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AI Provider for Vercel AI SDK (powered by @sap-ai-sdk/orchestration and @sap-ai-sdk/foundation-models)","maintainers":[{"name":"fraggle","email":"jerome.benoit@piment-noir.org"}],"readme":"# SAP AI Provider for Vercel AI SDK\n\n[![npm](https://img.shields.io/npm/v/@jerome-benoit/sap-ai-provider/latest?label=npm&color=blue)](https://www.npmjs.com/package/@jerome-benoit/sap-ai-provider)\n[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Vercel AI SDK](https://img.shields.io/badge/Vercel%20AI%20SDK-5%20%7C%206%20%7C%207-black.svg)](https://sdk.vercel.ai/docs)\n[![Language Model](https://img.shields.io/badge/Language%20Model-V2%20%7C%20V3%20%7C%20V4-green.svg)](https://sdk.vercel.ai/docs/ai-sdk-core/provider-management)\n[![Embedding Model](https://img.shields.io/badge/Embedding%20Model-V2%20%7C%20V3%20%7C%20V4-green.svg)](https://sdk.vercel.ai/docs/ai-sdk-core/embeddings)\n\nA community provider for SAP AI Core that integrates seamlessly with the Vercel\nAI SDK. Built on top of the official **@sap-ai-sdk/orchestration** and\n**@sap-ai-sdk/foundation-models** packages, this provider enables you to use\nSAP's enterprise-grade AI models through the familiar Vercel AI SDK interface.\n\n## Table of Contents\n\n- [Features](#features)\n- [Quick Start](#quick-start)\n- [Quick Reference](#quick-reference)\n- [Installation](#installation)\n- [Provider Creation](#provider-creation)\n  - [Option 1: Factory Function (Recommended for Custom Configuration)](#option-1-factory-function-recommended-for-custom-configuration)\n  - [API Selection](#api-selection)\n  - [Option 2: Default Instance (Quick Start)](#option-2-default-instance-quick-start)\n  - [Provider Methods](#provider-methods)\n- [Authentication](#authentication)\n- [Basic Usage](#basic-usage)\n  - [Text Generation](#text-generation)\n  - [Chat Conversations](#chat-conversations)\n  - [Streaming Responses](#streaming-responses)\n  - [Model Configuration](#model-configuration)\n  - [Embeddings](#embeddings)\n- [Supported Models](#supported-models)\n- [Advanced Features](#advanced-features)\n  - [Tool Calling](#tool-calling)\n  - [Multi-modal Input (Images)](#multi-modal-input-images)\n  - [Data Masking (SAP DPI)](#data-masking-sap-dpi)\n  - [Content Filtering](#content-filtering)\n  - [Document Grounding (RAG)](#document-grounding-rag)\n  - [Translation](#translation)\n  - [Provider Options (Per-Call Overrides)](#provider-options-per-call-overrides)\n- [Configuration Options](#configuration-options)\n- [Error Handling](#error-handling)\n- [Troubleshooting](#troubleshooting)\n- [Performance](#performance)\n- [Security](#security)\n- [Debug Mode](#debug-mode)\n- [Examples](#examples)\n- [Migration Guides](#migration-guides)\n  - [Upgrading from v4.x to v5.x](#upgrading-from-v4x-to-v5x)\n  - [Upgrading from v3.x to v4.x](#upgrading-from-v3x-to-v4x)\n  - [Upgrading from v2.x to v3.x](#upgrading-from-v2x-to-v3x)\n  - [Upgrading from v1.x to v2.x](#upgrading-from-v1x-to-v2x)\n- [Important Note](#important-note)\n- [Contributing](#contributing)\n- [Resources](#resources)\n  - [Documentation](#documentation)\n  - [Community](#community)\n  - [Related Projects](#related-projects)\n- [License](#license)\n\n## Features\n\n- 🔐 **Simplified Authentication** - Uses SAP AI SDK's built-in credential\n  handling\n- 🎯 **Tool Calling Support** - Full tool/function calling capabilities\n- 🧠 **Reasoning-Safe by Default** - Assistant reasoning parts are not forwarded\n  unless enabled\n- 🖼️ **Multi-modal Input** - Support for text and image inputs\n- 📡 **Streaming Support** - Real-time text generation with structured V3 blocks\n- 🔒 **Data Masking** - Built-in SAP DPI integration for privacy\n- 🛡️ **Content Filtering** - Azure Content Safety and Llama Guard support\n- 🔧 **TypeScript Support** - Full type safety and IntelliSense\n- 🎨 **Multiple Models** - Support for OpenAI, Claude, Gemini, Nova, and more\n- 🔄 **AI SDK 5–7 Compatibility** - Versioned V2, V3, and V4 entrypoints\n  preserve the matching Vercel AI SDK provider specification\n- 📊 **Text Embeddings** - Generate vector embeddings for RAG and semantic\n  search\n- 🔀 **Dual API Support** - Choose between Orchestration or Foundation Models\n  API per provider, model, or call\n- 📦 **Stored Configuration Support** - Reference orchestration configurations\n  or prompt templates from SAP AI Core\n\n## Quick Start\n\n```bash\nnpm install @jerome-benoit/sap-ai-provider ai@^6 dotenv\n```\n\n```typescript\nimport \"dotenv/config\"; // Load environment variables\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider\";\nimport { generateText } from \"ai\";\nimport { APICallError } from \"@ai-sdk/provider\";\n\n// Create provider (authentication via AICORE_SERVICE_KEY env var)\nconst provider = createSAPAIProvider();\n\ntry {\n  // Generate text with gpt-4.1\n  const result = await generateText({\n    model: provider(\"gpt-4.1\"),\n    prompt: \"Explain quantum computing in simple terms.