload: string) {
+    const response = JSON.parse(responsePayload) as AzureMLResponse;
+    return response.output;
+  }
+
+  /**
+   * Calls the AzureML endpoint. It takes a request payload as input and
+   * returns a Promise that resolves to the response payload.
+   * @param requestPayload The request payload for the AzureML endpoint.
+   * @returns A Promise that resolves to the response payload.
+   */
+  async call(requestPayload: string): Promise<string> {
+    const response = await fetch(this.endpointUrl, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: `Bearer ${this.endpointApiKey}`,
+      },
+      body: requestPayload,
+    });
+    if (!response.ok) {
+        const error = new Error(
+          `Azure ML LLM call failed with status code ${response.status}`
+        );
+        // eslint-disable-next-line @typescript-eslint/no-explicit-any
+        (error as any).response = response;
+        throw error;
+    }
+    return response.text();
+  }
+
+  /**
+   * Calls the AzureML endpoint with the provided prompt and model arguments.
+   * It returns a Promise that resolves to the generated text.
+   * @param prompt The prompt for the AzureML model.
+   * @param modelArgs A dictionary of model arguments.
+   * @param runManager An optional CallbackManagerForLLMRun instance.
+   * @returns A Promise that resolves to the generated text.
+   */
+  async _call(
+    prompt: string,
+    modelArgs: Record<string, unknown>
+  ): Promise<string> {
+    const requestPayload = this.formatRequestPayload(prompt, modelArgs);
+    const responsePayload = await this.call(requestPayload);
+    return this.formatResponsePayload(responsePayload);
+  }
+}

langchain/src/llms/tests/azure_ml.int.test.ts

@@ -0,0 +1,16 @@
+import { test, expect } from "@jest/globals";
+import { AzureMLModel } from "../azure_ml.js";
+
+test("Test AzureML Model", async () => {
+    const prompt = "Foo";
+    const model = new AzureMLModel({
+        endpointUrl: process.env.AZURE_ML_ENDPOINTURL,
+        endpointApiKey: process.env.AZURE_ML_APIKEY,
+        deploymentName: process.env.AZURE_ML_NAME
+    });
+
+    const res = await model.call(prompt);
+    expect(typeof res).toBe("string");
+
+    console.log(res);
+});

langchain/src/load/import_constants.ts

@@ -38,6 +38,7 @@ export const optionalImportEntrypoints = [
   "langchain/llms/llama_cpp",
   "langchain/llms/writer",
   "langchain/llms/portkey",
+  "langchain/llms/azure_ml",
   "langchain/prompts/load",
   "langchain/vectorstores/analyticdb",
   "langchain/vectorstores/cassandra",

langchain/src/load/import_type.d.ts

@@ -112,6 +112,9 @@ export interface OptionalImportMap {
   "langchain/llms/portkey"?:
     | typeof import("../llms/portkey.js")
     | Promise<typeof import("../llms/portkey.js")>;
+  "langchain/llms/azure_ml"?:
+    | typeof import("../llms/azure_ml.js")
+    | Promise<typeof import("../llms/azure_ml.js")>;
   "langchain/prompts/load"?:
     | typeof import("../prompts/load.js")
     | Promise<typeof import("../prompts/load.js")>;