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AI platform for SAP CAP. 11 providers behind one API (Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Ollama, Groq, Fireworks, DeepSeek, Mistral, OpenAI-compatible, SAP Generative AI Hub) with 30+ middleware primitives spanning cost/resi","maintainers":[{"name":"saptarishi","email":"jkalyan@alumni.iitm.ac.in"}],"readme":"# cds-plugin-llm\n\n[![CI](https://github.com/kalyanjanumpally/sap-joule-ai-plugin/actions/workflows/ci.yml/badge.svg)](https://github.com/kalyanjanumpally/sap-joule-ai-plugin/actions/workflows/ci.yml)\n[![npm](https://img.shields.io/npm/v/@saptarishi/cds-plugin-llm.svg)](https://www.npmjs.com/package/@saptarishi/cds-plugin-llm)\n[![license](https://img.shields.io/npm/l/@saptarishi/cds-plugin-llm.svg)](./LICENSE)\n\nLLM-agnostic AI platform for SAP CAP. **11 providers** behind one unified interface — Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Ollama, Groq, Fireworks, DeepSeek, Mistral, any OpenAI-compatible endpoint, and SAP Generative AI Hub — plus **30+ middleware primitives** spanning cost, resilience, security, observability, compliance, RAG, and CI eval.\n\n**Status:** stable (**v2.0.0**). Formal API stability contract in force for the 2.x line — see [`MIGRATION.md`](./MIGRATION.md). 11 providers implemented; **2188 automated tests** on Node 20 + 22 in CI; zero known bugs, zero deprecated APIs; TypeScript definitions ship in the package.\n\n## What it is\n\nA CAP service kind that turns `cds.connect.to('llm')` into a working LLM client — with one unified interface (`chat`, `stream`, `embed`, `batch`) that speaks to any of eleven backends. Swapping backends is a config change, not a code change.\n\nBeyond the provider abstraction, the package ships a comprehensive **middleware platform**: cost + budget guards, cost forecasting, circuit breakers, bulkheads (with latency- and quota-driven adaptive tuners), retries, deadlines, provider fallback, region failover, prompt-injection detection, PII redaction, content-safety classifiers, structured-output validators, response scoring, multi-model consensus voting, prompt-cache metrics, response caches (exact + semantic + pluggable-vector-store), embedding dedup, JSON logs, OTel spans, Prometheus metrics, tenant isolation, distributed locks, compliance audit trails, and more. See the [Middleware catalog](#middleware-catalog) below.\n\nThe package also ships **first-class helpers** for RAG orchestration (`ragChain`), LLM-as-judge scoring (`llmJudge`), CI eval harnesses (`promptRegression`, `lintPrompt`), and offline batch workflows (`runBatch`, `waitForBatch`) — plus a `saptarishi-llm` CLI with subcommands for `chat`/`stream`/`embed`/`verify`/`init`/`mcp`/`chain-visualize`/`doctor`/`export-dashboard`/`lint-prompts` and more.\n\nComplementary to [`@cap-js/ai`](https://github.com/cap-js/ai), which focuses on value-help recommendations and SAP AI Core integration. This plugin fills the more general \"I need a CAP-idiomatic way to call LLMs, with a local development story and multiple provider options\" gap.\n\n## Middleware catalog\n\nEvery primitive below is a shipping middleware (or top-level helper) with dedicated per-section documentation, tests, TypeScript types, and a `config://<primitive>` MCP resource for live introspection. All are stable in the 2.x line — see [`MIGRATION.md`](./MIGRATION.md).\n\n| Group | Primitives |\n| --- | --- |\n| **Cost** | `usageMetering` · `usageMeteringToCap` · `costBudget` · `costGuard` · `costForecast` · `quotaManager` (v2.23.0 — per-user USD quota with warnings) · `inMemoryQuotaStore` · `costOverrunPredictor` (v2.33.0 — calendar-window spend projection) · `startOfMonth` / `endOfMonth` (+day / quarter helpers) · `adaptiveMaxTokens` · `estimateCost` · `promptCacheStats` |\n| **Resilience** | `retryOnRateLimit` · `circuitBreaker` · `bulkhead` · `adaptiveBulkhead` · `adaptiveRateLimit` · `clientSideRateLimit` (v2.26.0 — proactive N-per-window throttle) · `deadline` · `gracePeriod` (v2.28.0 — soft-deadline warnings + optional hard timeout) · `chatWithFallback` · `regionFailover` · `autoRetry` · `providerHealthProbe` · `autoContinue` · `idempotency` · `distributedLock` · `speculativeHedge` (v2.12.0 — staggered parallel hedges for tail latency) · `retryBudget` (v2.15.0 — SRE-style global retry cap) |\n| **Security** | `guardrails` · `promptInjectionGuard` · `piiRedact` · `reversibleTokenization` (v2.13.0 — round-trip PII replacement) · `tokenizePII` / `detokenizePII` · `PII_PATTERNS` · `safetyClassifier` · `sensitiveDataAudit` · `requestSigning` (v2.21.0 — HMAC receipts + `verifyReceiptChain`) · `responseSigning` (v2.32.0 — HMAC responses + `verifyResponseSignature`) · `emptyResponseDetector` (v2.37.0 — catch empty/refusal responses with auto-retry) |\n| **Observability** | `jsonLog` · `otel` · `otelSpans` · `promMetrics` · `prometheusHandler` · `traceCorrelation` · `healthHandler` · `replayBuffer` · `retryAfterPropagation` · `providerHealthAggregate` (v2.31.0 — unified provider health score) · `latencyHistogram` (v2.35.0 — per-dimension p50/p95/p99 with Prometheus export) |\n| **Testing** | `chaosInjector` (v2.11.0 — deterministic seeded fault injection; test-only, refuses to construct without opt-in) |\n| **Routing** | `modelRouter` · `tenantIsolate` · `costAwareRouter` (v2.10.0 — cheap-first with quality escalation) · `fairShareScheduler` (v2.14.0 — per-tenant WRR admission control) · `semanticRouter` (v2.16.0 — embedding-based route selection) · `providerLoadBalancer` (v2.17.0 — rotate across N credentials of same kind) · `multimodalRouter` (v2.25.0 — capability-aware routing by attachment type) |\n| **Caching** | `responseCache` (exact + semantic) · `semanticCache` (pluggable vector store) · `cosineSimilarity` · `embeddingDedup` · `requestCoalescer` (in-flight dedup) · `fuzzyDedup` (v2.38.0 — near-duplicate detection via Jaccard-trigram / Levenshtein, no embedder required) · `inMemoryFuzzyStore` |\n| **Contract / GitOps** | `structuredOutputValidator` · `structuredOutputRepair` (v2.9.0 — multi-strategy repair) · `jsonAutoFix` · `functionCallArbitrator` (v2.18.0 — tool-call allowlist + validation) · `normalizeToolShape` · `normalizeToolList` · `contentLengthGate` (v2.36.0 — pre-flight size validation) · `defaultTokenEstimator` · `schemas` (Invoice, PurchaseOrder, SupplierRisk, ContractSummary, ExpenseReport, EmailDraft) · `validateMiddlewareOrder` · `chainSnapshot` · `chainDiff` · `preflight` · `capabilities` (+ `PROVIDER_CAPABILITY_MATRIX`, `MODEL_CAPABILITY_OVERRIDES`) |\n| **Prompts** | `PromptRegistry` · `builtInPrompts` · `gitPromptRegistry` (v2.1.0 — Git-backed prompt-as-code) · `promptVersionPin` (v2.30.0 — canary/rollback for prompt templates) · `PromptVersionRegistry` |\n| **Long-context** | `compactHistory` · `sessionContextStore` (v2.19.0 — per-session history with prune / summarize) · `inMemorySessionStore` |\n| **Streaming** | `wrapStreamCompletion` · `hasStreamCompletion` · `streamThrottle` · `streamAggregator` (v2.24.0 — coalesce per-token chunks for smoother UI) |\n| **RAG + Eval** | `ragChain` · `llmJudge` / `judgeMany` · `promptRegression` / `loadFixtures` / `formatRegressionReport` · `lintPrompt` / `lintPrompts` / `formatLintReport` · `scoreResponse` · `consensusVoting` · `promptExperiment` (v2.20.0 — live A/B testing with 95% CI winner detection) · `responseRevision` (v2.29.0 — quality-driven re-ask loop) · `userFeedbackAggregator` (v2.34.0 — human thumbs/stars aggregation) |\n| **Bulk workflows** | `runBatch` · `waitForBatch` · `batchAggregator` (v2.27.0 — window-based pooling of concurrent LLM calls) |\n| **Multimodal helpers** | `imageFromFile` / `pdfFromUrl` / `audioFromBase64` / `uploadPdfFromUrl` / etc. |\n| **Agent orchestration** | `runTools` · `streamTools` · `Agent` · `runAgents` · `streamAgents` · `autoToolChain` (v2.22.0 — cascading tool loop with cycle detection) |\n| **Error taxonomy** | `LLMError` base + `errorRegistry` with 18 stable codes (`CIRCUIT_OPEN`, `BUDGET_EXCEEDED`, `PROMPT_INJECTION`, `SAFETY_CLASSIFIER_BLOCKED`, `ALL_REGIONS_FAILED`, ...) |\n\nEvery primitive is composable via `llm.use(mw)` Koa-style — outermost first. `resilience.bundle()` wires the full resilience stack (retry → breaker → bulkhead → deadline → probes → tuner) with one call. `chainSnapshot(llm)` extracts the live config for GitOps drift detection; `chainDiff(baseline, live)` diffs; `validateMiddlewareOrder(chain)` warns on suspicious orderings.