fix: batch embedding queries, fix get client in tests (langchain-ai#2908

)

* fix: batch embedding queries, fix get client in tests

* fix: more efficient .push instead of .concat, fix jsdoc

* nit: console log error inside catch

* chore: lint files

* fix: rename, add @deprecated jsdoc to _embedText

* fix: jsdoc

* chore: lint files

* fix: scrap new method, instead refactor existing _embedText method

* chore: lint files

* fix: remove batching (caller does that) & update jsdoc comment

langchain/src/embeddings/bedrock.ts

@@ -2,7 +2,6 @@ import {
   BedrockRuntimeClient,
   InvokeModelCommand,
 } from "@aws-sdk/client-bedrock-runtime";
-
 import { Embeddings, EmbeddingsParams } from "./base.js";
 import type { CredentialType } from "../util/bedrock.js";
 
@@ -40,6 +39,8 @@ export class BedrockEmbeddings
 
   client: BedrockRuntimeClient;
 
+  batchSize = 512;
+
   constructor(fields?: BedrockEmbeddingsParams) {
     super(fields ?? {});
 
@@ -53,28 +54,48 @@ export class BedrockEmbeddings
       });
   }
 
+  /**
+   * Protected method to make a request to the Bedrock API to generate
+   * embeddings. Handles the retry logic and returns the response from the
+   * API.
+   * @param request Request to send to the Bedrock API.
+   * @returns Promise that resolves to the response from the API.
+   */
   protected async _embedText(text: string): Promise<number[]> {
-    // replace newlines, which can negatively affect performance.
-    const cleanedText = text.replace(/\n/g, " ");
-
-    const res = await this.client.send(
-      new InvokeModelCommand({
-        modelId: this.model,
-        body: JSON.stringify({
-          inputText: cleanedText,
-        }),
-        contentType: "application/json",
-        accept: "application/json",
-      })
-    );
-
-    try {
-      const body = new TextDecoder().decode(res.body);
-
-      return JSON.parse(body).embedding;
-    } catch (e) {
-      throw new Error("An invalid response was returned by Bedrock.");
-    }
+    return this.caller.call(async () => {
+      try {
+        // replace newlines, which can negatively affect performance.
+        const cleanedText = text.replace(/\n/g, " ");
+
+        const res = await this.client.send(
+          new InvokeModelCommand({
+            modelId: this.model,
+            body: JSON.stringify({
+              inputText: cleanedText,
+            }),
+            contentType: "application/json",
+            accept: "application/json",
+          })
+        );
+
+        const body = new TextDecoder().decode(res.body);
+        return JSON.parse(body).embedding;
+      } catch (e) {
+        console.error({
+          error: e,
+        });
+        // eslint-disable-next-line no-instanceof/no-instanceof
+        if (e instanceof Error) {
+          throw new Error(
+            `An error occurred while embedding documents with Bedrock: ${e.message}`
+          );
+        }
+
+        throw new Error(
+          "An error occurred while embedding documents with Bedrock"
+        );
+      }
+    });
   }
 
   /**
@@ -93,13 +114,12 @@ export class BedrockEmbeddings
   }
 
   /**
-   * Method that takes an array of documents as input and returns a promise
-   * that resolves to a 2D array of embeddings for each document. It calls
-   * the _embedText method for each document in the array.
-   * @param documents Array of documents for which to generate embeddings.
+   * Method to generate embeddings for an array of texts. Calls _embedText
+   * method which batches and handles retry logic when calling the AWS Bedrock API.
+   * @param documents Array of texts for which to generate embeddings.
    * @returns Promise that resolves to a 2D array of embeddings for each input document.
    */
-  embedDocuments(documents: string[]): Promise<number[][]> {
+  async embedDocuments(documents: string[]): Promise<number[][]> {
     return Promise.all(documents.map((document) => this._embedText(document)));
   }
 }

langchain/src/embeddings/tests/bedrock.int.test.ts

@@ -6,32 +6,42 @@ import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime";
 import { HNSWLib } from "../../vectorstores/hnswlib.js";
 import { BedrockEmbeddings } from "../bedrock.js";
 
-const client = new BedrockRuntimeClient({
-  region: process.env.BEDROCK_AWS_REGION!,
-  credentials: {
-    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
-    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
-  },
-});
+const getClient = () => {
+  if (
+    !process.env.BEDROCK_AWS_REGION ||
+    !process.env.BEDROCK_AWS_ACCESS_KEY_ID ||
+    !process.env.BEDROCK_AWS_SECRET_ACCESS_KEY
+  ) {
+    throw new Error("Missing environment variables for AWS");
+  }
+
+  const client = new BedrockRuntimeClient({
+    region: process.env.BEDROCK_AWS_REGION,
+    credentials: {
+      accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
+      secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
+    },
+  });
+
+  return client;
+};
 
 test("Test BedrockEmbeddings.embedQuery", async () => {
+  const client = getClient();
   const embeddings = new BedrockEmbeddings({
     maxRetries: 1,
     client,
   });
   const res = await embeddings.embedQuery("Hello world");
-  console.log(res);
+  // console.log(res);
   expect(typeof res[0]).toBe("number");
 });
 
 test("Test BedrockEmbeddings.embedDocuments with passed region and credentials", async () => {
+  const client = getClient();
   const embeddings = new BedrockEmbeddings({
     maxRetries: 1,
-    region: process.env.BEDROCK_AWS_REGION!,
-    credentials: {
-      accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
-      secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
-    },
+    client,
   });
   const res = await embeddings.embedDocuments([
     "Hello world",
@@ -41,14 +51,15 @@ test("Test BedrockEmbeddings.embedDocuments with passed region and credentials",
     "six documents",
     "to test pagination",
   ]);
-  console.log(res);
+  // console.log(res);
   expect(res).toHaveLength(6);
   res.forEach((r) => {
     expect(typeof r[0]).toBe("number");
   });
 });
 
 test("Test end to end with HNSWLib", async () => {
+  const client = getClient();
   const vectorStore = await HNSWLib.fromTexts(
     ["Hello world", "Bye bye", "hello nice world"],
     [{ id: 2 }, { id: 1 }, { id: 3 }],