{"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 Genkit,\n MessageData,\n ModelReference,\n Part,\n Role,\n StreamingCallback,\n ToolRequestPart,\n} from 'genkit';\nimport { GenerationCommonConfigSchema, Message, modelRef, z } from 'genkit';\nimport type { ModelAction, ModelInfo, ToolDefinition } from 'genkit/model';\nimport type OpenAI 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';\n\nconst VisualDetailLevelSchema = z.enum(['auto', 'low', 'high']).optional();\n\ntype VisualDetailLevel = z.infer;\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 * 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 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;\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 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 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 return {\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 };\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 return {\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 };\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 ...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 body.response_format = {\n type: 'json_object',\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 client: OpenAI,\n requestBuilder?: ModelRequestBuilder\n) {\n return async (\n request: GenerateRequest,\n options?: {\n streamingRequested?: boolean;\n sendChunk?: StreamingCallback;\n abortSignal?: AbortSignal;\n }\n ): Promise => {\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 };\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 ai: Genkit;\n name: string;\n client: OpenAI;\n modelRef?: ModelReference;\n requestBuilder?: ModelRequestBuilder;\n}): ModelAction {\n const { ai, name, client, modelRef, requestBuilder } = params;\n const modelName = name.substring(name.indexOf('/') + 1);\n\n return ai.defineModel(\n {\n name,\n apiVersion: 'v2',\n ...modelRef?.info,\n configSchema: modelRef?.configSchema,\n },\n openAIModelRunner(modelName!, client, requestBuilder)\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}): ModelReference {\n const {\n name,\n info = GENERIC_MODEL_INFO,\n configSchema,\n config = undefined,\n } = params;\n return modelRef({\n name,\n configSchema: configSchema || (ChatCompletionCommonConfigSchema as any),\n info: info,\n config,\n 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