feat(ai-gateway): add generate embedding Action

README.md

@@ -79,7 +79,7 @@ Visit [http://localhost:3000](http://localhost:3000) to get started.
 ### Action Nodes
 
 <!-- PLUGINS:START - Do not remove. Auto-generated by discover-plugins -->
-- **AI Gateway**: Generate Text, Generate Image
+- **AI Gateway**: Generate Text, Generate Image, Generate Embeddings
 - **Blob**: Put Blob, List Blobs
 - **Clerk**: Get User, Create User, Update User, Delete User
 - **fal.ai**: Generate Image, Generate Video, Upscale Image, Remove Background, Image to Image
@@ -92,6 +92,7 @@ Visit [http://localhost:3000](http://localhost:3000) to get started.
 - **Stripe**: Create Customer, Get Customer, Create Invoice
 - **Superagent**: Guard, Redact
 - **v0**: Create Chat, Send Message
+- **Webflow**: List Sites, Get Site, Publish Site
 <!-- PLUGINS:END -->
 
 ## Code Generation

plugins/ai-gateway/index.ts

@@ -5,7 +5,7 @@ import { AiGatewayIcon } from "./icon";
 const aiGatewayPlugin: IntegrationPlugin = {
   type: "ai-gateway",
   label: "AI Gateway",
-  description: "Generate text and images using AI models",
+  description: "Generate text, images, and embeddings using AI models",
 
   icon: AiGatewayIcon,
 
@@ -141,6 +141,75 @@ const aiGatewayPlugin: IntegrationPlugin = {
         },
       ],
     },
+    {
+      slug: "generate-embeddings",
+      label: "Generate Embeddings",
+      description: "Generate text embeddings using AI models",
+      category: "AI Gateway",
+      stepFunction: "generateEmbeddingsStep",
+      stepImportPath: "generate-embeddings",
+      outputFields: [
+        { field: "embedding", description: "Single embedding vector" },
+        { field: "embeddings", description: "Batch embedding vectors" },
+      ],
+      configFields: [
+        {
+          key: "embeddingMode",
+          label: "Embedding Mode",
+          type: "select",
+          defaultValue: "single",
+          options: [
+            { value: "single", label: "Single Value" },
+            { value: "batch", label: "Batch Values" },
+          ],
+          required: true,
+        },
+        {
+          key: "embeddingModel",
+          label: "Model",
+          type: "select",
+          defaultValue: "openai/text-embedding-3-small",
+          options: [
+						{ value: "amazon/titan-embed-text-v2", label: "Amazon Titan Embed Text V2"},
+						{ value: "cohere/embed-v4.0", label: "Cohere Embed V4.0"},
+						{ value: "google/gemini-embedding-001", label: "Gemini Embedding 001"},
+						{ value: "google/text-embedding-005", label: "Google Text Embedding 005" },
+						{ value: "google/text-multilingual-embedding-002", label: "Google Text Multilingual Embedding 002" },
+						{ value: "mistral/codestral-embed", label: "Mistral Codestral Embed" },
+						{ value: "mistral/mistral-embed", label: "Mistral Embed" },
+						{ value: "openai/text-embedding-3-large", label: "OpenAI Text Embedding 3 Large" },
+						{ value: "openai/text-embedding-3-small", label: "OpenAI Text Embedding 3 Small" },
+						{ value: "openai/text-embedding-ada-002", label: "OpenAI Text Embedding Ada 002" },
+						{ value: "voyage/voyage-3-large", label: "Voyage 3 Large" },
+						{ value: "voyage/voyage-3.5", label: "Voyage 3.5" },
+						{ value: "voyage/voyage-3.5-lite", label: "Voyage 3.5 Lite" },
+						{ value: "voyage/voyage-code-3", label: "Voyage Code 3" },
+          ],
+          required: true,
+        },
+        {
+          key: "embeddingValue",
+          label: "Text to Embed",
+          type: "template-input",
+          placeholder: "Enter text to embed. Use {{NodeName.field}} to reference previous outputs.",
+          example: "sunny day at the beach",
+          showWhen: { field: "embeddingMode", equals: "single" },
+          required: true,
+        },
+        {
+          key: "embeddingValues",
+          label: "Texts to Embed",
+          type: "template-textarea",
+          placeholder:
+						"Enter one text per line for batch embedding:\n- First text\n- Second text\nUse {{NodeName.field}} to reference previous outputs.",
+          example:
+            "sunny day at the beach\nrainy afternoon in the city\nsnowy night in the mountains",
+          rows: 6,
+          showWhen: { field: "embeddingMode", equals: "batch" },
+          required: true,
+        }
+      ],
+    },
   ],
 };
 