\",\n  });\n\n  console.log(result.text);\n} catch (error) {\n  process.exitCode = 1;\n  if (APICallError.isInstance(error)) {\n    console.error(\"SAP AI Core API error:\", error.statusCode, error.name);\n  } else {\n    console.error(\"Unexpected error:\", error instanceof Error ? error.name : \"Unknown error\");\n  }\n}\n```\n\n> **Note:** For local service-key setup, set `AICORE_SERVICE_KEY`. SAP BTP service\n> bindings and custom destinations are alternatives. See\n> [Environment Setup](./ENVIRONMENT_SETUP.md) for configuration.\n\n## Quick Reference\n\n| Task                | Code Pattern                                                     | Documentation                                                 |\n| ------------------- | ---------------------------------------------------------------- | ------------------------------------------------------------- |\n| **Install**         | `npm install @jerome-benoit/sap-ai-provider ai@^6`               | [Installation](#installation)                                 |\n| **Auth Setup**      | Add `AICORE_SERVICE_KEY` to `.env`                               | [Environment Setup](./ENVIRONMENT_SETUP.md)                   |\n| **Create Provider** | `createSAPAIProvider()` or use `sapai`                           | [Provider Creation](#provider-creation)                       |\n| **Text Generation** | `generateText({ model: provider(\"gpt-4.1\"), prompt })`           | [Basic Usage](#text-generation)                               |\n| **Streaming**       | `streamText({ model: provider(\"gpt-4.1\"), prompt })`             | [Streaming](#streaming-responses)                             |\n| **Tool Calling**    | `generateText({ tools: { myTool: tool({...}) } })`               | [Tool Calling](#tool-calling)                                 |\n| **Error Handling**  | `if (APICallError.isInstance(error)) { /* handle error */ }`     | [API Reference](./API_REFERENCE.md#error-handling--reference) |\n| **Choose Model**    | Discover models available in your tenant                         | [Models](./API_REFERENCE.md#models)                           |\n| **Embeddings**      | `embed({ model: provider.embedding(\"text-embedding-3-small\") })` | [Embeddings](#embeddings)                                     |\n\n## Installation\n\n**Requirements:** Node.js 22.12+. The provider entrypoint must match the installed\nAI SDK major.\n\nThe published package targets Node.js. Its ESM output uses Node\n`module.createRequire`, and the SAP SDK dependency chain relies on Node APIs.\nThe source-level Edge test suite does not establish deployability to pure Edge\nruntimes such as Cloudflare Workers; use a Node.js server runtime for deployment.\n\n| AI SDK | Install                                            | Provider import                                                         |\n| ------ | -------------------------------------------------- | ----------------------------------------------------------------------- |\n| 7      | `npm install @jerome-benoit/sap-ai-provider ai@^7` | `@jerome-benoit/sap-ai-provider/v4`                                     |\n| 6      | `npm install @jerome-benoit/sap-ai-provider ai@^6` | `@jerome-benoit/sap-ai-provider` or `@jerome-benoit/sap-ai-provider/v3` |\n| 5      | `npm install @jerome-benoit/sap-ai-provider ai@^5` | `@jerome-benoit/sap-ai-provider/v2`                                     |\n\n`/v3` is an explicit alias for the AI SDK 6 root entrypoint. Both resolve to\nthe same runtime modules and TypeScript declarations, including the same `sapai`\ninstance within each module format; existing root imports remain valid.\n\nThe Quick Start and inline snippets on this page use AI SDK 6 with the root V3\nentrypoint. The runnable files in `examples/` use the repository's installed\nAI SDK 7 with the V4 entrypoint.