SAP · deekshas8 · Mar 3, 2025 · Feb 21, 2025 · Feb 21, 2025 · Feb 21, 2025
diff --git a/.changeset/clear-suns-sleep.md b/.changeset/clear-suns-sleep.md
@@ -0,0 +1,5 @@
+---
+'@sap-ai-sdk/langchain': minor
+---
+
+[New Functionality] Add LangChain Orchestration client.
diff --git a/packages/langchain/README.md b/packages/langchain/README.md
@@ -2,17 +2,15 @@
 
 SAP Cloud SDK for AI is the official Software Development Kit (SDK) for **SAP AI Core**, **SAP Generative AI Hub**, and **Orchestration Service**.
 
-This package provides LangChain model clients built on top of the foundation model clients of the SAP Cloud SDK for AI.
+This package provides LangChain clients built on top of the foundation model and orchestration clients of the SAP Cloud SDK for AI.
 
 ### Table of Contents
 
 - [Installation](#installation)
 - [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)
+  - [Orchestration Client](#orchestration-client)
+  - [Azure OpenAI Client](#azure-openai-client)
 - [Local Testing](#local-testing)
 - [Support, Feedback, Contribution](#support-feedback-contribution)
 - [License](#license)
@@ -28,10 +26,11 @@ $ npm install @sap-ai-sdk/langchain
 - [Enable the AI Core service in SAP BTP](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/initial-setup).
 - Use the same `@langchain/core` version as the `@sap-ai-sdk/langchain` package, to see which langchain version this package is currently using, check our [package.json](./package.json).
 - Configure the project with **Node.js v20 or higher** and **native ESM** support.
-- Ensure a deployed OpenAI model is available in the SAP Generative AI Hub.
-  - Use the [`DeploymentApi`](https://github.com/SAP/ai-sdk-js/blob/main/packages/ai-api/README.md#create-a-deployment) from `@sap-ai-sdk/ai-api` [to deploy a model](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/create-deployment-for-generative-ai-model-in-sap-ai-core).
-    Alternatively, you can also create deployments using the [SAP AI Launchpad](https://help.sap.com/docs/sap-ai-core/generative-ai-hub/activate-generative-ai-hub-for-sap-ai-launchpad?locale=en-US&q=launchpad).
-  - Once deployment is complete, access the model via the `deploymentUrl`.
+- Ensure that a relevant deployment is available in the SAP Generative AI Hub:
+  - Use the [`DeploymentApi`](https://github.com/SAP/ai-sdk-js/blob/main/packages/ai-api/README.md#create-a-deployment) from `@sap-ai-sdk/ai-api` or the [SAP AI Launchpad](https://help.sap.com/docs/sap-ai-core/generative-ai-hub/activate-generative-ai-hub-for-sap-ai-launchpad?locale=en-US&q=launchpad) to create a deployment.
+    - For **OpenAI model**, follow [this guide](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/create-deployment-for-generative-ai-model-in-sap-ai-core).
+    - For **orchestration service**, follow [this guide](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/create-deployment-for-orchestration).
+  - Once deployed, access the service via the `deploymentUrl`.
 
 > **Accessing the AI Core Service via the SDK**
 >
@@ -40,168 +39,18 @@ $ npm install @sap-ai-sdk/langchain
 > - In Cloud Foundry, it's accessed from the `VCAP_SERVICES` environment variable.
 > - In Kubernetes / Kyma environments, you have to mount the service binding as a secret instead, for more information refer to [this documentation](https://www.npmjs.com/package/@sap/xsenv#usage-in-kubernetes).
 
-## Relationship between Models and Deployment ID
-
-SAP AI Core manages access to generative AI models through the global AI scenario `foundation-models`.
-Creating a deployment for a model requires access to this scenario.
-
-Each model, model version, and resource group allows for a one-time deployment.
-After deployment completion, the response includes a `deploymentUrl` and an `id`, which is the deployment ID.
-For more information, see [here](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/create-deployment-for-generative-ai-model-in-sap-ai-core).
-
-[Resource groups](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/resource-groups?q=resource+group) represent a virtual collection of related resources within the scope of one SAP AI Core tenant.
-
-Consequently, each deployment ID and resource group uniquely map to a combination of model and model version within the `foundation-models` scenario.
-
 ## Usage
 
-This package offers both chat and embedding clients, currently supporting Azure OpenAI.
+This package offers LangChain clients for Azure OpenAI and SAP Orchestration service.
 All clients comply with [LangChain's interface](https://js.langchain.com/docs/introduction).
 