\n\n## Architecture\n\n```\n    Your CAP handler / OData action\n          │\n          │  cds.connect.to('llm')  →  { chat, stream, embed, batch }\n          ↓\n    ┌─────────────────────────────────────────────────────────────┐\n    │  Middleware chain (llm.use(...), Koa-style, outer → inner)  │\n    │                                                             │\n    │   otelSpans / traceCorrelation / jsonLog                    │\n    │     → promptCacheStats                                      │\n    │       → modelRouter                                         │\n    │         → embeddingDedup                                    │\n    │           → safetyClassifier                                │\n    │             → autoContinue                                  │\n    │               → promptInjectionGuard                        │\n    │                 → guardrails                                │\n    │                   → piiRedact                               │\n    │                     → sensitiveDataAudit                    │\n    │                       → costGuard / costBudget              │\n    │                         → adaptiveMaxTokens                 │\n    │                           → idempotency / distributedLock   │\n    │                             → circuitBreaker                │\n    │                               → bulkhead                    │\n    │                                 → retryOnRateLimit          │\n    │                                   → responseCache           │\n    │                                     → structuredOutputValidator\n    │                                       → replayBuffer        │\n    │                                         → (provider)        │\n    └─────────────────────────────────────────────────────────────┘\n                 │\n                 ▼\n    ┌─────────────────────────────────────────────────────────────┐\n    │  LLMService (base class): retries · structured-output       │\n    │  parsing · unified chunk shape · batch API dispatch         │\n    └───────────────────┬─────────────────────────────────────────┘\n                        │\n                        ▼\n    ┌─── AnthropicLLM ────┬─── AzureOpenAILLM ──┬─── GeminiLLM ─┐\n    │    OpenAICompatible │    GroqLLM          │    BedrockLLM │\n    │    FireworksLLM     │    DeepSeekLLM      │    MistralLLM │\n    │    OllamaLLM        │    GenAIHubLLM      │               │\n    └─────────────────────┴─────────────────────┴───────────────┘\n```\n\nThe middleware sandwich is **fully composable** — every layer is optional, layer order is validated by `validateMiddlewareOrder`, and the full snapshot is exportable for GitOps drift detection via `chainSnapshot`. Each layer exposes an `asMcpResource()` handler for live observability.\n\n- **No CDS entities or served OData surface** — this is a client library, not an OData service. `cds.connect.to('llm')` returns the provider instance directly.\n- **Provider selection at connect time** via `cds.requires.llm.kind` — profile-aware, so `[development]`, `[production]`, `[genai-hub]`, etc. can each point at a different backend.\n- **Provider inheritance:** `GroqLLMService` and `GenAIHubLLMService` both extend `OpenAICompatibleLLMService` (they speak the OpenAI `/chat/completions` shape); the latter adds OAuth + resource-group headers on top.\n\n## Install\n\n```bash\nnpm install @saptarishi/cds-plugin-llm\n```\n\nOptional peer dep for the Anthropic path: `@anthropic-ai/sdk` (installed automatically as a dependency).\n\n**TypeScript:** full type definitions ship in the package (`lib/index.d.ts`). No `@types/*` package needed.\n\n**CLI:** a `saptarishi-llm` executable ships with the package. Use it via `npx` without installing globally, or install once with `npm i -g @saptarishi/cds-plugin-llm`. See the [CLI section](#cli-new-in-v150).\n\n## Configure\n\nAdd to your CAP app's `package.json` under `cds.requires`:\n\n```json\n{\n  \"cds\": {\n    \"requires\": {\n      \"llm\": {\n        \"[development]\": { \"kind\": \"llm-groq\",       \"modelId\": \"llama-3.3-70b-versatile\" },\n        \"[ollama]\":      { \"kind\": \"llm-ollama\",     \"modelId\": \"qwen2.5:14b\"             },\n        \"[production]\":  { \"kind\": \"llm-genai-hub\", \"credentials\": { \"deploymentId\": \"...\" } }\n      }\n    }\n  }\n}\n```\n\nSet the appropriate env var (see `.env.example`):\n- `ANTHROPIC_API_KEY` for `llm-anthropic`\n- `OLLAMA_BASE_URL` for `llm-ollama` (defaults to `http://localhost:11434`)\n- `GROQ_API_KEY` for `llm-groq`\n- `OPENAI_API_KEY` + `OPENAI_BASE_URL` for `llm-openai-compatible`\n\n## Providers\n\n| Kind | Backend | Cost to test | Status |\n|---|---|---|---|\n| `llm-anthropic` | Claude via Anthropic API | Pennies per call | Working |\n| `llm-ollama` | Local Ollama daemon | Free | Working |\n| `llm-groq` | Groq's hosted Llama/Mixtral/Qwen (sub-second inference) | Generous free tier | Working |\n| `llm-openai-compatible` | Any endpoint speaking OpenAI's `/chat/completions` (OpenAI, Together, Fireworks, DeepSeek direct, LM Studio, LocalAI...) | Varies | Working |\n| `llm-azure-openai` | Azure OpenAI Service (per-deployment URL + `api-key` header) | Paid via Azure subscription | Working (mock-verified end-to-end) |\n| `llm-gemini` (new in 1.19.0) | Google Gemini via Google AI Studio (chat, streaming, tools, vision, embeddings) | Free tier + paid per token | Working (mock-verified end-to-end) |\n| `llm-bedrock` (new in 1.19.0) | AWS Bedrock Converse API (chat, streaming, tools, vision) + Titan/Cohere embed. Uses `@aws-sdk/client-bedrock-runtime` (optional peer). | Paid via AWS Bedrock | Working (mock-verified end-to-end) |\n| `llm-fireworks` (new in 1.23.0) | Fireworks AI — hosted OSS models (Llama, Qwen, Mixtral, DeepSeek) behind an OpenAI-compatible endpoint | Pay-per-token via Fireworks | Working (mock-verified end-to-end) |\n| `llm-deepseek` (new in 1.23.0) | DeepSeek — direct API access to DeepSeek-V3 (chat) and DeepSeek-R1 (reasoning) | Very cheap per-token | Working (mock-verified end-to-end) |\n| `llm-mistral` (new in 1.23.0) | Mistral AI — Mistral Large, Codestral, and the open-weights family | Pay-per-token via Mistral | Working (mock-verified end-to-end) |\n| `llm-genai-hub` | SAP AI Core / Generative AI Hub | Paid (extended plan) | Spec-compliant · mock-verified end-to-end · live verify open (community help welcome) |\n\n## Stability\n\n- **Semantic Versioning.** `1.x` will preserve the public API contract documented in `lib/index.d.ts`. Breaking changes require a major version bump.\n- **What's covered:** all exported symbols in `lib/index.js` — `LLMService` and 5 provider subclasses, `chat` / `stream` / `embed` shapes, `ContentBlock` union (text / image / document), message shapes, tool call shapes, stream chunk shapes, image / PDF helpers, and provider option fields (`kind`, `modelId`, `credentials`, `retries`, `responseCache`).\n- **What's not covered:** provider-native `raw` response objects (shape follows the upstream API), CDS kind config field names (which follow CAP conventions), and behavior of individual model IDs (upstream provider concern).\n- **Deprecation policy:** any deprecated field or method will be marked in the JSDoc + CHANGELOG for at least one minor release before removal, and only removed in a subsequent major version.\n- **Full history** in [`CHANGELOG.md`](./CHANGELOG.md).\n\n## Use\n\nStandard CAP idiom — `cds.connect.to()`:\n\n```js\nconst cds = require('@sap/cds');\n\nmodule.exports = class ProcurementService extends cds.ApplicationService {\n  async init() {\n    const llm = await cds.connect.to('llm');\n\n    this.on('summarizePO', async (req) => {\n      const { poId } = req.data;\n      const po = await SELECT.one.from('PurchaseOrders').where({ ID: poId });\n\n      const { text } = await llm.chat({\n        system: 'You summarize purchase orders for approvers in 2 sentences.',\n        messages: [{ role: 'user', content: JSON.stringify(po) }],\n        cache: true,  // Anthropic-only: caches the system prompt\n      });\n\n      return text;\n    });\n\n    return super.init();\n  }\n};\n```\n\n## Structured outputs (new in v0.3.0)\n\nPass a JSON schema via `format` and get a parsed `.data` field back:\n\n```js\nconst { data, usage } = await llm.chat({\n  system: 'Assess supplier invoice risk for AP triage.',\n  messages: [{ role: 'user', content: invoiceJson }],\n  format: {\n    type: 'object',\n    properties: {\n      risk: { type: 'string', enum: ['low', 'medium', 'high'] },\n      rationale: { type: 'string' },\n    },\n    required: ['risk', 'rationale'],\n    additionalProperties: false,\n  },\n});\n\nconsole.log(data.risk);       // 'high'\nconsole.log(data.rationale);  // 'Amount over 100k EUR without matched PO...'