change visibility, naming, docs

SAP · Sep 16, 2024 · e64243e · e64243e
1 parent 21907d1
commit e64243e
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Showing 7 changed files with 17 additions and 17 deletions.
diff --git a/packages/ai-api/src/index.ts b/packages/ai-api/src/index.ts
@@ -5,5 +5,5 @@ export type {
   DeploymentIdConfiguration,
   ModelConfiguration,
   ResourceGroupConfiguration,
-  ConfigurationOptions
+  ModelDeploymentConfig
 } from './utils/index.js';
diff --git a/packages/ai-api/src/utils/deployment-resolver.ts b/packages/ai-api/src/utils/deployment-resolver.ts
@@ -46,13 +46,13 @@ export interface ResourceGroupConfiguration {
  */
 export type ModelDeployment<ModelNameT = string> =
   | ModelNameT
-  | ConfigurationOptions<ModelNameT>;
+  | ModelDeploymentConfig<ModelNameT>;
 
 /**
  * The configuration options for a model deployment.
  * @typeParam ModelNameT - String literal type representing the name of the model.
  */
-export type ConfigurationOptions<ModelNameT> = Xor<
+export type ModelDeploymentConfig<ModelNameT> = Xor<
   ModelConfiguration<ModelNameT>,
   DeploymentIdConfiguration
 > &

diff --git a/packages/langchain/src/openai/embedding.ts b/packages/langchain/src/openai/embedding.ts
@@ -29,17 +29,17 @@ export class AzureOpenAiEmbeddingClient extends AzureOpenAIEmbeddings {
     );
     const embeddings: number[][] = [];
     for await (const promptChunk of chunkedPrompts) {
-      const resArr = await this.createEmbedding({ input: promptChunk });
-      resArr.data.forEach(res => embeddings.push(res.embedding));
+      const embeddingResponse = await this.createEmbedding({ input: promptChunk });
+      embeddingResponse.data.forEach(entry => embeddings.push(entry.embedding));
     }
     return embeddings;
   }
 
   override async embedQuery(query: string): Promise<number[]> {
-    const resArr = await this.createEmbedding({
+    const embeddingResponse = await this.createEmbedding({
       input: this.stripNewLines ? query.replace(/\n/g, ' ') : query
     });
-    return resArr.data[0].embedding;
+    return embeddingResponse.data[0].embedding;
   }
 
   private async createEmbedding(

diff --git a/packages/langchain/src/openai/types.ts b/packages/langchain/src/openai/types.ts
@@ -10,7 +10,7 @@ import type {
   AzureOpenAiChatModel,
   AzureOpenAiEmbeddingModel
 } from '@sap-ai-sdk/core';
-import type { ConfigurationOptions } from '@sap-ai-sdk/ai-api';
+import type { ModelDeploymentConfig } from '@sap-ai-sdk/ai-api';
 
 /**
  * Input type for OpenAI Chat models.
@@ -33,7 +33,7 @@ export type OpenAiChatModelInput = Omit<
 > &
   Omit<OpenAiChatCompletionParameters, 'messages'> &
   BaseChatModelParams &
-  ConfigurationOptions<AzureOpenAiChatModel>;
+  ModelDeploymentConfig<AzureOpenAiChatModel>;
 
 /**
  * Chat model call options for OpenAI.
@@ -60,5 +60,5 @@ export type OpenAiEmbeddingInput = Omit<
   OpenAIEmbeddingsParams,
   'modelName' | 'model' | 'azureOpenAIApiKey' | 'apiKey'
 > &
-  ConfigurationOptions<AzureOpenAiEmbeddingModel> &
+  ModelDeploymentConfig<AzureOpenAiEmbeddingModel> &
   BaseLLMParams;
diff --git a/packages/langchain/src/openai/util.test.ts b/packages/langchain/src/openai/util.test.ts
@@ -1,6 +1,3 @@
-// mapResponseToChatResult
-// mapLangchainToAiClient
-
 import {
   OpenAiChatClient as OpenAiChatClientBase,
   OpenAiChatCompletionOutput

diff --git a/packages/langchain/src/openai/util.ts b/packages/langchain/src/openai/util.ts
@@ -143,6 +143,7 @@ export function mapBaseMessageToOpenAiChatMessage(
  * Converts a value to an array or returns undefined.
  * @param value - The value to convert.
  * @returns The value as an array, undefined if the input is falsy, or the original array if input is already an array.
+ * @internal
  */
 export function toArrayOrUndefined<T>(value?: T | T[]): T[] | undefined {
   if (value === undefined) {

diff --git a/sample-code/src/server.ts b/sample-code/src/server.ts
@@ -30,9 +30,10 @@ app.get('/embedding', async (req, res) => {
   try {
     const result = await computeEmbedding();
     if (!result.length) {
-      throw new Error('No embedding vector returned');
+      res.status(500).send('No embedding vector returned.');
+    } else {
+      res.send('Number crunching success, got a nice vector.');
     }
-    res.send('Number crunching success, got a nice vector.');
   } catch (error: any) {
     console.error(error);
     res
@@ -78,9 +79,10 @@ app.get('/langchain/embedding', async (req, res) => {
   try {
     const result = await embedQuery();
     if (!result.length) {
-      throw new Error('No embedding vector returned');
+      res.status(500).send('No embedding vector returned.');
+    } else {
+      res.send('Number crunching success, got a nice vector.');
     }
-    res.send('Number crunching success, got a nice vector.');
   } catch (error: any) {
     console.error(error);
     res