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AI Alliance"},"license":"Apache-2.0","homepage":"https://github.com/The-AI-Alliance/semiont#readme","keywords":["ai","inference","llm","anthropic","ollama","text-generation"],"repository":{"url":"git+https://github.com/The-AI-Alliance/semiont.git","type":"git","directory":"packages/inference"},"description":"AI inference primitives: provider-agnostic text generation clients for Anthropic and Ollama","maintainers":[{"name":"adamaialliance","email":"apingel@thealliance.ai"}],"readme":"# @semiont/inference\n\n[![Tests](https://github.com/The-AI-Alliance/semiont/actions/workflows/package-tests.yml/badge.svg)](https://github.com/The-AI-Alliance/semiont/actions/workflows/package-tests.yml?query=branch%3Amain+is%3Asuccess+job%3A%22Test+inference%22)\n[![codecov](https://codecov.io/gh/The-AI-Alliance/semiont/graph/badge.svg?flag=inference)](https://codecov.io/gh/The-AI-Alliance/semiont?flag=inference)\n[![npm version](https://img.shields.io/npm/v/@semiont/inference.svg)](https://www.npmjs.com/package/@semiont/inference)\n[![npm downloads](https://img.shields.io/npm/dm/@semiont/inference.svg)](https://www.npmjs.com/package/@semiont/inference)\n[![License](https://img.shields.io/npm/l/@semiont/inference.svg)](https://github.com/The-AI-Alliance/semiont/blob/main/LICENSE)\n\n**AI primitives for text generation: a provider-agnostic inference client.**\n\nThis package provides the **core AI primitives** for the Semiont platform:\n- The `InferenceClient` interface (provider abstraction)\n- Client implementations for Anthropic and Ollama, plus a scripted mock for tests\n- A `createInferenceClient()` factory that selects the implementation from config\n- Cross-provider structured generation (`generateStructured` — parsed elements or a throw, never a silent `[]`)\n- Usage metrics via `@semiont/observability`\n\nFor **application-specific AI logic** (semantic processing, prompt engineering, response parsing), see [@semiont/make-meaning](../make-meaning/).\n\n## Architecture Context\n\n**Infrastructure Ownership**: In production, inference clients are **created by [@semiont/make-meaning](../make-meaning/)'s `startMakeMeaning()`** (one client per knowledge-system actor — Gatherer, Matcher) and by [@semiont/jobs](../jobs/)' worker process (one client per job group). Both build an `InferenceClientConfig` from their own configuration and call `createInferenceClient()`.\n\nThe API below can also be used directly for **testing, CLI tools, or standalone scripts**.\n\n## Philosophy\n\nThis package is named `inference` rather than `ai-inference` to align with Semiont's core tenet: humans and AI agents have equal opportunity to work behind similar interfaces. The abstraction remains open for future human-agent parity.\n\n**Package Responsibility**: AI primitives only. No application logic, no prompt engineering, no response parsing. Those belong in `@semiont/make-meaning`.\n\n## Installation\n\n```bash\nnpm install @semiont/inference\n```\n\n## Quick Start\n\n```typescript\nimport { createInferenceClient } from '@semiont/inference';\n\n// Anthropic (apiKey required)\nconst claude = createInferenceClient({\n  type: 'anthropic',\n  model: 'claude-sonnet-4-6',\n  apiKey: process.env['ANTHROPIC_API_KEY']!,\n});\n\n// Ollama (no API key; endpoint defaults to http://localhost:11434)\nconst local = createInferenceClient({\n  type: 'ollama',\n  model: 'gemma2:9b',\n});\n\nconst text = await claude.generateText(\n  'Explain quantum computing in simple terms',\n  500,   // maxTokens\n  0.7    // temperature\n);\nconsole.log(text);\n```\n\n## API Reference\n\nSee [docs/API.md](docs/API.md) for the full reference.\n\n### `createInferenceClient(config, logger?): InferenceClient`\n\nFactory ([src/factory.ts](src/factory.ts)). Selects the implementation from `config.type`:\n\n```typescript\ninterface InferenceClientConfig {\n  type: 'anthropic' | 'ollama';\n  model: string;        // e.g. 'claude-sonnet-4-6', 'gemma2:9b'\n  apiKey?: string;      // required for 'anthropic' (throws if missing/empty)\n  endpoint?: string;    // provider URL; Ollama default: http://localhost:11434\n  baseURL?: string;     // fallback used when endpoint is not set\n}\n```\n\nThe optional second argument is a `Logger` from `@semiont/core`.\n\n### `InferenceClient`\n\nThe contract every implementation satisfies ([src/interface.ts](src/interface.ts)):\n\n```typescript\ninterface InferenceClient {\n  readonly type: string;     // 'anthropic' | 'ollama' | 'mock'\n  readonly modelId: string;  // configured model name\n\n  // Declared per-provider capabilities — consumers read these instead of\n  // switching on provider identity:\n  readonly maxConcurrency: number;        // independent calls that gain from running at once\n                                          // (Anthropic 4; Ollama 1 — one GPU, no parallel gain)\n  readonly verifyDetectionYield: boolean; // whether detection count-verifies extractions\n                                          // (true for real providers; Mock false — tests opt in)\n\n  limits(): Promise<InferenceLimits>;\n  generateText(prompt, maxTokens, temperature, signal?): Promise<string>;\n  generateTextWithMetadata(prompt, maxTokens, temperature, signal?): Promise<InferenceResponse>;\n  generateStructured<T>(prompt, maxTokens, temperature, elementSchema, signal?): Promise<StructuredResponse<T>>;\n}\n```\n\nWhatever varies by provider is a **capability declared here**, hard-coded per implementation — downstream packages are written purely in terms of this contract and never sniff provider identity. A new provider must take a position on each capability (tests pin the declarations).