@mymediset/sap-ai-provider/dist/index.cjs.map

SAP AI Core provider for AI SDK (powered by @sap-ai-sdk/orchestration)

{"version":3,"sources":["../src/index.ts","../src/sap-ai-chat-language-model.ts","../src/convert-to-sap-messages.ts","../src/sap-ai-provider.ts","../src/sap-ai-chat-settings.ts","../src/sap-ai-error.ts","../src/types/completion-response.ts"],"sourcesContent":["// Provider exports\nexport { createSAPAIProvider, sapai } from \"./sap-ai-provider\";\nexport type {\n  SAPAIProvider,\n  SAPAIProviderSettings,\n  DeploymentConfig,\n} from \"./sap-ai-provider\";\n\n// Settings and model types\nexport type { SAPAISettings, SAPAIModelId } from \"./sap-ai-chat-settings\";\n\n// Re-export masking/filtering module types and helpers from SAP AI SDK\nexport type { MaskingModule, FilteringModule } from \"./sap-ai-chat-settings\";\nexport {\n  buildDpiMaskingProvider,\n  buildAzureContentSafetyFilter,\n  buildLlamaGuard38BFilter,\n  buildDocumentGroundingConfig,\n  buildTranslationConfig,\n} from \"./sap-ai-chat-settings\";\n\n// Error handling\nexport { SAPAIError } from \"./sap-ai-error\";\nexport type { OrchestrationErrorResponse } from \"./sap-ai-error\";\n\n// Re-export useful types from SAP AI SDK for advanced usage\nexport type {\n  OrchestrationModuleConfig,\n  ChatCompletionRequest,\n  PromptTemplatingModule,\n  GroundingModule,\n  TranslationModule,\n  LlmModelParams,\n  LlmModelDetails,\n  ChatCompletionTool,\n  FunctionObject,\n} from \"./types/completion-request\";\n\nexport type {\n  ChatMessage,\n  SystemChatMessage,\n  UserChatMessage,\n  AssistantChatMessage,\n  ToolChatMessage,\n  DeveloperChatMessage,\n} from \"./types/completion-response\";\n\nexport {\n  OrchestrationResponse,\n  OrchestrationStreamResponse,\n  OrchestrationStreamChunkResponse,\n} from \"./types/completion-response\";\n\n// Re-export OrchestrationClient for advanced usage\nexport { OrchestrationClient } from \"@sap-ai-sdk/orchestration\";\n","import {\n  LanguageModelV2,\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2FunctionTool,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n} from \"@ai-sdk/provider\";\nimport {\n  OrchestrationClient,\n  OrchestrationModuleConfig,\n  ChatMessage,\n  ChatCompletionTool,\n} from \"@sap-ai-sdk/orchestration\";\nimport type { HttpDestinationOrFetchOptions } from \"@sap-cloud-sdk/connectivity\";\nimport type {\n  ResourceGroupConfig,\n  DeploymentIdConfig,\n} from \"@sap-ai-sdk/ai-api/internal.js\";\n// Note: zodToJsonSchema and isZodSchema are kept for potential future use\n// when AI SDK Zod conversion issues are resolved\nimport { zodToJsonSchema } from \"zod-to-json-schema\";\n// Import ZodSchema from zod/v3 for zod-to-json-schema\nimport type { ZodSchema } from \"zod/v3\";\nimport { convertToSAPMessages } from \"./convert-to-sap-messages\";\nimport { SAPAIModelId, SAPAISettings } from \"./sap-ai-chat-settings\";\n\n/**\n * Type guard to check if an object is a Zod schema.\n * @internal\n */\nfunction isZodSchema(obj: unknown): obj is ZodSchema {\n  return (\n    obj !== null &&\n    typeof obj === \"object\" &&\n    \"_def\" in obj &&\n    \"parse\" in obj &&\n    typeof (obj as { parse: unknown }).parse === \"function\"\n  );\n}\n\n/**\n * Internal configuration for the SAP AI Chat Language Model.\n * @internal\n */\ninterface SAPAIConfig {\n  /** Provider identifier */\n  provider: string;\n  /** Deployment configuration for SAP AI SDK */\n  deploymentConfig: ResourceGroupConfig | DeploymentIdConfig;\n  /** Optional custom destination */\n  destination?: HttpDestinationOrFetchOptions;\n}\n\n/**\n * SAP AI Chat Language Model implementation.\n *\n * This class implements the Vercel AI SDK's `LanguageModelV2` interface,\n * providing a bridge between the AI SDK and SAP AI Core's Orchestration API\n * using the official SAP AI SDK (@sap-ai-sdk/orchestration).\n *\n * **Features:**\n * - Text generation (streaming and non-streaming)\n * - Tool calling (function calling)\n * - Multi-modal input (text + images)\n * - Data masking (SAP DPI)\n * - Content filtering\n *\n * **Model Support:**\n * - Azure OpenAI models (gpt-4o, gpt-4o-mini, o1, o3, etc.)\n * - Google Vertex AI models (gemini-2.0-flash, gemini-2.5-pro, etc.)\n * - AWS Bedrock models (anthropic--claude-*, amazon--nova-*, etc.)\n * - AI Core open source models (mistralai--, cohere--, etc.)\n *\n * @example\n * ```typescript\n * // Create via provider\n * const provider = createSAPAIProvider();\n * const model = provider('gpt-4o');\n *\n * // Use with AI SDK\n * const result = await generateText({\n *   model,\n *   prompt: 'Hello, world!'\n * });\n * ```\n *\n * @implements {LanguageModelV2}\n */\nexport class SAPAIChatLanguageModel implements LanguageModelV2 {\n  /** AI SDK specification version */\n  readonly specificationVersion = \"v2\";\n  /** Default object generation mode */\n  readonly defaultObjectGenerationMode = \"json\";\n  /** Whether the model supports image URLs */\n  readonly supportsImageUrls = true;\n  /** The model identifier (e.g., 'gpt-4o', 'anthropic--claude-3.5-sonnet') */\n  readonly modelId: SAPAIModelId;\n  /** Whether the model supports structured outputs */\n  readonly supportsStructuredOutputs = true;\n\n  /** Internal configuration */\n  private readonly config: SAPAIConfig;\n  /** Model-specific settings */\n  private readonly settings: SAPAISettings;\n\n  /**\n   * Creates a new SAP AI Chat Language Model instance.\n   *\n   * @param modelId - The model identifier\n   * @param settings - Model-specific configuration settings\n   * @param config - Internal configuration (deployment config, destination, etc.)\n   *\n   * @internal This constructor is not meant to be called directly.\n   * Use the provider function instead.