@genkit-ai/compat-oai/lib/model.mjs.map

Genkit AI framework plugin for OpenAI APIs.

{"version":3,"sources":["../src/model.ts"],"sourcesContent":["/**\n * Copyright 2024 The Fire Company\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 type {\n  GenerateRequest,\n  GenerateResponseChunkData,\n  GenerateResponseData,\n  MessageData,\n  ModelReference,\n  Part,\n  Role,\n  StreamingCallback,\n  ToolRequestPart,\n} from 'genkit';\nimport {\n  GenerationCommonConfigSchema,\n  GenkitError,\n  Message,\n  StatusName,\n  modelRef,\n  z,\n  type ErrorResponseMetadata,\n} from 'genkit';\nimport type { ModelAction, ModelInfo, ToolDefinition } from 'genkit/model';\nimport { model } from 'genkit/plugin';\nimport OpenAI, { APIError } from 'openai';\nimport type {\n  ChatCompletion,\n  ChatCompletionChunk,\n  ChatCompletionContentPart,\n  ChatCompletionCreateParams,\n  ChatCompletionCreateParamsNonStreaming,\n  ChatCompletionMessageParam,\n  ChatCompletionMessageToolCall,\n  ChatCompletionRole,\n  ChatCompletionTool,\n  CompletionChoice,\n} from 'openai/resources/index.mjs';\nimport { PluginOptions } from './index.mjs';\nimport { maybeCreateRequestScopedOpenAIClient, toModelName } from './utils.mjs';\n\n/**\n * Parses a `Retry-After` header value into milliseconds.\n * Supports delay-seconds and HTTP-date formats (RFC 7231 §7.1.3).\n */\nfunction parseRetryAfterMs(value: string): number | undefined {\n  if (!value || !value.trim()) return undefined;\n  const seconds = Number(value);\n  if (!isNaN(seconds) && seconds >= 0) return seconds * 1000;\n  const date = new Date(value);\n  if (!isNaN(date.getTime())) return Math.max(0, date.getTime() - Date.now());\n  return undefined;\n}\n\nconst VisualDetailLevelSchema = z.enum(['auto', 'low', 'high']).optional();\n\ntype VisualDetailLevel = z.infer<typeof VisualDetailLevelSchema>;\n\nexport type ModelRequestBuilder = (\n  req: GenerateRequest,\n  params: ChatCompletionCreateParams\n) => void;\n\nexport const ChatCompletionCommonConfigSchema =\n  GenerationCommonConfigSchema.extend({\n    temperature: z.number().min(0).max(2).optional(),\n    frequencyPenalty: z.number().min(-2).max(2).optional(),\n    logProbs: z.boolean().optional(),\n    presencePenalty: z.number().min(-2).max(2).optional(),\n    topLogProbs: z.number().int().min(0).max(20).optional(),\n  });\n\nexport function toOpenAIRole(role: Role): ChatCompletionRole {\n  switch (role) {\n    case 'user':\n      return 'user';\n    case 'model':\n      return 'assistant';\n    case 'system':\n      return 'system';\n    case 'tool':\n      return 'tool';\n    default:\n      throw new Error(`role ${role} doesn't map to an OpenAI role.`);\n  }\n}\n\n/**\n * Converts a Genkit ToolDefinition to an OpenAI ChatCompletionTool object.\n * @param tool The Genkit ToolDefinition to convert.\n * @returns The converted OpenAI ChatCompletionTool object.\n */\nexport function toOpenAITool(tool: ToolDefinition): ChatCompletionTool {\n  return {\n    type: 'function',\n    function: {\n      name: tool.name,\n      parameters: tool.inputSchema !== null ? tool.inputSchema : undefined,\n    },\n  };\n}\n\n/**\n * Checks if a content type is an image type.\n * @param contentType The content type to check.\n * @returns True if the content type is an image type.\n */\nfunction isImageContentType(contentType?: string): boolean {\n  if (!contentType) return false;\n  return contentType.startsWith('image/');\n}\n\n/**\n * Extracts the base64 data and content type from a data URL.\n * @param url The data URL to parse.\n * @returns The base64 data and content type, or null if invalid.\n */\nfunction extractDataFromBase64Url(url: string): {\n  data: string;\n  contentType: string;\n} | null {\n  const match = url.match(/^data:([^;]+);base64,(.+)$/);\n  return (\n    match && {\n      contentType: match[1],\n      data: match[2],\n    }\n  );\n}\n\n/**\n * Map of content types to file extensions.\n */\nconst FILE_EXTENSIONS: Record<string, string> = {\n  'application/pdf': 'pdf',\n  'application/msword': 'doc',\n  'application/vnd.openxmlformats-officedocument.wordprocessingml.document':\n    'docx',\n  'text/plain': 'txt',\n  'text/csv': 'csv',\n};\n\n/**\n * Generates a filename from a content type.\n * @param contentType The content type.\n * @returns A filename with appropriate extension.\n */\nfunction generateFilenameFromContentType(contentType: string): string {\n  const ext = FILE_EXTENSIONS[contentType] || '';\n  return ext ? `file.