{"version":3,"file":"common.mjs","sources":["../../../../../src/llm/google/utils/common.ts"],"sourcesContent":["import {\n POSSIBLE_ROLES,\n type Part,\n type Content,\n type TextPart,\n type FileDataPart,\n type InlineDataPart,\n type FunctionCallPart,\n type GenerateContentCandidate,\n type EnhancedGenerateContentResponse,\n type FunctionDeclaration as GenerativeAIFunctionDeclaration,\n type FunctionDeclarationsTool as GoogleGenerativeAIFunctionDeclarationsTool,\n} from '@google/generative-ai';\nimport {\n AIMessage,\n AIMessageChunk,\n BaseMessage,\n ChatMessage,\n ToolMessage,\n ToolMessageChunk,\n MessageContent,\n MessageContentComplex,\n UsageMetadata,\n isAIMessage,\n isBaseMessage,\n isToolMessage,\n StandardContentBlockConverter,\n parseBase64DataUrl,\n convertToProviderContentBlock,\n isDataContentBlock,\n} from '@langchain/core/messages';\nimport { ChatGenerationChunk } from '@langchain/core/outputs';\nimport type { ChatGeneration, ChatResult } from '@langchain/core/outputs';\nimport { isLangChainTool } from '@langchain/core/utils/function_calling';\nimport { isOpenAITool } from '@langchain/core/language_models/base';\nimport { ToolCallChunk } from '@langchain/core/messages/tool';\nimport { v4 as uuidv4 } from 'uuid';\nimport {\n jsonSchemaToGeminiParameters,\n schemaToGenerativeAIParameters,\n} from './zod_to_genai_parameters';\nimport { GoogleGenerativeAIToolType } from '../types';\n\nexport const _FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY =\n '__gemini_function_call_thought_signatures__';\n\nconst DUMMY_SIGNATURE =\n 'ErYCCrMCAdHtim9kOoOkrPiCNVsmlpMIKd7ZMxgiFbVQOkgp7nlLcDMzVsZwIzvuT7nQROivoXA72ccC2lSDvR0Gh7dkWaGuj7ctv6t7ZceHnecx0QYa+ix8tYpRfjhyWozQ49lWiws6+YGjCt10KRTyWsZ2h6O7iHTYJwKIRwGUHRKy/qK/6kFxJm5ML00gLq4D8s5Z6DBpp2ZlR+uF4G8jJgeWQgyHWVdx2wGYElaceVAc66tZdPQRdOHpWtgYSI1YdaXgVI8KHY3/EfNc2YqqMIulvkDBAnuMhkAjV9xmBa54Tq+ih3Im4+r3DzqhGqYdsSkhS0kZMwte4Hjs65dZzCw9lANxIqYi1DJ639WNPYihp/DCJCos7o+/EeSPJaio5sgWDyUnMGkY1atsJZ+m7pj7DD5tvQ==';\n\n/**\n * Executes a function immediately and returns its result.\n * Functional utility similar to an Immediately Invoked Function Expression (IIFE).\n * @param fn The function to execute.\n * @returns The result of invoking fn.\n */\nexport const iife = (fn: () => T): T => fn();\n\nexport function getMessageAuthor(message: BaseMessage): string {\n const type = message._getType();\n if (ChatMessage.isInstance(message)) {\n return message.role;\n }\n if (type === 'tool') {\n return type;\n }\n return message.name ?? type;\n}\n\n/**\n * Maps a message type to a Google Generative AI chat author.\n * @param message The message to map.\n * @param model The model to use for mapping.\n * @returns The message type mapped to a Google Generative AI chat author.\n */\nexport function convertAuthorToRole(\n author: string\n): (typeof POSSIBLE_ROLES)[number] {\n switch (author) {\n /**\n * Note: Gemini currently is not supporting system messages\n * we will convert them to human messages and merge with following\n * */\n case 'supervisor':\n case 'ai':\n case 'model': // getMessageAuthor returns message.name. code ex.: return message.name ?? type;\n return 'model';\n case 'system':\n return 'system';\n case 'human':\n return 'user';\n case 'tool':\n case 'function':\n return 'function';\n default:\n throw new Error(`Unknown / unsupported author: ${author}`);\n }\n}\n\nfunction messageContentMedia(content: MessageContentComplex): Part {\n if ('mimeType' in content && 'data' in content) {\n return {\n inlineData: {\n mimeType: content.mimeType,\n data: content.data,\n },\n };\n }\n if ('mimeType' in content && 'fileUri' in content) {\n return {\n fileData: {\n mimeType: content.mimeType,\n fileUri: content.fileUri,\n },\n };\n }\n\n throw new Error('Invalid