community[minor]: DeepInfra embeddings integration #1

docs/core_docs/docs/integrations/text_embedding/deepinfra.mdx

@@ -0,0 +1,130 @@
+---
+sidebar_label: DeepInfra
+---
+
+
+# DeepInfra Embeddings
+
+The `DeepInfraEmbeddings` class utilizes the DeepInfra API to generate embeddings for given text inputs. This guide will walk you through the setup and usage of the `DeepInfraEmbeddings` class, helping you integrate it into your project seamlessly.
+
+## Installation
+
+Install the `@langchain/community` package as shown below:
+
+import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
+
+<IntegrationInstallTooltip></IntegrationInstallTooltip>
+
+```bash npm2yarn
+npm i @langchain/community
+```
+
+## Initialization
+
+With this integration, you can use the DeepInfra embeddings model to get embeddings for your text data. Here is the [link](https://deepinfra.com/models/embeddings) to the embeddings models.
+
+First, you need to sign up on the DeepInfra website and get the API token from [here](https://deepinfra.com/dash/api_keys). You can copy names from the model cards and start using them in your code.
+
+To use the `DeepInfraEmbeddings` class, you need an API token from DeepInfra. You can pass this token directly to the constructor or set it as an environment variable (`DEEPINFRA_API_TOKEN`).
+
+
+
+### Basic Usage
+
+Here’s how to create an instance of `DeepInfraEmbeddings`:
+
+```typescript
+import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";
+
+const embeddings = new DeepInfraEmbeddings({
+  apiToken: "YOUR_API_TOKEN",
+  modelName: "sentence-transformers/clip-ViT-B-32", // Optional, defaults to "sentence-transformers/clip-ViT-B-32"
+  batchSize: 1024, // Optional, defaults to 1024
+});
+```
+
+If the `apiToken` is not provided, it will be read from the `DEEPINFRA_API_TOKEN` environment variable.
+
+## Generating Embeddings
+
+### Embedding a Single Query
+
+To generate embeddings for a single text query, use the `embedQuery` method:
+
+```typescript
+const embedding = await embeddings.embedQuery("What would be a good company name for a company that makes colorful socks?");
+console.log(embedding);
+```
+
+### Embedding Multiple Documents
+
+To generate embeddings for multiple documents, use the `embedDocuments` method. This method will handle batching automatically based on the `batchSize` parameter:
+
+```typescript
+const documents = [
+  "Document 1 text...",
+  "Document 2 text...",
+  "Document 3 text...",
+];
+
+const embeddingsArray = await embeddings.embedDocuments(documents);
+console.log(embeddingsArray);
+```
+
+## Customizing Requests
+
+You can customize the base URL the SDK sends requests to by passing a `configuration` parameter:
+
+```typescript
+const customEmbeddings = new DeepInfraEmbeddings({
+  apiToken: "YOUR_API_TOKEN",
+  configuration: {
+    baseURL: "https://your_custom_url.com",
+  },
+});
+```
+
+This allows you to route requests through a custom endpoint if needed.
+
+## Error Handling
+
+If the API token is not provided and cannot be found in the environment variables, an error will be thrown:
+
+```typescript
+try {
+  const embeddings = new DeepInfraEmbeddings();
+} catch (error) {
+  console.error("DeepInfra API token not found");
+}
+```
+
+## Example
+
+Here’s a complete example of how to set up and use the `DeepInfraEmbeddings` class:
+
+```typescript
+import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";
+
+const embeddings = new DeepInfraEmbeddings({
+  apiToken: "YOUR_API_TOKEN",
+  modelName: "sentence-transformers/clip-ViT-B-32",
+  batchSize: 512,
+});
+
+async function runExample() {
+  const queryEmbedding = await embeddings.embedQuery("Example query text.");
+  console.log("Query Embedding:", queryEmbedding);
+
+  const documents = ["Text 1", "Text 2", "Text 3"];
+  const documentEmbeddings = await embeddings.embedDocuments(documents);
+  console.log("Document Embeddings:", documentEmbeddings);
+}
+
+runExample();
+```
+
+## Feedback and Support
+
+For feedback or questions, please contact [feedback@deepinfra.com](mailto:feedback@deepinfra.com).
+
+