\nFor AI SDK 7, install `ai@^7` and import `createSAPAIProvider` or `sapai`\nfrom `@jerome-benoit/sap-ai-provider/v4`:\n\n```bash\nnpm install @jerome-benoit/sap-ai-provider ai@^7\n```\n\nThe V4 entrypoint exposes the same provider aliases and version-independent\nhelpers as the root V3 entrypoint. Its standardized `reasoning` option maps to\nSAP's `reasoning_effort` model parameter. `provider-default` preserves an\nexplicit `modelParams.reasoning_effort`; a stored `orchestrationConfigRef` owns\nthe model configuration and ignores local reasoning options with a warning.\nSee the [V4 API reference](./API_REFERENCE.md#v4-facade-api-ai-sdk-7) for the\nfull normalization and precedence contract.\n\n**V2 facade:** AI SDK 5, AI SDK 6 through its V2 compatibility layer, and other `LanguageModelV2`/`EmbeddingModelV2`\nconsumers can use the main package's `v2` subpath or the dedicated V2 package:\n\n```bash\nnpm install @jerome-benoit/sap-ai-provider ai@^5\n# Alternatively: npm install @jerome-benoit/sap-ai-provider-v2 ai@^5\n```\n\nBoth entrypoints expose the same V2 facade:\n\n```typescript\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider/v2\";\n// Dedicated-package alternative:\n// import { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider-v2\";\n```\n\nThe V2 type contracts are bundled at build time; neither package installs a second\nprovider package through the `@ai-sdk/provider-v2` alias. When using V2 with AI SDK 6,\nuse the latest 6.x patch: the initial 6.0.0 embedding compatibility adapter has an\nupstream warning-handling failure that is absent in 6.0.280.\n\nSee [Architecture - Versioned Packages](./ARCHITECTURE.md#versioned-package-architecture-v4--v3--v2)\nfor the V4/V3/V2 packaging model.\n\n## Provider Creation\n\nYou can create an SAP AI provider in two ways:\n\n### Option 1: Factory Function (Recommended for Custom Configuration)\n\n```typescript\nimport \"dotenv/config\"; // Load environment variables\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider\";\n\nconst provider = createSAPAIProvider({\n  resourceGroup: \"production\",\n  deploymentId: \"your-deployment-id\", // Optional\n});\n```\n\n### API Selection\n\nThe provider supports two SAP AI Core APIs:\n\n- **Orchestration API** (default): Full-featured API with data masking, content\n  filtering, document grounding, and translation\n- **Foundation Models API**: Direct model access with additional parameters like\n  `logprobs`, `seed`, `logit_bias`, and `dataSources` (Azure On Your Data)\n\n**Complete example:**\n[examples/example-foundation-models.ts](./examples/example-foundation-models.ts)\\\n**Complete documentation:**\n[API Reference - Foundation Models API](./API_REFERENCE.md#api-comparison-orchestration-vs-foundation-models)\n\n```typescript\nimport { createSAPAIProvider, SAP_AI_PROVIDER_NAME } from \"@jerome-benoit/sap-ai-provider\";\n\n// Provider-level API selection\nconst provider = createSAPAIProvider({\n  api: \"foundation-models\", // All models use Foundation Models API\n});\n\n// Model-level API override\nconst model = provider(\"gpt-4.1\", {\n  api: \"orchestration\", // Override for this model only\n});\n\n// Per-call API override via providerOptions\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  prompt: \"Hello\",\n  providerOptions: {\n    [SAP_AI_PROVIDER_NAME]: {\n      api: \"foundation-models\", // Override for this call only\n    },\n  },\n});\n```\n\n**Run it:** `npx tsx examples/example-foundation-models.ts`\n\n> **Note:** The Foundation Models API does not support orchestration features\n> (masking, filtering, grounding, translation). Attempting to use these features\n> with Foundation Models API will throw an `UnsupportedFeatureError`.\n\n### Option 2: Default Instance (Quick Start)\n\n```typescript\nimport \"dotenv/config\"; // Load environment variables\nimport { sapai } from \"@jerome-benoit/sap-ai-provider\";\nimport { generateText } from \"ai\";\n\n// Use directly with auto-detected configuration\nconst result = await generateText({\n  model: sapai(\"gpt-4.1\"),\n  prompt: \"Hello!\",\n});\n```\n\nThe `sapai` export provides a convenient default provider instance with\nautomatic configuration from environment variables or service bindings.