Added requested changes

docs/core_docs/docs/integrations/chat/azure_ml.mdx

@@ -1,19 +1,22 @@
 # Azure Machine Learning Chat
 
-You can deploy models on Azure with the endpointUrl, apiKey, and deploymentName
+You must deploy models on Azure with the endpointUrl, apiKey, and deploymentName
 when creating the AzureMLChatParams to call upon later. Must import a ContentFormatter
 or create your own using the ChatContentFormatter interface.
 
 ```typescript
-import { AzureMLChatParams, LlamaContentFormatter } from "langchain/chat_models/azure_ml";
+import {
+  AzureMLChatParams,
+  LlamaContentFormatter,
+} from "langchain/chat_models/azure_ml";
 
-const model = new AzureMLModel({
-    endpointUrl: "YOUR_ENDPOINT_URL", 
-    endpointApiKey: "YOUR_ENDPOINT_API_KEY", 
-    deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME",
-    contentFormatter: new LlamaContentFormatter()
+const model = new AzureMLOnlineEndpoint({
+  endpointUrl: "YOUR_ENDPOINT_URL",
+  endpointApiKey: "YOUR_ENDPOINT_API_KEY",
+  deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME",
+  contentFormatter: new LlamaContentFormatter(),
 });
 
-const res = model.call(["Foo"]);
+const res = model.invoke(["Foo"]);
 console.log({ res });
-```
+```

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

@@ -1,19 +1,22 @@
 # Azure Machine Learning
 
-You can deploy models on Azure with the endpointUrl, apiKey, and deploymentName
-when creating the AzureMLModel to call upon later. Must import a ContentFormatter
+You must deploy models on Azure with the endpointUrl, apiKey, and deploymentName
+when creating the AzureMLOnlineEndpoint to call upon later. Must import a ContentFormatter
 or create your own using the ContentFormatter interface.
 
 ```typescript
-import { AzureMLModel, LlamaContentFormatter } from "langchain/llms/azure_ml";
+import {
+  AzureMLOnlineEndpoint,
+  LlamaContentFormatter,
+} from "langchain/llms/azure_ml";
 
-const model = new AzureMLModel({
-    endpointUrl: "YOUR_ENDPOINT_URL", 
-    endpointApiKey: "YOUR_ENDPOINT_API_KEY", 
-    deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME",
-    contentFormatter: new LlamaContentFormatter()
+const model = new AzureMLOnlineEndpoint({
+  endpointUrl: "YOUR_ENDPOINT_URL",
+  endpointApiKey: "YOUR_ENDPOINT_API_KEY",
+  deploymentName: "YOUR_MODEL_DEPLOYMENT_NAME",
+  contentFormatter: new LlamaContentFormatter(),
 });
 
-const res = model.call("Foo");
+const res = model.invoke("Foo");
 console.log({ res });
-```
+```

examples/src/models/chat/chat_azure_ml.ts

@@ -1,12 +1,14 @@
-import { AzureMLChatModel, LlamaContentFormatter } from "langchain/chat_models/azure_ml";
+import {
+  AzureMLChatOnlineEndpoint,
+  LlamaContentFormatter,
+} from "langchain/chat_models/azure_ml";
 
-const model = new AzureMLChatModel({
-    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
-    contentFormatter: new LlamaContentFormatter(), // Only LLAMA currently supported.
+const model = new AzureMLChatOnlineEndpoint({
+  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
+  contentFormatter: new LlamaContentFormatter(), // Only LLAMA currently supported.
 });
 
-const res = model.call("Foo");
+const res = model.invoke("Foo");
 
-console.log({ res });
+console.log({ res });

examples/src/models/llm/azure_ml.ts

@@ -1,12 +1,15 @@
-import { AzureMLModel, LlamaContentFormatter } from "langchain/llms/azure_ml";
+import {
+  AzureMLOnlineEndpoint,
+  LlamaContentFormatter,
+} 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
-    contentFormatter: new LlamaContentFormatter(), // Or any of the other Models: GPT2ContentFormatter, HFContentFormatter, DollyContentFormatter
+const model = new AzureMLOnlineEndpoint({
+  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
+  contentFormatter: new LlamaContentFormatter(), // Or any of the other Models: GPT2ContentFormatter, HFContentFormatter, DollyContentFormatter
 });
 