media content');\n}\n\nfunction inferToolNameFromPreviousMessages(\n message: ToolMessage | ToolMessageChunk,\n previousMessages: BaseMessage[]\n): string | undefined {\n return previousMessages\n .map((msg) => {\n if (isAIMessage(msg)) {\n return msg.tool_calls ?? [];\n }\n return [];\n })\n .flat()\n .find((toolCall) => {\n return toolCall.id === message.tool_call_id;\n })?.name;\n}\n\nfunction _getStandardContentBlockConverter(\n isMultimodalModel: boolean\n): StandardContentBlockConverter<{\n text: TextPart;\n image: FileDataPart | InlineDataPart;\n audio: FileDataPart | InlineDataPart;\n file: FileDataPart | InlineDataPart | TextPart;\n}> {\n const standardContentBlockConverter: StandardContentBlockConverter<{\n text: TextPart;\n image: FileDataPart | InlineDataPart;\n audio: FileDataPart | InlineDataPart;\n file: FileDataPart | InlineDataPart | TextPart;\n }> = {\n providerName: 'Google Gemini',\n\n fromStandardTextBlock(block) {\n return {\n text: block.text,\n };\n },\n\n fromStandardImageBlock(block): FileDataPart | InlineDataPart {\n if (!isMultimodalModel) {\n throw new Error('This model does not support images');\n }\n if (block.source_type === 'url') {\n const data = parseBase64DataUrl({ dataUrl: block.url });\n if (data) {\n return {\n inlineData: {\n mimeType: data.mime_type,\n data: data.data,\n },\n };\n } else {\n return {\n fileData: {\n mimeType: block.mime_type ?? '',\n fileUri: block.url,\n },\n };\n }\n }\n\n if (block.source_type === 'base64') {\n return {\n inlineData: {\n mimeType: block.mime_type ?? '',\n data: block.data,\n },\n };\n }\n\n throw new Error(`Unsupported source type: ${block.source_type}`);\n },\n\n fromStandardAudioBlock(block): FileDataPart | InlineDataPart {\n if (!isMultimodalModel) {\n throw new Error('This model does not support audio');\n }\n if (block.source_type === 'url') {\n const data = parseBase64DataUrl({ dataUrl: block.url });\n if (data) {\n return {\n inlineData: {\n mimeType: data.mime_type,\n data: data.data,\n },\n };\n } else {\n return {\n fileData: {\n mimeType: block.mime_type ?? '',\n fileUri: block.url,\n },\n };\n }\n }\n\n if (block.source_type === 'base64') {\n return {\n inlineData: {\n mimeType: block.mime_type ?? '',\n data: block.data,\n },\n };\n }\n\n throw new Error(`Unsupported source type: ${block.source_type}`);\n },\n\n fromStandardFileBlock(block): FileDataPart | InlineDataPart | TextPart {\n if (!isMultimodalModel) {\n throw new Error('This model does not support files');\n }\n if (block.source_type === 'text') {\n return {\n text: block.text,\n };\n }\n if (block.source_type === 'url') {\n const data = parseBase64DataUrl({ dataUrl: block.url });\n if (data) {\n return {\n inlineData: {\n mimeType: data.mime_type,\n data: data.data,\n },\n };\n } else {\n return {\n fileData: {\n mimeType: block.mime_type ?? '',\n fileUri: block.url,\n },\n };\n }\n }\n\n if (block.source_type === 'base64') {\n return {\n inlineData: {\n mimeType: block.mime_type ?? '',\n data: block.data,\n },\n };\n }\n throw new Error(`Unsupported source type: ${block.source_type}`);\n },\n };\n return standardContentBlockConverter;\n}\n\nfunction _convertLangChainContentToPart(\n content: MessageContentComplex,\n isMultimodalModel: boolean\n): Part | undefined {\n if (isDataContentBlock(content)) {\n return convertToProviderContentBlock(\n content,\n _getStandardContentBlockConverter(isMultimodalModel)\n );\n }\n\n if (content.type === 'text') {\n return { text: content.text };\n } else if (content.type === 'executableCode') {\n return { executableCode: content.executableCode };\n } else if (content.type === 'codeExecutionResult') {\n return { codeExecutionResult: content.codeExecutionResult };\n } else if (content.type === 'image_url') {\n