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AI Core embedding provider (IEmbedder) for @mcp-abap-adt/llm-agent.","maintainers":[{"name":"fr0ster","email":"oleksij.kyslytsja@gmail.com"}],"readme":"# @mcp-abap-adt/sap-aicore-embedder\n\n[![Stand With Ukraine](https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/badges/StandWithUkraine.svg)](https://stand-with-ukraine.pp.ua)\n[![License: LGPL v3](https://img.shields.io/badge/License-LGPL_v3-blue.svg)](https://www.gnu.org/licenses/lgpl-3.0)\n\nSAP AI Core embedding provider (IEmbedderBatch) for @mcp-abap-adt/llm-agent.\n\nGenerates text embeddings via SAP AI Core embedding model deployments using the @sap-ai-sdk/orchestration client.\n\n## Installation\n\n```bash\nnpm install @mcp-abap-adt/sap-aicore-embedder\n```\n\n## Usage\n\n```typescript\nimport { SapAiCoreEmbedder } from '@mcp-abap-adt/sap-aicore-embedder';\nimport { serviceKeyCredential } from '@mcp-abap-adt/sap-aicore-auth';\n\nconst { credential, apiBaseUrl } = serviceKeyCredential(process.env.AICORE_SERVICE_KEY!);\n\nconst embedder = new SapAiCoreEmbedder({\n  model: 'text-embedding-3-small',\n  resourceGroup: 'default', // optional\n  credential,\n  apiBaseUrl,\n});\n\n// Embed a single text\nconst result = await embedder.embed('Hello, world!');\nconsole.log(result.vector);\n\n// Batch embed multiple texts\nconst results = await embedder.embedBatch(['Text 1', 'Text 2', 'Text 3']);\nconsole.log(results.map(r => r.vector));\n```\n\n## Configuration\n\n```ts\ninterface SapAiCoreEmbedderConfig {\n  model: string;\n  resourceGroup?: string;                             // default: 'default'\n  scenario?: 'foundation-models' | 'orchestration';   // default: 'orchestration'\n  credential: IBearerCredential;                       // required, both scenarios — resolved fresh per call\n  apiBaseUrl: string;                                  // required, both scenarios\n}\n```\n\n`credential` and `apiBaseUrl` are required for both scenarios and are never\nread from the environment inside this package. Build them from a raw SAP AI\nCore service key with `serviceKeyCredential` from `@mcp-abap-adt/sap-aicore-auth`\n— see [Authentication](#authentication) below. `IBearerCredential` comes from\n`@mcp-abap-adt/interfaces-auth`.\n\n### Orchestration (default)\n\nThe default scenario — matches v11.0.0 behavior. Use this when the embedding model is deployed under the `orchestration` scenario.\n\n```ts\nimport { SapAiCoreEmbedder } from '@mcp-abap-adt/sap-aicore-embedder';\nimport { serviceKeyCredential } from '@mcp-abap-adt/sap-aicore-auth';\n\nconst embedder = new SapAiCoreEmbedder({\n  model: 'text-embedding-3-small',\n  ...serviceKeyCredential(process.env.AICORE_SERVICE_KEY!),\n});\n```\n\n### Foundation-models\n\nOpt-in path for tenants where embedding models (such as `gemini-embedding` or `text-embedding-3-small`) are deployed under the `foundation-models` scenario rather than the orchestration scenario. The embedder calls the AI Core REST inference API directly.\n\n```ts\nconst embedder = new SapAiCoreEmbedder({\n  model: 'gemini-embedding',\n  scenario: 'foundation-models',\n  ...serviceKeyCredential(process.env.AICORE_SERVICE_KEY!),\n});\n```\n\n#### Batch size cap\n\n`gemini-embedding` routes to Vertex, which rejects a batch of 251 or more\ninstances. `FoundationModelsEmbedder` therefore declares `maxBatchSize = 250`\nfor the `gemini` family (`IBatchSizeLimited`), and the embedder chain chunks to\nit automatically — no configuration needed.\n\nOther families declare no cap and fall back to the library default (100). No\ndocumented AI Core limit for the OpenAI family has been verified, so none is\nasserted. Override either with `rag.maxBatchSize` in YAML when the tenant's real\nquota is stricter than the model's documented limit.\n\n### Asymmetric models (`type: document | query`)\n\nRetrieval models such as `nvidia--llama-3.2-nv-embedqa-1b` embed stored text and\nsearch text differently and refuse a call without an input `type`. Build the\nmodel twice — `inputType: 'document'` and `'query'` — and give the two to\n`asymmetricEmbedder` (`@mcp-abap-adt/llm-agent`). The result is ONE\n`IRetrievalEmbedder`: a store writes through its `embedDocument` and searches\nthrough its `embedQuery`, so neither job can be done with the other's instance.\n\n```ts\nimport { asymmetricEmbedder } from '@mcp-abap-adt/llm-agent';\nimport { SapAiCoreEmbedder } from '@mcp-abap-adt/sap-aicore-embedder';\n\nconst config = {\n  model: 'nvidia--llama-3.2-nv-embedqa-1b',\n  ...serviceKeyCredential(process.env.AICORE_SERVICE_KEY!),\n};\nconst embedder = asymmetricEmbedder({\n  document: new SapAiCoreEmbedder({ ...config, inputType: 'document' }),\n  query: new SapAiCoreEmbedder({ ...config, inputType: 'query' }),\n});\n```\n\nA symmetric model (`text-embedding-3-*`, `gemini-embedding`,\n`amazon--titan-embed-text`) is one instance: `symmetricEmbedder(new\nSapAiCoreEmbedder(config))`. `inputType` works with the orchestration scenario\nonly — with `scenario: 'foundation-models'` it is refused at construction.\n\n## Authentication\n\nNeither scenario reads an environment variable inside this package — the\ncaller resolves the credential and passes it in. `serviceKeyCredential(raw)`\n(`@mcp-abap-adt/sap-aicore-auth`) turns a raw SAP AI Core service-key JSON\nstring into the `{ credential, apiBaseUrl }` pair both scenarios need:\n\n```ts\nimport { serviceKeyCredential } from '@mcp-abap-adt/sap-aicore-auth';\n\nconst { credential, apiBaseUrl } = serviceKeyCredential(process.env.AICORE_SERVICE_KEY!);\n```\n\n`credential.token()` is asked fresh on every `embed()`/`embedBatch()` call —\nnever cached on the embedder instance — so a rotating token keeps rotating.\nReading `AICORE_SERVICE_KEY` (or any other environment variable) is the\ncomposition root's job, not this package's.\n\n## License\n\n**GNU Lesser General Public License v3.0 only** (`LGPL-3.0-only`) — see\n[`LICENSE`](LICENSE) (LGPL) and [`GPL-3.0.txt`](GPL-3.0.txt) (the GPL it layers\npermissions onto; both are required, the LGPL is not standalone).\n\nCopyright © 2025–2026 Oleksii Kyslytsia\n\nImporting this package, or running it behind an HTTP endpoint, does not place\nyour program under the LGPL — the licence asks that modifications *to this\nlibrary* stay free and that your users can substitute their own build.\nVersions up to and including v20.9.5 were MIT and stay MIT; the change is not\nretroactive. Full detail, including what to do if you redistribute:\n[docs/LICENSING.md](https://github.com/fr0ster/llm-agent/blob/main/docs/LICENSING.md).\n","readmeFilename":"README.md"}