chore: sync sdk code with DeepLearning repo

package.json

@@ -1,6 +1,6 @@
 {
   "name": "assemblyai",
-  "version": "4.15.0",
+  "version": "4.16.0",
   "description": "The AssemblyAI JavaScript SDK provides an easy-to-use interface for interacting with the AssemblyAI API, which supports async and real-time transcription, as well as the latest LeMUR models.",
   "engines": {
     "node": ">=18"

src/types/openapi.generated.ts

@@ -672,6 +672,71 @@
  * }
  * ```
  */
+
+export type LemurRequestDetails = {
+  /**
+   * The endpoint used for the leMUR request
+   */
+  request_endpoint: string;
+  /**
+   * The temperature to use for the model.
+   * Higher values result in answers that are more creative, lower values are more conservative.
+   * Can be any value between 0.0 and 1.0 inclusive.
+   *
+   * @defaultValue 0
+   */
+  temperature: number;
+  /**
+   * The model that was used for the final prompt after compression is performed.
+   *
+   * @defaultValue "default"
+   */
+  final_model: LiteralUnion<LemurModel, string>;
+  /**
+   * Max output size in tokens, up to 4000
+   * @defaultValue 2000
+   */
+  max_output_size: number;
+  /**
+   * The date when the request was created
+   */
+  created_at: Date;
+  /**
+   * A list of completed transcripts with text.
+   * Use either transcript_ids or input_text as input into LeMUR.
+   */
+  transcript_ids?: string[];
+  /**
+   * Custom formatted transcript data. Maximum size is the context limit of the selected model, which defaults to 100000".
+   * Use either transcript_ids or input_text as input into LeMUR.
+   */
+  input_text?: string;
+  /**
+   * A list of questions asked in the request
+   * Each question can have its own context and answer format.
+   */
+  questions?: LemurQuestion[];
+  /**
+   * The prompt used for the model.
+   */
+  prompt?: string;
+  /**
+   * Context provided to the model. This can be a string or a free-form JSON value.
+   */
+  context?: OneOf<
+    [
+      string,
+      {
+        [key: string]: unknown;
+      },
+    ]
+  >;
+  /**
+   * The format to use for the model's answers.
+   */
+  answer_format?: string;
+};
+
 export type LemurBaseResponse = {
   /**
    * The ID of the LeMUR request
@@ -681,6 +746,7 @@
    * The usage numbers for the LeMUR request
    */
   usage: LemurUsage;
+  request?: LemurRequestDetails;
 };
 
 /**
@@ -971,7 +1037,7 @@
   /**
    * Only get throttled transcripts, overrides the status filter
    * @defaultValue false
    * @deprecated
    */
   throttled_only?: boolean;
 };

tests/unit/lemur.test.ts

@@ -149,6 +149,144 @@ describe("lemur", () => {
     expect(response.response).toBe("some response");
   });
 
+  it("should return response with request details", async () => {
+    const responseWithDetails = {
+      request_id: knownLemurRequestId,
+      response: "detailed response",
+      usage: {
+        input_tokens: 250,
+        output_tokens: 75,
+      },
+      request: {
+        request_endpoint: "/lemur/v3/generate/task",
+        temperature: 0.7,
+        final_model: "anthropic/claude-3-5-sonnet",
+        max_output_size: 1500,
+        created_at: "2024-01-01T10:30:00Z",
+        transcript_ids: knownTranscriptIds,
+        prompt: "Analyze the key themes in this conversation",
+        context: "Focus on business decisions and action items",
+      },
+    };
+
+    fetchMock.doMockOnceIf(
+      requestMatches({
+        method: "GET",
+        url: `/lemur/v3/${knownLemurRequestId}`,
+      }),
+      JSON.stringify(responseWithDetails),
+    );
+
+    const response = await assembly.lemur.getResponse(knownLemurRequestId);
+    expect(response.request_id).toBe(knownLemurRequestId);
+    expect(response.request).toBeDefined();
+    expect(response.request?.request_endpoint).toBe("/lemur/v3/generate/task");
+    expect(response.request?.temperature).toBe(0.7);
+    expect(response.request?.final_model).toBe("anthropic/claude-3-5-sonnet");
+    expect(response.request?.max_output_size).toBe(1500);
+    expect(response.request?.prompt).toBe(
+      "Analyze the key themes in this conversation",
+    );
+    expect(response.usage.input_tokens).toBe(250);
+    expect(response.usage.output_tokens).toBe(75);
+  });
+
+  it("should return response with question-answer request details", async () => {
+    const qaResponseWithDetails = {
+      request_id: knownLemurRequestId,
+      response: [
+        { question: "What was discussed?", answer: "Project updates" },
+      ],
+      usage: {
+        input_tokens: 300,
+        output_tokens: 100,
+      },
+      request: {
+        request_endpoint: "/lemur/v3/generate/question-answer",
+        temperature: 0.3,
+        final_model: "anthropic/claude-3-opus",
+        max_output_size: 2500,
+        created_at: "2024-01-01T14:15:00Z",
+        input_text: "Custom transcript content...",
+        questions: [
+          {
+            question: "What was discussed?",
+            answer_format: "concise summary",
+            context: "Meeting notes",
+          },
+          {
+            question: "Was the date of the next meeting called out?",
+            answer_options: ["Yes", "No", "Not mentioned"],
+          },
+        ],
+      },
+    };
+
+    fetchMock.doMockOnceIf(
+      requestMatches({
+        method: "GET",
+        url: `/lemur/v3/${knownLemurRequestId}`,
+      }),
+      JSON.stringify(qaResponseWithDetails),
+    );
+
+    const response = await assembly.lemur.getResponse(knownLemurRequestId);
+    expect(response.request?.request_endpoint).toBe(
+      "/lemur/v3/generate/question-answer",
+    );
+    expect(response.request?.input_text).toBe("Custom transcript content...");
+    expect(response.request?.questions).toHaveLength(2);
+    expect(response.request?.questions?.[0].question).toBe(
+      "What was discussed?",
+    );
+    expect(response.request?.questions?.[0].context).toBe("Meeting notes");
+    expect(response.request?.questions?.[1].answer_options).toEqual([
+      "Yes",
+      "No",
+      "Not mentioned",
+    ]);
+  });
+
+  it("should return response with context as object in request details", async () => {
+    const responseWithObjectContext = {
+      request_id: knownLemurRequestId,
+      response: "context-aware response",
+      usage: {
+        input_tokens: 180,
+        output_tokens: 60,
+      },
+      request: {
+        request_endpoint: "/lemur/v3/generate/summary",
+        temperature: 0.5,
+        final_model: "default",
+        max_output_size: 2000,
+        created_at: "2024-01-01T16:45:00Z",
+        transcript_ids: knownTranscriptIds,
+        context: {
+          meeting_type: "standup",
+          team: "engineering",
+          date: "2024-01-01",
+        },
+        answer_format: "bullet points",
+      },
+    };
+
+    fetchMock.doMockOnceIf(
+      requestMatches({
+        method: "GET",
+        url: `/lemur/v3/${knownLemurRequestId}`,
+      }),
+      JSON.stringify(responseWithObjectContext),
+    );
+
+    const response = await assembly.lemur.getResponse(knownLemurRequestId);
+    expect(response.request?.context).toEqual({
+      meeting_type: "standup",
+      team: "engineering",
+      date: "2024-01-01",
+    });
+  });
+
   it("should purge request data", async () => {
     fetchMock.doMockOnceIf(
       requestMatches({