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' },
          },
        },
      },
    },
  },
}