\n\n### Provider Methods\n\nThe provider is callable and also exposes explicit methods:\n\n```typescript\n// Callable syntax (creates language model)\nconst chatModel = provider(\"gpt-4.1\");\n\n// Explicit method syntax\nconst explicitChatModel = provider.chat(\"gpt-4.1\");\nconst embeddingModel = provider.embedding(\"text-embedding-3-small\");\n```\n\n**Available methods:**\n\n| Method                                 | Description                                   |\n| -------------------------------------- | --------------------------------------------- |\n| `provider(modelId)`                    | Callable syntax, creates language model       |\n| `provider.chat(modelId)`               | Creates language model (alias)                |\n| `provider.languageModel(modelId)`      | Creates language model (ProviderV3 standard)  |\n| `provider.embedding(modelId)`          | Creates embedding model (alias)               |\n| `provider.embeddingModel(modelId)`     | Creates embedding model (ProviderV3 standard) |\n| `provider.textEmbeddingModel(modelId)` | Creates embedding model (alias)               |\n\n> `embedding()` and `embeddingModel()` are identical. `textEmbeddingModel()` is\n> deprecated in the V3 and V4 entrypoints — use `embeddingModel()` instead.\n>\n> **Note:** The V2 facade package (`@jerome-benoit/sap-ai-provider-v2`) only exposes\n> `textEmbeddingModel()` for embeddings per the `ProviderV2` specification. Use the\n> V3 root with AI SDK 6 or V4 subpath with AI SDK 7 if you need these aliases.\n\n## Authentication\n\nAuthentication is handled automatically by the SAP AI SDK via the\n`AICORE_SERVICE_KEY` environment variable (local) or `VCAP_SERVICES` (SAP BTP).\n\n**→ [Environment Setup Guide](./ENVIRONMENT_SETUP.md)** - Complete setup\ninstructions, SAP BTP deployment, and troubleshooting.\n\n## Basic Usage\n\n### Text Generation\n\n**Complete example:**\n[examples/example-generate-text.ts](./examples/example-generate-text.ts)\n\n```typescript\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  prompt: \"Write a short story about a robot learning to paint.\",\n});\nconsole.log(result.text);\n```\n\n**Run it:** `npx tsx examples/example-generate-text.ts`\n\n### Chat Conversations\n\n**Complete example:**\n[examples/example-simple-chat-completion.ts](./examples/example-simple-chat-completion.ts)\n\n> **Note:** Assistant `reasoning` parts are dropped by default. Set\n> `includeReasoning: true` on the model settings if you explicitly want to\n> forward them.\n\n```typescript\nconst result = await generateText({\n  model: provider(\"anthropic--claude-4.5-sonnet\"),\n  messages: [\n    { role: \"system\", content: \"You are a helpful coding assistant.\" },\n    {\n      role: \"user\",\n      content: \"How do I implement binary search in TypeScript?\",\n    },\n  ],\n});\n```\n\n**Run it:** `npx tsx examples/example-simple-chat-completion.ts`\n\n### Streaming Responses\n\n**Complete example:**\n[examples/example-streaming-chat.ts](./examples/example-streaming-chat.ts)\n\n```typescript\nimport { streamText } from \"ai\";\nimport { APICallError } from \"@ai-sdk/provider\";\n\ntry {\n  let streamError: unknown;\n  const result = streamText({\n    model: provider(\"gpt-4.1\"),\n    prompt: \"Explain machine learning concepts.\",\n    onError({ error }) {\n      streamError = error;\n    },\n  });\n\n  for await (const delta of result.textStream) {\n    process.stdout.write(delta);\n  }\n\n  // textStream does not throw stream errors; preserve the original API error.\n  if (streamError !== undefined) throw streamError;\n\n  // streamText returns a result object; its usage property is a promise.\n  console.log(\"\\n\\nUsage:\", await result.usage);\n} catch (error) {\n  process.exitCode = 1;\n  if (APICallError.isInstance(error)) {\n    console.error(\"API error:\", error.statusCode, error.name);\n  } else {\n    console.error(\"Streaming failed:\", error instanceof Error ? error.name : \"Unknown error\");\n  }\n}\n```\n\n**Run it:** `npx tsx examples/example-streaming-chat.ts`\n\n> **Note:** For comprehensive error handling patterns, see the\n> [Error Handling](#error-handling) section and\n> [API Reference - Error Types](./API_REFERENCE.md#error-types).\n\n### Model Configuration\n\n```typescript\nimport \"dotenv/config\"; // Load environment variables\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider\";\nimport { generateText } from \"ai\";\n\nconst provider = createSAPAIProvider();\n\nconst model = provider(\"gpt-4.1\", {\n  // Optional: include assistant reasoning parts (chain-of-thought).\n  // Best practice is to keep this disabled.\n  includeReasoning: false,\n  modelParams: {\n    temperature: 0.3,\n    maxTokens: 2000,\n    topP: 0.9,\n  },\n});\n\nconst result = await generateText({\n  model,\n  prompt: \"Write a technical blog post about TypeScript.\",\n});\n```\n\n### Embeddings\n\nGenerate vector embeddings for RAG (Retrieval-Augmented Generation), semantic\nsearch, and similarity matching.