community[major]: Together AI embeddings

libs/langchain-community/.gitignore

@@ -109,6 +109,9 @@ embeddings/ollama.d.ts
 embeddings/tensorflow.cjs
 embeddings/tensorflow.js
 embeddings/tensorflow.d.ts
+embeddings/togetherai.cjs
+embeddings/togetherai.js
+embeddings/togetherai.d.ts
 embeddings/voyage.cjs
 embeddings/voyage.js
 embeddings/voyage.d.ts

libs/langchain-community/package.json

@@ -656,6 +656,11 @@
       "import": "./embeddings/tensorflow.js",
       "require": "./embeddings/tensorflow.cjs"
     },
+    "./embeddings/togetherai": {
+      "types": "./embeddings/togetherai.d.ts",
+      "import": "./embeddings/togetherai.js",
+      "require": "./embeddings/togetherai.cjs"
+    },
     "./embeddings/voyage": {
       "types": "./embeddings/voyage.d.ts",
       "import": "./embeddings/voyage.js",
@@ -1346,6 +1351,9 @@
     "embeddings/tensorflow.cjs",
     "embeddings/tensorflow.js",
     "embeddings/tensorflow.d.ts",
+    "embeddings/togetherai.cjs",
+    "embeddings/togetherai.js",
+    "embeddings/togetherai.d.ts",
     "embeddings/voyage.cjs",
     "embeddings/voyage.js",
     "embeddings/voyage.d.ts",

libs/langchain-community/scripts/create-entrypoints.js

@@ -48,6 +48,7 @@ const entrypoints = {
   "embeddings/minimax": "embeddings/minimax",
   "embeddings/ollama": "embeddings/ollama",
   "embeddings/tensorflow": "embeddings/tensorflow",
+  "embeddings/togetherai": "embeddings/togetherai",
   "embeddings/voyage": "embeddings/voyage",
   // llms
   "llms/ai21": "llms/ai21",

libs/langchain-community/src/embeddings/tests/togetherai.int.test.ts

@@ -0,0 +1,19 @@
+import { test, expect } from "@jest/globals";
+import { TogetherAIEmbeddings } from "../togetherai.js";
+
+test("Test TogetherAIEmbeddings.embedQuery", async () => {
+  const embeddings = new TogetherAIEmbeddings();
+  const res = await embeddings.embedQuery("Hello world");
+  expect(typeof res[0]).toBe("number");
+  expect(res.length).toBe(768);
+});
+
+test("Test TogetherAIEmbeddings.embedDocuments", async () => {
+  const embeddings = new TogetherAIEmbeddings();
+  const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
+  expect(res).toHaveLength(2);
+  expect(typeof res[0][0]).toBe("number");
+  expect(typeof res[1][0]).toBe("number");
+  expect(res[0].length).toBe(768);
+  expect(res[1].length).toBe(768);
+});

