@robota-sdk/google/dist/index.cjs.map

Google AI integration for Robota SDK - Gemini Pro, Gemini Flash, function calling, and tool integration with Google's Generative AI

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type { UniversalMessage, UserMessage, AssistantMessage, SystemMessage, ToolMessage } from '@robota-sdk/core';\nimport type { Content, Part } from '@google/generative-ai';\n\n/**\n * Google AI ConversationHistory adapter\n * \n * Converts UniversalMessage to Google Generative AI format\n */\nexport class GoogleConversationAdapter {\n    /**\n     * Convert UniversalMessage array to Google AI message format\n     */\n    static toGoogleFormat(messages: UniversalMessage[]): Content[] {\n        return messages\n            .filter(msg => msg.role !== 'system') // System messages are handled separately\n            .map(msg => this.convertMessage(msg));\n    }\n\n    /**\n     * Convert a single UniversalMessage to Google AI format\n     */\n    static convertMessage(msg: UniversalMessage): Content {\n        const messageRole = msg.role;\n\n        // Handle user messages\n        if (messageRole === 'user') {\n            const userMsg = msg as UserMessage;\n            return {\n                role: 'user',\n                parts: [{ text: userMsg.content }]\n            };\n        }\n\n        // Handle assistant messages\n        if (messageRole === 'assistant') {\n            const assistantMsg = msg as AssistantMessage;\n            if ((assistantMsg as any).toolCalls) {\n                const parts: Part[] = [{ text: assistantMsg.content || '' }];\n\n                // Add tool calls if they exist\n                const toolCalls = (assistantMsg as any).toolCalls;\n                for (const tc of toolCalls) {\n                    parts.push({\n                        functionCall: {\n                            name: tc.function.name,\n                            args: JSON.parse(tc.function.arguments)\n                        }\n                    } as Part);\n                }\n\n                return {\n                    role: 'model',\n                    parts\n                };\n            }\n            return {\n                role: 'model',\n                parts: [{ text: assistantMsg.content || '' }]\n            };\n        }\n\n        // Handle tool messages\n        if (messageRole === 'tool') {\n            const toolMsg = msg as ToolMessage;\n            return {\n                role: 'function',\n                parts: [\n                    {\n                        functionResponse: {\n                            name: toolMsg.name || 'unknown',\n                            response: toolMsg.content\n                        }\n                    } as any\n                ]\n            };\n        }\n\n        // Handle system messages (convert to user)\n        if (messageRole === 'system') {\n            const systemMsg = msg as SystemMessage;\n            return {\n                role: 'user',\n                parts: [{ text: `[System]: ${systemMsg.content}` }]\n            };\n        }\n\n        // This should never happen but TypeScript requires exhaustive checking\n        const _exhaustiveCheck: never = msg;\n        return _exhaustiveCheck;\n    }\n\n    /**\n     * Extract system messages and combine them as system instruction\n     */\n    static extractSystemInstruction(messages: UniversalMessage[], fallbackSystemPrompt?: string): string | undefined {\n        const systemMessages = messages.filter(msg => msg.role === 'system') as SystemMessage[];\n\n        if (systemMessages.length > 0) {\n            return systemMessages.map(msg => msg.content).join('\\n\\n');\n        }\n\n        return fallbackSystemPrompt;\n    }\n\n    /**\n     * Complete message conversion pipeline\n     */\n    static processMessages(\n        messages: UniversalMessage[],\n        systemPrompt?: string\n    ): {\n        contents: Content[],\n        systemInstruction?: string\n    } {\n        // 1. Extract system instruction\n        const systemInstruction = this.extractSystemInstruction(messages, systemPrompt);\n\n        // 2. Convert messages to Google AI format\n        const contents = this.toGoogleFormat(messages);\n\n        return {\n            contents,\n            systemInstruction\n        };\n    }\n} ","import { GoogleGenerativeAI } from '@google/generative-ai';\nimport { BaseAIProvider } from '@robota-sdk/core';\nimport type {\n    Context,\n    ModelResponse,\n    StreamingResponseChunk,\n    UniversalMessage\n} from '@robota-sdk/core';\nimport type { FunctionSchema } from '@robota-sdk/tools';\nimport type { GoogleProviderOptions } from './types';\nimport { GoogleConversationAdapter } from './adapter';\n\n/**\n * Google AI provider implementation for Robota\n * \n * Provides integration with Google's Generative AI services including Gemini models.\n * Extends BaseAIProvider for common functionality and tool calling support.