${ext}` : 'file';\n}\n\n/**\n * Converts a Genkit Part to the corresponding OpenAI ChatCompletionContentPart.\n * @param part The Genkit Part to convert.\n * @param visualDetailLevel The visual detail level to use for media parts.\n * @returns The corresponding OpenAI ChatCompletionContentPart.\n * @throws Error if the part contains unsupported fields for the current message role.\n */\nexport function toOpenAITextAndMedia(\n  part: Part,\n  visualDetailLevel: VisualDetailLevel\n): ChatCompletionContentPart {\n  if (part.text) {\n    return {\n      type: 'text',\n      text: part.text,\n    };\n  } else if (part.media) {\n    // Determine the content type from the media part or data URL\n    let contentType = part.media.contentType;\n    if (!contentType && part.media.url.startsWith('data:')) {\n      const extracted = extractDataFromBase64Url(part.media.url);\n      if (extracted) {\n        contentType = extracted.contentType;\n      }\n    }\n\n    // Check if this is an image type\n    // If no contentType is provided, preserve legacy behavior by treating the media\n    // as an image URL (e.g. signed URLs or remote images without metadata)\n    if (!contentType || isImageContentType(contentType)) {\n      return {\n        type: 'image_url',\n        image_url: {\n          url: part.media.url,\n          detail: visualDetailLevel,\n        },\n      };\n    }\n\n    // For non-image types (like PDF), use the file type\n    // OpenAI expects the full data URL (with data: prefix) in file_data\n    if (part.media.url.startsWith('data:')) {\n      const extracted = extractDataFromBase64Url(part.media.url);\n      if (!extracted) {\n        throw Error(\n          `Invalid data URL format for media: ${part.media.url.substring(0, 50)}...`\n        );\n      }\n      return {\n        type: 'file',\n        file: {\n          filename: generateFilenameFromContentType(extracted.contentType),\n          file_data: part.media.url, // Full data URL with prefix\n        },\n      } as ChatCompletionContentPart;\n    }\n\n    // If it's a remote URL with non-image content type, this is not supported\n    // for chat completions according to OpenAI docs\n    throw Error(\n      `File URLs are not supported for chat completions. Only base64-encoded files and image URLs are supported. Content type: ${contentType}`\n    );\n  }\n  throw Error(\n    `Unsupported genkit part fields encountered for current message role: ${JSON.stringify(part)}.`\n  );\n}\n\n/**\n * Converts a Genkit MessageData array to an OpenAI ChatCompletionMessageParam array.\n * @param messages The Genkit MessageData array to convert.\n * @param visualDetailLevel The visual detail level to use for media parts.\n * @returns The converted OpenAI ChatCompletionMessageParam array.\n */\nexport function toOpenAIMessages(\n  messages: MessageData[],\n  visualDetailLevel: VisualDetailLevel = 'auto'\n): ChatCompletionMessageParam[] {\n  const apiMessages: ChatCompletionMessageParam[] = [];\n  for (const message of messages) {\n    const msg = new Message(message);\n    const role = toOpenAIRole(message.role);\n    switch (role) {\n      case 'user':\n        const content = msg.content.map((part) =>\n          toOpenAITextAndMedia(part, visualDetailLevel)\n        );\n        // Check if we have only text content\n        const onlyTextContent = content.some((item) => item.type !== 'text');\n\n        // If all items are strings, just add them as text\n        if (!onlyTextContent) {\n          content.forEach((item) => {\n            if (item.type === 'text') {\n              apiMessages.push({\n                role: role,\n                content: item.text,\n              });\n            }\n          });\n        } else {\n          apiMessages.push({\n            role: role,\n            content: content,\n          });\n        }\n        break;\n      case 'system':\n        apiMessages.push({\n          role: role,\n          content: msg.text,\n        });\n        break;\n      case 'assistant': {\n        const toolCalls: ChatCompletionMessageToolCall[] = msg.content\n          .filter(\n            (\n              part\n            ): part is Part & {\n              toolRequest: NonNullable<Part['toolRequest']>;\n            } => Boolean(part.toolRequest)\n          )\n          .map((part) => ({\n            id: part.toolRequest.ref ?? '',\n            type: 'function',\n            function: {\n              name: part.toolRequest.name,\n              arguments: JSON.stringify(part.toolRequest.input),\n            },\n          }));\n        if (toolCalls.length > 0) {\n          apiMessages.push({\n            role: role,\n            tool_calls: toolCalls,\n          });\n        } else {\n          apiMessages.push({\n            role: role,\n            content: msg.text,\n          });\n        }\n        break;\n      }\n      case 'tool': {\n        const toolResponseParts = msg.toolResponseParts();\n        toolResponseParts.map((part) => {\n          apiMessages.push({\n            role: role,\n            tool_call_id: part.toolResponse.ref ?? '',\n            content:\n              typeof part.toolResponse.output === 'string'\n                ? part.toolResponse.output\n                : JSON.stringify(part.toolResponse.output),\n          });\n        });\n        break;\n      }\n    }\n  }\n  return apiMessages;\n}\n\nconst finishReasonMap: Record<\n  // OpenAI Node SDK doesn't support tool_call in the enum, but it is returned from the API\n  CompletionChoice['finish_reason'] | 'tool_calls',\n  GenerateResponseData['finishReason']\n> = {\n  length: 'length',\n  stop: 'stop',\n  tool_calls: 'stop',\n  content_filter: 'blocked',\n};\n\n/**\n * Converts an OpenAI tool call to a Genkit ToolRequestPart.\n * @param toolCall The OpenAI tool call to convert.\n * @returns The converted Genkit ToolRequestPart.\n */\nexport function fromOpenAIToolCall(\n  toolCall:\n    | ChatCompletionMessageToolCall\n    | ChatCompletionChunk.Choice.Delta.ToolCall,\n  choice: ChatCompletion.Choice | ChatCompletionChunk.Choice\n): ToolRequestPart {\n  if (!toolCall.function) {\n    throw Error(\n      `Unexpected openAI chunk choice. tool_calls was provided but one or more tool_calls is missing.`\n    );\n  }\n  const f = toolCall.function;\n\n  // Only parse arguments when it is a JSON object and the finish reason is tool_calls to avoid parsing errors\n  if (choice.finish_reason === 'tool_calls') {\n    return {\n      toolRequest: {\n        name: f.name!,\n        ref: toolCall.id,\n        input: f.arguments ? JSON.parse(f.arguments) : f.arguments,\n      },\n    };\n  } else {\n    return {\n      toolRequest: {\n        name: f.name!,\n        ref: toolCall.id,\n        input: '',\n      },\n    };\n  }\n}\n\n/**\n * Converts an OpenAI message event to a Genkit GenerateResponseData object.\n * @param choice The OpenAI message event to convert.\n * @param jsonMode Whether the event is a JSON response.\n * @returns The converted Genkit GenerateResponseData object.\n */\nexport function fromOpenAIChoice(\n  choice: ChatCompletion.Choice,\n  jsonMode = false\n): GenerateResponseData {\n  const toolRequestParts = choice.message.tool_calls?.map((toolCall) =>\n    fromOpenAIToolCall(toolCall, choice)\n  );\n\n  // Build content array based on what's present in the message\n  let content: Part[] = [];\n\n  if (toolRequestParts && toolRequestParts.length > 0) {\n    content = toolRequestParts as ToolRequestPart[];\n  } else {\n    // Handle reasoning_content if present\n    if (\n      'reasoning_content' in choice.message &&\n      choice.message.reasoning_content\n    ) {\n      content.push({ reasoning: choice.message.reasoning_content as string });\n    }\n\n    // Handle regular content if present\n    if (choice.message.content) {\n      content.push(\n        jsonMode\n          ? { data: JSON.parse(choice.message.content!) }\n          : { text: choice.message.content! }\n      );\n    }\n  }\n\n  return {\n    finishReason: finishReasonMap[choice.finish_reason] || 'other',\n    message: {\n      role: 'model',\n      content,\n    },\n  };\n}\n\n/**\n * Converts an OpenAI message stream event to a Genkit GenerateResponseData\n * object.\n * @param choice The OpenAI message stream event to convert.\n * @param jsonMode Whether the event is a JSON response.\n * @returns The converted Genkit GenerateResponseData object.