\n   */\n  constructor(\n    modelId: SAPAIModelId,\n    settings: SAPAISettings,\n    config: SAPAIConfig,\n  ) {\n    this.settings = settings;\n    this.config = config;\n    this.modelId = modelId;\n  }\n\n  /**\n   * Checks if a URL is supported for file/image uploads.\n   *\n   * @param url - The URL to check\n   * @returns True if the URL protocol is HTTPS\n   */\n  supportsUrl(url: URL): boolean {\n    return url.protocol === \"https:\";\n  }\n\n  /**\n   * Returns supported URL patterns for different content types.\n   *\n   * @returns Record of content types to regex patterns\n   */\n  get supportedUrls(): Record<string, RegExp[]> {\n    return {\n      \"image/*\": [\n        /^https:\\/\\/.*\\.(?:png|jpg|jpeg|gif|webp)$/i,\n        /^data:image\\/.*$/,\n      ],\n    };\n  }\n\n  /**\n   * Gets the provider identifier.\n   *\n   * @returns The provider name ('sap-ai')\n   */\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  /**\n   * Builds orchestration module config for SAP AI SDK.\n   *\n   * @param options - Call options from the AI SDK\n   * @returns Object containing orchestration config and warnings\n   *\n   * @internal\n   */\n  private buildOrchestrationConfig(options: LanguageModelV2CallOptions): {\n    orchestrationConfig: OrchestrationModuleConfig;\n    messages: ChatMessage[];\n    warnings: LanguageModelV2CallWarning[];\n  } {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    // Convert AI SDK prompt to SAP messages\n    const messages = convertToSAPMessages(options.prompt);\n\n    // Get tools - prefer settings.tools if provided (proper JSON Schema),\n    // otherwise try to convert from AI SDK tools\n    let tools: ChatCompletionTool[] | undefined;\n\n    if (this.settings.tools && this.settings.tools.length > 0) {\n      // Use tools from settings (already in SAP format with proper schemas)\n      tools = this.settings.tools;\n    } else {\n      // Extract tools from options and convert\n      const availableTools = options.tools;\n\n      tools = availableTools\n        ?.map((tool): ChatCompletionTool | null => {\n          if (tool.type === \"function\") {\n            // Get the input schema - AI SDK provides this as JSONSchema7\n            // But in some cases, it might be a Zod schema or have empty properties\n            const inputSchema = tool.inputSchema as\n              | Record<string, unknown>\n              | undefined;\n\n            // Also check for raw Zod schema in 'parameters' field (AI SDK internal)\n            const toolWithParams = tool as LanguageModelV2FunctionTool & {\n              parameters?: unknown;\n            };\n\n            // Build parameters ensuring type: \"object\" is always present\n            // SAP AI Core requires explicit type: \"object\" in the schema\n            let parameters: Record<string, unknown>;\n\n            // First, check if there's a Zod schema we need to convert\n            if (\n              toolWithParams.parameters &&\n              isZodSchema(toolWithParams.parameters)\n            ) {\n              // Convert Zod schema to JSON Schema\n              const jsonSchema = zodToJsonSchema(toolWithParams.parameters, {\n                $refStrategy: \"none\",\n              }) as Record<string, unknown>;\n              // Remove $schema property as SAP doesn't need it\n              delete jsonSchema.$schema;\n              parameters = {\n                type: \"object\",\n                ...jsonSchema,\n              };\n            } else if (inputSchema && Object.keys(inputSchema).length > 0) {\n              // Check if schema has properties (it's a proper object schema)\n              const hasProperties =\n                inputSchema.properties &&\n                typeof inputSchema.properties === \"object\" &&\n                Object.keys(inputSchema.properties).length > 0;\n\n              if (hasProperties) {\n                parameters = {\n                  type: \"object\",\n                  ...inputSchema,\n                };\n              } else {\n                // Schema exists but has no properties - use default empty schema\n                parameters = {\n                  type: \"object\",\n                  properties: {},\n                  required: [],\n                };\n              }\n            } else {\n              // No schema provided - use default empty schema\n              parameters = {\n                type: \"object\",\n                properties: {},\n                required: [],\n              };\n            }\n\n            return {\n              type: \"function\",\n              function: {\n                name: tool.name,\n                description: tool.description,\n                parameters,\n              },\n            };\n          } else {\n            warnings.push({\n              type: \"unsupported-tool\",\n              tool: tool,\n            });\n            return null;\n          }\n        })\n        .filter((t): t is ChatCompletionTool => t !== null);\n    }\n\n    // Check if model supports certain features\n    const supportsN =\n      !this.modelId.startsWith(\"amazon--\") &&\n      !this.modelId.startsWith(\"anthropic--\");\n\n    // Build orchestration config\n    const orchestrationConfig: OrchestrationModuleConfig = {\n      promptTemplating: {\n        model: {\n          name: this.modelId,\n          version: this.settings.modelVersion ?? \"latest\",\n          params: {\n            max_tokens: this.settings.modelParams?.maxTokens,\n            temperature: this.settings.modelParams?.temperature,\n            top_p: this.settings.modelParams?.topP,\n            frequency_penalty: this.settings.modelParams?.frequencyPenalty,\n            presence_penalty: this.settings.modelParams?.presencePenalty,\n            n: supportsN ? (this.settings.modelParams?.n ?? 