plugins/ai-gateway/steps/generate-embeddings.ts

@@ -0,0 +1,144 @@
+import "server-only";
+
+import { createGateway, embed, embedMany } from "ai";
+import { fetchCredentials } from "@/lib/credential-fetcher";
+import { type StepInput, withStepLogging } from "@/lib/steps/step-handler";
+import { getErrorMessageAsync } from "@/lib/utils";
+import type { AiGatewayCredentials } from "../credentials";
+
+type GenerateEmbeddingsResult =
+	| { success: true; embedding: number[]; }
+	| {
+		success: true;
+		embeddings: number[][];
+	}
+	| { success: false; error: string };
+
+export type GenerateEmbeddingsCoreInput = {
+	embeddingMode: "single" | "batch";
+	embeddingModel: string;
+	embeddingValue?: string;
+	embeddingValues?: string;
+};
+
+export type GenerateEmbeddingsInput = StepInput &
+	GenerateEmbeddingsCoreInput & {
+		integrationId?: string;
+	};
+
+
+/**
+ * Parse batch embedding values from textarea input (one per line)
+ */
+function parseEmbeddingValues(valuesText?: string): string[] {
+	if (!valuesText) {
+		return [];
+	}
+
+	return valuesText
+		.split("\n")
+		.map((line) => line.trim())
+		.filter((line) => line.length > 0);
+}
+
+/**
+ * Core logic - portable between app and export
+ */
+async function stepHandler(
+	input: GenerateEmbeddingsCoreInput,
+	credentials: AiGatewayCredentials,
+): Promise<GenerateEmbeddingsResult> {
+	const apiKey = credentials.AI_GATEWAY_API_KEY;
+
+	if (!apiKey) {
+		return {
+			success: false,
+			error:
+				"AI_GATEWAY_API_KEY is not configured. Please add it in Project Integrations.",
+		};
+	}
+
+	const mode = input.embeddingMode || "single";
+	const modelId = input.embeddingModel || "openai/text-embedding-3-small";
+
+	try {
+		const gateway = createGateway({
+			apiKey,
+		});
+
+		// Handle single embedding mode
+		if (mode === "single") {
+			const value = input.embeddingValue?.trim() || "";
+
+			if (!value) {
+				return {
+					success: false,
+					error: "Embedding value is required for single mode",
+				};
+			}
+
+			const { embedding } = await embed({
+				model: gateway.textEmbeddingModel(modelId),
+				value,
+			});
+
+			return {
+				success: true,
+				embedding,
+			};
+		}
+
+		// Handle batch embedding mode
+		if (mode === "batch") {
+			const valuesText = input.embeddingValues?.trim() || "";
+			const values = parseEmbeddingValues(valuesText);
+
+			if (values.length === 0) {
+				return {
+					success: false,
+					error:
+						"At least one embedding value is required for batch mode (one per line)",
+				};
+			}
+
+			const { embeddings } = await embedMany({
+				model: gateway.textEmbeddingModel(modelId),
+				values,
+			});
+
+			return {
+				success: true,
+				embeddings,
+			};
+		}
+
+		return {
+			success: false,
+			error: `Invalid embedding mode: ${mode}`,
+		};
+	} catch (error) {
+		const message = await getErrorMessageAsync(error);
+		return {
+			success: false,
+			error: `Embedding generation failed: ${message}`,
+		};
+	}
+}
+
+/**
+ * App entry point - fetches credentials and wraps with logging
+ */
+export async function generateEmbeddingsStep(
+	input: GenerateEmbeddingsInput,
+): Promise<GenerateEmbeddingsResult> {
+	"use step";
+
+	const credentials = input.integrationId
+		? await fetchCredentials(input.integrationId)
+		: {};
+
+	return withStepLogging(input, () => stepHandler(input, credentials));
+}
+generateEmbeddingsStep.maxRetries = 0;
+
+export const _integrationType = "ai-gateway";