{"version":3,"sources":["../src/gpt.ts"],"sourcesContent":["/**\n * Copyright 2024 The Fire Company\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 { CandidateData, ModelInfo } from 'genkit/model';\nimport type {\n StreamingCallback,\n GenerateRequest,\n GenerateResponseData,\n Genkit,\n MessageData,\n ModelReference,\n Part,\n Role,\n ToolRequestPart,\n} from 'genkit';\nimport { Message, GenerationCommonConfigSchema, z } from 'genkit';\nimport type {\n GenerateResponseChunkData,\n ModelAction,\n ToolDefinition,\n} from 'genkit/model';\nimport { modelRef } from 'genkit/model';\nimport type OpenAI from 'openai';\nimport {\n type ChatCompletion,\n type ChatCompletionChunk,\n type ChatCompletionContentPart,\n type ChatCompletionCreateParamsNonStreaming,\n type ChatCompletionMessageParam,\n type ChatCompletionMessageToolCall,\n type ChatCompletionRole,\n type ChatCompletionTool,\n type CompletionChoice,\n} from 'openai/resources/index.mjs';\n\nconst MODELS_SUPPORTING_OPENAI_RESPONSE_FORMAT = [\n 'gpt-4.5-preview',\n 'gpt-4o',\n 'gpt-4o-2024-05-13',\n 'gpt-4o-mini',\n 'gpt-4o-mini-2024-07-18',\n 'gpt-4-turbo',\n 'gpt-4-turbo-2024-04-09',\n 'gpt-4-turbo-preview',\n 'gpt-4-0125-preview',\n 'gpt-4-1106-preview',\n 'gpt-3.5-turbo-0125',\n 'gpt-3.5-turbo',\n 'gpt-3.5-turbo-1106',\n 'o1-preview',\n];\n\nexport const OpenAiConfigSchema = GenerationCommonConfigSchema.extend({\n frequencyPenalty: z.number().min(-2).max(2).optional(),\n logitBias: z.record(z.string(), z.number().min(-100).max(100)).optional(),\n logProbs: z.boolean().optional(),\n presencePenalty: z.number().min(-2).max(2).optional(),\n seed: z.number().int().optional(),\n topLogProbs: z.number().int().min(0).max(20).optional(),\n user: z.string().optional(),\n visualDetailLevel: z.enum(['auto', 'low', 'high']).optional(),\n});\n\ntype VisualDetailLevel = z.infer<\n typeof OpenAiConfigSchema\n>['visualDetailLevel'];\n\nexport const gpt45 = modelRef({\n name: 'openai/gpt-4.5',\n info: {\n versions: ['gpt-4.5-preview'],\n label: 'OpenAI - GPT-4.5',\n supports: {\n multiturn: true,\n tools: true,\n media: true,\n systemRole: true,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt4o = modelRef({\n name: 'openai/gpt-4o',\n info: {\n versions: ['gpt-4o', 'gpt-4o-2024-05-13'],\n label: 'OpenAI - GPT-4o',\n supports: {\n multiturn: true,\n tools: true,\n media: true,\n systemRole: true,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const o1Preview = modelRef({\n name: 'openai/o1-preview',\n info: {\n versions: ['o1-preview'],\n label: 'OpenAI - o1 Preview',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: false,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const o1Mini = modelRef({\n name: 'openai/o1',\n info: {\n versions: ['o1-mini'],\n label: 'OpenAI - o1 Mini',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: false,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const o1 = modelRef({\n name: 'openai/o1',\n info: {\n versions: ['o1'],\n label: 'OpenAI - o1',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: false,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const o3Mini = modelRef({\n name: 'openai/o3-mini',\n info: {\n versions: ['o3-mini'],\n label: 'OpenAI - o3 Mini',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: false,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt4oMini = modelRef({\n name: 'openai/gpt-4o-mini',\n info: {\n versions: ['gpt-4o-mini', 'gpt-4o-mini-2024-07-18'],\n label: 'OpenAI - GPT-4o mini',\n supports: {\n multiturn: true,\n tools: true,\n media: true,\n systemRole: true,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt4Turbo = modelRef({\n name: 'openai/gpt-4-turbo',\n info: {\n versions: [\n 'gpt-4-turbo',\n 'gpt-4-turbo-2024-04-09',\n 'gpt-4-turbo-preview',\n 'gpt-4-0125-preview',\n 'gpt-4-1106-preview',\n ],\n label: 'OpenAI - GPT-4 Turbo',\n supports: {\n multiturn: true,\n tools: true,\n