\n\n**Complete example:**\n[examples/example-embeddings.ts](./examples/example-embeddings.ts)\n\n```typescript\nimport \"dotenv/config\"; // Load environment variables\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider\";\nimport { embed, embedMany } from \"ai\";\n\nconst provider = createSAPAIProvider();\n\n// Single embedding\nconst { embedding } = await embed({\n  model: provider.embedding(\"text-embedding-3-small\"),\n  value: \"What is machine learning?\",\n});\n\n// Multiple embeddings\nconst { embeddings } = await embedMany({\n  model: provider.embedding(\"text-embedding-3-small\"),\n  values: [\"Hello world\", \"AI is amazing\", \"Vector search\"],\n});\n```\n\n**Run it:** `npx tsx examples/example-embeddings.ts`\n\n> **Note:** Embedding model availability depends on your SAP AI Core tenant\n> configuration. Common providers include OpenAI, Amazon Titan, and NVIDIA.\n\nFor complete embedding API documentation, see\n**[API Reference: Embeddings](./API_REFERENCE.md#embeddings)**.\n\n## Supported Models\n\nThis provider supports all models available through SAP AI Core, including models\nfrom **OpenAI**, **Anthropic Claude**, **Google Gemini**, **Amazon Nova**,\n**Mistral AI**, **Cohere**, and **SAP** (ABAP, RPT).\n\n> **Note:** Model availability depends on your SAP AI Core tenant configuration,\n> region, and subscription. Use `provider(\"model-name\")` with any model ID\n> available in your environment.\n\nFor details on discovering available models, see\n**[API Reference: Supported Models](./API_REFERENCE.md#supported-models)**.\n\n## Advanced Features\n\nThe following helper functions are exported by this package for convenient\nconfiguration of SAP AI Core features. These builders provide type-safe\nconfiguration for data masking, content filtering, grounding, and translation\nmodules.\n\n### Tool Calling\n\n> **Note on Terminology:** This documentation uses \"tool calling\" (Vercel AI SDK\n> convention), equivalent to \"function calling\" in OpenAI documentation. Both\n> terms refer to the same capability of models invoking external functions.\n\n📖 **Complete guide:**\n[API Reference - Tool Calling](./API_REFERENCE.md#tool-calling-function-calling)\\\n**Complete example:**\n[examples/example-chat-completion-tool.ts](./examples/example-chat-completion-tool.ts)\n\n```typescript\nimport { generateText, stepCountIs, tool } from \"ai\";\nimport { z } from \"zod\";\nimport { createSAPAIProvider } from \"@jerome-benoit/sap-ai-provider\";\n\nconst provider = createSAPAIProvider();\n\nconst weatherTool = tool({\n  description: \"Get weather for a location\",\n  inputSchema: z.object({ location: z.string() }),\n  execute: async (args) => `Weather in ${args.location}: sunny, 72°F`,\n});\n\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  prompt: \"What's the weather in Tokyo?\",\n  tools: { getWeather: weatherTool },\n  stopWhen: stepCountIs(3),\n});\n```\n\n**Run it:** `npx tsx examples/example-chat-completion-tool.ts`\n\n⚠️ **Model Limitations:** Some models have tool calling restrictions. See\n[API Reference - Model-Specific Tool Limitations](./API_REFERENCE.md#model-specific-tool-limitations)\nfor upstream support documentation.\n\n### Multi-modal Input (Images)\n\n**Complete example:**\n[examples/example-image-recognition.ts](./examples/example-image-recognition.ts)\n\n```typescript\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  messages: [\n    {\n      role: \"user\",\n      content: [\n        { type: \"text\", text: \"What do you see in this image?\" },\n        { type: \"image\", image: new URL(\"https://example.com/image.jpg\") },\n      ],\n    },\n  ],\n});\n```\n\n**Run it:** `npx tsx examples/example-image-recognition.ts`\n\n### Data Masking (SAP DPI)\n\nUse SAP's Data Privacy Integration to mask sensitive data:\n\n**Complete example:**\n[examples/example-data-masking.ts](./examples/example-data-masking.ts)\\\n**Complete documentation:**\n[API Reference - Data Masking](./API_REFERENCE.md#builddpimaskingproviderconfig)\n\n```typescript\nimport { buildDpiMaskingProvider } from \"@jerome-benoit/sap-ai-provider\";\n\nconst dpiConfig = buildDpiMaskingProvider({\n  method: \"anonymization\",\n  entities: [\"profile-email\", \"profile-person\", \"profile-phone\"],\n});\n```\n\n**Run it:** `npx tsx examples/example-data-masking.ts`\n\n### Content Filtering\n\nContent filtering is available through the Orchestration API.