if (!isMultimodalModel) {\n throw new Error('This model does not support images');\n }\n let source: string;\n if (typeof content.image_url === 'string') {\n source = content.image_url;\n } else if (\n typeof content.image_url === 'object' &&\n 'url' in content.image_url\n ) {\n source = content.image_url.url;\n } else {\n throw new Error('Please provide image as base64 encoded data URL');\n }\n const [dm, data] = source.split(',');\n if (!dm.startsWith('data:')) {\n throw new Error('Please provide image as base64 encoded data URL');\n }\n\n const [mimeType, encoding] = dm.replace(/^data:/, '').split(';');\n if (encoding !== 'base64') {\n throw new Error('Please provide image as base64 encoded data URL');\n }\n\n return {\n inlineData: {\n data,\n mimeType,\n },\n };\n } else if (content.type === 'media') {\n return messageContentMedia(content);\n } else if (content.type === 'tool_use') {\n return {\n functionCall: {\n name: content.name,\n args: content.input,\n },\n };\n } else if (\n content.type?.includes('/') === true &&\n // Ensure it's a single slash.\n content.type.split('/').length === 2 &&\n 'data' in content &&\n typeof content.data === 'string'\n ) {\n return {\n inlineData: {\n mimeType: content.type,\n data: content.data,\n },\n };\n } else if ('functionCall' in content) {\n // No action needed here — function calls will be added later from message.tool_calls\n return undefined;\n } else {\n if ('type' in content) {\n throw new Error(`Unknown content type ${content.type}`);\n } else {\n throw new Error(`Unknown content ${JSON.stringify(content)}`);\n }\n }\n}\n\nexport function convertMessageContentToParts(\n message: BaseMessage,\n isMultimodalModel: boolean,\n previousMessages: BaseMessage[],\n model?: string\n): Part[] {\n if (isToolMessage(message)) {\n const messageName =\n message.name ??\n inferToolNameFromPreviousMessages(message, previousMessages);\n if (messageName === undefined) {\n throw new Error(\n `Google requires a tool name for each tool call response, and we could not infer a called tool name for ToolMessage \"${message.id}\" from your passed messages. Please populate a \"name\" field on that ToolMessage explicitly.`\n );\n }\n\n const result = Array.isArray(message.content)\n ? (message.content\n .map((c) => _convertLangChainContentToPart(c, isMultimodalModel))\n .filter((p) => p !== undefined) as Part[])\n : message.content;\n\n if (message.status === 'error') {\n return [\n {\n functionResponse: {\n name: messageName,\n // The API expects an object with an `error` field if the function call fails.\n // `error` must be a valid object (not a string or array), so we wrap `message.content` here\n response: { error: { details: result } },\n },\n },\n ];\n }\n\n return [\n {\n functionResponse: {\n name: messageName,\n // again, can't have a string or array value for `response`, so we wrap it as an object here\n response: { result },\n },\n },\n ];\n }\n\n let functionCalls: FunctionCallPart[] = [];\n const messageParts: Part[] = [];\n\n if (typeof message.content === 'string' && message.content) {\n messageParts.push({ text: message.content });\n }\n\n if (Array.isArray(message.content)) {\n messageParts.push(\n ...(message.content\n .map((c) => _convertLangChainContentToPart(c, isMultimodalModel))\n .filter((p) => p !== undefined) as Part[])\n );\n }\n\n const functionThoughtSignatures = (\n message.additional_kwargs as BaseMessage['additional_kwargs'] | undefined\n )?.[_FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY] as\n | Record\n | undefined;\n\n if (isAIMessage(message) && (message.tool_calls?.length ?? 0) > 0) {\n functionCalls = (message.tool_calls ?? []).map((tc) => {\n const thoughtSignature = iife(() => {\n if (tc.id != null && tc.id !