\n```\n\nUnder the hood:\n- **Anthropic**: uses `output_config.format` (native JSON schema)\n- **OpenAI-compatible / Groq**: uses `response_format: { type: 'json_object' }` and prepends the schema to the system prompt for broadest model coverage\n- **Ollama**: uses native `format` field (schema-strict on recent Ollama versions)\n- **Base class** post-parses `.text` into `.data` uniformly; falls back to first-`{...}`-block extraction if the model wrapped the JSON in prose\n\n## Tool use / function calling (new in v0.3.0)\n\nPass a unified tool schema; get normalized `toolCalls` back:\n\n```js\nconst turn1 = await llm.chat({\n  system: 'Help procurement approvers. Use tools to fetch data.',\n  messages: [{ role: 'user', content: 'Fetch PO 4500000123' }],\n  tools: [{\n    name: 'get_purchase_order',\n    description: 'Fetch a purchase order by its 10-digit ID',\n    input_schema: {\n      type: 'object',\n      properties: { purchaseOrderId: { type: 'string' } },\n      required: ['purchaseOrderId'],\n    },\n  }],\n});\n\nif (turn1.toolCalls?.length) {\n  const call = turn1.toolCalls[0];  // { id, name, input }\n  const result = await fetchPO(call.input.purchaseOrderId);  // your app logic\n\n  // Feed the result back for turn 2\n  const turn2 = await llm.chat({\n    system: '...',\n    messages: [\n      { role: 'user',      content: 'Fetch PO 4500000123' },\n      { role: 'assistant', toolCalls: turn1.toolCalls },\n      { role: 'tool',      tool_call_id: call.id, content: JSON.stringify(result) },\n    ],\n    tools: [...],\n  });\n  console.log(turn2.text);  // model's final answer\n}\n```\n\nWorks across providers with matching `{ id, name, input }` shape. Individual model quality varies for multi-tool scenarios — llama-3.3-70b on Groq is solid for single-tool cases; Claude and qwen2.5 are more reliable for chained tool use.\n\n### Tool runner — automatic multi-turn loop (new in v1.1.0)\n\nFor agent-style code that would otherwise write the \"call → execute → feed back → repeat\" loop by hand, `runTools()` wraps the pattern:\n\n```js\nconst { runTools } = require('@saptarishi/cds-plugin-llm');\n\nconst result = await runTools({\n  llm,\n  system: 'You help procurement approvers.',\n  messages: [{ role: 'user', content: 'Fetch PO 4500000123 and summarize it.' }],\n  tools: [{\n    name: 'get_purchase_order',\n    description: 'Fetch a PO by 10-digit ID',\n    input_schema: {\n      type: 'object',\n      properties: { purchaseOrderId: { type: 'string' } },\n      required: ['purchaseOrderId'],\n    },\n    run: async ({ purchaseOrderId }) =>\n      await SELECT.one.from('PurchaseOrders').where({ ID: purchaseOrderId }),\n  }],\n  maxSteps: 10,\n});\n\nresult.text        // final assistant answer\nresult.steps       // number of chat() calls made\nresult.toolCalls   // [{ id, name, input, result, isError }, ...] — every call executed\nresult.usage       // aggregated tokens across all turns\n```\n\nWhat it handles for you:\n- Executes every tool call in every turn (multiple in one turn all run)\n- Appends assistant + `tool` messages correctly (matches the format both Anthropic and OpenAI-compat expect)\n- Catches tool exceptions and surfaces them as `tool_result` with `is_error: true` so the model can recover\n- Rejects with a clear message when an unknown tool name is called\n- `maxSteps` safety cap (default 10) — throws if the model loops forever\n- Optional `onStep({ step, response })` callback to observe each turn\n\n## Streaming (new in v0.6.0)\n\nGet tokens as they arrive from the model instead of waiting for the full response:\n\n```js\nfor await (const chunk of llm.stream({\n  system: 'You are a poet.',\n  messages: [{ role: 'user', content: 'Write a haiku about SAP procurement.' }],\n})) {\n  if (chunk.type === 'text_delta') {\n    process.stdout.write(chunk.text);\n  }\n  if (chunk.type === 'done') {\n    console.log(`\\n[${chunk.usage.output_tokens} tokens, stopReason: ${chunk.stopReason}]`);\n  }\n}\n```\n\nChunk types:\n\n| type | payload | when |\n|---|---|---|\n| `text_delta` | `{ text }` — incremental piece of text | fires per token/token-group as the model generates |\n| `done` | `{ text, usage, stopReason, model }` — accumulated text + final metadata | once at the end |\n\nWire-shape parsing per provider:\n- **Anthropic**: uses the SDK's `messages.stream()` — SSE under the hood, events adapted to unified chunks\n- **OpenAI-compatible / Groq / GenAI Hub**: parses SSE (`data: {json}\\n\\n`) from the `/chat/completions` streaming endpoint, adds `stream_options: {include_usage: true}` so `usage` populates on the `done` chunk\n- **Ollama**: parses NDJSON from `/api/chat`, emits `done` when the stream's final message carries `done:true`\n\nRetries are **not** applied to streams (partial-response semantics are unclear). If a stream fails mid-way, the caller sees the error thrown from the generator.\n\nTry the demo:\n\n```sh\nnode scripts/stream-demo.js \"Explain streaming LLM responses in 3 sentences.\"\n```\n\n## Embeddings (expanded in v0.7.0)\n\n```js\nconst { embeddings } = await llm.embed({\n  input: ['first document', 'second document', 'third'],\n  model: 'text-embedding-3-small',  // optional; falls back to configured modelId\n});\n// embeddings is number[][] — one vector per input string\n```\n\nSupported providers:\n- **Ollama** — `mxbai-embed-large`, `nomic-embed-text`, `all-minilm`, any embedding model you've pulled\n- **OpenAI-compatible** (including Groq, Together AI, DeepSeek, LM Studio) — `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`, provider-specific models\n- **Anthropic**: not supported (no first-party embeddings)\n- **GenAI Hub**: needs a separate embedding-model deployment; not yet plumbed (planned for 0.9)\n\nSingle string or array of strings both work. Returns `{ embeddings: number[][], model: string }` — the outer array always matches the input length.\n\n## Vision / multimodal input (new in v0.5.0)\n\nPass images inline as content blocks. Works across all providers with vision-capable models (Claude 3.5+, GPT-4o, Groq's `llama-3.2-*-vision`, Ollama's `llava` / `moondream` / `llama3.2-vision`).\n\n```js\nconst { imageFromFile, imageFromUrl, imageFromBase64 } = require('@saptarishi/cds-plugin-llm');\n\n// Load from disk\nconst image = await imageFromFile('/tmp/scanned-invoice.png');\n\n// Or from a URL (Anthropic + OpenAI-compat; Ollama needs base64)\nconst image = imageFromUrl('https://example.com/invoice.png');\n\n// Or from base64 data you already have\nconst image = imageFromBase64(base64Data, 'image/png');\n\nconst { data } = await llm.chat({\n  model: 'gpt-4o',  // or claude-opus-4-7, llama-3.2-11b-vision-preview, llava, ...\n  system: 'Extract structured data from scanned invoices.',\n  messages: [{\n    role: 'user',\n    content: [\n      image,\n      { type: 'text', text: 'Return the vendor, invoice number, and line items.' },\n    ],\n  }],\n  format: {\n    type: 'object',\n    properties: {\n      vendor: { type: 'string' },\n      invoiceNumber: { type: 'string' },\n      lineItems: {\n        type: 'array',\n        items: {\n          type: 'object',\n          properties: {\n            description: { type: 'string' },\n            quantity: { type: 'number' },\n            unitPrice: { type: 'number' },\n          },\n        },\n      },\n    },\n  },\n});\n```\n\nWire-shape translation is provider-aware:\n- **Anthropic**: native content blocks (source can be `url` or `base64`)\n- **OpenAI-compatible / Groq**: `image_url` blocks with data URLs for base64\n- **Ollama**: text goes in `content`, images extracted to `images: [base64, ...]` (Ollama does not accept URLs — use `imageFromFile()` or `imageFromBase64()`)\n\n## PDF documents (v0.8.0 · expanded in v0.9.0)\n\nPass PDF documents inline. Full native support on Anthropic (Claude 3.5+ parses text + visuals in one pass). Since **v0.9.0** OpenAI-compat providers accept base64 PDFs too via the `file` content-block shape — works on GPT-4o and newer OpenAI models. Groq and other OpenAI-compat providers that don't accept files will 400 upstream.