feat: Use literal string for orchestration models

packages/core/src/index.ts

@@ -7,3 +7,4 @@ export type {
 export { executeRequest } from './http-client.js';
 export { getAiCoreDestination } from './context.js';
 export { OpenApiRequestBuilder } from './openapi-request-builder.js';
+export type * from './model-types.js';

packages/core/src/internal.ts

@@ -1,3 +1,4 @@
 export * from './context.js';
 export * from './http-client.js';
 export * from './openapi-request-builder.js';
+export * from './model-types.js';

packages/core/src/model-types.ts

@@ -0,0 +1,52 @@
+type LiteralUnion<T extends U, U = string> = T | (U & Record<never, never>);
+
+/**
+ * Azure OpenAI models for chat completion.
+ */
+export type AzureOpenAiChatModel = LiteralUnion<
+  | 'gpt-4o-mini'
+  | 'gpt-4o'
+  | 'gpt-4'
+  | 'gpt-4-32k'
+  | 'gpt-35-turbo'
+  | 'gpt-35-turbo-0125'
+  | 'gpt-35-turbo-16k'
+>;
+
+/**
+ * Azure OpenAI models for embedding.
+ */
+export type AzureOpenAiEmbeddingModel = LiteralUnion<
+  'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large'
+>;
+
+/**
+ * GCP Vertex AI models for chat completion.
+ */
+export type GcpVertexAiChatModel = LiteralUnion<
+  'gemini-1.0-pro' | 'gemini-1.5-pro' | 'gemini-1.5-flash' | 'chat-bison'
+>;
+
+/**
+ * AWS Bedrock models for chat completion.
+ */
+export type AwsBedrockChatModel = LiteralUnion<
+  | 'amazon--titan-text-express'
+  | 'amazon--titan-text-lite'
+  | 'anthropic--claude-3-haiku'
+  | 'anthropic--claude-3-opus'
+  | 'anthropic--claude-3-sonnet'
+  | 'anthropic--claude-3.5-sonnet'
+>;
+
+/**
+ * All available models for chat completion.
+ */
+export type ChatModel = LiteralUnion<
+  AzureOpenAiChatModel | GcpVertexAiChatModel | AwsBedrockChatModel
+>;
+
+/**
+ * All available models for embeddings.
+ */
+export type EmbeddingModel = LiteralUnion<AzureOpenAiEmbeddingModel>;

packages/foundation-models/src/index.ts

@@ -1,6 +1,4 @@
 export type {
-  OpenAiChatModel,
-  OpenAiEmbeddingModel,
   OpenAiChatMessage,
   OpenAiChatSystemMessage,
   OpenAiChatUserMessage,

packages/foundation-models/src/openai/openai-chat-client.ts

@@ -1,12 +1,13 @@
-import { type CustomRequestConfig, executeRequest } from '@sap-ai-sdk/core';
+import {
+  type CustomRequestConfig,
+  type AzureOpenAiChatModel,
+  executeRequest
+} from '@sap-ai-sdk/core';
 import {
   getDeploymentId,
   type ModelDeployment
 } from '@sap-ai-sdk/ai-api/internal.js';
-import type {
-  OpenAiChatCompletionParameters,
-  OpenAiChatModel
-} from './openai-types.js';
+import type { OpenAiChatCompletionParameters } from './openai-types.js';
 import { OpenAiChatCompletionResponse } from './openai-response.js';
 
 const apiVersion = '2024-02-01';
@@ -19,7 +20,7 @@ export class OpenAiChatClient {
    * Creates an instance of the OpenAI chat client.
    * @param modelDeployment - This configuration is used to retrieve a deployment. Depending on the configuration use either the given deployment ID or the model name to retrieve matching deployments. If model and deployment ID are given, the model is verified against the deployment.
    */
-  constructor(private modelDeployment: ModelDeployment<OpenAiChatModel>) {}
+  constructor(private modelDeployment: ModelDeployment<AzureOpenAiChatModel>) {}
 
   /**
    * Creates a completion for the chat messages.

packages/foundation-models/src/openai/openai-embedding-client.ts

@@ -1,12 +1,15 @@
-import { type CustomRequestConfig, executeRequest } from '@sap-ai-sdk/core';
+import {
+  type CustomRequestConfig,
+  type AzureOpenAiEmbeddingModel,
+  executeRequest
+} from '@sap-ai-sdk/core';
 import {
   getDeploymentId,
   type ModelDeployment
 } from '@sap-ai-sdk/ai-api/internal.js';
 import type {
   OpenAiEmbeddingParameters,
-  OpenAiEmbeddingOutput,
-  OpenAiEmbeddingModel
+  OpenAiEmbeddingOutput
 } from './openai-types.js';
 
 const apiVersion = '2024-02-01';
@@ -19,7 +22,9 @@ export class OpenAiEmbeddingClient {
    * Creates an instance of the OpenAI embedding client.
    * @param modelDeployment - This configuration is used to retrieve a deployment. Depending on the configuration use either the given deployment ID or the model name to retrieve matching deployments. If model and deployment ID are given, the model is verified against the deployment.
    */
-  constructor(private modelDeployment: ModelDeployment<OpenAiEmbeddingModel>) {}
+  constructor(
+    private modelDeployment: ModelDeployment<AzureOpenAiEmbeddingModel>
+  ) {}
 
   /**
    * Creates an embedding vector representing the given text.

packages/foundation-models/src/openai/openai-types.ts

@@ -1,22 +1,3 @@
-/**
- * Available OpenAI models for chat completion.
- */
-export type OpenAiChatModel =
-  | 'gpt-4o'
-  | 'gpt-4'
-  | 'gpt-4-32k'
-  | 'gpt-35-turbo'
-  | 'gpt-35-turbo-0125'
-  | 'gpt-35-turbo-16k';
-
-/**
- * OpenAI embedding models.
- */
-export type OpenAiEmbeddingModel =
-  | 'text-embedding-ada-002'
-  | 'text-embedding-3-small'
-  | 'text-embedding-3-large';
-
 /**
  * OpenAI system message.
  */

packages/orchestration/src/orchestration-types.ts

@@ -1,3 +1,4 @@
+import { ChatModel } from '@sap-ai-sdk/core';
 import {
   ChatMessages,
   FilteringModuleConfig,
@@ -20,11 +21,13 @@ export interface Prompt {
   inputParams?: Record<string, string>;
 }
 
-// TODO: why do we have this extra type? and if there is a reason, why does it not apply to the filtering module?
 /**
  * LLMModule configuration.
  */
-export type LlmConfig = LLMModuleConfig;
+export type LlmConfig = LLMModuleConfig & {
+  /** */
+  model_name: ChatModel;
+};
 
 /**
  * Orchestration module configuration.

tests/type-tests/test/model.test-d.ts

@@ -0,0 +1,10 @@
+import {
+  ChatModel,
+  AzureOpenAiChatModel,
+  EmbeddingModel
+} from '@sap-ai-sdk/core';
+
+expect<ChatModel>('custom-model');
+expect<AzureOpenAiChatModel>('custom-model');
+expect<ChatModel>('gpt-4-32k');
+expect<EmbeddingModel>('custom-model');