1) : undefined,\n          },\n        },\n        prompt: {\n          template: [],\n          tools: tools && tools.length > 0 ? tools : undefined,\n        },\n      },\n      // Include masking module if provided\n      ...(this.settings.masking ? { masking: this.settings.masking } : {}),\n      // Include filtering module if provided\n      ...(this.settings.filtering\n        ? { filtering: this.settings.filtering }\n        : {}),\n    };\n\n    return { orchestrationConfig, messages, warnings };\n  }\n\n  /**\n   * Creates an OrchestrationClient instance.\n   *\n   * @param config - Orchestration module configuration\n   * @returns OrchestrationClient instance\n   *\n   * @internal\n   */\n  private createClient(config: OrchestrationModuleConfig): OrchestrationClient {\n    return new OrchestrationClient(\n      config,\n      this.config.deploymentConfig,\n      this.config.destination,\n    );\n  }\n\n  /**\n   * Generates a single completion (non-streaming).\n   *\n   * This method implements the `LanguageModelV2.doGenerate` interface,\n   * sending a request to SAP AI Core and returning the complete response.\n   *\n   * **Features:**\n   * - Tool calling support\n   * - Multi-modal input (text + images)\n   * - Data masking (if configured)\n   * - Content filtering (if configured)\n   *\n   * @param options - Generation options including prompt, tools, and settings\n   * @returns Promise resolving to the generation result with content, usage, and metadata\n   *\n   * @example\n   * ```typescript\n   * const result = await model.doGenerate({\n   *   prompt: [\n   *     { role: 'user', content: [{ type: 'text', text: 'Hello!' }] }\n   *   ]\n   * });\n   *\n   * console.log(result.content); // Generated content\n   * console.log(result.usage);   // Token usage\n   * ```\n   */\n  async doGenerate(options: LanguageModelV2CallOptions): Promise<{\n    content: LanguageModelV2Content[];\n    finishReason: LanguageModelV2FinishReason;\n    usage: LanguageModelV2Usage;\n    rawCall: { rawPrompt: unknown; rawSettings: Record<string, unknown> };\n    warnings: LanguageModelV2CallWarning[];\n  }> {\n    const { orchestrationConfig, messages, warnings } =\n      this.buildOrchestrationConfig(options);\n\n    const client = this.createClient(orchestrationConfig);\n\n    const response = await client.chatCompletion({\n      messages,\n    });\n\n    const content: LanguageModelV2Content[] = [];\n\n    // Extract text content\n    const textContent = response.getContent();\n    if (textContent) {\n      content.push({\n        type: \"text\",\n        text: textContent,\n      });\n    }\n\n    // Extract tool calls\n    const toolCalls = response.getToolCalls();\n    if (toolCalls) {\n      for (const toolCall of toolCalls) {\n        content.push({\n          type: \"tool-call\",\n          toolCallId: toolCall.id,\n          toolName: toolCall.function.name,\n          // AI SDK expects input as a JSON string, which it parses internally\n          input: toolCall.function.arguments,\n        });\n      }\n    }\n\n    // Get usage\n    const tokenUsage = response.getTokenUsage();\n\n    // Map finish reason\n    const finishReasonRaw = response.getFinishReason();\n    const finishReason = mapFinishReason(finishReasonRaw);\n\n    return {\n      content,\n      finishReason,\n      usage: {\n        inputTokens: tokenUsage.prompt_tokens,\n        outputTokens: tokenUsage.completion_tokens,\n        totalTokens: tokenUsage.total_tokens,\n      },\n      rawCall: {\n        rawPrompt: { config: orchestrationConfig, messages },\n        rawSettings: {},\n      },\n      warnings,\n    };\n  }\n\n  /**\n   * Generates a streaming completion.\n   *\n   * This method implements the `LanguageModelV2.doStream` interface,\n   * sending a streaming request to SAP AI Core and returning a stream of response parts.\n   *\n   * **Stream Events:**\n   * - `stream-start` - Stream initialization\n   * - `response-metadata` - Response metadata (model, timestamp)\n   * - `text-start` - Text generation starts\n   * - `text-delta` - Incremental text chunks\n   * - `text-end` - Text generation completes\n   * - `tool-call` - Tool call detected\n   * - `finish` - Stream completes with usage and finish reason\n   * - `error` - Error occurred\n   *\n   * @param options - Streaming options including prompt, tools, and settings\n   * @returns Promise resolving to stream and raw call metadata\n   *\n   * @example\n   * ```typescript\n   * const { stream } = await model.doStream({\n   *   prompt: [\n   *     { role: 'user', content: [{ type: 'text', text: 'Write a story' }] }\n   *   ]\n   * });\n   *\n   * for await (const part of stream) {\n   *   if (part.type === 'text-delta') {\n   *     process.stdout.write(part.delta);\n   *   }\n   * }\n   * ```\n   */\n  async doStream(options: LanguageModelV2CallOptions): Promise<{\n    stream: ReadableStream<LanguageModelV2StreamPart>;\n    rawCall: { rawPrompt: unknown; rawSettings: Record<string, unknown> };\n  }> {\n    const { orchestrationConfig, messages, warnings } =\n      this.buildOrchestrationConfig(options);\n\n    const client = this.createClient(orchestrationConfig);\n\n    const streamResponse = await client.stream(\n      { messages },\n      options.abortSignal,\n      { promptTemplating: { include_usage: true } },\n    );\n\n    let finishReason: LanguageModelV2FinishReason = \"unknown\";\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n\n    let isFirstChunk = true;\n    let activeText = false;\n\n    // Track tool calls being built up\n    const toolCallsInProgress = new Map<\n      number,\n      { id: string; name: string; arguments: string }\n    >();\n\n    const sdkStream = streamResponse.stream;\n\n    const transformedStream = new ReadableStream<LanguageModelV2StreamPart>({\n      async start(controller) {\n        controller.enqueue({ type: \"stream-start\", warnings });\n\n        try {\n          for await (const chunk of sdkStream) {\n            if (isFirstChunk) {\n              isFirstChunk = false;\n              controller.enqueue({\n                type: \"response-metadata\",\n                id: undefined,\n                modelId: undefined,\n                timestamp: new Date(),\n              });\n            }\n\n            // Get delta content\n            const deltaContent = chunk.getDeltaContent();\n            if (deltaContent) {\n              if (!activeText) {\n                controller.enqueue({ type: \"text-start\", id: \"0\" });\n                activeText = true;\n              }\n              controller.enqueue({\n                type: \"text-delta\",\n                id: \"0\",\n                delta: deltaContent,\n              });\n            }\n\n            // Handle tool calls\n            const deltaToolCalls = chunk.getDeltaToolCalls();\n            if (deltaToolCalls) {\n              for (const toolCallChunk of deltaToolCalls) {\n                const index = toolCallChunk.index;\n\n                // Initialize tool call if new\n                if (!toolCallsInProgress.has(index)) {\n                  toolCallsInProgress.set(index, {\n                    id: toolCallChunk.id ?? `tool_${String(index)}`,\n                    name: toolCallChunk.function?.name ?? \"\",\n                    arguments: \"\",\n                  });\n\n                  // Emit tool-input-start\n                  const tc = toolCallsInProgress.get(index);\n                  if (!tc) continue;\n                  if (toolCallChunk.function?.name) {\n                    controller.enqueue({\n                      type: \"tool-input-start\",\n                      id: tc.id,\n                      toolName: tc.name,\n                    });\n                  }\n                }\n\n                const tc = toolCallsInProgress.get(index);\n                if (!tc) continue;\n\n                // Update tool call ID if provided\n                if (toolCallChunk.id) {\n                  tc.id = toolCallChunk.id;\n                }\n\n                // Update function name if provided\n                if (toolCallChunk.function?.name) {\n                  tc.name = toolCallChunk.function.name;\n                }\n\n                // Accumulate arguments\n                if (toolCallChunk.function?.arguments) {\n                  tc.arguments += toolCallChunk.function.arguments;\n                  controller.enqueue({\n                    type: \"tool-input-delta\",\n                    id: tc.id,\n                    delta: toolCallChunk.function.arguments,\n                  });\n                }\n              }\n            }\n\n            // Check for finish reason\n            const chunkFinishReason = chunk.getFinishReason();\n            if (chunkFinishReason) {\n              finishReason = mapFinishReason(chunkFinishReason);\n            }\n\n            // Get usage from chunk\n            const chunkUsage = chunk.getTokenUsage();\n            if (chunkUsage) {\n              usage.inputTokens = chunkUsage.prompt_tokens;\n              usage.outputTokens = chunkUsage.completion_tokens;\n              usage.totalTokens = chunkUsage.total_tokens;\n            }\n          }\n\n          // Emit completed tool calls\n          const toolCalls = Array.from(toolCallsInProgress.values());\n          for (const tc of toolCalls) {\n            controller.enqueue({\n              type: \"tool-input-end\",\n              id: tc.id,\n            });\n            controller.enqueue({\n              type: \"tool-call\",\n              toolCallId: tc.id,\n              toolName: tc.name,\n              input: tc.arguments,\n            });\n          }\n\n          if (activeText) {\n            controller.enqueue({ type: \"text-end\", id: \"0\" });\n          }\n\n          // Try to get final usage from stream response\n          const finalUsage = streamResponse.getTokenUsage();\n          if (finalUsage) {\n            usage.inputTokens = finalUsage.prompt_tokens;\n            usage.outputTokens = finalUsage.completion_tokens;\n            usage.totalTokens = finalUsage.total_tokens;\n          }\n\n          // Get final finish reason\n          const finalFinishReason = streamResponse.getFinishReason();\n          if (finalFinishReason) {\n            finishReason = mapFinishReason(finalFinishReason);\n          }\n\n          controller.enqueue({\n            type: \"finish\",\n            finishReason,\n            usage,\n          });\n\n          controller.close();\n        } catch (error) {\n          controller.enqueue({\n            type: \"error\",\n            error: error instanceof Error ? error : new Error(String(error)),\n          });\n          controller.close();\n        }\n      },\n    });\n\n    return {\n      stream: transformedStream,\n      rawCall: {\n        rawPrompt: { config: orchestrationConfig, messages },\n        rawSettings: {},\n      },\n    };\n  }\n}\n\n/**\n * Maps SAP AI Core finish reasons to AI SDK finish reasons.\n */\nfunction mapFinishReason(\n  reason: string | undefined,\n): LanguageModelV2FinishReason {\n  if (!reason) return \"unknown\";\n\n  switch (reason.toLowerCase()) {\n    case \"stop\":\n      return \"stop\";\n    case \"length\":\n      return \"length\";\n    case \"tool_calls\":\n    case \"function_call\":\n      return \"tool-calls\";\n    case \"content_filter\":\n      return \"content-filter\";\n    default:\n      return \"unknown\";\n  }\n}\n","import {\n  LanguageModelV2Prompt,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\nimport type {\n  ChatMessage,\n  SystemChatMessage,\n  UserChatMessage,\n  AssistantChatMessage,\n  ToolChatMessage,\n} from \"@sap-ai-sdk/orchestration\";\n\n/**\n * User chat message content item for multi-modal messages.