\n\n```js\nconst { pdfFromFile, pdfFromUrl, pdfFromBase64 } = require('@saptarishi/cds-plugin-llm');\n\nconst pdf = await pdfFromFile('/tmp/scanned-invoice.pdf');\n// or: const pdf = pdfFromUrl('https://example.com/invoice.pdf');\n// or: const pdf = pdfFromBase64(base64Data);\n\nconst { data } = await llm.chat({\n  model: 'claude-opus-4-7',\n  system: 'Extract structured data from scanned invoices.',\n  messages: [{\n    role: 'user',\n    content: [\n      pdf,\n      { type: 'text', text: 'Return vendor, invoice number, line items.' },\n    ],\n  }],\n  format: { /* JSON schema */ },\n});\n```\n\nProvider notes:\n- **Anthropic** — native, both base64 and URL sources\n- **OpenAI-compat (GPT-4o+)** — base64 works out of the box. **URL PDFs (new in v1.14.0)** via the Files API: call `uploadPdfFromUrl(url, {apiKey})` first — it fetches, uploads to `/v1/files`, and returns a document block with `source.type='file_id'` that the provider translates to `{type:'file', file:{file_id}}`.\n- **Groq / other OpenAI-compat that don't accept files** — base64 will 400 upstream; `/v1/files` isn't exposed so `uploadPdfFromUrl` will also 404 there\n- **Ollama** — no PDF support; render pages to images via `pdftoppm` (poppler) and pass to a vision model like `llava` or `llama3.2-vision`\n\n### URL PDFs on OpenAI (v1.14.0+)\n\n```js\nconst { uploadPdfFromUrl } = require('@saptarishi/cds-plugin-llm');\n\nconst doc = await uploadPdfFromUrl('https://example.com/contract.pdf', {\n  apiKey: process.env.OPENAI_API_KEY,\n  // baseUrl:  'https://api.openai.com/v1'  (default)\n  // purpose:  'user_data'                  (default)\n  // filename: <inferred from URL basename, .pdf appended if missing>\n});\n\nawait openai.chat({\n  messages: [{ role: 'user', content: [doc, { type: 'text', text: 'Summarize' }] }],\n});\n```\n\nThe returned document block has `source: { type: 'file_id', file_id: 'file-xxx' }`. Reuse the block across multiple chat calls to avoid re-uploading. OpenAI retains uploaded files by account — bring your own retention policy.\n\n## SAP Generative AI Hub setup\n\nThe `llm-genai-hub` kind targets a **deployment** in your BTP AI Core instance. Prerequisites:\n\n1. **Provision AI Core** — BTP Cockpit → Service Marketplace → *AI Core* → **extended** plan (free plan does not include Generative AI Hub).\n2. **Create a resource group** (or use `default`).\n3. **Deploy a model** via SAP AI Launchpad, `ai-api-cli`, or the SDK — e.g. `gpt-4o`, `mistral-large-instruct`, `claude-3-5-sonnet`. Note the deployment ID.\n4. **Configure the plugin** — three ways depending on where your CAP app runs.\n\n### On BTP Cloud Foundry (recommended)\n\nBind the AI Core service instance to your CAP app:\n\n```sh\ncf bind-service <your-app> <ai-core-instance>\ncf restage <your-app>\n```\n\nThen set only the deployment ID (credentials auto-discovered from `VCAP_SERVICES`):\n\n```sh\ncf set-env <your-app> AICORE_DEPLOYMENT_ID <deployment-id>\ncf set-env <your-app> AICORE_RESOURCE_GROUP default   # optional; defaults to 'default'\n```\n\nIn `package.json`:\n\n```json\n{\n  \"cds\": { \"requires\": { \"llm\": {\n    \"[production]\": { \"kind\": \"llm-genai-hub\", \"modelId\": \"gpt-4o\" }\n  }}}\n}\n```\n\n### On Kyma\n\nAttach the service binding manifest, then set the same env vars via a ConfigMap or Secret. The `VCAP_SERVICES` layout is preserved by the SBO (Service Binding Operator).\n\n### Local dev pointing at a BTP-hosted AI Core\n\nExtract the service key JSON from BTP Cockpit (Service Instance → Service Keys → View). Put values in `.env`:\n\n```\nAICORE_API_URL=https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com\nAICORE_AUTH_URL=https://<subaccount>.authentication.<region>.hana.ondemand.com\nAICORE_CLIENT_ID=sb-...\nAICORE_CLIENT_SECRET=...\nAICORE_DEPLOYMENT_ID=abc123                # chat model deployment\nAICORE_EMBEDDING_DEPLOYMENT_ID=def456      # optional; enables llm.embed()\nAICORE_MODEL=gpt-4o\n```\n\nOr pass explicitly in `package.json`:\n\n```json\n{\n  \"cds\": { \"requires\": { \"llm\": {\n    \"[genai-hub]\": {\n      \"kind\": \"llm-genai-hub\",\n      \"modelId\": \"gpt-4o\",\n      \"credentials\": {\n        \"aiCoreUrl\":     \"https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com\",\n        \"tokenUrl\":      \"https://<subaccount>.authentication.<region>.hana.ondemand.com\",\n        \"clientId\":      \"sb-...\",\n        \"clientSecret\":  \"...\",\n        \"deploymentId\":  \"abc123\",\n        \"resourceGroup\": \"default\"\n      }\n    }\n  }}}\n}\n```\n\n### What it handles for you\n\n- OAuth2 client-credentials flow against XSUAA\n- Token caching + refresh (60s before expiry)\n- `AI-Resource-Group` header\n- Deployment-based inference endpoint construction\n- `VCAP_SERVICES.aicore` auto-discovery when the service is bound\n\n### Known limitations (v0.4.0)\n\n- **OpenAI-shape only.** Deployments that expose the OpenAI `/chat/completions` shape (GPT, Mistral, Llama, Gemini, and Anthropic-via-shim) work. Native Anthropic-shape deployments (Claude via `/invoke`) are not yet supported — use the `llm-anthropic` kind directly for Claude.\n- **Not yet live-verified.** Built to the SAP-documented API contract and unit-tested against mocks. Live verification against an AI Core `extended` deployment is the next contribution wanted.\n\n## Automatic retries (new in v0.3.0)\n\nEvery `chat()` and `embed()` call is wrapped with exponential-backoff retry on 429 / 5xx responses. Honors `Retry-After` headers. Configurable per-call or globally:\n\n```js\n// Per-call override\nawait llm.chat({ messages: [...], retries: { max: 5, baseMs: 1000, maxMs: 30000 } });\n\n// Or via cds.requires.llm config:\n{ \"cds\": { \"requires\": { \"llm\": {\n  \"kind\": \"llm-groq\",\n  \"retries\": { \"max\": 5 }\n}}}}\n```\n\n## Middleware / interceptors (new in v1.2.0)\n\nRegister hooks around every `chat` / `stream` / `embed` call. Koa-style compose — outermost first, `next()` returns the next middleware's result (or the provider's response). Middleware may inspect or transform the request AND the response, share state via `ctx.meta`, or short-circuit by returning without calling `next()`.\n\n```js\nconst llm = await cds.connect.to('llm');\n\n// 1. Logging + duration\nllm.use(async (ctx, next) => {\n  const start = Date.now();\n  const res = await next();\n  console.log(`[${ctx.method}] ${Date.now() - start}ms`);\n  return res;\n});\n\n// 2. Cost tracking (aggregate tokens across every call)\nconst totals = { in: 0, out: 0 };\nllm.use(async (ctx, next) => {\n  const res = await next();\n  totals.in  += res?.usage?.input_tokens  ?? 0;\n  totals.out += res?.usage?.output_tokens ?? 0;\n  return res;\n});\n\n// 3. Auto-injected system prompt suffix\nllm.use(async (ctx, next) => {\n  if (ctx.method === 'chat' && ctx.request.system) {\n    ctx.request.system += '\\n\\nBe concise. If uncertain, say so.';\n  }\n  return next();\n});\n\n// 4. Streams: wrap the iterator to observe each chunk\nllm.use(async (ctx, next) => {\n  if (ctx.method !== 'stream') return next();\n  const inner = await next();\n  return (async function* () {\n    for await (const chunk of inner) {\n      if (chunk.type === 'text_delta') myLiveUI.append(chunk.text);\n      yield chunk;\n    }\n  })();\n});\n```\n\nContext object:\n- `ctx.method` — `'chat'` | `'stream'` | `'embed'`\n- `ctx.request` — mutable request options (modify before `next()` to affect the provider call)\n- `ctx.meta` — scratchpad for cross-middleware state (e.g. timing marks, request IDs)\n\nNotes:\n- Middleware runs **around** the response cache, retries, and format-parsing — those are internal concerns your middleware can observe. Cache hits arrive at your middleware with `cached: true` set.\n- Calling `next()` more than once from the same middleware throws.\n- Errors propagate up the chain.\n- For streams, `next()` returns an async iterable. To observe/transform chunks, wrap it into a new async generator.\n\n### Built-in middleware (new in v1.3.0)\n\nTwo production-oriented middlewares ship in the box: rate limiting and OpenTelemetry tracing.\n\n#### `rateLimit` — token-bucket limiter\n\n```js\nconst { rateLimit } = require('@saptarishi/cds-plugin-llm');\n\nconst llm = await cds.connect.to('llm');\n\n// Global: 60 requests burst, refill at 1/s\nllm.use(rateLimit({ capacity: 60, refillPerSecond: 1 }));\n\n// Per-user: keyed off ctx.meta.user (populate this from an earlier middleware\n// that inspects your CAP request)\nllm.use(rateLimit({\n  capacity: 10,\n  refillPerSecond: 0.2,\n  keyFn: (ctx) => ctx.meta.user ?? 