\n\n**Complete example:**\n[examples/example-content-filtering.ts](./examples/example-content-filtering.ts)\\\n**Complete documentation:**\n[API Reference - Content Filtering](./API_REFERENCE.md#buildazurecontentsafetyfiltertype-config)\n\n**Run it:** `npx tsx examples/example-content-filtering.ts`\n\n### Document Grounding (RAG)\n\nGround LLM responses in your own documents using vector databases.\n\n**Complete example:**\n[examples/example-document-grounding.ts](./examples/example-document-grounding.ts)\\\n**Complete documentation:**\n[API Reference - Document Grounding](./API_REFERENCE.md#builddocumentgroundingconfigconfig)\n\n```typescript\nconst provider = createSAPAIProvider({\n  defaultSettings: {\n    grounding: buildDocumentGroundingConfig({\n      filters: [{ id: \"knowledge-filter\", data_repositories: [\"*\"] }],\n      placeholders: { input: [\"groundingRequest\"], output: \"groundingOutput\" },\n    }),\n  },\n});\n\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  prompt: \"Question: {{?groundingRequest}}\\nContext: {{?groundingOutput}}\",\n  providerOptions: {\n    \"sap-ai\": {\n      escapeTemplatePlaceholders: false,\n      placeholderValues: { groundingRequest: \"What is SAP?\" },\n    },\n  },\n});\n```\n\nSet `escapeTemplatePlaceholders` to `false` only when intentionally sending SAP\nor Jinja placeholders in prompts or messages.\n\n**Run it:** `npx tsx examples/example-document-grounding.ts`\n\n### Translation\n\nAutomatically translate user queries and model responses.\n\n**Complete example:**\n[examples/example-translation.ts](./examples/example-translation.ts)\\\n**Complete documentation:**\n[API Reference - Translation](./API_REFERENCE.md#buildtranslationconfigtype-config)\n\n```typescript\nconst provider = createSAPAIProvider({\n  defaultSettings: {\n    translation: {\n      // Translate user input from German to English\n      input: buildTranslationConfig(\"input\", {\n        sourceLanguage: \"de\",\n        targetLanguage: \"en\",\n      }),\n      // Translate model output from English to German\n      output: buildTranslationConfig(\"output\", {\n        targetLanguage: \"de\",\n      }),\n    },\n  },\n});\n\n// Model handles German input/output automatically\nconst model = provider(\"gpt-4.1\");\n```\n\n**Run it:** `npx tsx examples/example-translation.ts`\n\n### Provider Options (Per-Call Overrides)\n\nOverride constructor settings on a per-call basis using `providerOptions`.\nOnly the documented per-call fields are supported: unknown top-level fields\nare stripped by the Zod schemas, while additional `modelParams` keys pass\nthrough. Standard AI SDK generation options (for example, `temperature`) take\nprecedence over the corresponding `providerOptions` model parameters.\n\n```typescript\nimport { generateText } from \"ai\";\nimport { createSAPAIProvider, SAP_AI_PROVIDER_NAME } from \"@jerome-benoit/sap-ai-provider\";\n\nconst provider = createSAPAIProvider();\n\nconst result = await generateText({\n  model: provider(\"gpt-4.1\"),\n  prompt: \"Explain quantum computing\",\n  providerOptions: {\n    [SAP_AI_PROVIDER_NAME]: {\n      includeReasoning: true,\n      modelParams: {\n        temperature: 0.7,\n        maxTokens: 1000,\n      },\n    },\n  },\n});\n```\n\n**Complete documentation:**\n[API Reference - Provider Options](./API_REFERENCE.md#provider-options)\n\n## Configuration Options\n\nThe provider and models can be configured with various settings for\nauthentication, model parameters, data masking, content filtering, and more.\n\n**Common Configuration:**\n\n- `name`: Provider identifier prefix (default: `'sap-ai'`). The segment before\n  the first dot is used as the key in `providerOptions`/`providerMetadata`.\n- `resourceGroup`: SAP AI Core resource group (default: 'default')\n- `deploymentId`: Specific deployment ID (auto-resolved if not set)\n- `requestConfig`: Custom HTTP request configuration (headers, params, timeout, etc.)\n  forwarded to the underlying SAP AI SDK client on every call. Provider-level scope\n  only; per-call `headers` can override individual headers. Use `requestConfig.headers`\n  for SAP-specific headers such as\n  `AI-Object-Store-Secret-Name` (feedback service). See\n  [API Reference](./API_REFERENCE.md#sapaiprovidersettings) for portability caveats.\n- `modelParams`: Temperature, maxTokens, topP, and other generation parameters\n- `masking`: SAP Data Privacy Integration (DPI) configuration\n- `filtering`: Content safety filters (Azure Content Safety, Llama Guard)\n\nFor complete configuration reference including all available options, types, and\nexamples, see\n**[API Reference - Configuration](./API_REFERENCE.md#sapaiprovidersettings)**.