\n */\ninterface UserContentItem {\n  type: \"text\" | \"image_url\";\n  text?: string;\n  image_url?: {\n    url: string;\n  };\n}\n\n/**\n * Converts Vercel AI SDK prompt format to SAP AI SDK ChatMessage format.\n *\n * This function transforms the standardized LanguageModelV2Prompt format\n * used by the Vercel AI SDK into the ChatMessage format expected\n * by SAP AI SDK's OrchestrationClient.\n *\n * **Supported Features:**\n * - Text messages (system, user, assistant)\n * - Multi-modal messages (text + images)\n * - Tool calls and tool results\n * - Conversation history\n *\n * **Limitations:**\n * - Images must be in data URL format or accessible HTTP URLs\n * - Audio messages are not supported\n * - File attachments (non-image) are not supported\n *\n * @param prompt - The Vercel AI SDK prompt to convert\n * @returns Array of SAP AI SDK compatible ChatMessage objects\n *\n * @throws {UnsupportedFunctionalityError} When unsupported message types are encountered\n *\n * @example\n * ```typescript\n * const prompt = [\n *   { role: 'system', content: 'You are a helpful assistant.' },\n *   { role: 'user', content: [{ type: 'text', text: 'Hello!' }] }\n * ];\n *\n * const sapMessages = convertToSAPMessages(prompt);\n * // Result: [\n * //   { role: 'system', content: 'You are a helpful assistant.' },\n * //   { role: 'user', content: 'Hello!' }\n * // ]\n * ```\n *\n * @example\n * **Multi-modal with Image**\n * ```typescript\n * const prompt = [\n *   {\n *     role: 'user',\n *     content: [\n *       { type: 'text', text: 'What do you see in this image?' },\n *       { type: 'file', mediaType: 'image/jpeg', data: 'base64...' }\n *     ]\n *   }\n * ];\n *\n * const sapMessages = convertToSAPMessages(prompt);\n * ```\n */\nexport function convertToSAPMessages(\n  prompt: LanguageModelV2Prompt,\n): ChatMessage[] {\n  const messages: ChatMessage[] = [];\n\n  for (const message of prompt) {\n    switch (message.role) {\n      case \"system\": {\n        const systemMessage: SystemChatMessage = {\n          role: \"system\",\n          content: message.content,\n        };\n        messages.push(systemMessage);\n        break;\n      }\n\n      case \"user\": {\n        // Build content parts for user messages\n        const contentParts: UserContentItem[] = [];\n\n        for (const part of message.content) {\n          switch (part.type) {\n            case \"text\": {\n              contentParts.push({\n                type: \"text\",\n                text: part.text,\n              });\n              break;\n            }\n            case \"file\": {\n              // SAP AI Core only supports image files\n              if (!part.mediaType.startsWith(\"image/\")) {\n                throw new UnsupportedFunctionalityError({\n                  functionality: \"Only image files are supported\",\n                });\n              }\n\n              const imageUrl =\n                part.data instanceof URL\n                  ? part.data.toString()\n                  : `data:${part.mediaType};base64,${String(part.data)}`;\n\n              contentParts.push({\n                type: \"image_url\",\n                image_url: {\n                  url: imageUrl,\n                },\n              });\n              break;\n            }\n            default: {\n              throw new UnsupportedFunctionalityError({\n                functionality: `Content type ${(part as { type: string }).type}`,\n              });\n            }\n          }\n        }\n\n        // If only text content, use simple string format\n        // Otherwise use array format for multi-modal\n        const userMessage: UserChatMessage =\n          contentParts.length === 1 && contentParts[0].type === \"text\"\n            ? {\n                role: \"user\",\n                content: contentParts[0].text ?? \"\",\n              }\n            : {\n                role: \"user\",\n                content: contentParts as UserChatMessage[\"content\"],\n              };\n\n        messages.push(userMessage);\n        break;\n      }\n\n      case \"assistant\": {\n        let text = \"\";\n        const toolCalls: {\n          id: string;\n          type: \"function\";\n          function: { name: string; arguments: string };\n        }[] = [];\n\n        for (const part of message.content) {\n          switch (part.type) {\n            case \"text\": {\n              text += part.text;\n              break;\n            }\n            case \"tool-call\": {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: \"function\",\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.input),\n                },\n              });\n              break;\n            }\n          }\n        }\n\n        const assistantMessage: AssistantChatMessage = {\n          role: \"assistant\",\n          content: text || \"\",\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        };\n        messages.push(assistantMessage);\n        break;\n      }\n\n      case \"tool\": {\n        // Convert tool results to tool messages\n        for (const part of message.content) {\n          const toolMessage: ToolChatMessage = {\n            role: \"tool\",\n            tool_call_id: part.toolCallId,\n            content: JSON.stringify(part.output),\n          };\n          messages.push(toolMessage);\n        }\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = message;\n        throw new Error(\n          `Unsupported role: ${(_exhaustiveCheck as { role: string }).role}`,\n        );\n      }\n    }\n  }\n\n  return messages;\n}\n","import { ProviderV2 } from \"@ai-sdk/provider\";\nimport type { HttpDestinationOrFetchOptions } from \"@sap-cloud-sdk/connectivity\";\nimport type {\n  ResourceGroupConfig,\n  DeploymentIdConfig,\n} from \"@sap-ai-sdk/ai-api/internal.js\";\nimport { SAPAIChatLanguageModel } from \"./sap-ai-chat-language-model\";\nimport { SAPAIModelId, SAPAISettings } from \"./sap-ai-chat-settings\";\n\n/**\n * SAP AI Provider interface.