@genkit-ai/ai/lib/model/middleware.mjs.map

Genkit AI framework generative AI APIs.

{"version":3,"sources":["../../src/model/middleware.ts"],"sourcesContent":["/**\n * Copyright 2024 Google LLC\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nimport { GenkitError, StatusName } from '@genkit-ai/core';\nimport { HasRegistry } from '@genkit-ai/core/registry';\nimport { Document } from '../document.js';\nimport { injectInstructions } from '../formats/index.js';\nimport { ModelArgument } from '../index.js';\nimport type {\n  MediaPart,\n  MessageData,\n  ModelInfo,\n  ModelMiddleware,\n  Part,\n} from '../model.js';\nimport { resolveModel } from '../model.js';\n\n/**\n * Preprocess a GenerateRequest to download referenced http(s) media URLs and\n * inline them as data URIs.\n */\nexport function downloadRequestMedia(options?: {\n  maxBytes?: number;\n  filter?: (part: MediaPart) => boolean;\n}): ModelMiddleware {\n  return async (req, next) => {\n    const { default: fetch } = await import('node-fetch');\n\n    const newReq = {\n      ...req,\n      messages: await Promise.all(\n        req.messages.map(async (message) => {\n          const content: Part[] = await Promise.all(\n            message.content.map(async (part) => {\n              // skip non-media parts and non-http urls, or parts that have been\n              // filtered out by user config\n              if (\n                !part.media ||\n                !part.media.url.startsWith('http') ||\n                (options?.filter && !options?.filter(part))\n              ) {\n                return part;\n              }\n\n              const response = await fetch(part.media.url, {\n                size: options?.maxBytes,\n              });\n              if (response.status !== 200)\n                throw new Error(\n                  `HTTP error downloading media '${\n                    part.media.url\n                  }': ${await response.text()}`\n                );\n\n              // use provided contentType or sniff from response\n              const contentType =\n                part.media.contentType ||\n                response.headers.get('content-type') ||\n                '';\n\n              return {\n                media: {\n                  contentType,\n                  url: `data:${contentType};base64,${Buffer.from(\n                    await response.arrayBuffer()\n                  ).toString('base64')}`,\n                },\n              };\n            })\n          );\n\n          return {\n            ...message,\n            content,\n          };\n        })\n      ),\n    };\n\n    return next(newReq);\n  };\n}\n\n/**\n * Validates that a GenerateRequest does not include unsupported features.\n */\nexport function validateSupport(options: {\n  name: string;\n  supports?: ModelInfo['supports'];\n}): ModelMiddleware {\n  const supports = options.supports || {};\n  return async (req, next) => {\n    function invalid(message: string): never {\n      throw new Error(\n        `Model '${\n          options.name\n        }' does not support ${message}. Request: ${JSON.stringify(\n          req,\n          null,\n          2\n        )}`\n      );\n    }\n\n    if (\n      supports.media === false &&\n      req.messages.some((message) => message.content.some((part) => part.media))\n    )\n      invalid('media, but media was provided');\n    if (supports.tools === false && req.tools?.length)\n      invalid('tool use, but tools were provided');\n    if (supports.multiturn === false && req.messages.length > 1)\n      invalid(`multiple messages, but ${req.messages.length} were provided`);\n    // if (\n    //   typeof supports.output !== 'undefined' &&\n    //   req.output?.format &&\n    //   !supports.output.includes(req.output?.format)\n    // )\n    //   invalid(`requested output format '${req.output?.format}'`);\n    return next();\n  };\n}\n\n// N.B. Figure out why array.findLast isn't available despite setting target\n// to ES2022 (Node 16.14.0)\nfunction lastUserMessage(messages: MessageData[]) {\n  for (let i = messages.length - 1; i >= 0; i--) {\n    if (messages[i].role === 'user') {\n      return messages[i];\n    }\n  }\n  return undefined;\n}\n\n/**\n * Provide a simulated system prompt for models that don't support it natively.