\n\n## Error Handling\n\nThe provider uses standard Vercel AI SDK error types (`APICallError`,\n`LoadAPIKeyError`, `NoSuchModelError` from `@ai-sdk/provider`) for consistent\nerror handling across providers.\n\n**Documentation:**\n\n- **[API Reference - Error Handling](./API_REFERENCE.md#error-handling--reference)** -\n  Complete examples, error types, and SAP-specific metadata\n- **[Troubleshooting Guide](./TROUBLESHOOTING.md)** - Solutions for common\n  errors (401, 404, 429, 5xx)\n\n## Troubleshooting\n\n**Quick Reference:**\n\n- **Authentication (401)**: Check `AICORE_SERVICE_KEY` or `VCAP_SERVICES`\n- **Model not found (404)**: Confirm tenant/region supports the model ID\n- **Rate limit (429)**: High-level AI SDK calls retry eligible errors with\n  exponential backoff, bounded by `maxRetries`; direct provider calls do not\n  implement retries\n- **Streaming**: Iterate `textStream` correctly; don't mix `generateText` and\n  `streamText`\n\n**For detailed solutions**, see **[Troubleshooting Guide](./TROUBLESHOOTING.md)**\ncovering authentication, model discovery, rate limiting, server errors,\nstreaming, and tool calling.\n\n**Error codes:**\n[API Reference - HTTP Status Codes](./API_REFERENCE.md#http-status-code-reference)\n\n## Performance\n\n- Prefer streaming (`streamText`) for long outputs to reduce latency and memory.\n- Tune `modelParams` carefully: lower `temperature` for less variable results;\n  set `maxTokens` to expected response size.\n- Use `defaultSettings` at provider creation to share configuration across models;\n  settings are still merged and validated when models and calls are prepared.\n- Avoid unnecessary history: keep `messages` concise to reduce prompt size and\n  cost.\n\n## Security\n\nFollow security best practices when handling credentials. See\n[Environment Setup - Security Best Practices](./ENVIRONMENT_SETUP.md#security-best-practices)\nfor detailed guidance on credential management, key rotation, and secure\ndeployment.\n\n## Debug Mode\n\n- Use the curl guide `CURL_API_TESTING_GUIDE.md` to diagnose raw API behavior\n  independent of the SDK.\n- Log request IDs from `error.responseBody` (parse JSON for `request_id`) to\n  correlate with backend traces.\n- Temporarily enable verbose logging in your app around provider calls; redact\n  secrets.\n\n## Examples\n\nThe `examples/` directory contains complete, runnable examples using the\nrepository's AI SDK 7 dependency and the local `../src/index-v4` entrypoint.\nIn an AI SDK 7 application, import from `@jerome-benoit/sap-ai-provider/v4`.\nSee [Installation](#installation) for other AI SDK versions.\n\n| Example                             | Description                 | Key Features                             |\n| ----------------------------------- | --------------------------- | ---------------------------------------- |\n| `example-generate-text.ts`          | Basic text generation       | Simple prompts, non-streaming generation |\n| `example-simple-chat-completion.ts` | Simple chat conversation    | System messages, user prompts            |\n| `example-chat-completion-tool.ts`   | Tool calling with functions | Demo weather tool, function execution    |\n| `example-streaming-chat.ts`         | Streaming responses         | Real-time text generation, SSE           |\n| `example-image-recognition.ts`      | Multi-modal with images     | Vision models, image analysis            |\n| `example-data-masking.ts`           | Data privacy integration    | DPI masking, anonymization               |\n| `example-content-filtering.ts`      | Content filtering           | Azure Content Safety, orchestration      |\n| `example-document-grounding.ts`     | Document grounding (RAG)    | Vector store, retrieval-augmented gen    |\n| `example-translation.ts`            | Input/output translation    | Multi-language support, SAP translation  |\n| `example-embeddings.ts`             | Text embeddings             | Vector generation, semantic similarity   |\n| `example-foundation-models.ts`      | Foundation Models API       | Direct model access, logprobs, seed      |\n\n**Running Examples:**\n\n```bash\nnpx tsx examples/example-generate-text.ts\n```\n\n> **Note:** Configure `AICORE_SERVICE_KEY` locally or bind the application to\n> SAP AI Core on SAP BTP. See [Environment Setup](./ENVIRONMENT_SETUP.md).