\n */\nexport function fromOpenAIChunkChoice(\n  choice: ChatCompletionChunk.Choice,\n  jsonMode = false\n): GenerateResponseData {\n  const toolRequestParts = choice.delta.tool_calls?.map((toolCall) =>\n    fromOpenAIToolCall(toolCall, choice)\n  );\n\n  // Build content array based on what's present in the delta\n  let content: Part[] = [];\n\n  if (toolRequestParts && toolRequestParts.length > 0) {\n    content = toolRequestParts as ToolRequestPart[];\n  } else {\n    // Handle reasoning_content if present\n    if ('reasoning_content' in choice.delta && choice.delta.reasoning_content) {\n      content.push({ reasoning: choice.delta.reasoning_content as string });\n    }\n\n    // Handle regular content if present\n    if (choice.delta.content) {\n      content.push(\n        jsonMode\n          ? { data: JSON.parse(choice.delta.content!) }\n          : { text: choice.delta.content! }\n      );\n    }\n  }\n\n  return {\n    finishReason: choice.finish_reason\n      ? finishReasonMap[choice.finish_reason] || 'other'\n      : 'unknown',\n    message: {\n      role: 'model',\n      content,\n    },\n  };\n}\n\n/**\n * Converts an OpenAI request to an OpenAI API request body.\n * @param modelName The name of the OpenAI model to use.\n * @param request The Genkit GenerateRequest to convert.\n * @returns The converted OpenAI API request body.\n * @throws An error if the specified model is not supported or if an unsupported output format is requested.\n */\nexport function toOpenAIRequestBody(\n  modelName: string,\n  request: GenerateRequest,\n  requestBuilder?: ModelRequestBuilder\n) {\n  const messages = toOpenAIMessages(\n    request.messages,\n    request.config?.visualDetailLevel\n  );\n  const {\n    temperature,\n    maxOutputTokens, // unused\n    topK, // unused\n    topP: top_p,\n    frequencyPenalty: frequency_penalty,\n    logProbs: logprobs,\n    presencePenalty: presence_penalty,\n    topLogProbs: top_logprobs,\n    stopSequences: stop,\n    version: modelVersion,\n    tools: toolsFromConfig,\n    apiKey,\n    ...restOfConfig\n  } = request.config ?? {};\n\n  const tools: ChatCompletionTool[] = request.tools?.map(toOpenAITool) ?? [];\n  if (toolsFromConfig) {\n    tools.push(...(toolsFromConfig as any[]));\n  }\n  let body = {\n    model: modelVersion ?? modelName,\n    messages,\n    tools: tools.length > 0 ? tools : undefined,\n    temperature,\n    top_p,\n    stop,\n    frequency_penalty,\n    presence_penalty,\n    top_logprobs,\n    logprobs,\n  } as ChatCompletionCreateParamsNonStreaming;\n  if (requestBuilder) {\n    // If override provided, apply the override to the OpenAI request.\n    // User must control passthrough config too.\n    requestBuilder(request, body);\n  } else {\n    body = { ...body, ...restOfConfig }; // passthrough for other config\n  }\n  const response_format = request.output?.format;\n  if (response_format === 'json') {\n    if (request.output?.schema) {\n      body.response_format = {\n        type: 'json_schema',\n        json_schema: {\n          name: 'output',\n          schema: request.output!.schema,\n        },\n      };\n    } else {\n      body.response_format = {\n        type: 'json_object',\n      };\n    }\n  } else if (response_format === 'text') {\n    body.response_format = {\n      type: 'text',\n    };\n  }\n  for (const key in body) {\n    if (!body[key] || (Array.isArray(body[key]) && !body[key].length))\n      delete body[key];\n  }\n  return body;\n}\n\n/**\n * Creates the runner used by Genkit to interact with an OpenAI compatible\n * model.\n * @param name The name of the GPT model.\n * @param client The OpenAI client instance.\n * @returns The runner that Genkit will call when the model is invoked.\n */\nexport function openAIModelRunner(\n  name: string,\n  defaultClient: OpenAI,\n  requestBuilder?: ModelRequestBuilder,\n  pluginOptions?: Omit<PluginOptions, 'apiKey'>\n) {\n  return async (\n    request: GenerateRequest,\n    options?: {\n      streamingRequested?: boolean;\n      sendChunk?: StreamingCallback<GenerateResponseChunkData>;\n      abortSignal?: AbortSignal;\n    }\n  ): Promise<GenerateResponseData> => {\n    const client = maybeCreateRequestScopedOpenAIClient(\n      pluginOptions,\n      request,\n      defaultClient\n    );\n    try {\n      let response: ChatCompletion;\n      const body = toOpenAIRequestBody(name, request, requestBuilder);\n      if (options?.streamingRequested) {\n        const stream = client.beta.chat.completions.stream(\n          {\n            ...body,\n            stream: true,\n            stream_options: {\n              include_usage: true,\n            },\n          },\n          { signal: options?.abortSignal }\n        );\n        for await (const chunk of stream) {\n          chunk.choices?.forEach((chunk) => {\n            const c = fromOpenAIChunkChoice(chunk);\n            options?.sendChunk!