\n *\n * This is the main interface for creating and configuring SAP AI Core models.\n * It extends the standard Vercel AI SDK ProviderV2 interface with SAP-specific functionality.\n *\n * @example\n * ```typescript\n * const provider = createSAPAIProvider({\n *   resourceGroup: 'default'\n * });\n *\n * // Create a model instance\n * const model = provider('gpt-4o', {\n *   modelParams: {\n *     temperature: 0.7,\n *     maxTokens: 1000\n *   }\n * });\n *\n * // Or use the explicit chat method\n * const chatModel = provider.chat('gpt-4o');\n * ```\n */\nexport interface SAPAIProvider extends ProviderV2 {\n  /**\n   * Create a language model instance.\n   *\n   * @param modelId - The SAP AI Core model identifier (e.g., 'gpt-4o', 'anthropic--claude-3.5-sonnet')\n   * @param settings - Optional model configuration settings\n   * @returns Configured SAP AI chat language model instance\n   */\n  (modelId: SAPAIModelId, settings?: SAPAISettings): SAPAIChatLanguageModel;\n\n  /**\n   * Explicit method for creating chat models.\n   *\n   * This method is equivalent to calling the provider function directly,\n   * but provides a more explicit API for chat-based interactions.\n   *\n   * @param modelId - The SAP AI Core model identifier\n   * @param settings - Optional model configuration settings\n   * @returns Configured SAP AI chat language model instance\n   */\n  chat(modelId: SAPAIModelId, settings?: SAPAISettings): SAPAIChatLanguageModel;\n}\n\n/**\n * Configuration settings for the SAP AI Provider.\n *\n * This interface defines all available options for configuring the SAP AI Core connection\n * using the official SAP AI SDK. The SDK handles authentication automatically when\n * running on SAP BTP (via service binding) or locally (via AICORE_SERVICE_KEY env var).\n *\n * @example\n * ```typescript\n * // Using default configuration (auto-detects service binding or env var)\n * const provider = createSAPAIProvider();\n *\n * // With specific resource group\n * const provider = createSAPAIProvider({\n *   resourceGroup: 'production'\n * });\n *\n * // With custom destination\n * const provider = createSAPAIProvider({\n *   destination: {\n *     url: 'https://my-ai-core-instance.cfapps.eu10.hana.ondemand.com'\n *   }\n * });\n * ```\n */\nexport interface SAPAIProviderSettings {\n  /**\n   * SAP AI Core resource group.\n   *\n   * Logical grouping of AI resources in SAP AI Core.\n   * Used for resource isolation and access control.\n   * Different resource groups can have different permissions and quotas.\n   *\n   * @default 'default'\n   * @example\n   * ```typescript\n   * resourceGroup: 'default'     // Default resource group\n   * resourceGroup: 'production'  // Production environment\n   * resourceGroup: 'development' // Development environment\n   * ```\n   */\n  resourceGroup?: string;\n\n  /**\n   * SAP AI Core deployment ID.\n   *\n   * A specific deployment ID to use for orchestration requests.\n   * If not provided, the SDK will resolve the deployment automatically.\n   *\n   * @example\n   * ```typescript\n   * deploymentId: 'd65d81e7c077e583'\n   * ```\n   */\n  deploymentId?: string;\n\n  /**\n   * Custom destination configuration for SAP AI Core.\n   *\n   * Override the default destination detection. Useful for:\n   * - Custom proxy configurations\n   * - Non-standard SAP AI Core setups\n   * - Testing environments\n   *\n   * @example\n   * ```typescript\n   * destination: {\n   *   url: 'https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com'\n   * }\n   * ```\n   */\n  destination?: HttpDestinationOrFetchOptions;\n\n  /**\n   * Default model settings applied to every model instance created by this provider.\n   * Per-call settings provided to the model will override these.\n   */\n  defaultSettings?: SAPAISettings;\n}\n\n/**\n * Deployment configuration type used by SAP AI SDK.\n */\nexport type DeploymentConfig = ResourceGroupConfig | DeploymentIdConfig;\n\n/**\n * Creates a SAP AI Core provider instance for use with Vercel AI SDK.\n *\n * This is the main entry point for integrating SAP AI Core with the Vercel AI SDK.\n * It uses the official SAP AI SDK (@sap-ai-sdk/orchestration) under the hood,\n * which handles authentication and API communication automatically.\n *\n * **Authentication:**\n * The SAP AI SDK automatically handles authentication:\n * 1. On SAP BTP: Uses service binding (VCAP_SERVICES)\n * 2. Locally: Uses AICORE_SERVICE_KEY environment variable\n *\n * **Key Features:**\n * - Automatic authentication via SAP AI SDK\n * - Support for all SAP AI Core orchestration models\n * - Streaming and non-streaming responses\n * - Tool calling support\n * - Data masking (DPI)\n * - Content filtering\n *\n * @param options - Configuration options for the provider\n * @returns A configured SAP AI provider\n *\n * @example\n * **Basic Usage**\n * ```typescript\n * import { createSAPAIProvider } from '@mymediset/sap-ai-provider';\n * import { generateText } from 'ai';\n *\n * const provider = createSAPAIProvider();\n *\n * const result = await generateText({\n *   model: provider('gpt-4o'),\n *   prompt: 'Hello, world!'