\n */\nexport function simulateSystemPrompt(options?: {\n  preface: string;\n  acknowledgement: string;\n}): ModelMiddleware {\n  const preface = options?.preface || 'SYSTEM INSTRUCTIONS:\\n';\n  const acknowledgement = options?.acknowledgement || 'Understood.';\n\n  return (req, next) => {\n    const messages = [...req.messages];\n    for (let i = 0; i < messages.length; i++) {\n      if (req.messages[i].role === 'system') {\n        const systemPrompt = messages[i].content;\n        messages.splice(\n          i,\n          1,\n          { role: 'user', content: [{ text: preface }, ...systemPrompt] },\n          { role: 'model', content: [{ text: acknowledgement }] }\n        );\n        break;\n      }\n    }\n    return next({ ...req, messages });\n  };\n}\n\nexport interface AugmentWithContextOptions {\n  /** Preceding text to place before the rendered context documents. */\n  preface?: string | null;\n  /** A function to render a document into a text part to be included in the message. */\n  itemTemplate?: (d: Document, options?: AugmentWithContextOptions) => string;\n  /** The metadata key to use for citation reference. Pass `null` to provide no citations. */\n  citationKey?: string | null;\n}\n\nexport const CONTEXT_PREFACE =\n  '\\n\\nUse the following information to complete your task:\\n\\n';\nconst CONTEXT_ITEM_TEMPLATE = (\n  d: Document,\n  index: number,\n  options?: AugmentWithContextOptions\n) => {\n  let out = '- ';\n  if (options?.citationKey) {\n    out += `[${d.metadata![options.citationKey]}]: `;\n  } else if (options?.citationKey === undefined) {\n    out += `[${d.metadata?.['ref'] || d.metadata?.['id'] || index}]: `;\n  }\n  out += d.text + '\\n';\n  return out;\n};\n\nexport function augmentWithContext(\n  options?: AugmentWithContextOptions\n): ModelMiddleware {\n  const preface =\n    typeof options?.preface === 'undefined' ? CONTEXT_PREFACE : options.preface;\n  const itemTemplate = options?.itemTemplate || CONTEXT_ITEM_TEMPLATE;\n  return (req, next) => {\n    // if there is no context in the request, no-op\n    if (!req.docs?.length) return next(req);\n    const userMessage = lastUserMessage(req.messages);\n    // if there are no messages, no-op\n    if (!userMessage) return next(req);\n    // if there is already a context part, no-op\n    const contextPartIndex = userMessage?.content.findIndex(\n      (p) => p.metadata?.purpose === 'context'\n    );\n    const contextPart =\n      contextPartIndex >= 0 && userMessage.content[contextPartIndex];\n\n    if (contextPart && !contextPart.metadata?.pending) {\n      return next(req);\n    }\n    let out = `${preface || ''}`;\n    req.docs?.forEach((d, i) => {\n      out += itemTemplate(new Document(d), i, options);\n    });\n    out += '\\n';\n    if (contextPartIndex >= 0) {\n      userMessage.content[contextPartIndex] = {\n        ...contextPart,\n        text: out,\n        metadata: { purpose: 'context' },\n      } as Part;\n    } else {\n      userMessage.content.push({ text: out, metadata: { purpose: 'context' } });\n    }\n\n    return next(req);\n  };\n}\n\n/**\n * Options for the `retry` middleware.\n */\nexport interface RetryOptions {\n  /**\n   * The maximum number of times to retry a failed request.\n   * @default 3\n   */\n  maxRetries?: number;\n  /**\n   * An array of `StatusName` values that should trigger a retry.\n   * @default ['UNAVAILABLE', 'DEADLINE_EXCEEDED', 'RESOURCE_EXHAUSTED', 'ABORTED', 'INTERNAL']\n   */\n  statuses?: StatusName[];\n  /**\n   * The initial delay between retries, in milliseconds.\n   * @default 1000\n   */\n  initialDelayMs?: number;\n  /**\n   * The maximum delay between retries, in milliseconds.\n   * @default 60000\n   */\n  maxDelayMs?: number;\n  /**\n   * The factor by which the delay increases after each retry (exponential backoff).\n   * @default 2\n   */\n  backoffFactor?: number;\n  /**\n   * Whether to disable jitter on the delay. Jitter adds a random factor to the\n   * delay to help prevent a \"thundering herd\" of clients all retrying at the\n   * same time.\n   * @default false\n   */\n  noJitter?: boolean;\n  /**\n   * A callback to be executed on each retry attempt.\n   */\n  onError?: (error: Error, attempt: number) => void;\n}\n\nlet __setTimeout: (\n  callback: (...args: any[]) => void,\n  ms?: number\n) => NodeJS.Timeout = setTimeout;\n\n/**\n * FOR TESTING ONLY.