({\n              index: chunk.index,\n              content: c.message?.content ?? [],\n            });\n          });\n        }\n        response = await stream.finalChatCompletion();\n      } else {\n        response = await client.chat.completions.create(body, {\n          signal: options?.abortSignal,\n        });\n      }\n      const standardResponse: GenerateResponseData = {\n        usage: {\n          inputTokens: response.usage?.prompt_tokens,\n          outputTokens: response.usage?.completion_tokens,\n          totalTokens: response.usage?.total_tokens,\n        },\n        raw: response,\n      };\n      if (response.choices.length === 0) {\n        return standardResponse;\n      } else {\n        const choice = response.choices[0];\n        return {\n          ...fromOpenAIChoice(choice, request.output?.format === 'json'),\n          ...standardResponse,\n        };\n      }\n    } catch (e) {\n      if (e instanceof APIError) {\n        let status: StatusName = 'UNKNOWN';\n        switch (e.status) {\n          case 429:\n            status = 'RESOURCE_EXHAUSTED';\n            break;\n          case 401:\n            status = 'PERMISSION_DENIED';\n            break;\n          case 403:\n            status = 'UNAUTHENTICATED';\n            break;\n          case 400:\n            status = 'INVALID_ARGUMENT';\n            break;\n          case 500:\n            status = 'INTERNAL';\n            break;\n          case 503:\n            status = 'UNAVAILABLE';\n            break;\n        }\n        const retryAfterHeader =\n          e.headers?.get?.('retry-after') ??\n          (e.headers as any)?.['retry-after'];\n        const retryAfterMs = retryAfterHeader\n          ? parseRetryAfterMs(retryAfterHeader)\n          : undefined;\n        const responseMetadata: ErrorResponseMetadata | undefined =\n          retryAfterMs !== undefined ? { retryAfterMs } : undefined;\n        throw new GenkitError({\n          status,\n          message: e.message,\n          responseMetadata,\n        });\n      }\n      throw e;\n    }\n  };\n}\n\n/**\n * Method to define a new Genkit Model that is compatible with Open AI\n * Chat Completions API.\n *\n * These models are to be used to chat with a large language model.\n *\n * @param params An object containing parameters for defining the OpenAI\n * Chat model.\n * @param params.ai The Genkit AI instance.\n * @param params.name The name of the model.\n * @param params.client The OpenAI client instance.\n * @param params.modelRef Optional reference to the model's configuration and\n * custom options.\n\n * @returns the created {@link ModelAction}\n */\nexport function defineCompatOpenAIModel<\n  CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n>(params: {\n  name: string;\n  client: OpenAI;\n  modelRef?: ModelReference<CustomOptions>;\n  requestBuilder?: ModelRequestBuilder;\n  pluginOptions?: PluginOptions;\n}): ModelAction {\n  const { name, client, pluginOptions, modelRef, requestBuilder } = params;\n  const modelName = toModelName(name, pluginOptions?.name);\n  const actionName =\n    modelRef?.name ?? `${pluginOptions?.name ?? 'compat-oai'}/${modelName}`;\n\n  return model(\n    {\n      name: actionName,\n      ...modelRef?.info,\n      configSchema: modelRef?.configSchema,\n    },\n    openAIModelRunner(modelName, client, requestBuilder, pluginOptions)\n  );\n}\n\nconst GENERIC_MODEL_INFO: ModelInfo = {\n  supports: {\n    multiturn: true,\n    media: true,\n    tools: true,\n    toolChoice: true,\n    systemRole: true,\n  },\n};\n\n/** ModelRef helper, with reasonable defaults for OpenAI-compatible providers */\nexport function compatOaiModelRef<\n  CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n>(params: {\n  name: string;\n  info?: ModelInfo;\n  configSchema?: CustomOptions;\n  config?: any;\n  namespace?: string;\n}): ModelReference<CustomOptions> {\n  const {\n    name,\n    info = GENERIC_MODEL_INFO,\n    configSchema,\n    config = undefined,\n    namespace,\n  } = params;\n  return modelRef({\n    name,\n    configSchema: configSchema || (ChatCompletionCommonConfigSchema as any),\n    info: info,\n    config,\n    namespace,\n  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