\n\nEvery generation method takes a trailing optional `AbortSignal`: aborting tears down the underlying transport (and, on Anthropic, the SDK's internal retry loop) so a cancelled call rejects promptly instead of surviving as a billed background request. Implementations must honor it — accepting and ignoring the signal is a defect.\n\n```typescript\n\ninterface InferenceLimits {\n  contextTokens: number;         // context window (Anthropic: max input; Ollama: shared input+output)\n  maxOutputTokens: number;       // max output per generation (Ollama mirrors the shared window here)\n  outputTokensPerHour?: number;  // provider's worst-case output-rate model, when it\n                                 // publishes one (Anthropic: 128_000; absent for Ollama)\n}\n\ninterface InferenceResponse {\n  text: string;\n  stopReason: 'end_turn' | 'max_tokens' | 'stop_sequence' | string;\n  usage?: TokenUsage;   // the PROVIDER's own token counts — never estimated;\n}                       // absent means unreported, not zero\n\ninterface TokenUsage {\n  inputTokens: number;\n  outputTokens: number;\n}\n```\n\n`StructuredResponse<T>` carries the same optional `usage`, so a consumer can pair what a call yielded with what it cost from one return value.\n\n### Structured generation\n\n`generateStructured(prompt, maxTokens, temperature, elementSchema)` returns **parsed array elements**, not text — the JSON guarantee lives in the return type (`StructuredResponse<T> = { items: T[]; stopReason }`), not in a comment:\n\n```typescript\nconst { items } = await client.generateStructured<Entity>(prompt, 1000, 0, {\n  type: 'object',\n  properties: { exact: { type: 'string' } },\n  required: ['exact'],\n  additionalProperties: false,\n});\n```\n\nEach implementation honors the contract with its provider's mechanism:\n- **Ollama**: grammar-constrained sampling — the request's `format` field carries the caller's element schema wrapped in an array schema.\n- **Anthropic**: response-level structured output — `output_config.format` carries the caller's element schema under an **array root** (accepted on both live-config models; `.plans/spikes/output-config-array-root.md`), so the response text IS the schema-conforming JSON. No tools, no wrapper, no unwrap.\n\nA response that cannot be read as an array — unparseable output, a missing array, an unhonoured grammar, or an **empty response** (which throws with the stop reason that produced it, so thinking-exhaustion classifies as the truncation it is) — **throws a typed `StructuredReadError`** carrying the provider's `stopReason`, because the cause classifies differently downstream: `max_tokens` is truncation (an identical retry truncates identically, so detection subdivides), `'unknown'` (no stop reason at all) is measured size-correlated on real documents (detection subdivides that too, while keeping it retryable), and everything else is model misbehavior a retry may fix. It is never coerced to `[]`: an empty extraction is a legitimate, distinct outcome, and conflating the two silently discards real data.\n\nCurrent callers all expect arrays (entity extraction, motivation detection). If an object-emitting caller appears, `generateStructured` grows a sibling, not an option — see the notes in [src/interface.ts](src/interface.ts).\n\n### Provider limits\n\n`limits()` publishes the provider's **actual** context/output ceilings for the configured model — discovered from the provider itself, never hand-maintained constants:\n\n- **Anthropic**: the Models API (`models.retrieve`) — `max_input_tokens` / `max_tokens` — plus `outputTokensPerHour: 128_000`, the SDK's own worst-case rate model (the `calculateNonstreamingTimeout` constant): the one duration statement the provider surface makes, which detection's duration-safe budgets derive from.\n- **Ollama**: `POST /api/show` — the model's context window. Input and output share that window, so it is published as both fields (`maxOutputTokens === contextTokens` signals a shared window). No rate is published — local hardware's rate is unknowable a priori. Absence does **not** mean no duration bound: the detection consumer applies its own conservative assumed floor rate instead, because an unbounded output budget turned model repetition loops into hour-long transient burns.\n\nDiscovery is lazy and cached per client; a failed discovery is **not** cached — the next call retries. When ceilings cannot be determined (unknown model, endpoint unreachable), `limits()` **throws**: fail-loud, never a guessed floor.