\n\n## Migration Guides\n\n### Upgrading from v4.x to v5.x\n\nVersion 5.0 adds **AI SDK 7** support through the **V4** facade and requires\n**Node.js 22.12 or newer** for both packages. The root entrypoint remains\n**V3 for AI SDK 6**; upgrading the provider does not require switching SDKs.\n\n**Key changes:**\n\n- **Runtime**: Upgrade local, CI and deployment environments from Node.js 20\n  to Node.js 22.12 or newer.\n- **Entrypoints**: Use `/v4` with SDK 7, the root with SDK 6, or `/v2` with\n  SDK 5. The standalone V2 package remains available for SDK 5/6.\n- **Multimodal inputs**: V4 tagged JPEG/PDF inputs are normalized correctly;\n  unsupported binary objects are rejected instead of silently stringified.\n\nSee the [4.x to 5.x migration guide](./MIGRATION_GUIDE.md#version-4x-to-5x-breaking-changes)\nfor installation commands, compatibility details and the migration checklist.\n\n### Upgrading from v3.x to v4.x\n\nVersion 4.0 migrates from **LanguageModelV2** to **LanguageModelV3**\nspecification (AI SDK 6). Package release 4.x is not the V4 provider\nspecification used by AI SDK 7. **See the\n[Migration Guide](./MIGRATION_GUIDE.md#version-3x-to-4x-breaking-changes) for\ncomplete upgrade instructions.**\n\n**Key changes in direct provider results (`doGenerate`/`doStream`):**\n\n- **Finish Reason**: Changed from string to object\n  (`result.finishReason.unified`)\n- **Usage Structure**: Nested format with detailed token breakdown\n  (`result.usage.inputTokens.total`)\n- **Stream Events**: Text blocks retain `text-start`, `text-delta`, and\n  `text-end`; finish and warning payloads use the V3 format\n- **Warning Types**: Updated format with `feature` field for categorization\n\n**Impact by user type:**\n\n- High-level API users (`generateText`/`streamText`): use AI SDK 6 with the root\n  entrypoint. High-level token totals remain flat numbers.\n- Direct provider users: ⚠️ Update type imports (`LanguageModelV2` →\n  `LanguageModelV3`)\n- Custom stream parsers: ⚠️ Update parsing logic for V3 structure\n\n### Upgrading from v2.x to v3.x\n\nVersion 3.0 standardizes error handling to use Vercel AI SDK native error types.\n**See the\n[Migration Guide](./MIGRATION_GUIDE.md#version-2x-to-3x-breaking-changes) for\ncomplete upgrade instructions.**\n\n**Key changes:**\n\n- `SAPAIError` removed → Use `APICallError` from `@ai-sdk/provider`\n- Error properties: `error.code` → `error.statusCode`\n- Retryable error classification for the AI SDK's rate-limit/server-error retries\n\n### Upgrading from v1.x to v2.x\n\nVersion 2.0 uses the official SAP AI SDK. **See the\n[Migration Guide](./MIGRATION_GUIDE.md#version-1x-to-2x-breaking-changes) for\ncomplete upgrade instructions.**\n\n**Key changes:**\n\n- Authentication via `AICORE_SERVICE_KEY` environment variable\n- Synchronous provider creation: `createSAPAIProvider()` (no await)\n- Helper functions from SAP AI SDK\n\n**For detailed migration instructions with code examples, see the\n[complete Migration Guide](./MIGRATION_GUIDE.md).**\n\n## Important Note\n\n> **Third-Party Provider**: This SAP AI Provider\n> (`@jerome-benoit/sap-ai-provider`) is developed and maintained by\n> jerome-benoit, not by SAP SE. While it uses the official SAP AI SDK and\n> integrates with SAP AI Core services, it is not an official SAP product.\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](./CONTRIBUTING.md)\nfor details.\n\n## Resources\n\n### Documentation\n\n- [Migration Guide](./MIGRATION_GUIDE.md) - Version upgrade instructions (v1.x →\n  v2.x → v3.x → v4.x → v5.x)\n- [API Reference](./API_REFERENCE.md) - Complete API documentation with all\n  types and functions\n- [Environment Setup](./ENVIRONMENT_SETUP.md) - Authentication and configuration\n  setup\n- [Troubleshooting](./TROUBLESHOOTING.md) - Common issues and solutions\n- [Architecture](./ARCHITECTURE.md) - Internal architecture, design decisions,\n  and request flows\n- [cURL API Testing Guide](./CURL_API_TESTING_GUIDE.md) - Direct API testing for\n  debugging\n\n### Community\n\n- 🐛 [Issue Tracker](https://github.com/jerome-benoit/sap-ai-provider/issues) -\n  Report bugs, request features, and ask questions\n\n### Related Projects\n\n- [Vercel AI SDK](https://sdk.vercel.ai/) - The AI SDK this provider extends\n- [SAP AI SDK](https://sap.github.io/ai-sdk/) - Official SAP Cloud SDK for AI\n- [SAP AI Core Documentation](https://help.sap.com/docs/ai-core) - Official SAP\n  AI Core docs\n\n## License\n\nApache License 2.0 - see [LICENSE](./LICENSE) for details.\n","readmeFilename":"README.md"}