\n * });\n * ```\n *\n * @example\n * **With Resource Group**\n * ```typescript\n * const provider = createSAPAIProvider({\n *   resourceGroup: 'production'\n * });\n *\n * const model = provider('anthropic--claude-3.5-sonnet', {\n *   modelParams: {\n *     temperature: 0.3,\n *     maxTokens: 2000\n *   }\n * });\n * ```\n *\n * @example\n * **With Default Settings**\n * ```typescript\n * const provider = createSAPAIProvider({\n *   defaultSettings: {\n *     modelParams: {\n *       temperature: 0.7\n *     }\n *   }\n * });\n * ```\n */\nexport function createSAPAIProvider(\n  options: SAPAIProviderSettings = {},\n): SAPAIProvider {\n  const resourceGroup = options.resourceGroup ?? \"default\";\n\n  // Build deployment config for SAP AI SDK\n  const deploymentConfig: DeploymentConfig = options.deploymentId\n    ? { deploymentId: options.deploymentId }\n    : { resourceGroup };\n\n  // Create the model factory function\n  const createModel = (modelId: SAPAIModelId, settings: SAPAISettings = {}) => {\n    const mergedSettings: SAPAISettings = {\n      ...options.defaultSettings,\n      ...settings,\n      modelParams: {\n        ...(options.defaultSettings?.modelParams ?? {}),\n        ...(settings.modelParams ?? {}),\n      },\n    };\n\n    return new SAPAIChatLanguageModel(modelId, mergedSettings, {\n      provider: \"sap-ai\",\n      deploymentConfig,\n      destination: options.destination,\n    });\n  };\n\n  // Create the provider function\n  const provider = function (modelId: SAPAIModelId, settings?: SAPAISettings) {\n    // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n    if (new.target) {\n      throw new Error(\n        \"The SAP AI provider function cannot be called with the new keyword.\",\n      );\n    }\n\n    return createModel(modelId, settings);\n  };\n\n  provider.chat = createModel;\n\n  return provider as SAPAIProvider;\n}\n\n/**\n * Default SAP AI provider instance.\n *\n * Uses the default configuration which auto-detects authentication\n * from service binding (SAP BTP) or AICORE_SERVICE_KEY environment variable.\n *\n * @example\n * ```typescript\n * import { sapai } from '@mymediset/sap-ai-provider';\n * import { generateText } from 'ai';\n *\n * const result = await generateText({\n *   model: sapai('gpt-4o'),\n *   prompt: 'Hello!'\n * });\n * ```\n */\nexport const sapai = createSAPAIProvider();\n","import type {\n  MaskingModule,\n  FilteringModule,\n  ChatModel,\n  ChatCompletionTool,\n} from \"@sap-ai-sdk/orchestration\";\n\n/**\n * Settings for configuring SAP AI Core model behavior.\n */\nexport interface SAPAISettings {\n  /**\n   * Specific version of the model to use.\n   * If not provided, the latest version will be used.\n   */\n  modelVersion?: string;\n\n  /**\n   * Model generation parameters that control the output.\n   */\n  modelParams?: {\n    /**\n     * Maximum number of tokens to generate.\n     * Higher values allow for longer responses but increase latency and cost.\n     */\n    maxTokens?: number;\n\n    /**\n     * Sampling temperature between 0 and 2.\n     * Higher values make output more random, lower values more deterministic.\n     * No default; omitted when unspecified or unsupported by the target model.\n     */\n    temperature?: number;\n\n    /**\n     * Nucleus sampling parameter between 0 and 1.\n     * Controls diversity via cumulative probability cutoff.\n     * @default 1\n     */\n    topP?: number;\n\n    /**\n     * Frequency penalty between -2.0 and 2.0.\n     * Positive values penalize tokens based on their frequency.\n     * @default 0\n     */\n    frequencyPenalty?: number;\n\n    /**\n     * Presence penalty between -2.0 and 2.0.\n     * Positive values penalize tokens that have appeared in the text.\n     * @default 0\n     */\n    presencePenalty?: number;\n\n    /**\n     * Number of completions to generate.\n     * Multiple completions provide alternative responses.\n     * Note: Not supported by Amazon and Anthropic models.\n     * @default 1\n     */\n    n?: number;\n\n    /**\n     * Whether to enable parallel tool calls.\n     * When enabled, the model can call multiple tools in parallel.\n     */\n    parallel_tool_calls?: boolean;\n  };\n\n  /**\n   * Masking configuration for SAP AI Core orchestration.\n   * When provided, sensitive information in prompts can be anonymized or\n   * pseudonymized by SAP Data Privacy Integration (DPI).\n   *\n   * @example\n   * ```typescript\n   * import { buildDpiMaskingProvider } from '@sap-ai-sdk/orchestration';\n   *\n   * const model = provider('gpt-4o', {\n   *   masking: {\n   *     masking_providers: [\n   *       buildDpiMaskingProvider({\n   *         method: 'anonymization',\n   *         entities: ['profile-email', 'profile-phone']\n   *       })\n   *     ]\n   *   }\n   * });\n   * ```\n   */\n  masking?: MaskingModule;\n\n  /**\n   * Filtering configuration for input and output content safety.\n   * Supports Azure Content Safety and Llama Guard filters.\n   *\n   * @example\n   * ```typescript\n   * import { buildAzureContentSafetyFilter } from '@sap-ai-sdk/orchestration';\n   *\n   * const model = provider('gpt-4o', {\n   *   filtering: {\n   *     input: {\n   *       filters: [\n   *         buildAzureContentSafetyFilter('input', {\n   *           hate: 'ALLOW_SAFE',\n   *           violence: 'ALLOW_SAFE_LOW_MEDIUM'\n   *         })\n   *       ]\n   *     }\n   *   }\n   * });\n   * ```\n   */\n  filtering?: FilteringModule;\n\n  /**\n   * Response format for templating prompt (OpenAI-compatible).\n   * Allows specifying structured output formats.\n   *\n   * @example\n   * ```typescript\n   * const model = provider('gpt-4o', {\n   *   responseFormat: {\n   *     type: 'json_schema',\n   *     json_schema: {\n   *       name: 'response',\n   *       schema: { type: 'object', properties: { answer: { type: 'string' } } }\n   *     }\n   *   }\n   * });\n   * ```\n   */\n  responseFormat?:\n    | { type: \"text\" }\n    | { type: \"json_object\" }\n    | {\n        type: \"json_schema\";\n        json_schema: {\n          name: string;\n          description?: string;\n          schema?: unknown;\n          strict?: boolean | null;\n        };\n      };\n\n  /**\n   * Tool definitions in SAP AI SDK format.\n   *\n   * Use this to pass tools directly with proper JSON Schema definitions.