\n * \n * @see {@link ../../../apps/examples/03-integrations | Provider Integration Examples}\n * \n * @public\n */\nexport class GoogleProvider extends BaseAIProvider {\n    /**\n     * Provider identifier name\n     * @readonly\n     */\n    public readonly name: string = 'google';\n\n    /**\n     * Google AI client instance\n     * @internal\n     */\n    private readonly client: GoogleGenerativeAI;\n\n    /**\n     * Provider configuration options\n     * @readonly\n     */\n    public readonly options: GoogleProviderOptions;\n\n    /**\n     * Create a new Google AI provider instance\n     * \n     * @param options - Configuration options for the Google provider\n     * \n     * @throws {Error} When client is not provided in options\n     */\n    constructor(options: GoogleProviderOptions) {\n        super();\n\n        this.options = {\n            temperature: 0.7,\n            maxTokens: undefined,\n            ...options\n        };\n\n        // Validate required client injection\n        if (!options.client) {\n            throw new Error('Google AI client is not injected. The client option is required.');\n        }\n\n        this.client = options.client;\n    }\n\n    /**\n     * Send a chat request to Google AI and receive a complete response\n     * \n     * @param model - Model name to use (e.g., 'gemini-1.5-pro', 'gemini-1.5-flash')\n     * @param context - Context object containing messages and system prompt\n     * @param options - Optional generation parameters and tools\n     * @returns Promise resolving to the model's response\n     * \n     * @throws {Error} When context is invalid\n     * @throws {Error} When messages array is invalid\n     * @throws {Error} When Google AI API call fails\n     */\n    async chat(model: string, context: Context, options?: any): Promise<ModelResponse> {\n        // Use base class validation\n        this.validateContext(context);\n\n        const { messages, systemPrompt } = context;\n\n        try {\n            // Convert UniversalMessage[] to Google AI format\n            const { contents, systemInstruction } = GoogleConversationAdapter.processMessages(\n                messages as UniversalMessage[],\n                systemPrompt\n            );\n\n            // Configure tools if provided\n            const toolConfig = this.configureTools(options?.tools);\n            const modelConfig: any = {\n                model: model || this.options.model || 'gemini-1.5-flash',\n                systemInstruction: systemInstruction\n            };\n\n            // Add tools to model configuration if available\n            if (toolConfig) {\n                modelConfig.tools = toolConfig.tools;\n            }\n\n            // Get Google AI model instance\n            const generativeModel = this.client.getGenerativeModel(modelConfig);\n\n            // Configure generation parameters\n            const generationConfig = {\n                temperature: options?.temperature ?? this.options.temperature,\n                maxOutputTokens: options?.maxTokens ?? this.options.maxTokens,\n                ...(this.options.responseMimeType && {\n                    responseMimeType: this.options.responseMimeType\n                }),\n                ...(this.options.responseSchema && {\n                    responseSchema: this.options.responseSchema\n                })\n            };\n\n            // Generate content\n            const result = await generativeModel.generateContent({\n                contents,\n                generationConfig\n            });\n\n            return this.parseResponse(result);\n        } catch (error) {\n            this.handleApiError(error, 'chat');\n        }\n    }\n\n    /**\n     * Send a streaming chat request to Google AI and receive response chunks\n     * \n     * Generates an async iterator that yields response chunks as they arrive.\n     * Useful for real-time display of responses or handling large responses incrementally.\n     * \n     * @param model - Model name to use\n     * @param context - Context object containing messages and system prompt\n     * @param options - Optional generation parameters and tools\n     * @returns Async generator yielding response chunks\n     * \n     * @throws {Error} When context is invalid\n     * @throws {Error} When messages array is invalid\n     * @throws {Error} When Google AI API streaming call fails\n     */\n    async *chatStream(model: string, context: Context, options?: any): AsyncGenerator<StreamingResponseChunk, void, unknown> {\n        // Use base class validation\n        this.validateContext(context);\n\n        const { messages, systemPrompt } = context;\n\n        try {\n            // Convert UniversalMessage[] to Google AI format\n            const { contents, systemInstruction } = GoogleConversationAdapter.processMessages(\n                messages as UniversalMessage[],\n                systemPrompt\n            );\n\n            // Configure tools if provided\n            const toolConfig = this.configureTools(options?.tools);\n            const modelConfig: any = {\n                model: model || this.options.model || 'gemini-1.5-flash',\n                systemInstruction: systemInstruction\n            };\n\n            // Add tools to model configuration if available\n            if (toolConfig) {\n                modelConfig.tools = toolConfig.tools;\n            }\n\n            // Get Google AI model instance\n            const generativeModel = this.client.getGenerativeModel(modelConfig);\n\n            // Configure generation parameters\n            const generationConfig = {\n                temperature: options?.temperature ?? this.options.temperature,\n                maxOutputTokens: options?.maxTokens ?? this.options.maxTokens,\n                ...