diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/deepinfra.md b/apps/docs/docs/modules/embeddings/available_embeddings/deepinfra.md
new file mode 100644
index 0000000000000000000000000000000000000000..6f049ea864f319d2ef1ca5485db2bc2d4cc2e554
--- /dev/null
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/deepinfra.md
@@ -0,0 +1,79 @@
+# DeepInfra
+
+To use DeepInfra embeddings, you need to import `DeepInfraEmbedding` from llamaindex.
+Check out available embedding models [here](https://deepinfra.com/models/embeddings).
+
+```ts
+import {
+ DeepInfraEmbedding,
+ Settings,
+ Document,
+ VectorStoreIndex,
+} from "llamaindex";
+
+// Update Embed Model
+Settings.embedModel = new DeepInfraEmbedding();
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+ query,
+});
+```
+
+By default, DeepInfraEmbedding is using the sentence-transformers/clip-ViT-B-32 model. You can change the model by passing the model parameter to the constructor.
+For example:
+
+```ts
+import { DeepInfraEmbedding } from "llamaindex";
+
+const model = "intfloat/e5-large-v2";
+Settings.embedModel = new DeepInfraEmbedding({
+ model,
+});
+```
+
+You can also set the `maxRetries` and `timeout` parameters when initializing `DeepInfraEmbedding` for better control over the request behavior.
+
+For example:
+
+```ts
+import { DeepInfraEmbedding, Settings } from "llamaindex";
+
+const model = "intfloat/e5-large-v2";
+const maxRetries = 5;
+const timeout = 5000; // 5 seconds
+
+Settings.embedModel = new DeepInfraEmbedding({
+ model,
+ maxRetries,
+ timeout,
+});
+```
+
+Standalone usage:
+
+```ts
+import { DeepInfraEmbedding } from "llamaindex";
+import { config } from "dotenv";
+// For standalone usage, you need to configure DEEPINFRA_API_TOKEN in .env file
+config();
+
+const main = async () => {
+ const model = "intfloat/e5-large-v2";
+ const embeddings = new DeepInfraEmbedding({ model });
+ const text = "What is the meaning of life?";
+ const response = await embeddings.embed([text]);
+ console.log(response);
+};
+
+main();
+```
+
+For questions or feedback, please contact us at [feedback@deepinfra.com](mailto:feedback@deepinfra.com)
diff --git a/examples/deepinfra/embedding.ts b/examples/deepinfra/embedding.ts
new file mode 100644
index 0000000000000000000000000000000000000000..809fd7fe694d7d753263df8ec6cbabc62369cc71
--- /dev/null
+++ b/examples/deepinfra/embedding.ts
@@ -0,0 +1,17 @@
+import { DeepInfraEmbedding } from "llamaindex";
+
+async function main() {
+ // API token can be provided as an environment variable too
+ // using DEEPINFRA_API_TOKEN variable
+ const apiToken = "YOUR_API_TOKEN" ?? process.env.DEEPINFRA_API_TOKEN;
+ const model = "BAAI/bge-large-en-v1.5";
+ const embedModel = new DeepInfraEmbedding({
+ model,
+ apiToken,
+ });
+ const texts = ["hello", "world"];
+ const embeddings = await embedModel.getTextEmbeddingsBatch(texts);
+ console.log(`\nWe have ${embeddings.length} embeddings`);
+}
+
+main().catch(console.error);
diff --git a/packages/core/src/embeddings/DeepInfraEmbedding.ts b/packages/core/src/embeddings/DeepInfraEmbedding.ts
new file mode 100644
index 0000000000000000000000000000000000000000..59f1c827e4ff166526b94fae3ba9e6d22c8546c1
--- /dev/null
+++ b/packages/core/src/embeddings/DeepInfraEmbedding.ts
@@ -0,0 +1,152 @@
+import { getEnv } from "@llamaindex/env";
+import type { MessageContentDetail } from "../llm/index.js";
+import { extractSingleText } from "../llm/utils.js";
+import { BaseEmbedding } from "./types.js";
+
+const DEFAULT_MODEL = "sentence-transformers/clip-ViT-B-32";
+
+const API_TOKEN_ENV_VARIABLE_NAME = "DEEPINFRA_API_TOKEN";
+
+const API_ROOT = "https://api.deepinfra.com/v1/inference";
+
+const DEFAULT_TIMEOUT = 60 * 1000;
+
+const DEFAULT_MAX_RETRIES = 5;
+
+export interface DeepInfraEmbeddingResponse {
+ embeddings: number[][];
+ request_id: string;
+ inference_status: InferenceStatus;
+}
+
+export interface InferenceStatus {
+ status: string;
+ runtime_ms: number;
+ cost: number;
+ tokens_input: number;
+}
+
+const mapPrefixWithInputs = (prefix: string, inputs: string[]): string[] => {
+ return inputs.map((input) => `${prefix} ${input}`);
+};
+
+/**
+ * DeepInfraEmbedding is an alias for DeepInfra that implements the BaseEmbedding interface.