'anon',\n  mode: 'wait',   // 'throw' (default) or 'wait' — pause instead of erroring\n}));\n```\n\nWhen `mode: 'throw'` and the bucket is empty, the middleware throws an `Error` with `code: 'RATE_LIMITED'` and `retryAfterMs` so you can surface a proper 429 to your caller. Buckets are in-process — for multi-instance CF apps that need a shared counter, back with Redis via your own middleware.\n\n#### `otel` — OpenTelemetry spans\n\n```js\nconst { trace } = require('@opentelemetry/api');\nconst { otel } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(otel({\n  tracer: trace.getTracer('cap-app'),\n  systemAttribute: 'anthropic',   // sets gen_ai.system on every span\n}));\n```\n\nEmits one span per `chat` / `stream` / `embed` call. Attributes follow the emerging GenAI semantic conventions where possible: `gen_ai.system`, `gen_ai.operation.name`, `gen_ai.request.model`, `gen_ai.response.model`, `gen_ai.usage.input_tokens` / `output_tokens`, `gen_ai.response.stop_reason`. Plus a few plugin-specific ones: `llm.cached`, `llm.tool_calls.count`, `llm.stream.chunks`, `llm.embed.count`. Duck-typed against `@opentelemetry/api` — no hard dependency, works with any object exposing `startSpan()`.\n\nStream spans stay open through the whole iterator (span ends on the `done` chunk, on early break, or on error — never leaks).\n\n#### `redisRateLimit` — shared bucket across CF instances (new in v1.4.0)\n\nThe in-process `rateLimit` is fine for single-instance apps. For multi-instance CF deployments where a shared quota must be enforced globally, back the bucket with Redis.\n\n```js\nconst Redis = require('ioredis');\nconst { redisRateLimit } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(redisRateLimit({\n  redis: new Redis(process.env.REDIS_URL),\n  capacity: 60,\n  refillPerSecond: 1,\n  keyFn: (ctx) => ctx.meta.user ?? 'anon',\n  keyPrefix: 'ratelimit:llm:',   // default 'saptarishi:llm:rl:'\n  mode: 'throw',                  // 'throw' | 'wait'\n}));\n```\n\nUses an atomic Lua `EVAL` so two instances checking the bucket at the same time cannot both succeed when only one token is left. Duck-typed client — any object with an `eval(script, numKeys, ...args)` promise API satisfies (works with `ioredis` and `node-redis` v4+ out of the box). On BTP, bind a Redis service to your CF app and pull the URL from `VCAP_SERVICES`.\n\n## CLI (new in v1.5.0)\n\nA `saptarishi-llm` executable ships with the package. Handy for provider health checks in CI, quick prompt experiments from the shell, pipelining embeddings into a downstream tool, or **scaffolding a fresh CAP project** pre-wired to the plugin (v1.6.0).\n\n```bash\nnpx @saptarishi/cds-plugin-llm --help\n\n# or install globally\nnpm install -g @saptarishi/cds-plugin-llm\nsaptarishi-llm --help\n```\n\n### Commands\n\n```bash\nsaptarishi-llm chat -p \"explain SAP CAP in one sentence\"\nsaptarishi-llm stream -p \"write a haiku about procurement\"\nsaptarishi-llm embed -p \"purchase order for steel coils\" --json\nsaptarishi-llm verify --provider anthropic\nsaptarishi-llm providers\n```\n\n### Provider selection\n\n`--provider <kind>` or `SAPTARISHI_LLM_PROVIDER` env var. Same five kinds as the CAP plugin: `anthropic`, `ollama`, `groq`, `openai-compatible`, `genai-hub`.\n\nCredentials come from env vars (never CLI flags — avoids leaking secrets into shell history):\n\n```bash\nANTHROPIC_API_KEY=sk-ant-... saptarishi-llm chat -p \"hello\"\nOLLAMA_URL=http://192.168.5.13:11434 saptarishi-llm chat --provider ollama -p \"hello\"\nGROQ_API_KEY=gsk-... saptarishi-llm verify --provider groq\n```\n\n### Input sources\n\nPrompt can come from `--prompt` / `-p`, `--file` / `-f`, stdin, or a positional arg. Multiple sources concatenate with a blank line between them.\n\n```bash\necho \"summarize this\" | saptarishi-llm chat -f contract.pdf.txt\nsaptarishi-llm embed -p \"one\\n---\\ntwo\\n---\\nthree\"      # 3 vectors from 1 call\n```\n\n### CI health checks\n\n`verify` connects, runs a tiny probe, reports latency, and exits `0` on success / `1` on unexpected reply / `1` on error. Drop it in a nightly workflow to catch expired credentials or endpoint outages before your CAP app does.\n\n```bash\nsaptarishi-llm verify --provider genai-hub --json\n```\n\n### Expose as an MCP server (new in v1.7.0)\n\n`saptarishi-llm mcp` runs a Model Context Protocol server over stdio that exposes the configured provider as tools any MCP client can call. Register it in Claude Desktop, Cursor, Zed, or any other MCP-capable client and those clients gain a `chat` / `embed` / `verify` / `list_providers` tool backed by **your** provider config — with all its middleware, caching, rate limits, and OTel tracing.\n\nThe point: one MCP server = \"the sanctioned way to call an LLM at MyCompany\". Centralized credentials, centralized policy, developer productivity everywhere.\n\nClaude Desktop (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):\n\n```jsonc\n{\n  \"mcpServers\": {\n    \"saptarishi-llm\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@saptarishi/cds-plugin-llm\", \"mcp\"],\n      \"env\": {\n        \"SAPTARISHI_LLM_PROVIDER\": \"groq\",\n        \"GROQ_API_KEY\": \"gsk-...\"\n      }\n    }\n  }\n}\n```\n\nTools exposed:\n\n| Tool | Purpose |\n|------|---------|\n| `chat` | Send a prompt, return text. `{ prompt, system?, maxTokens? }` |\n| `embed` | Embed input(s) into vectors. `{ input: string \\| string[] }` |\n| `verify` | Tiny probe against the provider. Returns `{ ok, latencyMs, model, text }`. |\n| `list_providers` | Enumerate every supported provider kind with default models. |\n\nUses a hand-rolled MCP implementation (2024-11-05 spec) over stdio JSON-RPC 2.0 — **zero new dependencies**. Full protocol coverage: `initialize`, `tools/list`, `tools/call`, `resources/list`, `resources/read`, `resources/subscribe`, `resources/unsubscribe`, `prompts/list`, `prompts/get`, `ping`, notifications. Tool errors surface as `result.isError: true` per spec so the model can recover, not as JSON-RPC errors.\n\n**Resources exposed** (readable via `resources/read`):\n\n| URI | Description |\n|-----|-------------|\n| `config://active-provider` | Current provider kind + model + middleware count. |\n| `config://supported-providers` | Every provider kind + default model. |\n\n**Prompts registered** (invokable via `prompts/get`) — see [Prompt-template registry](#prompt-template-registry-new-in-v180) below for the full built-in list.\n\n**Resource templates** (new in v1.9.0; parametrized URIs discoverable via `resources/templates/list`):\n\n| URI template | What clients can read |\n|---|---|\n| `provider://{kind}` | Default model for a specific provider kind (`provider://groq` → `{ kind: 'groq', defaultModel: 'llama-3.3-70b-versatile' }`). |\n| `prompt://{name}` | Metadata (arguments, description) for a registered prompt template. To render, use `prompts/get`. |\n\n### Scaffold a fresh CAP project (new in v1.6.0)\n\n`init` creates a fully-wired CAP app in seconds — no manual `package.json` editing, no CDS boilerplate:\n\n```bash\nnpx @saptarishi/cds-plugin-llm init joule-demo --provider groq\ncd joule-demo\ncp .env.example .env               # then fill in real credentials\nnpm install\ncds watch\n```\n\nThen:\n\n```bash\ncurl 'http://localhost:4004/ai/chat(prompt='\"'\"'hello'\"'\"')'\n```\n\nGenerated:\n\n```\njoule-demo/\n├── package.json          # cds.requires.llm pointing at chosen provider\n├── srv/\n│   ├── ai-service.cds    # service AIService { chat(prompt), summarize(text) }\n│   └── ai-service.js     # handlers using cds.connect.to('llm')\n│                         # + SSE streaming endpoint at POST /stream/chat\n├── .env.example          # provider-specific env vars\n├── .gitignore            # excludes .env, node_modules/, gen/\n└── README.md             # how to run\n```\n\n**Streaming out of the box (v1.9.0+):** the generated `srv/ai-service.js` registers `POST /stream/chat` inline, streaming tokens as they arrive:\n\n```bash\ncurl -N -X POST http://localhost:4004/stream/chat \\\n  -H 'content-type: application/json' \\\n  -d '{\"prompt\":\"write a haiku about SAP CAP\"}'\n```\n\nFlags:\n- `--provider <kind>` — `anthropic` (default) | `ollama` | `groq` | `openai-compatible` | `genai-hub`\n- `--model <id>` — override the default model for the chosen provider\n- `--force` — overwrite a non-empty target directory\n- `--dry-run` — print the file list without writing anything\n\n## Prompt-template registry (new in v1.8.0)\n\nRegister named prompt templates once, invoke them by name from any CAP handler, and automatically expose them over MCP so external clients (Claude Desktop, Cursor, Zed) can invoke them too. Same registry, three surfaces.