add sample code

Author: tomfrenken

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';

packages/langchain/src/orchestration/chat.ts

@@ -18,17 +18,15 @@ import type { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager
 import type { OrchestrationCallOptions } from './types.js';
 import type { HttpDestinationOrFetchOptions } from '@sap-cloud-sdk/connectivity';
 
-// TODO: Update all docs
-
 /**
- * LangChain chat client for Azure OpenAI consumption on SAP BTP.
+ * The Orchestration client.
  */
 export class OrchestrationClient extends BaseChatModel<
   OrchestrationCallOptions,
   OrchestrationMessageChunk
 > {
   constructor(
-    // Omit streaming until supported
+    // TODO: Omit streaming until supported
     public orchestrationConfig: Omit<OrchestrationModuleConfig, 'streaming'>,
     public langchainOptions: BaseChatModelParams = {},
     public deploymentConfig?: ResourceGroupConfig,
@@ -45,8 +43,8 @@ export class OrchestrationClient extends BaseChatModel<
    * Decisions:
    * bind only supports ParsedCallOptions, we don't support arbitrary LLM options, only tool calls & default BaseLanguageModelCallOptions, e.g. stop ✅
    * this aligns with other vendors' client designs (e.g. openai, google) ✅
-   * top of the array (array[array.length - 1]) contains the current message, everything before then is history.
-   * Module results are part of our own message type, which extends AI Message to work with all other langchain functionality.
+   * inputParams are a seperate call option, history = history ✅
+   * Module results are part of our own message type, which extends AI Message to work with all other langchain functionality. ✅.
    *
    * For timeout, we need to apply our own middleware, it is not handled by langchain. ✅.
    */
@@ -71,7 +69,6 @@ export class OrchestrationClient extends BaseChatModel<
         signal: options.signal
       },
       () => {
-        // consider this.tools & this.stop property, merge it with template orchestration config
         const { inputParams } = options;
         const mergedOrchestrationConfig =
           this.mergeOrchestrationConfig(options);
@@ -88,8 +85,6 @@ export class OrchestrationClient extends BaseChatModel<
         };
         return orchestrationClient.chatCompletion(
           {
-            // how to handle tools here? doesn't really exist as input in orchestration as message history
-            // make template a call option, to merge it ??
             messagesHistory,
             inputParams
           },
@@ -100,7 +95,7 @@ export class OrchestrationClient extends BaseChatModel<
 
     const content = res.getContent();
 
-    // we currently do not support streaming
+    // TODO: Add streaming as soon as we support it
     await runManager?.handleLLMNewToken(
       typeof content === 'string' ? content : ''
     );

packages/langchain/src/orchestration/index.ts

@@ -0,0 +1,5 @@
+export * from './chat.js';
+export * from './orchestration-message.js';
+export * from './orchestration-message-chunk.js';
+export * from './types.js';
+export * from './util.js';

packages/langchain/src/orchestration/orchestration-message-chunk.ts

@@ -3,7 +3,8 @@ import type { AIMessageChunkFields } from '@langchain/core/messages';
 import type { ModuleResults } from '@sap-ai-sdk/orchestration';
 
 /**
- * TODO: Add docs.
+ * An AI Message Chunk containing module results and request ID.
+ * @internal
  */
 export class OrchestrationMessageChunk extends AIMessageChunk {
   module_results: ModuleResults;

packages/langchain/src/orchestration/orchestration-message.ts

@@ -3,7 +3,8 @@ import type { AIMessageFields } from '@langchain/core/messages';
 import type { ModuleResults } from '@sap-ai-sdk/orchestration';
 
 /**
- * TODO: Add docs.
+ * An AI Message containing module results and request ID.
+ * @internal
  */
 export class OrchestrationMessage extends AIMessage {
   module_results: ModuleResults;

packages/langchain/src/orchestration/types.ts

@@ -3,7 +3,7 @@ import type { BaseChatModelCallOptions } from '@langchain/core/language_models/c
 import type { CustomRequestConfig } from '@sap-ai-sdk/core';
 