== '') {\n const signature = functionThoughtSignatures?.[tc.id];\n if (signature != null && signature !== '') {\n return signature;\n }\n }\n if (model?.includes('gemini-3') === true) {\n return DUMMY_SIGNATURE;\n }\n return '';\n });\n\n return {\n functionCall: {\n name: tc.name,\n args: tc.args,\n },\n ...(thoughtSignature ? { thoughtSignature } : {}),\n };\n });\n }\n\n return [...messageParts, ...functionCalls];\n}\n\nexport function convertBaseMessagesToContent(\n messages: BaseMessage[],\n isMultimodalModel: boolean,\n convertSystemMessageToHumanContent: boolean = false,\n\n model?: string\n): Content[] | undefined {\n return messages.reduce<{\n content: Content[] | undefined;\n mergeWithPreviousContent: boolean;\n }>(\n (acc, message, index) => {\n if (!isBaseMessage(message)) {\n throw new Error('Unsupported message input');\n }\n const author = getMessageAuthor(message);\n if (author === 'system' && index !== 0) {\n throw new Error('System message should be the first one');\n }\n const role = convertAuthorToRole(author);\n\n const prevContent = acc.content?.[acc.content.length];\n if (\n !acc.mergeWithPreviousContent &&\n prevContent &&\n prevContent.role === role\n ) {\n throw new Error(\n 'Google Generative AI requires alternate messages between authors'\n );\n }\n\n const parts = convertMessageContentToParts(\n message,\n isMultimodalModel,\n messages.slice(0, index),\n model\n );\n\n if (acc.mergeWithPreviousContent) {\n const prevContent = acc.content?.[acc.content.length - 1];\n if (!prevContent) {\n throw new Error(\n 'There was a problem parsing your system message. Please try a prompt without one.'\n );\n }\n prevContent.parts.push(...parts);\n\n return {\n mergeWithPreviousContent: false,\n content: acc.content,\n };\n }\n let actualRole = role;\n if (\n actualRole === 'function' ||\n (actualRole === 'system' && !convertSystemMessageToHumanContent)\n ) {\n // GenerativeAI API will throw an error if the role is not \"user\" or \"model.\"\n actualRole = 'user';\n }\n const content: Content = {\n role: actualRole,\n parts,\n };\n return {\n mergeWithPreviousContent:\n author === 'system' && !convertSystemMessageToHumanContent,\n content: [...(acc.content ?? []), content],\n };\n },\n { content: [], mergeWithPreviousContent: false }\n ).content;\n}\n\nexport function convertResponseContentToChatGenerationChunk(\n response: EnhancedGenerateContentResponse,\n extra: {\n usageMetadata?: UsageMetadata | undefined;\n index: number;\n }\n): ChatGenerationChunk | null {\n if (!response.candidates || response.candidates.length === 0) {\n return null;\n }\n const [candidate] = response.candidates as [\n Partial | undefined,\n ];\n const { content: candidateContent, ...generationInfo } = candidate ?? {};\n\n // Extract function calls directly from parts to preserve thoughtSignature\n const functionCalls =\n (candidateContent?.parts as Part[] | undefined)?.reduce(\n (acc, p) => {\n if ('functionCall' in p && p.functionCall) {\n acc.push({\n ...p,\n id:\n 'id' in p.functionCall && typeof p.functionCall.id === 'string'\n ? p.functionCall.id\n : uuidv4(),\n });\n }\n return acc;\n },\n [] as (\n | undefined\n | (FunctionCallPart & { id: string; thoughtSignature?: string })\n )[]\n ) ?? [];\n\n let content: MessageContent | undefined;\n // Checks if some parts do not have text. If false, it means that the content is a string.