\n\n```js\nconst { PromptRegistry, builtInPrompts } = require('@saptarishi/cds-plugin-llm');\n\nconst prompts = new PromptRegistry()\n  .registerAll(builtInPrompts())                    // 5 built-ins\n  .register({                                       // your own\n    name: 'invoice_dispute_response',\n    description: 'Draft a courteous response to an invoice dispute.',\n    arguments: [\n      { name: 'dispute', required: true, description: 'The dispute text' },\n      { name: 'tone',    required: false, description: 'formal | friendly' },\n    ],\n    render: ({ dispute, tone = 'formal' }) => ({\n      system: `You are AP support. Reply in a ${tone} tone. Never admit liability.`,\n      messages: [{ role: 'user', content: dispute }],\n    }),\n  });\n\n// From a CAP handler:\nconst req = prompts.render('invoice_dispute_response', { dispute });\nconst res = await llm.chat({ ...req, maxTokens: 512 });\n```\n\nBuilt-ins (`builtInPrompts()`):\n\n| Name | Purpose |\n|------|---------|\n| `summarize` | Condense text to N sentences (`text`, `sentences?`). |\n| `extract_json` | Extract structured JSON against a schema (`text`, `schema`). |\n| `classify` | Assign one label from a set (`text`, `labels`). |\n| `translate` | Translate to a target language (`text`, `targetLanguage`). |\n| `procurement_risk_scorer` | SAP-flavored risk analyst prompt (`text`). |\n\nTemplates auto-appear as MCP prompts when you run `saptarishi-llm mcp`. Claude Desktop shows them as slash-commands the user can pick.\n\n### Load templates from a folder (new in v1.9.0)\n\nInstead of registering templates inline, drop `*.mjs` or `*.js` files into a directory and load them at boot:\n\n```\nprompts/\n├── invoice_dispute.mjs        # export default { name, render, ... }\n├── kpi_extractor.mjs          # export default { ... }\n└── shared.mjs                 # export const foo = ...; export const bar = ...\n```\n\n```js\nawait registry.loadFromDir('./prompts');\n```\n\nOr expose the whole folder via MCP without writing any code:\n\n```bash\nsaptarishi-llm mcp --prompts-dir ./prompts\n# or: SAPTARISHI_LLM_PROMPTS_DIR=./prompts saptarishi-llm mcp\n```\n\nThree export conventions handled in one scan: `export default <template>`, `export default [<t1>, <t2>]`, or named exports.\n\nAdd `--watch-prompts` and the server hot-reloads templates when files change — iterate without restart (new in v1.10.0):\n\n```bash\nsaptarishi-llm mcp --prompts-dir ./prompts --watch-prompts\n```\n\n### Git-backed prompt registry (new in v2.1.0)\n\n`gitPromptRegistry` extends `PromptRegistry` with a loader that pulls templates from a Git repository, caches them locally, and optionally polls for updates. Prompt changes go through PR review separately from code deploys — a real prompt-as-code workflow.\n\n```js\nconst { gitPromptRegistry } = require('@saptarishi/cds-plugin-llm');\n\nconst registry = await gitPromptRegistry({\n  url:     'https://github.com/your-org/prompts.git',\n  branch:  'main',\n  subdir:  'templates',      // optional — load only a subfolder\n  pollMs:  60_000,           // poll every 60s for new commits\n  onUpdate: (info) => cds.log('llm:prompts').info('reloaded', info),\n});\n\n// Same API as PromptRegistry:\nconst req = registry.render('invoice_dispute_response', { dispute });\n```\n\n- Exposes `config://git-prompt-registry` (URL, branch, current SHA, refresh timestamp, counters) — live-visible in your MCP client.\n- Pin to a specific `ref` (SHA / tag) for immutable prompts in prod; use `branch` for rolling updates in staging.\n- Works with any prompt directory the shipped `loadFromDir` scanner accepts.\n\n### HTTP+SSE transport (new in v1.10.0)\n\nBy default `saptarishi-llm mcp` speaks stdio (for Claude Desktop / Cursor / Zed as a local subprocess). Add `--http` and it runs as a network service instead — the same MCP server, addressable over HTTP. Deploy to CF, put behind an auth proxy, share with a team.\n\n```bash\nsaptarishi-llm mcp --http --port 3333 --host 0.0.0.0\n# → MCP HTTP+SSE listening on http://0.0.0.0:3333/sse\n# → GET /health for liveness/session count\n```\n\nWire protocol (MCP 2024-11-05 SSE spec):\n\n| Method | Path | Purpose |\n|---|---|---|\n| `GET` | `/sse` | Client opens SSE stream. Server sends `event: endpoint\\ndata: /messages?sessionId=<uuid>`. Server replies to that session flow back on this stream. |\n| `POST` | `/messages?sessionId=<uuid>` | Client sends a single JSON-RPC message. Server acknowledges 202; reply arrives on the SSE stream. |\n| `GET` | `/health` | `{ server, version, transport, sessions }` — plug into monitoring. |\n\nMulti-session — N concurrent clients each get their own session and stream. Graceful shutdown on SIGINT/SIGTERM.\n\n#### Bearer-token auth (new in v1.11.0)\n\nDeploying `--http` to anything beyond `127.0.0.1` should require a credential. Set `--auth-token` (or `SAPTARISHI_LLM_MCP_TOKEN` env) and every `/sse` and `/messages` request must send `Authorization: Bearer <token>`:\n\n```bash\nsaptarishi-llm mcp --http --host 0.0.0.0 --port 3333 \\\n  --auth-token \"$(openssl rand -hex 32)\"\n```\n\nBehavior:\n- Missing / wrong token → `401 Unauthorized` with `WWW-Authenticate: Bearer realm=\"mcp\"`.\n- `/health` stays **public** so load balancers can probe without credentials. Its response now includes `authRequired: <bool>` so clients know whether they need a token.\n- Constant-time comparison on the byte-by-byte match to avoid trivial timing side-channels.\n- Binding to a non-loopback host without a token prints a loud stderr warning — no hard failure (upstream proxies may legitimately terminate auth), but the intent is impossible to miss.\n\nClient-side example (curl):\n\n```bash\ncurl -N -H 'Authorization: Bearer <token>' http://host:3333/sse\n```\n\n#### JWT / JWKS validation (new in v1.16.0)\n\nFor real enterprise deployments, validate signed JWTs against your IdP's JWKS endpoint. Works with SAP XSUAA, Auth0, Okta, Azure AD, Google, Keycloak, AWS Cognito, and any other standards-compliant IdP.\n\n```bash\nnpm install jose   # optional peer dep, only needed for JWT auth\n\nsaptarishi-llm mcp --http --host 0.0.0.0 \\\n  --jwks-url https://tenant.authentication.us10.hana.ondemand.com/token_keys \\\n  --jwt-issuer https://tenant.authentication.us10.hana.ondemand.com \\\n  --jwt-audience sb-my-cap-app!t12345\n```\n\nBehavior:\n- Each request's `Authorization: Bearer <jwt>` is verified against the JWKS (signature, `exp`, `iat`, `nbf`, optional `iss` / `aud`).\n- JWKS cached + auto-refreshed on unknown `kid` by `jose`.\n- Any failure — bad signature, expired, wrong issuer, wrong audience, unknown key — collapses to a uniform 401. Never leaks *which* check failed (avoids oracle attacks; same rationale as bearer-token constant-time compare).\n\nOn BTP with an XSUAA service binding, use the values from `VCAP_SERVICES.xsuaa[0].credentials`:\n- `--jwks-url  <url>/token_keys` (where `<url>` = credentials.url)\n- `--jwt-issuer <url>`\n- `--jwt-audience credentials.xsappname`\n\nProgrammatic use — build your own verifier for custom flows (introspection endpoints, mTLS metadata → role mapping, JWT + local RBAC check):\n\n```js\nconst { createHttpTransport, createJwtVerifier } = require('@saptarishi/cds-plugin-llm');\n\nconst jwtVerifier = createJwtVerifier({ jwksUrl, issuer, audience });\ncreateHttpTransport({\n  server,\n  authTokenVerifier: async (token) => {\n    const claims = await jwtVerifier(token);\n    if (!claims) return null;\n    // Extra check: reject unless the client has the 'llm-user' scope\n    if (!claims.scope?.includes('llm-user')) return null;\n    return claims;\n  },\n});\n```\n\n#### Progress notifications (new in v1.12.0)\n\nLong-running tools can push `notifications/progress` back to the client instead of leaving them hanging silently. Tool handlers grow an optional second argument — a `ctx` object with `reportProgress(current, total?)