\n\nTwo request-time behaviors ride on the limits:\n- **Ollama sets `num_ctx` explicitly** on every generate request — sized to the prompt estimate + output budget, capped at the model window. Without it, Ollama's model-*default* window silently clips large prompts. A request that genuinely cannot fit **throws** instead of being clipped.\n- **The Ollama adapter owns its transport timeouts.** With `stream: false`, Ollama sends no response headers until generation completes, and Node's default fetch would kill any call generating longer than ~5 minutes (undici's `headersTimeout`) — a ceiling below every deliberate bound, owned by nobody. Generate requests run on a per-request undici@7 dispatcher with those timeouts disabled; the caller's `AbortSignal` is the one bound. The undici `^7` pin is load-bearing (the built-in fetch rejects an undici@8 Agent) and test-gated.\n- **Cloud-routed Ollama models are reported honestly, not corrected**: hidden thinking returned despite `think: false` is surfaced on the response and warned (it inflates `eval_count`, which is documented at the field), and the structured `format` is advisory rather than grammar-enforced on that path — violations surface as `StructuredReadError`.\n- **Anthropic streams internally** above the SDK's non-streaming output ceiling (≈21K tokens) — same interface, same response shape.\n\n### `MockInferenceClient`\n\nA scripted test double ([src/implementations/mock.ts](src/implementations/mock.ts)): construct it with a list of canned responses, then inspect `calls` (recorded prompt/maxTokens/temperature/options per invocation). `reset()` and `setResponses()` helpers included. An optional third constructor argument injects `InferenceLimits` for chunking/budget tests; the default is generous (1M/1M window plus a generous published rate) so ordinary tests never trip window guards, duration caps, or the count-verifier. Its capabilities are deterministic-test defaults — `maxConcurrency: 1`, `verifyDetectionYield: false` — so a test exercising concurrency or verification declares its own client rather than paying a surprise call.\n\n```typescript\nimport { MockInferenceClient } from '@semiont/inference';\n\nconst mock = new MockInferenceClient(['first reply', 'second reply']);\nawait mock.generateText('hi', 100, 0);\nexpect(mock.calls[0].prompt).toBe('hi');\n```\n\n## Observability\n\nEvery generation records a usage metric through `@semiont/observability`'s `recordInferenceUsage`: provider, model, duration, outcome (`success`/`error`), and token counts when the provider reports them.\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────┐\n│  @semiont/make-meaning   @semiont/jobs      │\n│  (application logic)     (job workers)      │\n│  - builds InferenceClientConfig             │\n│  - calls createInferenceClient()            │\n└──────────────────┬──────────────────────────┘\n                   │ uses\n┌──────────────────▼──────────────────────────┐\n│      @semiont/inference                     │\n│  (AI primitives only)                       │\n│  - InferenceClient interface                │\n│  - createInferenceClient() factory          │\n│  - cross-provider JSON output mode          │\n└──────────┬───────────────────┬──────────────┘\n           │                   │\n┌──────────▼──────────┐ ┌─────▼──────────────┐\n│  AnthropicInference │ │  OllamaInference   │\n│  Client             │ │  Client            │\n│  (@anthropic-ai/sdk)│ │  (native HTTP API) │\n└─────────────────────┘ └────────────────────┘\n```\n\n**Key Principles:**\n- **@semiont/inference**: provider abstraction, text generation, output discipline\n- **@semiont/make-meaning**: semantic processing, prompt engineering, response parsing\n- **Clean separation**: adding a new provider only affects @semiont/inference\n\n## Supported Providers\n\n| Provider | Type | API Key | Models |\n|----------|------|---------|--------|\n| Anthropic | `anthropic` | Required | Claude family |\n| Ollama | `ollama` | Not required | gemma2:9b, llama3.1:8b, mistral, etc. |\n\n### Adding a New Provider\n\n1. Implement the `InferenceClient` interface in `src/implementations/` — including a\n   position on each declared capability (`maxConcurrency`: does this provider gain from\n   concurrent independent calls? `verifyDetectionYield`: should detection count-verify\n   its extractions?). The capability pins in `factory.test.ts` fail until you take one.\n2. Add type to `InferenceClientType` union in `src/factory.ts`\n3. Add case in `createInferenceClient()` switch\n4. Application code in `@semiont/make-meaning` and `@semiont/jobs` requires no changes —\n   consumers read capabilities off the contract instead of switching on provider identity\n\n## Dependencies\n\nFrom [package.json](package.json):\n\n- `@anthropic-ai/sdk` - Anthropic API client\n- `@semiont/core` - `Logger` type\n- `@semiont/observability` - usage metrics\n\nOllama uses native HTTP (`fetch`) with no SDK dependency.\n\n## Testing\n\n```bash\nnpm test                # Run tests\nnpm run test:watch      # Watch mode\nnpm run test:coverage   # Coverage report\n```\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}