(this.options.responseMimeType && {\n                    responseMimeType: this.options.responseMimeType\n                }),\n                ...(this.options.responseSchema && {\n                    responseSchema: this.options.responseSchema\n                })\n            };\n\n            // Generate streaming content\n            const result = await generativeModel.generateContentStream({\n                contents,\n                generationConfig\n            });\n\n            for await (const chunk of result.stream) {\n                yield this.parseStreamingChunk(chunk);\n            }\n        } catch (error) {\n            this.handleApiError(error, 'chatStream');\n        }\n    }\n\n    /**\n     * Configure tools for Google AI API request\n     * \n     * Google AI supports function calling with Gemini models.\n     * Transforms function schemas into Google AI tool format.\n     * \n     * @param tools - Array of function schemas\n     * @returns Google AI tool configuration object or undefined\n     */\n    protected configureTools(tools?: FunctionSchema[]): { tools: any[] } | undefined {\n        if (!tools || !Array.isArray(tools)) {\n            return undefined;\n        }\n\n        return {\n            tools: [{\n                functionDeclarations: tools.map(fn => ({\n                    name: fn.name,\n                    description: fn.description || '',\n                    parameters: fn.parameters\n                }))\n            }]\n        };\n    }\n\n    /**\n     * Parse Google AI response into universal ModelResponse format\n     * \n     * Extracts content, usage information, and metadata from the Google AI response\n     * and converts it to the standard format used across all providers.\n     * Supports function calling with Gemini models.\n     * \n     * @param response - Raw response from Google AI API\n     * @returns Parsed model response in universal format\n     * \n     * @internal\n     */\n    parseResponse(response: any): ModelResponse {\n        let content = '';\n        const toolCalls: any[] = [];\n\n        // Extract content and function calls from response\n        const candidate = response.response?.candidates?.[0];\n        if (candidate?.content?.parts) {\n            for (const part of candidate.content.parts) {\n                if (part.text) {\n                    content += part.text;\n                } else if (part.functionCall) {\n                    // Convert Google AI function call to OpenAI format for consistency\n                    toolCalls.push({\n                        id: `call_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,\n                        type: 'function' as const,\n                        function: {\n                            name: part.functionCall.name,\n                            arguments: JSON.stringify(part.functionCall.args || {})\n                        }\n                    });\n                }\n            }\n        }\n\n        // Extract usage information from response if available\n        const usageMetadata = response.response?.usageMetadata;\n        const usage = usageMetadata ? {\n            promptTokens: usageMetadata.promptTokenCount || 0,\n            completionTokens: usageMetadata.candidatesTokenCount || 0,\n            totalTokens: usageMetadata.totalTokenCount || 0\n        } : {\n            promptTokens: 0,\n            completionTokens: 0,\n            totalTokens: 0\n        };\n\n        const result: ModelResponse = {\n            content: content || undefined,\n            usage,\n            metadata: {\n                model: response.response?.model,\n                finishReason: candidate?.finishReason,\n                safetyRatings: candidate?.safetyRatings\n            }\n        };\n\n        // Add tool calls if present\n        if (toolCalls.length > 0) {\n            result.toolCalls = toolCalls;\n        }\n\n        return result;\n    }\n\n    /**\n     * Parse Google AI streaming response chunk into universal format\n     * \n     * Converts individual chunks from the streaming response into the standard\n     * StreamingResponseChunk format used across all providers.\n     * \n     * @param chunk - Raw chunk from Google AI streaming API\n     * @returns Parsed streaming response chunk\n     * \n     * @internal\n     */\n    parseStreamingChunk(chunk: any): StreamingResponseChunk {\n        const text = chunk.text() || '';\n\n        // Determine if this is the final chunk\n        const candidate = chunk.candidates?.[0];\n        const isComplete = candidate?.finishReason !== undefined && candidate.finishReason !== null;\n\n        return {\n            content: text,\n            isComplete\n        };\n    }\n\n    /**\n     * Release resources and close connections\n     * \n     * Performs cleanup operations when the provider is no longer needed.\n     * Google AI client doesn't require explicit cleanup, so this is a no-op.\n     * \n     * @returns Promise that resolves when cleanup is complete\n     */\n    async close(): Promise<void> {\n        // Google AI client doesn't have explicit close method\n        // This is implemented as no-op for interface compliance\n    }\n} "]}