-const res = model.call("Foo");
+const res = model.invoke("Foo");
 
-console.log({ res });
+console.log({ res });

langchain/src/chat_models/azure_ml.ts

@@ -4,15 +4,18 @@ import { getEnvironmentVariable } from "../util/env.js";
 import { BaseMessage } from "../schema/index.js";
 
 export interface ChatContentFormatter {
-    /**
+  /**
    * 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 messages A list of messages for the chat so far.
    * @param modelArgs A dictionary of model arguments.
    * @returns A string representing the formatted request payload.
    */
-  formatRequestPayload:(messages:BaseMessage[], modelArgs:Record<string, unknown>) => string;
+  formatRequestPayload: (
+    messages: BaseMessage[],
+    modelArgs: Record<string, unknown>
+  ) => string;
   /**
    * Formats the response payload from the AzureML endpoint. It takes a
    * response payload as input and returns a string representing the
@@ -24,98 +27,86 @@ export interface ChatContentFormatter {
 }
 
 export class LlamaContentFormatter implements ChatContentFormatter {
-    _convertMessageToRecord(message:BaseMessage):Record<string, unknown> {
-        if (message._getType() === 'human') {
-            return {role: "user", content: message.content}
-        } else if (message._getType() === 'ai') {
-            return {role: "assistant", content: message.content}
-        } else {
-            return {role: message._getType(), content: message.content}
-        }
+  _convertMessageToRecord(message: BaseMessage): Record<string, unknown> {
+    if (message._getType() === "human") {
+      return { role: "user", content: message.content };
+    } else if (message._getType() === "ai") {
+      return { role: "assistant", content: message.content };
+    } else {
+      return { role: message._getType(), content: message.content };
     }
+  }
 
-    formatRequestPayload(
-        messages: BaseMessage[],
-        modelArgs: Record<string, unknown>
-    ): string {
-        let msgs = messages.map(message => {
-            this._convertMessageToRecord(message)
-        });
-        return JSON.stringify(
-            {"input_data": {
-                "input_string": msgs,
-                "parameters": modelArgs
-            }}
-        )
-    }
+  formatRequestPayload(
+    messages: BaseMessage[],
+    modelArgs: Record<string, unknown>
+  ): string {
+    let msgs = messages.map((message) => {
+      this._convertMessageToRecord(message);
+    });
+    return JSON.stringify({
+      input_data: {
+        input_string: msgs,
+        parameters: modelArgs,
+      },
+    });
+  }
 
-    formatResponsePayload(
-        responsePayload: string
-    ) {
-        const response = JSON.parse(responsePayload);
-        return response.output
-    }
+  formatResponsePayload(responsePayload: string) {
+    const response = JSON.parse(responsePayload);
+    return response.output;
+  }
 }
 
 /**
  * Type definition for the input parameters of the AzureMLChatOnlineEndpoint class.
  */
-export interface AzureMLChatParams extends BaseChatModelParams  {
-    endpointUrl?: string;
-    endpointApiKey?: string;
-    modelArgs?: Record<string, unknown>;
-    contentFormatter?: ChatContentFormatter;
-  };
-
+export interface AzureMLChatParams extends BaseChatModelParams {
+  endpointUrl?: string;
+  endpointApiKey?: string;
+  modelArgs?: Record<string, unknown>;
+  contentFormatter?: ChatContentFormatter;
+}
 