`:\n\n```js\nserver.registerTool({\n  name: 'batch_summarize',\n  description: 'Summarize N documents',\n  inputSchema: { type: 'object', properties: { docs: { type: 'array', items: { type: 'string' } } } },\n  handler: async ({ docs }, ctx) => {\n    const summaries = [];\n    for (let i = 0; i < docs.length; i++) {\n      summaries.push(await llm.chat({ /* ... */ }));\n      ctx.reportProgress(i + 1, docs.length);\n    }\n    return summaries;\n  },\n});\n```\n\nWhen the client calls this tool with `_meta.progressToken: 'x'`, each `reportProgress(...)` becomes a server-pushed notification carrying that token. Delivered on **both** transports — stdio writes to the same stdout; HTTP+SSE pushes on the requesting session's SSE stream. Existing tools that ignore the second arg keep working (backwards-compatible).\n\n#### Resource subscriptions (new in v1.17.0)\n\nComplements the list-changed notifications from v1.13.0 with per-URI push updates. Use `list_changed` when the *set* of resources shifts, `resources/subscribe` when a *specific* resource's content changes.\n\n```jsonc\n// client -> server\n{ \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"resources/subscribe\",\n  \"params\": { \"uri\": \"prompt://summarize\" } }\n\n// server -> client (later, when that prompt is hot-reloaded)\n{ \"jsonrpc\": \"2.0\", \"method\": \"notifications/resources/updated\",\n  \"params\": { \"uri\": \"prompt://summarize\" } }\n```\n\n- **Per-connection state** — session A subscribing never affects session B. Cleaned up when the SSE stream / stdio pipe closes.\n- **Both transports** — stdio writes updates to stdout on the reply stream; HTTP+SSE pushes to the subscribing session's stream only.\n- **Unknown URIs rejected** at subscribe time so client typos surface immediately. Matches both static resources and template URIs (`prompt://{name}`, `provider://{kind}`).\n- **`resources/unsubscribe`** is idempotent — safe to call for URIs you never subscribed to.\n\n`--watch-prompts` now fires `resources/updated` for every subscribed `prompt://<name>` URI on hot-reload (in addition to the existing `prompts/list_changed` broadcast). Pin a client to a specific prompt and every edit refreshes without a `resources/read`.\n\nFor third-party transports / programmatic MCP setups, `MCPServer` exposes:\n\n- `notifyResourceUpdated(uri)` — broadcast to every subscriber of `uri`. Silent no-op with none.\n- `subscribedUris(prefix?)` — distinct URIs any connected client subscribed to, optionally prefix-filtered.\n\n#### Per-session provider overrides (new in v1.18.0)\n\nOne `saptarishi-llm mcp` process, multiple named provider configs behind a single MCP endpoint. Clients pick which one to use per session or per tool call. Real enterprise use: one authenticated endpoint, different agents hit different backends — DevOps on `cheap`, compliance reviewer on `smart`, local dev on `local`. Credentials centralized server-side; agents never touch API keys.\n\n```bash\nsaptarishi-llm mcp --http --host 0.0.0.0 \\\n  --providers-config ./providers.json\n```\n\n```jsonc\n// providers.json  —  chmod 600 and keep out of git\n{\n  \"cheap\": {\n    \"kind\": \"groq\",\n    \"model\": \"llama-3.1-8b-instant\",\n    \"credentials\": { \"apiKey\": \"gsk_...\" }\n  },\n  \"smart\": {\n    \"kind\": \"anthropic\",\n    \"model\": \"claude-opus-4-7\",\n    \"credentials\": { \"apiKey\": \"sk-ant-...\" }\n  },\n  \"local\": {\n    \"kind\": \"ollama\",\n    \"model\": \"qwen2.5:14b\",\n    \"credentials\": { \"baseUrl\": \"http://localhost:11434\" }\n  }\n}\n```\n\n**Resolution order per tool call**: `arguments.provider` → `sessionState.provider` (from `initialize._meta.provider`) → top-level default.\n\n**Session default** — client declares once at initialize; every subsequent call on that session uses it:\n\n```jsonc\n{ \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"initialize\",\n  \"params\": { \"protocolVersion\": \"2024-11-05\",\n              \"_meta\": { \"provider\": \"smart\" } } }\n```\n\n**Per-call override** — bypass the session default one call at a time:\n\n```jsonc\n{ \"jsonrpc\": \"2.0\", \"id\": 2, \"method\": \"tools/call\",\n  \"params\": { \"name\": \"chat\",\n              \"arguments\": { \"prompt\": \"dispatch this\", \"provider\": \"cheap\" } } }\n```\n\n**Discovery** — `list_providers` tool and `config://providers` resource both dump the alias list (kind + model per alias; credentials never returned). Unknown aliases surface as tool-result errors with the configured list so the model can self-correct on the next turn.\n\n**Boot-time validation** — every alias is instantiated + `init()`'d at server startup. Bad credentials or unknown kinds die on `saptarishi-llm mcp`, not on the first client call.\n\n## Response caching (new in v0.9.0)\n\nOpt-in per-instance LRU cache with TTL. Skips tool-use calls (side-effects) and streaming (partial responses). Hits return the same `ChatResponse` shape with `cached: true` set.\n\n```jsonc\n{\n  \"cds\": { \"requires\": { \"llm\": {\n    \"kind\": \"llm-groq\",\n    \"modelId\": \"llama-3.3-70b-versatile\",\n    \"responseCache\": true                              // defaults: 5min TTL, 100 entries\n    // or: \"responseCache\": { \"ttlMs\": 600000, \"maxEntries\": 500 }\n  }}}\n}\n```\n\nCache key is a SHA-1 of the request's stable JSON representation (`model + maxTokens + system + messages + tools + format + thinking`). Requests with any of those fields differing miss the cache.\n\n```js\nconst r1 = await llm.chat({ messages: [{ role: 'user', content: 'hi' }] });\nr1.cached  // undefined  (miss + fresh call)\n\nconst r2 = await llm.chat({ messages: [{ role: 'user', content: 'hi' }] });\nr2.cached  // true        (hit — no upstream call)\n\nllm.responseCache.hits    // 1\nllm.responseCache.misses  // 1\nllm.responseCache.size()  // 1\n```\n\nCommon wins:\n- **Same PO summarized twice** (approver reopens the review) — instant, zero tokens\n- **Batch classification** with duplicate inputs — deduplicates automatically\n- **Load-testing** — dev-env queries hit cache after the first pass\n\n## Semantic response cache (new in v2.7.0)\n\n`semanticCache` is a middleware-form cache keyed by **embedding similarity**, not byte-equality — useful when the same question keeps showing up in slightly different wording. It's separate from the built-in `responseCache` (which is exact-match + LRU). Bring your own embedder and vector store; an in-memory linear-scan store is provided so you can wire it up with zero infra.\n\n```js\nconst { semanticCache, inMemorySemanticStore } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(semanticCache({\n  embedder: async (text) => (await llm.embed({ input: text })).embeddings[0],\n  store:    inMemorySemanticStore({ maxEntries: 1000, ttlMs: 3600_000 }),\n  threshold: 0.92,          // cosine similarity → hit\n  keyPrefix: `tenant:${tenantId}:`,\n  shouldCache: (_ctx, r) => r?.status !== 'error',\n  onHit:  (i) => cds.log('llm:sc').info('hit', i),\n}));\n```\n\nHighlights:\n- **Fail-open.** Any exception from the embedder or store falls through to `next()` — a broken cache never takes the request path down.\n- **Multi-tenant safe.** `keyPrefix` is post-filtered by the middleware even if the store ignores the hint, so hits cannot leak across tenants.\n- **Swap the store for prod.** The interface is three async methods (`get`, `put`, `findSimilar`) — implement against pgvector, Redis, Pinecone, Weaviate, etc.\n- **MCP resource** at `config://semantic-cache` exposes `hitRate`, hit/miss/store counts, threshold, and last similarity for live dashboards.\n\nComposition rule: wrap `semanticCache` **outside** `bulkhead`/`retry` (cache hits shouldn't burn concurrency slots or retry budget) and **inside** `guardrails`/`promptInjectionGuard` (don't cache answers whose inputs were rejected).\n\n## Fuzzy dedup (new in v2.38.0)\n\n`fuzzyDedup` is **near-duplicate request detection** using cheap character-level similarity — Jaccard on character trigrams (default, no library deps) or normalized Levenshtein. Cache-like semantics: on match, returns the prior response without calling the provider. No embedder required, so it complements (rather than competes with) `semanticCache` (v2.7.0). Ideal for support-ticket dedup + spam prevention where users retype the same question with typos or slight rewording.