diff --git a/src/aws_bedrock_embedders.ts b/src/aws_bedrock_embedders.ts
index 50fbfcc..5e8874a 100644
--- a/src/aws_bedrock_embedders.ts
+++ b/src/aws_bedrock_embedders.ts
@@ -1,98 +1,92 @@
-// /**
-//  * Copyright 2024 The Fire Company
-//  *
-//  * Licensed under the Apache License, Version 2.0 (the "License");
-//  * you may not use this file except in compliance with the License.
-//  * You may obtain a copy of the License at
-//  *
-//  *     http://www.apache.org/licenses/LICENSE-2.0
-//  *
-//  * Unless required by applicable law or agreed to in writing, software
-//  * distributed under the License is distributed on an "AS IS" BASIS,
-//  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-//  * See the License for the specific language governing permissions and
-//  * limitations under the License.
-//  */
-// /* eslint-disable  @typescript-eslint/no-explicit-any */
+/**
+ * Copyright 2024 The Fire Company
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+/* eslint-disable  @typescript-eslint/no-explicit-any */
 
-// import { embedderRef, Genkit } from "genkit";
-// import ModelClient, {
-//   GetEmbeddings200Response,
-//   GetEmbeddingsParameters,
-// } from "@azure-rest/ai-inference";
-// import { z } from "zod";
-// import { type PluginOptions } from "./index.js";
-// import { AzureKeyCredential } from "@azure/core-auth";
+import { embedderRef, Genkit } from "genkit";
 
-// export const TextEmbeddingConfigSchema = z.object({
-//   dimensions: z.number().optional(),
-//   encodingFormat: z.union([z.literal("float"), z.literal("base64")]).optional(),
-// });
+import { z } from "zod";
+import {
+  BedrockRuntimeClient,
+  InvokeModelCommand,
+  InvokeModelCommandInput,
+  InvokeModelCommandOutput,
+} from "@aws-sdk/client-bedrock-runtime";
 
-// export type TextEmbeddingGeckoConfig = z.infer<
-//   typeof TextEmbeddingConfigSchema
-// >;
+export const TextEmbeddingConfigSchema = z.object({
+  dimensions: z.number().optional(),
+});
 
-// export const TextEmbeddingInputSchema = z.string();
+export type TextEmbeddingGeckoConfig = z.infer<
+  typeof TextEmbeddingConfigSchema
+>;
 
-// export const openAITextEmbedding3Small = embedderRef({
-//   name: "github/text-embedding-3-small",
-//   configSchema: TextEmbeddingConfigSchema,
-//   info: {
-//     dimensions: 1536,
-//     label: "OpenAI - Text-embedding-3-small",
-//     supports: {
-//       input: ["text"],
-//     },
-//   },
-// });
+export const TextEmbeddingInputSchema = z.string();
 
-// export const SUPPORTED_EMBEDDING_MODELS: Record<string, any> = {
-//   "text-embedding-3-small": openAITextEmbedding3Small,
-// };
+export const amazonTitanEmbedTextV2 = embedderRef({
+  name: "aws-bedrock/amazon.titan-embed-text-v2:0",
+  configSchema: TextEmbeddingConfigSchema,
+  info: {
+    dimensions: 512,
+    label: "Amazon - titan-embed-text-v2:0",
+    supports: {
+      input: ["text"],
+    },
+  },
+});
 
-// export function awsBedrockEmbedder(
-//   name: string,
-//   ai: Genkit,
-//   options?: PluginOptions,
-// ) {
-//   const token = options?.githubToken || process.env.GITHUB_TOKEN;
-//   let endpoint = options?.endpoint || process.env.GITHUB_ENDPOINT;
-//   if (!token) {
-//     throw new Error(
-//       "Please pass in the TOKEN key or set the GITHUB_TOKEN environment variable",
-//     );
-//   }
-//   if (!endpoint) {
-//     endpoint = "https://models.inference.ai.azure.com";
-//   }
+export const SUPPORTED_EMBEDDING_MODELS: Record<string, any> = {
+  "amazon.titan-embed-text-v2:0": amazonTitanEmbedTextV2,
+};
 
