update readmes and types

Author: tomfrenken

.changeset/clear-suns-sleep.md

@@ -1,10 +1,5 @@
 ---
-'@sap-ai-sdk/orchestration': minor
 '@sap-ai-sdk/langchain': minor
-'@sap-ai-sdk/smoke-tests': minor
-'@sap-ai-sdk/e2e-tests': minor
-'@sap-ai-sdk/sample-code': minor
-'@sap-ai-sdk/sample-cap': minor
 ---
 
 [New Functionality] Add LangChain Orchestration client.

packages/langchain/README.md

@@ -10,9 +10,8 @@ This package provides LangChain model clients built on top of the foundation mod
 - [Prerequisites](#prerequisites)
 - [Relationship between Models and Deployment ID](#relationship-between-models-and-deployment-id)
 - [Usage](#usage)
-  - [Client Initialization](#client-initialization)
-  - [Chat Client](#chat-client)
-  - [Embedding Client](#embedding-client)
+    - [SAP Orchestration Service](#sap-orchestration-service)
+    - [Azure OpenAI](#azure-openai)
 - [Local Testing](#local-testing)
 - [Support, Feedback, Contribution](#support-feedback-contribution)
 - [License](#license)
@@ -55,12 +54,17 @@ Consequently, each deployment ID and resource group uniquely map to a combinatio
 
 ## Usage
 
-This package offers both chat and embedding clients, currently supporting Azure OpenAI.
-Also supports the SAP Orchestration service.
+This package offers LangChain clients for Azure OpenAI as well as the SAP orchestration service
 All clients comply with [LangChain's interface](https://js.langchain.com/docs/introduction).
 
 ### SAP Orchestration Service
 
+For details on the orchestration client, refer to this [document](./src/orchestration/README.md).
+
+### Azure OpenAI
+
+For details on the Azure OpenAI clients, refer to this [document](./src/openai/README.md).
+
 ## Local Testing
 
 For local testing instructions, refer to this [section](https://github.com/SAP/ai-sdk-js/blob/main/README.md#local-testing).

packages/langchain/src/README.md

@@ -55,7 +55,7 @@ Consequently, each deployment ID and resource group uniquely map to a combinatio
 
 ## Usage
 
-This package offers both chat and embedding clients, currently supporting Azure OpenAI.
+This package offers both chat and embedding clients for Azure OpenAI.
 All clients comply with [LangChain's interface](https://js.langchain.com/docs/introduction).
 
 ### Client Initialization
@@ -74,7 +74,7 @@ const chatClient = new AzureOpenAiChatClient({ modelName: 'gpt-4o' });
 const embeddingClient = new AzureOpenAiEmbeddingClient({ modelName: 'gpt-4o' });
 ```
 
-In addition to the default parameters of the model vendor (e.g., OpenAI) and LangChain, additional parameters can be used to help narrow down the search for the desired model:
+In addition to the default parameters of Azure OpenAI and LangChain, additional parameters can be used to help narrow down the search for the desired model:
 
 ```ts
 const chatClient = new AzureOpenAiChatClient({

packages/langchain/src/openai/README.md

@@ -155,54 +155,6 @@ return llmChain.invoke({
 });
 ```
 
-### Embedding Client
-
-Embedding clients allow embedding either text or document chunks (represented as arrays of strings).
-While you can use them standalone, they are usually used in combination with other LangChain utilities, like a text splitter for preprocessing and a vector store for storage and retrieval of the relevant embeddings.
-For a complete example how to implement RAG with our LangChain client, take a look at our [sample code](https://github.com/SAP/ai-sdk-js/blob/main/sample-code/src/langchain-azure-openai.ts).
-
-#### Embed Text
-
-```ts
-const embeddedText = await embeddingClient.embedQuery(
-  'Paris is the capital of France.'
-);
-```
-
-#### Embed Document Chunks
-
-```ts
-const embeddedDocuments = await embeddingClient.embedDocuments([
-  'Page 1: Paris is the capital of France.',
-  'Page 2: It is a beautiful city.'
-]);
-```
-
-#### Preprocess, embed, and store documents
-
-```ts
-// Create a text splitter and split the document
-const textSplitter = new RecursiveCharacterTextSplitter({
-  chunkSize: 2000,
-  chunkOverlap: 200
-});
-const splits = await textSplitter.splitDocuments(docs);
-
-// Initialize the embedding client
-const embeddingClient = new AzureOpenAiEmbeddingClient({
-  modelName: 'text-embedding-ada-002'
-});
-
-// Create a vector store from the document
-const vectorStore = await MemoryVectorStore.fromDocuments(
-  splits,
-  embeddingClient
-);
-
-// Create a retriever for the vector store
-const retriever = vectorStore.asRetriever();
-```
-
 ## Local Testing
 
 For local testing instructions, refer to this [section](https://github.com/SAP/ai-sdk-js/blob/main/README.md#local-testing).

packages/langchain/src/orchestration/README.md

@@ -7,7 +7,7 @@ import type { CustomRequestConfig } from '@sap-ai-sdk/core';
  */
 export type OrchestrationCallOptions = Pick<
   BaseChatModelCallOptions,
-  'stop' | 'signal' | 'timeout'
+  'stop' | 'signal' | 'timeout' | 'callbacks' | 'metadata' | 'runId' | 'runName' | 'tags'
 > & {
   customRequestConfig?: CustomRequestConfig;
   tools?: Template['tools'];

packages/langchain/src/orchestration/types.ts

@@ -204,13 +204,13 @@ The `toContentStream()` method is called to extract the content of the chunk for
 
 Once the streaming is done, finish reason and token usage are printed out.
 
-### Langchain
+### LangChain
 
 #### Invoke with a Simple Input
 
 `GET /langchain/invoke`
 
-Invoke langchain Azure OpenAI client with a simple input to get chat completion response.
+Invoke LangChain Azure OpenAI client with a simple input to get chat completion response.
 
 #### Invoke a Chain for Templating