\n\n```js\nconst { fuzzyDedup, inMemoryFuzzyStore } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(fuzzyDedup({\n  similarityKind: 'jaccard-trigram',   // or 'levenshtein'\n  threshold:      0.85,\n  store:          inMemoryFuzzyStore({ maxEntries: 5000, ttlMs: 24 * 3600_000 }),\n  minKeyLength:   8,\n  onHit:          (i) => cds.log('llm:fuzzy').info('near-dup', i),\n}));\n```\n\nFills the third slot in the dedup story alongside existing primitives:\n- **`requestCoalescer`** (v2.8.0) — BYTE-IDENTICAL, IN-FLIGHT\n- **`responseCache`** (v0.9.0) — BYTE-IDENTICAL, AT-REST\n- **`semanticCache`** (v2.7.0) — EMBEDDING-SIMILAR (requires embedder)\n- **`fuzzyDedup`** (v2.38.0, this) — CHARACTER-SIMILAR (no embedder)\n\nHighlights:\n- **Two-phase matching** — exact-key hit via `store.get` first (fast), then `store.findSimilar` fuzzy scan only as needed. `stats.exactHits` and `stats.fuzzyHits` are broken out separately so operators can see how much lift comes from typo-level matching vs plain repeats.\n- **Two algorithms** — `jaccard-trigram` (O(n+m), scales to a few thousand chars) or `levenshtein` (O(n*m), catches char-level typos more precisely — cap prompt length in practice).\n- **In-memory reference store** ships with LRU eviction (`maxEntries`) and optional TTL — same `{ get, put, findSimilar }` contract used by any custom store (Redis, PostgreSQL, etc.).\n- **Multi-tenant safe** via `keyPrefix` — namespace one shared store across many dedup instances; post-filter enforces isolation even if the store ignored the hint.\n- **`minKeyLength: 8`** default skips trivially-short prompts (`\"hi\"`, `\"ok\"`) where every prompt would collide.\n- **Fail-open on store errors** — increments `storeErrors`, passes through; cache degrades to no-op rather than blocking.\n- Standalone `jaccardTrigram(a, b)`, `normalizedLevenshtein(a, b)`, `levenshteinDistance(a, b)`, `trigrams(text)` exported for custom similarity strategies.\n- MCP resource: `config://fuzzy-dedup`.\n\nComposition rules: compose OUTSIDE `requestCoalescer` (v2.8.0) so byte-identical concurrent requests coalesce first, and OUTSIDE `responseCache` (v0.9.0) so exact repeats short-circuit before the fuzzy scan runs at all. Compose with `semanticCache` (v2.7.0) when both are available — fuzzyDedup catches typos + trivial rewordings cheaply; the semantic layer catches genuine paraphrases (higher recall, embedder cost). Order dedup → quota/cost primitives so fuzzy hits never accrue cost against `quotaManager` (v2.23.0) or `costOverrunPredictor` (v2.33.0).\n\n## Empty-response detector (new in v2.37.0)\n\n`emptyResponseDetector` catches broken model responses (empty string, whitespace-only, single-char replies, refusal patterns like `\"I can't help with that\"`) BEFORE they reach the caller. Common failure modes caught: provider hiccups (empty payload), safety-filter or guardrail evasion that returned `\"\"`, or soft-refusals on legitimate requests.\n\n```js\nconst { emptyResponseDetector } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(emptyResponseDetector({\n  minChars:   10,\n  onEmpty:    'retry',       // 'throw' | 'retry' | 'log'\n  maxRetries: 1,\n  onDetected: (i) => cds.log('llm:empty').warn('empty response', i),\n}));\n```\n\nDistinct from other quality primitives:\n- **`structuredOutputRepair`** (v2.9.0) — SCHEMA-driven repair\n- **`responseRevision`** (v2.29.0) — RUBRIC-driven re-ask\n- **`emptyResponseDetector`** (v2.37.0) — EMPTY / REFUSAL detection\n\nBuilt-in refusal patterns (6 anchored-to-start regexes): `I can't/cannot/won't help/assist`, `I'm unable/not able/sorry to`, `Sorry, but I can't`, `I must decline/refuse`, `This request cannot be`, `As an AI assistant, I can't`. Anchored to start so mid-response mentions don't false-positive.\n\nHighlights:\n- **Custom detection**: pass `detectEmpty(result)` for full override; return `true`/`false` or `{ empty, reason }`. Return `null` to fall back to defaults.\n- **Reason-aware retry prompt**: refusal detection asks the model to explain WHY rather than refuse outright; too-short asks for a substantive answer.\n- **Per-reason stats** (`byReason`) — dashboard breakdowns across `too-short` / `whitespace` / `refusal-pattern` / custom.\n- **Fail-safe custom detector** — exceptions fall back to default detection.\n- MCP resource: `config://empty-response-detector`.\n\nCompose with `responseRevision` (v2.29.0) OUTSIDE this detector — first check for emptiness; if substantive but low-scoring, then revise. Two-stage quality gate.\n\n## Content-length gate (new in v2.36.0)\n\n`contentLengthGate` is **pre-flight size validation**: reject (or truncate) prompts that exceed a per-model token budget BEFORE they hit the provider. Prevents 400 errors on over-limit contexts + saves tokens on obviously-too-large inputs. Distinct from time-based limiters (`deadline`, `gracePeriod`) — this is *size-based*.\n\n```js\nconst { contentLengthGate } = require('@saptarishi/cds-plugin-llm');\n\nllm.use(contentLengthGate({\n  modelLimits: {\n    'gpt-4o':          128_000,\n    'gpt-4o-mini':     128_000,\n    'claude-opus-4-7': 200_000,\n    default:            64_000,\n  },\n  overageMode: 'truncate-oldest',   // 'throw' | 'truncate-oldest' | 'log'\n  onOverage:   (i) => cds.log('llm:size').warn('over-limit', i),\n}));\n```\n\nThree-way overage policy:\n- **`'throw'`** (default) — raises `ContentLengthExceededError` (code `CONTENT_LENGTH_EXCEEDED`) with `.tokens`, `.chars`, `.limitTokens`, `.model`. Fail-fast.\n- **`'truncate-oldest'`** — drops oldest messages until under limit, preserving `system` + latest user message by default (`preserveSystem` / `preserveLatestUser` opt-out).\n- **`'log'`** — pass through unmodified with `onOverage` observability; let the provider decide (useful for shadow deployments before enforcing).\n\nHighlights:\n- **GPT-family token heuristic** by default (`char / 4`); users with real tokenizers pass `tokenEstimator(text) → tokens`.\n- **Per-model limits** with `default` fallback; unknown models pass through unchecked with `unknownModelCount` counter for observability.\n- **Original request restored** after truncation-mode calls — caller state unmodified, only the downstream provider saw the trimmed version.\n- **`overageRate()`** returns `overageCount / totalCalls` for real-time tuning.\n- MCP resource: `config://content-length-gate`.\n\nComposition rule: put `contentLengthGate` **outside** providers so the check runs before any network work. Compose with `compactHistory` (v1.91.0) as complementary primitives — compact keeps multi-turn conversations bounded via LLM summarization; gate catches single-turn over-limit as the hard backstop.\n\n## Latency histogram (new in v2.35.0)\n\n`latencyHistogram` tracks per-dimension latency distributions using Prometheus-style bucketed counts (**bucket counts + sum + count** — not raw samples). Reports p50/p95/p99 percentiles cheaply. O(1) memory per dimension regardless of sample count. Complements `providerHealthAggregate` (v2.31.0 — single p95 verdict) with the **full percentile shape** + exportable Prometheus format.\n\n```js\nconst { latencyHistogram } = require('@saptarishi/cds-plugin-llm');\n\nconst hist = latencyHistogram({\n  dimensionsOf:    (ctx, result) => ({ model: result?.model ?? 'unknown' }),\n  overThresholdMs: 15_000,             // p95 threshold\n  onOverThreshold: (i) => cds.log('llm:slo').warn('p95 breach', i),\n});\nllm.use(hist);\n\n// Query percentiles:\nconst p = hist.getPercentiles({ model: 'gpt-4o' });\n// → { count: 145, sum: 92500, mean: 638, p50: 500, p95: 2500, p99: 5000 }\n\n// Export for Prometheus scraping:\nconst prom = hist.prometheusHistograms('llm_latency_ms');\n// llm_latency_ms_bucket{model=\"gpt-4o\",le=\"500\"} 87\n// llm_latency_ms_bucket{model=\"gpt-4o\",le=\"1000\"} 120\n// ...\n// llm_latency_ms_sum{model=\"gpt-4o\"} 92500\n// llm_latency_ms_count{model=\"gpt-4o\"} 145\n```\n\nHighlights:\n- **Prometheus-canonical bucket layout** by default: `[5, 10, 25, 50, 100, 250, 500, 1000, 2500, 5000, 10000]` ms. Users with different SLOs pass a custom `buckets` array.\n- **Percentile computation** via cumulative-count walk — O(buckets) regardless of sample count.\n- **Filtered percentiles** — `getPercentiles({ model: 'gpt-4o' })` aggregates matching dimensions.\n- **Custom percentile arrays** — `getPercentiles(filter, [10, 90, 99.9])` for tail-latency analysis.\n- **Rising-edge over-threshold** callback rearms on recovery so subsequent breaches also fire.\n- **Fail-safe**: dimens","readmeFilename":"README.md"}