\n const reasoningParts: string[] = [];\n if (\n candidateContent != null &&\n Array.isArray(candidateContent.parts) &&\n candidateContent.parts.every((p) => 'text' in p)\n ) {\n // content = candidateContent.parts.map((p) => p.text).join('');\n const textParts: string[] = [];\n for (const part of candidateContent.parts) {\n if ('thought' in part && part.thought === true) {\n reasoningParts.push(part.text ?? '');\n continue;\n }\n textParts.push(part.text ?? '');\n }\n content = textParts.join('');\n } else if (candidateContent && Array.isArray(candidateContent.parts)) {\n content = candidateContent.parts\n .map((p) => {\n if ('text' in p && 'thought' in p && p.thought === true) {\n reasoningParts.push(p.text ?? '');\n return undefined;\n } else if ('text' in p) {\n return {\n type: 'text',\n text: p.text,\n };\n } else if ('executableCode' in p) {\n return {\n type: 'executableCode',\n executableCode: p.executableCode,\n };\n } else if ('codeExecutionResult' in p) {\n return {\n type: 'codeExecutionResult',\n codeExecutionResult: p.codeExecutionResult,\n };\n }\n return p;\n })\n .filter((p) => p !== undefined);\n } else {\n // no content returned - likely due to abnormal stop reason, e.g. malformed function call\n content = [];\n }\n\n let text = '';\n if (typeof content === 'string' && content) {\n text = content;\n } else if (Array.isArray(content)) {\n const block = content.find((b) => 'text' in b) as\n | { text: string }\n | undefined;\n text = block?.text ?? '';\n }\n\n const toolCallChunks: ToolCallChunk[] = [];\n if (functionCalls.length > 0) {\n toolCallChunks.push(\n ...functionCalls.map((fc) => ({\n type: 'tool_call_chunk' as const,\n id: fc?.id,\n name: fc?.functionCall.name,\n args: JSON.stringify(fc?.functionCall.args),\n }))\n );\n }\n\n // Extract thought signatures from function calls for Gemini 3+\n const functionThoughtSignatures = functionCalls.reduce(\n (acc, fc) => {\n if (\n fc &&\n 'thoughtSignature' in fc &&\n typeof fc.thoughtSignature === 'string'\n ) {\n acc[fc.id] = fc.thoughtSignature;\n }\n return acc;\n },\n {} as Record\n );\n\n const additional_kwargs: ChatGeneration['message']['additional_kwargs'] = {\n [_FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY]: functionThoughtSignatures,\n };\n\n if (reasoningParts.length > 0) {\n additional_kwargs.reasoning = reasoningParts.join('');\n }\n\n if (candidate?.groundingMetadata) {\n additional_kwargs.groundingMetadata = candidate.groundingMetadata;\n }\n\n const isFinalChunk =\n response.candidates[0]?.finishReason === 'STOP' ||\n response.candidates[0]?.finishReason === 'MAX_TOKENS' ||\n response.candidates[0]?.finishReason === 'SAFETY';\n\n return new ChatGenerationChunk({\n text,\n message: new AIMessageChunk({\n content: content,\n name: !candidateContent ? undefined : candidateContent.role,\n tool_call_chunks: toolCallChunks,\n // Each chunk can have unique \"generationInfo\", and merging strategy is unclear,\n // so leave blank for now.\n additional_kwargs,\n usage_metadata: isFinalChunk ? extra.usageMetadata : undefined,\n }),\n generationInfo,\n });\n}\n\n/**\n * Maps a Google GenerateContentResult to a LangChain ChatResult\n */\nexport function mapGenerateContentResultToChatResult(\n response: EnhancedGenerateContentResponse,\n extra?: {\n usageMetadata: UsageMetadata | undefined;\n }\n): ChatResult {\n if (\n !response.candidates ||\n response.candidates.length === 0 ||\n !response.candidates[0]\n ) {\n return {\n generations: [],\n llmOutput: {\n filters: response.promptFeedback,\n },\n };\n }\n const [candidate] = response.candidates as [\n Partial | undefined,\n ];\n const { content: candidateContent, ...generationInfo } = candidate ?? {};\n\n // Extract function calls directly from parts to preserve thoughtSignature\n const functionCalls =\n candidateContent?.parts.reduce(\n (acc, p) => {\n if ('functionCall' in p && p.functionCall) {\n acc.push({\n ...p,\n id:\n 'id' in p.functionCall && typeof p.functionCall.id === 'string'\n ? p.functionCall.id\n : uuidv4(),\n });\n }\n return acc;\n },\n [] as (FunctionCallPart & { id: string; thoughtSignature?: string })[]\n ) ?? [];\n\n let content: MessageContent | undefined;\n const reasoningParts: string[] = [];\n if (\n Array.isArray(candidateContent?.parts) &&\n candidateContent.parts.length === 1 &&\n candidateContent.parts[0].text &&\n !