 /**
- * TODO: Add docs.
+ * Options for an orchestration call.
  */
 export type OrchestrationCallOptions = Pick<
   BaseChatModelCallOptions,

packages/langchain/src/orchestration/util.ts

@@ -19,20 +19,19 @@ import type {
  * Checks if the object is a {@link Template}.
  * @param object - The object to check.
  * @returns True if the object is a {@link Template}.
+ * @internal
  */
 export function isTemplate(object: TemplatingModuleConfig): object is Template {
   return 'template' in object;
 }
 
 /**
- * Maps {@link BaseMessage} to {@link AzureOpenAiChatMessage}.
+ * Maps {@link BaseMessage} to {@link ChatMessage}.
  * @param message - The message to map.
- * @returns The {@link AzureOpenAiChatMessage}.
+ * @returns The {@link ChatMessage}.
  */
 // TODO: Add mapping of refusal property, once LangChain base class supports it natively.
-function mapBaseMessageToAzureOpenAiChatMessage(
-  message: BaseMessage
-): ChatMessage {
+function mapBaseMessageToChatMessage(message: BaseMessage): ChatMessage {
   switch (message.getType()) {
     case 'ai':
       return mapAiMessageToAzureOpenAiAssistantMessage(message);
@@ -97,15 +96,15 @@ function mapSystemMessageToAzureOpenAiSystemMessage(
 }
 
 /**
- * TODO: adjust
  * Maps LangChain messages to orchestration messages.
  * @param messages - The LangChain messages to map.
  * @returns The orchestration messages mapped from LangChain messages.
+ * @internal
  */
 export function mapLangchainMessagesToOrchestrationMessages(
   messages: BaseMessage[]
 ): ChatMessage[] {
-  return messages.map(mapBaseMessageToAzureOpenAiChatMessage);
+  return messages.map(mapBaseMessageToChatMessage);
 }
 
 /**

sample-code/src/langchain-orchestration.ts

@@ -0,0 +1,42 @@
+import { StringOutputParser } from '@langchain/core/output_parsers';
+import { OrchestrationClient } from '@sap-ai-sdk/langchain';
+import type { BaseLanguageModelInput } from '@langchain/core/language_models/base';
+import type { Runnable } from '@langchain/core/runnables';
+import type { OrchestrationCallOptions } from '@sap-ai-sdk/langchain';
+
+/**
+ * Ask GPT about the capital of France, as part of a chain.
+ * @returns The answer from ChatGPT.
+ */
+export async function invokeChain(): Promise<string> {
+  const orchestrationConfig = {
+    // define the language model to be used
+    llm: {
+      model_name: 'gpt-35-turbo',
+      model_params: {}
+    },
+    // define the template
+    templating: {
+      template: [
+        {
+          role: 'user',
+          content: 'Give me a long introduction of {{?input}}'
+        }
+      ]
+    }
+  };
+
+  const callOptions = { inputParams: { input: 'SAP Cloud SDK' } };
+
+  // initialize the client
+  const client = new OrchestrationClient(orchestrationConfig);
+
+  // create an output parser
+  const parser = new StringOutputParser();
+
+  // chain together template, client, and parser
+  const llmChain = client.pipe(parser) as Runnable<BaseLanguageModelInput, string, OrchestrationCallOptions>;
+
+  // invoke the chain
+  return llmChain.invoke('My Message History', callOptions);
+}

sample-code/src/server.ts

@@ -42,6 +42,9 @@ import {
   invoke,
   invokeToolChain
 } from './langchain-azure-openai.js';
+import {
+  invokeChain as invokeChainOrchestration
+} from './langchain-orchestration.js';
 import {
   createCollection,
   createDocumentsWithTimestamp,
@@ -416,6 +419,17 @@ app.get('/langchain/invoke-chain', async (req, res) => {
   }
 });
 
+app.get('/langchain/invoke-chain-orchestration', async (req, res) => {
+  try {
+    res.send(await invokeChainOrchestration());
+  } catch (error: any) {
+    console.error(error);
+    res
+      .status(500)
+      .send('Yikes, vibes are off apparently 😬 -> ' + error.request.data);
+  }
+});
+
 app.get('/langchain/invoke-rag-chain', async (req, res) => {
   try {
     res.send(await invokeRagChain());