examples/src/embeddings/deepinfra.ts

@@ -0,0 +1,12 @@
+import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";
+
+const model = new DeepInfraEmbeddings({
+  apiToken: process.env.DEEPINFRA_API_TOKEN,
+  batchSize: 1024, // Default value
+  modelName: "sentence-transformers/clip-ViT-B-32", // Default value
+});
+
+const embeddings = await model.embedQuery(
+  "Tell me a story about a dragon and a princess."
+);
+console.log(embeddings);

examples/src/models/embeddings/deepinfra.ts

@@ -0,0 +1,12 @@
+import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";
+
+const model = new DeepInfraEmbeddings({
+  apiToken: process.env.DEEPINFRA_API_TOKEN,
+  batchSize: 1024, // Default value
+  modelName: "sentence-transformers/clip-ViT-B-32", // Default value
+});
+
+const embeddings = await model.embedQuery(
+  "Tell me a story about a dragon and a princess."
+);
+console.log(embeddings);

libs/langchain-community/.gitignore

@@ -142,6 +142,10 @@ embeddings/cohere.cjs
 embeddings/cohere.js
 embeddings/cohere.d.ts
 embeddings/cohere.d.cts
+embeddings/deepinfra.cjs
+embeddings/deepinfra.js
+embeddings/deepinfra.d.ts
+embeddings/deepinfra.d.cts
 embeddings/fireworks.cjs
 embeddings/fireworks.js
 embeddings/fireworks.d.ts

libs/langchain-community/langchain.config.js

@@ -69,6 +69,7 @@ export const config = {
     "embeddings/bedrock": "embeddings/bedrock",
     "embeddings/cloudflare_workersai": "embeddings/cloudflare_workersai",
     "embeddings/cohere": "embeddings/cohere",
+    "embeddings/deepinfra": "embeddings/deepinfra",
     "embeddings/fireworks": "embeddings/fireworks",
     "embeddings/googlepalm": "embeddings/googlepalm",
     "embeddings/googlevertexai": "embeddings/googlevertexai",

libs/langchain-community/package.json

@@ -1022,6 +1022,15 @@
       "import": "./embeddings/cohere.js",
       "require": "./embeddings/cohere.cjs"
     },
+    "./embeddings/deepinfra": {
+      "types": {
+        "import": "./embeddings/deepinfra.d.ts",
+        "require": "./embeddings/deepinfra.d.cts",
+        "default": "./embeddings/deepinfra.d.ts"
+      },
+      "import": "./embeddings/deepinfra.js",
+      "require": "./embeddings/deepinfra.cjs"
+    },
     "./embeddings/fireworks": {
       "types": {
         "import": "./embeddings/fireworks.d.ts",
@@ -3096,6 +3105,10 @@
     "embeddings/cohere.js",
     "embeddings/cohere.d.ts",
     "embeddings/cohere.d.cts",
+    "embeddings/deepinfra.cjs",
+    "embeddings/deepinfra.js",
+    "embeddings/deepinfra.d.ts",
+    "embeddings/deepinfra.d.cts",
     "embeddings/fireworks.cjs",
     "embeddings/fireworks.js",
     "embeddings/fireworks.d.ts",

