provider-sarvam

Sarvam AI behind llm-router, as one worker for every Sarvam model family:

• Chat: provider::sarvam::stream runs sarvam-105b, sarvam-30b and sarvam-m on Sarvam's OpenAI-compatible Chat Completions API (SSE streaming, tool calling, reasoning_effort), so router::chat and the harness use them like any other provider's models.
• Speech to text: provider::sarvam::transcribe runs Saaras (saaras:v3 default, saaras:v4, saarika:v2.5) behind router::transcribe: 22 Indian languages and English, detected when no hint is given, word timings folded into sentence segments.
• Text to speech: provider::sarvam::speak runs Bulbul (bulbul:v3 default, bulbul:v2) behind router::speak: 11 languages, 35+ speakers, mp3, wav, pcm16 or opus back.
• provider::sarvam::count_tokens estimates prompt tokens locally with tiktoken (Sarvam publishes no tokenizer for the 105B and 30B models).

The voice worker reaches the speech half with stt.backend: router / tts.backend: router and a sarvam::… model.

Install

iii trigger compose::add worker=provider-sarvam

Put the key where the router reads it: SARVAM_API_KEY in the router's environment, or paste it into the sarvam block of the llm-router entry in the console's Settings. The catalog fills the moment a key lands.

Behavior

• Auth: the same key goes out as Authorization: Bearer (what the chat endpoint documents) and as api-subscription-key (what every other endpoint takes), so a gateway in front of either shape is satisfied.
• Endpoints: chat at the configured api_url (default https://api.sarvam.ai/v1/chat/completions); the speech endpoints at that URL's origin (/speech-to-text, /text-to-speech), so a residency host override moves all three.
• Reasoning: every Sarvam chat model reasons by default; the router's thinking_level folds onto reasoning_effort low | medium | high. response_format maps to json_object without schema validation and the catalog says so (supports_structured_output: false).
• Languages: hints such as hi, hi-IN or en-US become Sarvam's xx-IN codes; Odia's ISO code or becomes od-IN. Unknown hints let Saaras detect the language and make Bulbul speak English.
• Speakers: shubh is the Bulbul v3 default (anushka for v2). Pass any speaker name Sarvam lists; v2 voices do not work on v3 and vice versa.
• Limits: Bulbul takes up to 2500 characters per request; the REST speech-to-text endpoint is for recordings, not hour-long files (use Sarvam's batch API for those).

Surfaces

Function	Request	Response
provider::sarvam::stream	router chat stream contract	AssistantMessageEvent frames
provider::sarvam::transcribe	{model?, audio_base64, mime?, language?}	{model, text, segments[{text, start_secs, end_secs}], language, duration_secs}
provider::sarvam::speak	{model?, text, voice?, format?, language?, speed?}	{model, audio_base64, mime, voice}
provider::sarvam::count_tokens	{model, system_prompt?, tools?, messages}	{model, tokens, estimator}
provider::sarvam::refresh_models	{}	{ok, count}

Errors carry stable prefixes: provider/not_configured, provider/auth_expired, provider/quota_exceeded, provider/rate_limited, provider/invalid_input, provider/upstream_transient; chat failures use the shared ErrorKind taxonomy on their error frames.

Configuration

Nothing in this worker's own config file. The credential, api_url and max_tokens resolve per request through router::provider::resolve.

Tests

cargo test                      # unit tests + wire-schema goldens (+ engine-backed suite when `iii` is on PATH)
UPDATE_GOLDENS=1 cargo test     # regenerate tests/golden after a surface change

Running

cargo run -- --url ws://127.0.0.1:49134