chore: import upstream snapshot with attribution

apps/sim/tools/openai/embeddings.ts

@@ -0,0 +1,87 @@
+import type { OpenAIEmbeddingsParams } from '@/tools/openai/types'
+import type { ToolConfig } from '@/tools/types'
+
+export const embeddingsTool: ToolConfig<OpenAIEmbeddingsParams> = {
+  id: 'openai_embeddings',
+  name: 'OpenAI Embeddings',
+  description: "Generate embeddings from text using OpenAI's embedding models",
+  version: '1.0',
+
+  params: {
+    input: {
+      type: 'string',
+      required: true,
+      visibility: 'user-or-llm',
+      description: 'Text to generate embeddings for',
+    },
+    model: {
+      type: 'string',
+      required: false,
+      visibility: 'user-only',
+      description: 'Model to use for embeddings',
+      default: 'text-embedding-3-small',
+    },
+    encodingFormat: {
+      type: 'string',
+      required: false,
+      visibility: 'hidden',
+      description: 'The format to return the embeddings in',
+      default: 'float',
+    },
+    apiKey: {
+      type: 'string',
+      required: true,
+      visibility: 'user-only',
+      description: 'OpenAI API key',
+    },
+  },
+
+  request: {
+    method: 'POST',
+    url: () => 'https://api.openai.com/v1/embeddings',
+    headers: (params) => ({
+      Authorization: `Bearer ${params.apiKey}`,
+      'Content-Type': 'application/json',
+    }),
+    body: (params) => ({
+      input: params.input,
+      model: params.model || 'text-embedding-3-small',
+      encoding_format: params.encodingFormat || 'float',
+    }),
+  },
+
+  transformResponse: async (response) => {
+    const data = await response.json()
+    return {
+      success: true,
+      output: {
+        embeddings: data.data.map((item: any) => item.embedding),
+        model: data.model,
+        usage: {
+          prompt_tokens: data.usage.prompt_tokens,
+          total_tokens: data.usage.total_tokens,
+        },
+      },
+    }
+  },
+
+  outputs: {
+    success: { type: 'boolean', description: 'Operation success status' },
+    output: {
+      type: 'object',
+      description: 'Embeddings generation results',
+      properties: {
+        embeddings: { type: 'array', description: 'Array of embedding vectors' },
+        model: { type: 'string', description: 'Model used for generating embeddings' },
+        usage: {
+          type: 'object',
+          description: 'Token usage information',
+          properties: {
+            prompt_tokens: { type: 'number', description: 'Number of tokens in the prompt' },
+            total_tokens: { type: 'number', description: 'Total number of tokens used' },
+          },
+        },
+      },
+    },
+  },
+}