media: true,\n systemRole: true,\n output: ['text', 'json'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt4Vision = modelRef({\n name: 'openai/gpt-4-vision',\n info: {\n versions: ['gpt-4-vision-preview', 'gpt-4-1106-vision-preview'],\n label: 'OpenAI - GPT-4 Vision',\n supports: {\n multiturn: true,\n tools: false,\n media: true,\n systemRole: true,\n output: ['text'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt4 = modelRef({\n name: 'openai/gpt-4',\n info: {\n versions: ['gpt-4', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-32k-0613'],\n label: 'OpenAI - GPT-4',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: true,\n output: ['text'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const gpt35Turbo = modelRef({\n name: 'openai/gpt-3.5-turbo',\n info: {\n versions: ['gpt-3.5-turbo-0125', 'gpt-3.5-turbo', 'gpt-3.5-turbo-1106'],\n label: 'OpenAI - GPT-3.5 Turbo',\n supports: {\n multiturn: true,\n tools: true,\n media: false,\n systemRole: true,\n output: ['json', 'text'],\n },\n },\n configSchema: OpenAiConfigSchema,\n});\n\nexport const SUPPORTED_GPT_MODELS: Record<\n string,\n ModelReference\n> = {\n 'gpt-4.5': gpt45,\n 'gpt-4o': gpt4o,\n 'gpt-4o-mini': gpt4oMini,\n 'gpt-4-turbo': gpt4Turbo,\n 'gpt-4-vision': gpt4Vision,\n 'gpt-4': gpt4,\n 'gpt-3.5-turbo': gpt35Turbo,\n 'o1-preview': o1Preview,\n o1: o1,\n 'o1-mini': o1Mini,\n 'o3-mini': o3Mini,\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 */\nfunction 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 * 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 return {\n type: 'image_url',\n image_url: {\n url: part.media.url,\n detail: visualDetailLevel,\n },\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 openAiMsgs: 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, index) => {\n if (item.type === 'text') {\n openAiMsgs.push({\n role: role,\n content: item.text,\n });\n }\n });\n } else {\n openAiMsgs.push({\n role: role,\n content: content,\n });\n }\n break;\n case 'system':\n openAiMsgs.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;\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 openAiMsgs.push({\n role: role,\n tool_calls: toolCalls,\n });\n } else {\n openAiMsgs.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 openAiMsgs.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 openAiMsgs;\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 CandidateData['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 arugments 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 CandidateData 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 CandidateData object.\n */\nexport function fromOpenAiChoice(\n choice: ChatCompletion.Choice,\n jsonMode = false\n): CandidateData {\n const toolRequestParts = choice.message.tool_calls?.map((toolCall) =>\n fromOpenAiToolCall(toolCall, choice)\n );\n return {\n index: choice.index,\n finishReason: finishReasonMap[choice.finish_reason] || 'other',\n message: {\n role: 'model',\n content: toolRequestParts\n ? // Note: Not sure why I have to cast here exactly.\n // Otherwise it thinks toolRequest must be 'undefined' if provided\n (toolRequestParts as ToolRequestPart[])\n : [\n jsonMode\n ? { data: JSON.parse(choice.message.content!) }\n : { text: choice.message.content! },\n ],\n },\n custom: {},\n };\n}\n\n/**\n * Converts an OpenAI message stream event to a Genkit CandidateData 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 CandidateData object.\n */\nexport function fromOpenAiChunkChoice(\n choice: ChatCompletionChunk.Choice,\n jsonMode = false\n): CandidateData {\n const toolRequestParts = choice.delta.tool_calls?.map((toolCall) =>\n fromOpenAiToolCall(toolCall, choice)\n );\n return {\n index: choice.index,\n finishReason: choice.finish_reason\n ? finishReasonMap[choice.finish_reason] || 'other'\n : 'unknown',\n message: {\n role: 'model',\n content: toolRequestParts\n ? // Note: Not sure why I have to cast here exactly.