+ */
+export class DeepInfraEmbedding extends BaseEmbedding {
+ /**
+ * DeepInfra model to use
+ * @default "sentence-transformers/clip-ViT-B-32"
+ * @see https://deepinfra.com/models/embeddings
+ */
+ model: string;
+
+ /**
+ * DeepInfra API token
+ * @see https://deepinfra.com/dash/api_keys
+ * If not provided, it will try to get the token from the environment variable `DEEPINFRA_API_TOKEN`
+ *
+ */
+ apiToken: string;
+
+ /**
+ * Prefix to add to the query
+ * @default ""
+ */
+ queryPrefix: string;
+
+ /**
+ * Prefix to add to the text
+ * @default ""
+ */
+ textPrefix: string;
+
+ /**
+ *
+ * @default 5
+ */
+ maxRetries: number;
+
+ /**
+ *
+ * @default 60 * 1000
+ */
+ timeout: number;
+
+ constructor(init?: Partial<DeepInfraEmbedding>) {
+ super();
+
+ this.model = init?.model ?? DEFAULT_MODEL;
+ this.apiToken = init?.apiToken ?? getEnv(API_TOKEN_ENV_VARIABLE_NAME) ?? "";
+ this.queryPrefix = init?.queryPrefix ?? "";
+ this.textPrefix = init?.textPrefix ?? "";
+ this.maxRetries = init?.maxRetries ?? DEFAULT_MAX_RETRIES;
+ this.timeout = init?.timeout ?? DEFAULT_TIMEOUT;
+ }
+
+ async getTextEmbedding(text: string): Promise<number[]> {
+ const texts = mapPrefixWithInputs(this.textPrefix, [text]);
+ const embeddings = await this.getDeepInfraEmbedding(texts);
+ return embeddings[0];
+ }
+
+ async getQueryEmbedding(
+ query: MessageContentDetail,
+ ): Promise<number[] | null> {
+ const text = extractSingleText(query);
+ if (text) {
+ const queries = mapPrefixWithInputs(this.queryPrefix, [text]);
+ const embeddings = await this.getDeepInfraEmbedding(queries);
+ return embeddings[0];
+ } else {
+ return null;
+ }
+ }
+
+ async getTextEmbeddings(texts: string[]): Promise<number[][]> {
+ const textsWithPrefix = mapPrefixWithInputs(this.textPrefix, texts);
+ return await this.getDeepInfraEmbedding(textsWithPrefix);
+ }
+
+ async getQueryEmbeddings(queries: string[]): Promise<number[][]> {
+ const queriesWithPrefix = mapPrefixWithInputs(this.queryPrefix, queries);
+ return await this.getDeepInfraEmbedding(queriesWithPrefix);
+ }
+
+ private async getDeepInfraEmbedding(inputs: string[]): Promise<number[][]> {
+ const url = this.getUrl(this.model);
+
+ for (let attempt = 0; attempt < this.maxRetries; attempt++) {
+ const controller = new AbortController();
+ const id = setTimeout(() => controller.abort(), this.timeout);
+
+ try {
+ const response = await fetch(url, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ Authorization: `Bearer ${this.apiToken}`,
+ },
+ body: JSON.stringify({ inputs }),
+ signal: controller.signal,
+ });
+ if (!response.ok) {
+ throw new Error(`Request failed with status ${response.status}`);
+ }
+
+ const responseJson: DeepInfraEmbeddingResponse = await response.json();
+ return responseJson.embeddings;
+ } catch (error) {
+ console.error(`Attempt ${attempt + 1} failed: ${error}`);
+ } finally {
+ clearTimeout(id);
+ }
+ }
+
+ throw new Error("Exceeded maximum retries");
+ }
+
+ private getUrl(model: string): string {
+ return `${API_ROOT}/${model}`;
+ }
+}
diff --git a/packages/core/src/embeddings/index.ts b/packages/core/src/embeddings/index.ts
index e2cc200689319aad904e5da68a854d1c5eb4179f..e4f7c5bf17f2d1890b1f7894d81d923738eceba4 100644
--- a/packages/core/src/embeddings/index.ts
+++ b/packages/core/src/embeddings/index.ts
@@ -1,3 +1,4 @@
+export { DeepInfraEmbedding } from "./DeepInfraEmbedding.js";
export * from "./GeminiEmbedding.js";
export * from "./JinaAIEmbedding.js";
export * from "./MistralAIEmbedding.js";