\n   * This bypasses the AI SDK's Zod conversion which may have issues.\n   *\n   * Note: This should be used in conjunction with AI SDK's tool handling\n   * to provide the actual tool implementations (execute functions).\n   *\n   * @example\n   * ```typescript\n   * const model = provider('gpt-4o', {\n   *   tools: [\n   *     {\n   *       type: 'function',\n   *       function: {\n   *         name: 'get_weather',\n   *         description: 'Get weather for a location',\n   *         parameters: {\n   *           type: 'object',\n   *           properties: {\n   *             location: { type: 'string', description: 'City name' }\n   *           },\n   *           required: ['location']\n   *         }\n   *       }\n   *     }\n   *   ]\n   * });\n   * ```\n   */\n  tools?: ChatCompletionTool[];\n}\n\n/**\n * Supported model IDs in SAP AI Core.\n *\n * These models are available through the SAP AI Core Orchestration service.\n * Model availability depends on your subscription and region.\n *\n * **Azure OpenAI Models:**\n * - gpt-4o, gpt-4o-mini\n * - gpt-4.1, gpt-4.1-mini, gpt-4.1-nano\n * - o1, o3, o3-mini, o4-mini\n *\n * **Google Vertex AI Models:**\n * - gemini-2.0-flash, gemini-2.0-flash-lite\n * - gemini-2.5-flash, gemini-2.5-pro\n *\n * **AWS Bedrock Models:**\n * - anthropic--claude-3-haiku, anthropic--claude-3-sonnet, anthropic--claude-3-opus\n * - anthropic--claude-3.5-sonnet, anthropic--claude-3.7-sonnet\n * - anthropic--claude-4-sonnet, anthropic--claude-4-opus\n * - amazon--nova-pro, amazon--nova-lite, amazon--nova-micro, amazon--nova-premier\n *\n * **AI Core Open Source Models:**\n * - mistralai--mistral-large-instruct, mistralai--mistral-medium-instruct, mistralai--mistral-small-instruct\n * - cohere--command-a-reasoning\n */\nexport type SAPAIModelId = ChatModel;\n\n// Re-export useful types from SAP AI SDK for convenience\nexport type { MaskingModule, FilteringModule } from \"@sap-ai-sdk/orchestration\";\n\n// Re-export DPI masking helpers\nexport {\n  buildDpiMaskingProvider,\n  buildAzureContentSafetyFilter,\n  buildLlamaGuard38BFilter,\n  buildDocumentGroundingConfig,\n  buildTranslationConfig,\n} from \"@sap-ai-sdk/orchestration\";\n","import type { OrchestrationErrorResponse } from \"@sap-ai-sdk/orchestration\";\n\n/**\n * Custom error class for SAP AI Core errors.\n * Provides structured access to error details returned by the API.\n *\n * The SAP AI SDK handles most error responses internally, but this class\n * can be used to wrap and provide additional context for errors.\n *\n * @example\n * ```typescript\n * try {\n *   await model.doGenerate({ prompt });\n * } catch (error) {\n *   if (error instanceof SAPAIError) {\n *     console.error('Error Code:', error.code);\n *     console.error('Request ID:', error.requestId);\n *     console.error('Location:', error.location);\n *   }\n * }\n * ```\n */\nexport class SAPAIError extends Error {\n  /** HTTP status code or custom error code */\n  public readonly code?: number;\n\n  /** Where the error occurred (e.g., module name) */\n  public readonly location?: string;\n\n  /** Unique identifier for tracking the request */\n  public readonly requestId?: string;\n\n  /** Additional error context or debugging information */\n  public readonly details?: string;\n\n  /** Original cause of the error */\n  public readonly cause?: unknown;\n\n  constructor(\n    message: string,\n    options?: {\n      code?: number;\n      location?: string;\n      requestId?: string;\n      details?: string;\n      cause?: unknown;\n    },\n  ) {\n    super(message);\n    this.name = \"SAPAIError\";\n    this.code = options?.code;\n    this.location = options?.location;\n    this.requestId = options?.requestId;\n    this.details = options?.details;\n    this.cause = options?.cause;\n  }\n\n  /**\n   * Creates a SAPAIError from an OrchestrationErrorResponse.\n   *\n   * @param errorResponse - The error response from SAP AI SDK\n   * @returns A new SAPAIError instance\n   */\n  static fromOrchestrationError(\n    errorResponse: OrchestrationErrorResponse,\n  ): SAPAIError {\n    const error = errorResponse.error;\n\n    // Handle both single error and error list\n    if (Array.isArray(error)) {\n      // ErrorList - get first error\n      const firstError = error[0];\n      return new SAPAIError(\n        // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n        firstError?.message ?? \"Unknown orchestration error\",\n        {\n          // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n          code: firstError?.code,\n          // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n          location: firstError?.location,\n          // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n          requestId: firstError?.request_id,\n        },\n      );\n    } else {\n      // Single Error object\n      // eslint-disable-next-line @typescript-eslint/no-unnecessary-condition\n      return new SAPAIError(error.message ?? \"Unknown orchestration error\", {\n        code: error.code,\n        location: error.location,\n        requestId: error.request_id,\n      });\n    }\n  }\n\n  /**\n   * Creates a SAPAIError from a generic error.\n   *\n   * @param error - The original error\n   * @param context - Optional context about where the error occurred\n   * @returns A new SAPAIError instance\n   */\n  static fromError(error: unknown, context?: string): SAPAIError {\n    if (error instanceof SAPAIError) {\n      return error;\n    }\n\n    let message: string;\n    if (error instanceof Error) {\n      message = error.message;\n    } else if (error == null) {\n      message = \"Unknown error\";\n    } else if (\n      typeof error === \"string\" ||\n      typeof error === \"number\" ||\n      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