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

@@ -0,0 +1,181 @@
+import { getEnvironmentVariable } from "@langchain/core/utils/env";
+import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
+import { chunkArray } from "@langchain/core/utils/chunk_array";
+
+/**
+ * The default model name to use for generating embeddings.
+ */
+const DEFAULT_MODEL_NAME = "sentence-transformers/clip-ViT-B-32";
+
+/**
+ * The default batch size to use for generating embeddings.
+ * This is limited by the DeepInfra API to a maximum of 1024.
+ */
+const DEFAULT_BATCH_SIZE = 1024;
+
+/**
+ * Environment variable name for the DeepInfra API token.
+ */
+const API_TOKEN_ENV_VAR = "DEEPINFRA_API_TOKEN";
+
+export interface DeepInfraEmbeddingsRequest {
+  inputs: string[];
+  normalize?: boolean;
+  image?: string;
+  webhook?: string;
+}
+
+/**
+ * Input parameters for the DeepInfra embeddings
+ */
+export interface DeepInfraEmbeddingsParams extends EmbeddingsParams {
+  /**
+   * The API token to use for authentication.
+   * If not provided, it will be read from the `DEEPINFRA_API_TOKEN` environment variable.
+   */
+  apiToken?: string;
+
+  /**
+   * The model ID to use for generating completions.
+   * Default: `sentence-transformers/clip-ViT-B-32`
+   */
+  modelName?: string;
+
+  /**
+   * The maximum number of texts to embed in a single request. This is
+   * limited by the DeepInfra API to a maximum of 1024.
+   */
+  batchSize?: number;
+}
+
+/**
+ * Response from the DeepInfra embeddings API.
+ */
+export interface DeepInfraEmbeddingsResponse {
+  /**
+   * The embeddings generated for the input texts.
+   */
+  embeddings: number[][];
+  /**
+   * The number of tokens in the input texts.
+   */
+  input_tokens: number;
+  /**
+   * The status of the inference.
+   */
+  request_id?: string;
+}
+
+/**
+ * A class for generating embeddings using the DeepInfra API.
+ * @example
+ * ```typescript
+ * // Embed a query using the DeepInfraEmbeddings class
+ * const model = new DeepInfraEmbeddings();
+ * const res = await model.embedQuery(
+ *   "What would be a good company name for a company that makes colorful socks?",
+ * );
+ * console.log({ res });
+ * ```
+ */
+export class DeepInfraEmbeddings
+  extends Embeddings
+  implements DeepInfraEmbeddingsParams
+{
+  apiToken: string;
+
+  batchSize: number;
+
+  modelName: string;
+
+  /**
+   * Constructor for the DeepInfraEmbeddings class.
+   * @param fields - An optional object with properties to configure the instance.
+   */
+  constructor(
+    fields?: Partial<DeepInfraEmbeddingsParams> & {
+      verbose?: boolean;
+    }
+  ) {
+    const fieldsWithDefaults = {
+      modelName: DEFAULT_MODEL_NAME,
+      batchSize: DEFAULT_BATCH_SIZE,
+      ...fields,
+    };
+
+    super(fieldsWithDefaults);
+
+    const apiKey =
+      fieldsWithDefaults?.apiToken || getEnvironmentVariable(API_TOKEN_ENV_VAR);
+
+    if (!apiKey) {
+      throw new Error("DeepInfra API token not found");
+    }
+
+    this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
+    this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
+    this.apiToken = apiKey;
+  }
+
+  /**
+   * Generates embeddings for an array of texts.
+   * @param inputs - An array of strings to generate embeddings for.
+   * @returns A Promise that resolves to an array of embeddings.
+   */
+  async embedDocuments(inputs: string[]): Promise<number[][]> {
+    const batches = chunkArray(inputs, this.batchSize);
+
+    const batchRequests = batches.map((batch: string[]) =>
+      this.embeddingWithRetry({
+        inputs: batch,
+      })
+    );
+
+    const batchResponses = await Promise.all(batchRequests);
+
+    const out: number[][] = [];
+
+    for (let i = 0; i < batchResponses.length; i += 1) {
+      const batch = batches[i];
+      const { embeddings } = batchResponses[i];
+      for (let j = 0; j < batch.length; j += 1) {
+        out.push(embeddings[j]);
+      }
+    }
+
+    return out;
+  }
+
+  /**
+   * Generates an embedding for a single text.
+   * @param text - A string to generate an embedding for.
+   * @returns A Promise that resolves to an array of numbers representing the embedding.
+   */
+  async embedQuery(text: string): Promise<number[]> {
+    const { embeddings } = await this.embeddingWithRetry({
+      inputs: [text],
+    });
+    return embeddings[0];
+  }
+
+  /**
+   * Generates embeddings with retry capabilities.
+   * @param request - An object containing the request parameters for generating embeddings.
+   * @returns A Promise that resolves to the API response.
+   */
+  private async embeddingWithRetry(
+    request: DeepInfraEmbeddingsRequest
+  ): Promise<DeepInfraEmbeddingsResponse> {
+    const response = await this.caller.call(() =>
+      fetch(`https://api.deepinfra.com/v1/inference/${this.modelName}`, {
+        method: "POST",
+        headers: {
+          Authorization: `Bearer ${this.apiToken}`,
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify(request),
+      }).then((res) => res.json())
+    );
+    return response as DeepInfraEmbeddingsResponse;
+  }
+}