-//   const client = ModelClient(endpoint, new AzureKeyCredential(token));
-//   const model = SUPPORTED_EMBEDDING_MODELS[name];
+export function awsBedrockEmbedder(
+  name: string,
+  ai: Genkit,
+  client: BedrockRuntimeClient,
+) {
+  const model = SUPPORTED_EMBEDDING_MODELS[name];
 
-//   return ai.defineEmbedder(
-//     {
-//       info: model.info!,
-//       configSchema: TextEmbeddingConfigSchema,
-//       name: model.name,
-//     },
-//     async (input, options) => {
-//       const body = {
-//         body: {
-//           model: name,
-//           input: input.map((d) => d.text),
-//           dimensions: options?.dimensions,
-//           encoding_format: options?.encodingFormat,
-//         },
-//       } as GetEmbeddingsParameters;
-//       const embeddings = (await client
-//         .path("/embeddings")
-//         .post(body)) as GetEmbeddings200Response;
-//       return {
-//         embeddings: embeddings.body.data.map((d) => ({
-//           embedding: Array.isArray(d.embedding) ? d.embedding : [],
-//         })),
-//       };
-//     },
-//   );
-// }
+  return ai.defineEmbedder(
+    {
+      info: model.info!,
+      configSchema: TextEmbeddingConfigSchema,
+      name: model.name,
+    },
+    async (input, options) => {
+      const body: InvokeModelCommandInput = {
+        modelId: name,
+        contentType: "application/json",
+        body: JSON.stringify({
+          inputText: input.map((d) => d.text).join(","),
+          dimensions: options?.dimensions,
+        }),
+      };
+
+      const command = new InvokeModelCommand(body);
+
+      const response = (await client.send(command)) as InvokeModelCommandOutput;
+      const embeddings = new TextDecoder().decode(response.body)
+        ? JSON.parse(new TextDecoder().decode(response.body))
+        : [];
+      return {
+        embeddings: [
+          {
+            embedding: embeddings.embedding as number[],
+          },
+        ],
+      };
+    },
+  );
+}
diff --git a/src/aws_bedrock_llms.ts b/src/aws_bedrock_llms.ts
index cbda432..0cf68f4 100644
--- a/src/aws_bedrock_llms.ts
+++ b/src/aws_bedrock_llms.ts
@@ -14,7 +14,6 @@
  * limitations under the License.
  */
 /* eslint-disable  @typescript-eslint/no-explicit-any */
-import * as fs from "fs";
 
 import {
   Message,
@@ -103,7 +102,7 @@ export function toAwsBedrockTextAndMedia(
       text: part.text,
     };
   } else if (part.media) {
-    const imageBuffer = new Uint8Array(fs.readFileSync(part.media.url).buffer);
+    const imageBuffer = new Uint8Array(Buffer.from(part.media.url, "base64"));
 
     return {
       image: {
diff --git a/src/index.ts b/src/index.ts
index 48f1d29..f8db113 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -10,15 +10,15 @@ import {
   amazonNovaProV1,
   SUPPORTED_AWS_BEDROCK_MODELS,
 } from "./aws_bedrock_llms.js";
-// import {
-//   awsBedrockEmbedder,
-//   openAITextEmbedding3Small,
-//   SUPPORTED_EMBEDDING_MODELS,
-// } from "./aws_bedrock_embedders.js";
+import {
+  awsBedrockEmbedder,
+  amazonTitanEmbedTextV2,
+  SUPPORTED_EMBEDDING_MODELS,
+} from "./aws_bedrock_embedders.js";
 
 export { amazonNovaProV1 };
 
-// export { openAITextEmbedding3Small };
+export { amazonTitanEmbedTextV2 };
 
 export type PluginOptions = BedrockRuntimeClientConfig;
 
@@ -30,9 +30,9 @@ export function awsBedrock(options?: PluginOptions) {
       awsBedrockModel(name, client, ai);
     });
 
-    // Object.keys(SUPPORTED_EMBEDDING_MODELS).forEach((name) =>
-    //   awsBedrockEmbedder(name, ai, options),
-    // );
+    Object.keys(SUPPORTED_EMBEDDING_MODELS).forEach((name) =>
+      awsBedrockEmbedder(name, ai, client),
+    );
   });
 }