\n // Otherwise it thinks toolRequest must be 'undefined' if provided\n (toolRequestParts as ToolRequestPart[])\n : [\n jsonMode\n ? { data: JSON.parse(choice.delta.content!) }\n : { text: choice.delta.content! },\n ],\n },\n custom: {},\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) {\n const model = SUPPORTED_GPT_MODELS[modelName];\n if (!model) throw new Error(`Unsupported model: ${modelName}`);\n const openAiMessages = toOpenAiMessages(\n request.messages,\n request.config?.visualDetailLevel\n );\n const mappedModelName = request.config?.version || model.version || modelName;\n const body = {\n model: mappedModelName,\n messages: openAiMessages,\n temperature: request.config?.temperature,\n max_tokens: request.config?.maxOutputTokens,\n top_p: request.config?.topP,\n stop: request.config?.stopSequences,\n frequency_penalty: request.config?.frequencyPenalty,\n logit_bias: request.config?.logitBias,\n logprobs: request.config?.logProbs, // logprobs not snake case!\n presence_penalty: request.config?.presencePenalty,\n seed: request.config?.seed,\n top_logprobs: request.config?.topLogProbs, // logprobs not snake case!\n user: request.config?.user,\n tools: request.tools?.map(toOpenAiTool),\n n: request.candidates,\n } as ChatCompletionCreateParamsNonStreaming;\n\n const response_format = request.output?.format;\n if (\n response_format &&\n MODELS_SUPPORTING_OPENAI_RESPONSE_FORMAT.includes(mappedModelName)\n ) {\n if (\n response_format === 'json' &&\n model.info?.supports?.output?.includes('json')\n ) {\n body.response_format = {\n type: 'json_object',\n };\n } else if (\n response_format === 'text' &&\n model.info?.supports?.output?.includes('text')\n ) {\n body.response_format = {\n type: 'text',\n };\n } else {\n throw new Error(\n `${response_format} format is not supported for GPT models currently`\n );\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 the GPT 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 gptRunner(name: string, client: OpenAI) {\n return async (\n request: GenerateRequest,\n streamingCallback?: StreamingCallback\n ): Promise => {\n let response: ChatCompletion;\n const body = toOpenAiRequestBody(name, request);\n if (streamingCallback) {\n const stream = client.beta.chat.completions.stream({\n ...body,\n stream: true,\n });\n for await (const chunk of stream) {\n chunk.choices?.forEach((chunk) => {\n const c = fromOpenAiChunkChoice(chunk);\n streamingCallback({\n index: c.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 }\n return {\n candidates: response.choices.map((c) =>\n fromOpenAiChoice(c, request.output?.format === 'json')\n ),\n usage: {\n inputTokens: response.usage?.prompt_tokens,\n outputTokens: response.usage?.completion_tokens,\n totalTokens: response.usage?.total_tokens,\n },\n custom: response,\n };\n };\n}\n\n/**\n * Defines a GPT model with the given name and OpenAI client.\n * @param name The name of the GPT model.\n * @param client The OpenAI client instance.\n * @returns The defined GPT model.\n * @throws An error if the specified model is not supported.\n */\nexport function gptModel(\n ai: Genkit,\n name: string,\n client: OpenAI,\n modelInfo?: ModelInfo,\n modelConfig?: any\n): ModelAction {\n const modelId = `openai/${name}`;\n const model = SUPPORTED_GPT_MODELS[name];\n if (!model) {\n SUPPORTED_GPT_MODELS[name] = modelRef({\n name: modelId,\n info: modelInfo,\n configSchema: modelConfig?.configSchema,\n });\n }\n\n // Use the built-in model info and config schema or override if provided\n const modelInformation = modelInfo ? modelInfo : model.info;\n const configSchema = modelConfig\n ? modelConfig.configSchema\n : model.configSchema;\n\n return ai.defineModel(\n {\n name: modelId,\n ...modelInformation,\n configSchema,\n },\n gptRunner(name, client)\n 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