\n * @internal\n */\nexport const TEST_ONLY = {\n  setRetryTimeout(\n    impl: (callback: (...args: any[]) => void, ms?: number) => NodeJS.Timeout\n  ) {\n    __setTimeout = impl;\n  },\n};\n\nconst DEFAULT_RETRY_STATUSES: StatusName[] = [\n  'UNAVAILABLE',\n  'DEADLINE_EXCEEDED',\n  'RESOURCE_EXHAUSTED',\n  'ABORTED',\n  'INTERNAL',\n];\n\nconst DEFAULT_FALLBACK_STATUSES: StatusName[] = [\n  'UNAVAILABLE',\n  'DEADLINE_EXCEEDED',\n  'RESOURCE_EXHAUSTED',\n  'ABORTED',\n  'INTERNAL',\n  'NOT_FOUND',\n  'UNIMPLEMENTED',\n];\n\n/**\n * Creates a middleware that retries requests on specific error statuses.\n *\n * ```ts\n * const { text } = await ai.generate({\n *   model: googleAI.model('gemini-2.5-pro'),\n *   prompt: 'You are a helpful AI assistant named Walt, say hello',\n *   use: [\n *     retry({\n *       maxRetries: 2,\n *       initialDelayMs: 1000,\n *       backoffFactor: 2,\n *     }),\n *   ],\n * });\n * ```\n */\nexport function retry(options: RetryOptions = {}): ModelMiddleware {\n  const {\n    maxRetries = 3,\n    statuses = DEFAULT_RETRY_STATUSES,\n    initialDelayMs = 1000,\n    maxDelayMs = 60000,\n    backoffFactor = 2,\n    noJitter = false,\n    onError,\n  } = options;\n\n  return async (req, next) => {\n    let lastError: any;\n    let currentDelay = initialDelayMs;\n    for (let i = 0; i <= maxRetries; i++) {\n      try {\n        return await next(req);\n      } catch (e) {\n        lastError = e;\n        const error = e as Error;\n        if (i < maxRetries) {\n          let shouldRetry = false;\n          if (error instanceof GenkitError) {\n            if (statuses.includes(error.status)) {\n              shouldRetry = true;\n            }\n          } else {\n            shouldRetry = true;\n          }\n\n          if (shouldRetry) {\n            onError?.(error, i + 1);\n            let delay = currentDelay;\n            if (!noJitter) {\n              delay = delay + 1000 * Math.pow(2, i) * Math.random();\n            }\n            await new Promise((resolve) => __setTimeout(resolve, delay));\n            currentDelay = Math.min(currentDelay * backoffFactor, maxDelayMs);\n            continue;\n          }\n        }\n        throw error;\n      }\n    }\n    throw lastError;\n  };\n}\n\n/**\n * Options for the `fallback` middleware.\n */\nexport interface FallbackOptions {\n  /**\n   * An array of models to try in order.\n   */\n  models: ModelArgument[];\n  /**\n   * An array of `StatusName` values that should trigger a fallback.\n   * @default ['UNAVAILABLE', 'DEADLINE_EXCEEDED', 'RESOURCE_EXHAUSTED', 'ABORTED', 'INTERNAL', 'NOT_FOUND', 'UNIMPLEMENTED']\n   */\n  statuses?: StatusName[];\n  /**\n   * A callback to be executed on each fallback attempt.\n   */\n  onError?: (error: Error) => void;\n}\n\n/**\n * Creates a middleware that falls back to a different model on specific error statuses.\n *\n * ```ts\n * const { text } = await ai.generate({\n *   model: googleAI.model('gemini-2.5-pro'),\n *   prompt: 'You are a helpful AI assistant named Walt, say hello',\n *   use: [\n *     fallback(ai, {\n *       models: [googleAI.model('gemini-2.5-flash')],\n *       statuses: ['RESOURCE_EXHAUSTED'],\n *     }),\n *   ],\n * });\n * ```\n */\nexport function fallback(\n  ai: HasRegistry,\n  options: FallbackOptions\n): ModelMiddleware {\n  const { models, statuses = DEFAULT_FALLBACK_STATUSES, onError } = options;\n\n  return async (req, next) => {\n    try {\n      return await next(req);\n    } catch (e) {\n      if (e instanceof GenkitError && statuses.includes(e.status)) {\n        onError?.(e);\n        let lastError: any = e;\n        for (const model of models) {\n          try {\n            const resolved = await resolveModel(ai.registry, model);\n            return await resolved.modelAction(req);\n          } catch (e2) {\n            lastError = e2;\n            if (e2 instanceof GenkitError && statuses.includes(e2.status)) {\n              onError?.(e2);\n              continue;\n            }\n            throw e2;\n          }\n        }\n        throw lastError;\n      }\n      throw e;\n    }\n  };\n}\n\nexport interface SimulatedConstrainedGenerationOptions {\n  instructionsRenderer?: (schema: Record<string, any>) => string;\n}\n\nconst DEFAULT_CONSTRAINED_GENERATION_INSTRUCTIONS = (\n  schema: Record<string, any>\n) => `Output should be in JSON format and conform to the following schema:\n\n\\`\\`\\`\n${JSON.stringify(schema)}\n\\`\\`\\`\n`;\n\n/**\n * Model middleware that simulates constrained generation by 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