libs/langchain-community/src/embeddings/togetherai.ts

@@ -0,0 +1,188 @@
+import { getEnvironmentVariable } from "@langchain/core/utils/env";
+import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
+import { chunkArray } from "../utils/chunk.js";
+
+/**
+ * Interface for TogetherAIEmbeddingsParams parameters. Extends EmbeddingsParams and
+ * defines additional parameters specific to the TogetherAIEmbeddings class.
+ */
+export interface TogetherAIEmbeddingsParams extends EmbeddingsParams {
+  /**
+   * The API key to use for the TogetherAI API.
+   * @default {process.env.TOGETHER_AI_API_KEY}
+   */
+  apiKey?: string;
+
+  /**
+   * Model name to use
+   * @default {"togethercomputer/m2-bert-80M-8k-retrieval"}
+   */
+  modelName?: string;
+
+  /**
+   * Timeout to use when making requests to TogetherAI.
+   * @default {undefined}
+   */
+  timeout?: number;
+
+  /**
+   * The maximum number of documents to embed in a single request.
+   * @default {512}
+   */
+  batchSize?: number;
+
+  /**
+   * Whether to strip new lines from the input text. May not be suitable
+   * for all use cases.
+   * @default {false}
+   */
+  stripNewLines?: boolean;
+}
+
+/** @ignore */
+interface TogetherAIEmbeddingsResult {
+  object: string;
+  data: Array<{
+    object: "embedding";
+    embedding: number[];
+    index: number;
+  }>;
+  model: string;
+  request_id: string;
+}
+
+/**
+ * Class for generating embeddings using the TogetherAI API. Extends the
+ * Embeddings class and implements TogetherAIEmbeddingsParams.
+ * @example
+ * ```typescript
+ * const embeddings = new TogetherAIEmbeddings({
+ *   apiKey: process.env.TOGETHER_AI_API_KEY, // Default value
+ *   model: "togethercomputer/m2-bert-80M-8k-retrieval", // Default value
+ * });
+ * const res = await embeddings.embedQuery(
+ *   "What would be a good company name a company that makes colorful socks?"
+ * );
+ * ```
+ */
+export class TogetherAIEmbeddings
+  extends Embeddings
+  implements TogetherAIEmbeddingsParams
+{
+  modelName = "togethercomputer/m2-bert-80M-8k-retrieval";
+
+  apiKey: string;
+
+  batchSize = 512;
+
+  stripNewLines = false;
+
+  timeout?: number;
+
+  private embeddingsAPIUrl = "https://api.together.xyz/api/v1/embeddings";
+
+  constructor(fields?: Partial<TogetherAIEmbeddingsParams>) {
+    super(fields ?? {});
+
+    let apiKey =
+      fields?.apiKey ?? getEnvironmentVariable("TOGETHER_AI_API_KEY");
+    if (!apiKey) {
+      throw new Error("TOGETHER_AI_API_KEY not found.");
+    }
+
+    this.apiKey = apiKey;
+    this.modelName = fields?.modelName ?? this.modelName;
+    this.timeout = fields?.timeout;
+    this.batchSize = fields?.batchSize ?? this.batchSize;
+    this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
+  }
+
+  private constructHeaders() {
+    return {
+      accept: "application/json",
+      "content-type": "application/json",
+      Authorization: `Bearer ${this.apiKey}`
+    };
+  }
+
+  private constructBody(input: string) {
+    const body = {
+      model: this?.modelName,
+      input
+    };
+    return body;
+  }
+
+  /**
+   * Method to generate embeddings for an array of documents. Splits the
+   * documents into batches and makes requests to the TogetherAI API to generate
+   * embeddings.
+   * @param texts Array of documents to generate embeddings for.
+   * @returns Promise that resolves to a 2D array of embeddings for each document.
+   */
+  async embedDocuments(texts: string[]): Promise<number[][]> {
+    const batches = chunkArray(
+      this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
+      this.batchSize
+    );
+
+    let batchResponses: TogetherAIEmbeddingsResult[] = [];
+    for await (const batch of batches) {
+      const batchRequests = batch.map((item) => this.embeddingWithRetry(item));
+      const response = await Promise.all(batchRequests);
+      batchResponses = batchResponses.concat(response);
+    }
+
+    const embeddings: number[][] = batchResponses.map(
+      (response) => response.data[0].embedding
+    );
+    return embeddings;
+  }
+
+  /**
+   * Method to generate an embedding for a single document. Calls the
+   * embeddingWithRetry method with the document as the input.
+   * @param {string} text Document to generate an embedding for.
+   * @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
+   */
+  async embedQuery(text: string): Promise<number[]> {
+    const { data } = await this.embeddingWithRetry(
+      this.stripNewLines ? text.replace(/\n/g, " ") : text
+    );
+    return data[0].embedding;
+  }
+
+  /**
+   * Private method to make a request to the TogetherAI API to generate
+   * embeddings. Handles the retry logic and returns the response from the
+   * API.
+   * @param {string} input The input text to embed.
+   * @returns Promise that resolves to the response from the API.
+   * @TODO Figure out return type and statically type it.
+   */
+  private async embeddingWithRetry(
+    input: string
+  ): Promise<TogetherAIEmbeddingsResult> {
+    const body = JSON.stringify(this.constructBody(input));
+    const headers = this.constructHeaders();
+
+    return this.caller.call(async () => {
+      const fetchResponse = await fetch(this.embeddingsAPIUrl, {
+        method: "POST",
+        headers,
+        body
+      });
+
+      if (fetchResponse.status === 200) {
+        return fetchResponse.json();
+      }
+      throw new Error(
+        `Error getting prompt completion from Together AI. ${JSON.stringify(
+          await fetchResponse.json(),
+          null,
+          2
+        )}`
+      );
+    });
+  }
+}

libs/langchain-community/src/llms/togetherai.ts

@@ -139,7 +139,7 @@ export class TogetherAI extends LLM<TogetherAICallOptions> {
 
   private apiKey: string;
 
-  private inferenceUrl = "https://api.together.xyz/inference";
+  private inferenceAPIUrl = "https://api.together.xyz/inference";
 
   static lc_name() {
     return "TogetherAI";
@@ -197,7 +197,7 @@ export class TogetherAI extends LLM<TogetherAICallOptions> {
     options?: this["ParsedCallOptions"]
   ) {
     return this.caller.call(async () => {
-      const fetchResponse = await fetch(this.inferenceUrl, {
+      const fetchResponse = await fetch(this.inferenceAPIUrl, {
         method: "POST",
         headers: {
           ...this.constructHeaders(),
@@ -236,7 +236,7 @@ export class TogetherAI extends LLM<TogetherAICallOptions> {
     options: this["ParsedCallOptions"],
     runManager?: CallbackManagerForLLMRun
   ): AsyncGenerator<GenerationChunk> {
-    const fetchResponse = await fetch(this.inferenceUrl, {
+    const fetchResponse = await fetch(this.inferenceAPIUrl, {
       method: "POST",
       headers: {
         ...this.constructHeaders(),

libs/langchain-community/src/load/import_map.ts

@@ -21,6 +21,7 @@ export * as agents__toolkits__base from "../agents/toolkits/base.js";
 export * as agents__toolkits__connery from "../agents/toolkits/connery/index.js";
 export * as embeddings__minimax from "../embeddings/minimax.js";
 export * as embeddings__ollama from "../embeddings/ollama.js";
+export * as embeddings__togetherai from "../embeddings/togetherai.js";
 export * as embeddings__voyage from "../embeddings/voyage.js";
 export * as llms__ai21 from "../llms/ai21.js";
 export * as llms__aleph_alpha from "../llms/aleph_alpha.js";