(\n 'thought' in candidateContent.parts[0] &&\n candidateContent.parts[0].thought === true\n )\n ) {\n content = candidateContent.parts[0].text;\n } else if (\n Array.isArray(candidateContent?.parts) &&\n candidateContent.parts.length > 0\n ) {\n content = candidateContent.parts\n .map((p) => {\n if ('text' in p && 'thought' in p && p.thought === true) {\n reasoningParts.push(p.text ?? '');\n return undefined;\n } else if ('text' in p) {\n return {\n type: 'text',\n text: p.text,\n };\n } else if ('executableCode' in p) {\n return {\n type: 'executableCode',\n executableCode: p.executableCode,\n };\n } else if ('codeExecutionResult' in p) {\n return {\n type: 'codeExecutionResult',\n codeExecutionResult: p.codeExecutionResult,\n };\n }\n return p;\n })\n .filter((p) => p !== undefined);\n } else {\n content = [];\n }\n let text = '';\n if (typeof content === 'string') {\n text = content;\n } else if (Array.isArray(content) && content.length > 0) {\n const block = content.find((b) => 'text' in b) as\n | { text: string }\n | undefined;\n text = block?.text ?? text;\n }\n\n const additional_kwargs: ChatGeneration['message']['additional_kwargs'] = {\n ...generationInfo,\n };\n if (reasoningParts.length > 0) {\n additional_kwargs.reasoning = reasoningParts.join('');\n }\n\n // Extract thought signatures from function calls for Gemini 3+\n const functionThoughtSignatures = functionCalls.reduce(\n (acc, fc) => {\n if ('thoughtSignature' in fc && typeof fc.thoughtSignature === 'string') {\n acc[fc.id] = fc.thoughtSignature;\n }\n return acc;\n },\n {} as Record\n );\n\n const tool_calls = functionCalls.map((fc) => ({\n type: 'tool_call' as const,\n id: fc.id,\n name: fc.functionCall.name,\n args: fc.functionCall.args,\n }));\n\n // Store thought signatures map for later retrieval\n additional_kwargs[_FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY] =\n functionThoughtSignatures;\n\n const generation: ChatGeneration = {\n text,\n message: new AIMessage({\n content: content ?? '',\n tool_calls,\n additional_kwargs,\n usage_metadata: extra?.usageMetadata,\n }),\n generationInfo,\n };\n return {\n generations: [generation],\n llmOutput: {\n tokenUsage: {\n promptTokens: extra?.usageMetadata?.input_tokens,\n completionTokens: extra?.usageMetadata?.output_tokens,\n totalTokens: extra?.usageMetadata?.total_tokens,\n },\n },\n };\n}\n\nexport function convertToGenerativeAITools(\n tools: GoogleGenerativeAIToolType[]\n): GoogleGenerativeAIFunctionDeclarationsTool[] {\n if (\n tools.every(\n (tool) =>\n 'functionDeclarations' in tool &&\n Array.isArray(tool.functionDeclarations)\n )\n ) {\n return tools as GoogleGenerativeAIFunctionDeclarationsTool[];\n }\n return [\n {\n functionDeclarations: tools.map(\n (tool): GenerativeAIFunctionDeclaration => {\n if (isLangChainTool(tool)) {\n const jsonSchema = schemaToGenerativeAIParameters(tool.schema);\n if (\n jsonSchema.type === 'object' &&\n 'properties' in jsonSchema &&\n Object.keys(jsonSchema.properties).length === 0\n ) {\n return {\n name: tool.name,\n description: tool.description,\n };\n }\n return {\n name: tool.name,\n description: tool.description,\n parameters: jsonSchema,\n };\n }\n if (isOpenAITool(tool)) {\n return {\n name: tool.function.name,\n description:\n tool.function.description ?? 'A function available to call.',\n parameters: jsonSchemaToGeminiParameters(\n tool.function.parameters\n ),\n };\n }\n return tool as unknown as GenerativeAIFunctionDeclaration;\n }\n ),\n },\n 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