-### Client Initialization
-
-To initialize a client, provide the model name:
-
-```ts
-import {
-  AzureOpenAiChatClient,
-  AzureOpenAiEmbeddingClient
-} from '@sap-ai-sdk/langchain';
-
-// For a chat client
-const chatClient = new AzureOpenAiChatClient({ modelName: 'gpt-4o' });
-// For an embedding client
-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:
-
-```ts
-const chatClient = new AzureOpenAiChatClient({
-  modelName: 'gpt-4o',
-  modelVersion: '24-07-2021',
-  resourceGroup: 'my-resource-group'
-});
-```
-
-**Do not pass a `deployment ID` to initialize the client.**
-For the LangChain model clients, initialization is done using the model name, model version and resource group.
-
-An important note is that LangChain clients by default attempt 6 retries with exponential backoff in case of a failure.
-Especially in testing environments you might want to reduce this number to speed up the process:
-
-```ts
-const embeddingClient = new AzureOpenAiEmbeddingClient({
-  modelName: 'gpt-4o',
-  maxRetries: 0
-});
-```
-
-#### Custom Destination
-
-When initializing the `AzureOpenAiChatClient` and `AzureOpenAiEmbeddingClient` clients, it is possible to provide a custom destination.
-For example, when targeting a destination with the name `my-destination`, the following code can be used:
-
-```ts
-const chatClient = new AzureOpenAiChatClient(
-  {
-    modelName: 'gpt-4o',
-    modelVersion: '24-07-2021',
-    resourceGroup: 'my-resource-group'
-  },
-  {
-    destinationName: 'my-destination'
-  }
-);
-```
-
-By default, the fetched destination is cached.
-To disable caching, set the `useCache` parameter to `false` together with the `destinationName` parameter.
+### Orchestration Client
 
-### Chat Client
+For more information about the Orchestration client, refer to the [documentation](https://github.com/SAP/ai-sdk-js/tree/main/packages/langchain/src/orchestration/README.md).
 
-The chat client allows you to interact with Azure OpenAI chat models, accessible via the generative AI hub of SAP AI Core.
-To invoke the client, pass a prompt:
+### Azure OpenAI Client
 
-```ts
-const response = await chatClient.invoke("What's the capital of France?");
-```
-
-#### Advanced Example with Templating and Output Parsing
-
-```ts
-import { AzureOpenAiChatClient } from '@sap-ai-sdk/langchain';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { ChatPromptTemplate } from '@langchain/core/prompts';
-
-// initialize the client
-const client = new AzureOpenAiChatClient({ modelName: 'gpt-35-turbo' });
-
-// create a prompt template
-const promptTemplate = ChatPromptTemplate.fromMessages([
-  ['system', 'Answer the following in {language}:'],
-  ['user', '{text}']
-]);
-// create an output parser
-const parser = new StringOutputParser();
-
-// chain together template, client, and parser
-const llmChain = promptTemplate.pipe(client).pipe(parser);
-
-// invoke the chain
-return llmChain.invoke({
-  language: 'german',
-  text: 'What is the capital of France?'
-});
-```
-
-### 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();
-```
+For more information about Azure OpenAI client, refer to the [documentation](https://github.com/SAP/ai-sdk-js/tree/main/packages/langchain/src/openai/README.md).
 
 ## Local Testing
 

diff --git a/packages/langchain/package.json b/packages/langchain/package.json
@@ -29,6 +29,7 @@
     "@sap-ai-sdk/ai-api": "workspace:^",
     "@sap-ai-sdk/core": "workspace:^",
     "@sap-ai-sdk/foundation-models": "workspace:^",
+    "@sap-ai-sdk/orchestration": "workspace:^",
     "@sap-cloud-sdk/connectivity": "^3.26.1",
     "uuid": "^11.1.0",
     "@langchain/core": "0.3.40",

diff --git a/packages/langchain/src/index.ts b/packages/langchain/src/index.ts
@@ -7,3 +7,5 @@ export type {
   AzureOpenAiEmbeddingModelParams,
   AzureOpenAiChatCallOptions
 } from './openai/index.js';
+export { OrchestrationClient } from './orchestration/index.js';
+export type { OrchestrationCallOptions } from './orchestration/index.js';