 /**
  * Class that represents the chat model. It extends the SimpleChatModel class and implements the AzureMLChatInput interface.
  */
-export class AzureMLChatModel extends SimpleChatModel implements AzureMLChatParams {
+export class AzureMLChatOnlineEndpoint
+  extends SimpleChatModel
+  implements AzureMLChatParams
+{
   static lc_name() {
     return "AzureMLChat";
   }
   static lc_description() {
     return "A class for interacting with AzureML Chat models.";
   }
-
-  static lc_fields() {
-    return {
-      endpointUrl: {
-        lc_description: "The URL of the AzureML endpoint.",
-        lc_env: "AZUREML_URL",
-      },
-      endpointApiKey: {
-        lc_description: "The API key for the AzureML endpoint.",
-        lc_env: "AZUREML_API_KEY",
-      },
-      contentFormatter: {
-        lc_description: "The formatter for AzureML API",
-      }
-    };
-  }
   endpointUrl: string;
   endpointApiKey: string;
   modelArgs?: Record<string, unknown>;
   contentFormatter: ChatContentFormatter;
   httpClient: AzureMLHttpClient;
 
-
   constructor(fields: AzureMLChatParams) {
     super(fields ?? {});
-    if (!fields?.endpointUrl && !getEnvironmentVariable('AZUREML_URL')) {
+    if (!fields?.endpointUrl && !getEnvironmentVariable("AZUREML_URL")) {
       throw new Error("No Azure ML Url found.");
     }
-    if (!fields?.endpointApiKey && !getEnvironmentVariable('AZUREML_API_KEY')) {
+    if (!fields?.endpointApiKey && !getEnvironmentVariable("AZUREML_API_KEY")) {
       throw new Error("No Azure ML ApiKey found.");
     }
     if (!fields?.contentFormatter) {
-        throw new Error("No Content Formatter provided.")
+      throw new Error("No Content Formatter provided.");
     }
-
-    this.endpointUrl = fields.endpointUrl || getEnvironmentVariable('AZUREML_URL')+'';
-    this.endpointApiKey = fields.endpointApiKey || getEnvironmentVariable('AZUREML_API_KEY')+'';
-    this.httpClient = new AzureMLHttpClient(this.endpointUrl, this.endpointApiKey);
+
+    this.endpointUrl =
+      fields.endpointUrl || getEnvironmentVariable("AZUREML_URL") + "";
+    this.endpointApiKey =
+      fields.endpointApiKey || getEnvironmentVariable("AZUREML_API_KEY") + "";
+    this.httpClient = new AzureMLHttpClient(
+      this.endpointUrl,
+      this.endpointApiKey
+    );
     this.contentFormatter = fields.contentFormatter;
     this.modelArgs = fields?.modelArgs;
   }
@@ -132,20 +123,20 @@ export class AzureMLChatModel extends SimpleChatModel implements AzureMLChatPara
   }
 
   _combineLLMOutput(): Record<string, any> | undefined {
-      return []
+    return [];
   }
 
   async _call(
     messages: BaseMessage[],
     modelArgs: Record<string, unknown>
   ): Promise<string> {
     const requestPayload = this.contentFormatter.formatRequestPayload(
-        messages,
-        modelArgs
+      messages,
+      modelArgs
     );
     const responsePayload = await this.httpClient.call(requestPayload);
-    const generatedText = this.contentFormatter.formatResponsePayload(responsePayload);
+    const generatedText =
+      this.contentFormatter.formatResponsePayload(responsePayload);
     return generatedText;
   }
 }
-

langchain/src/chat_models/tests/chatazure_ml.int.test.ts

@@ -1,14 +1,17 @@
 import { test, expect } from "@jest/globals";
-import { AzureMLChatModel, LlamaContentFormatter } from "../azure_ml.js";
+import {
+  AzureMLChatOnlineEndpoint,
+  LlamaContentFormatter,
+} from "../azure_ml.js";
 
 test("Test AzureML LLama Call", async () => {
-    const prompt = "Hi Llama!";
-    const chat = new AzureMLChatModel({
-        contentFormatter: new LlamaContentFormatter()
-    });
-  
-    const res = await chat.call([prompt]);
-    expect(typeof res).toBe("string");
-  
-    console.log(res);
-});
+  const prompt = "Hi Llama!";
+  const chat = new AzureMLChatOnlineEndpoint({
+    contentFormatter: new LlamaContentFormatter(),
+  });
+
+  const res = await chat.call([prompt]);
+  expect(typeof res).toBe("string");
+
+  console.log(res);
+});