PolarGrid Provider¶

PolarGrid is a Canadian-hosted inference network with regional edges in Toronto, Vancouver, and Montreal. Use it when data residency on Canadian soil matters. It serves OpenAI-shaped chat completions with streaming and tool / function calling; voice is limited (see below).

Install¶

pip install roomkit[polargrid]   # requires polargrid-sdk>=0.10.0

Quick start¶

from roomkit import AIChannel, RoomKit
from roomkit.providers.polargrid import PolarGridAIProvider, PolarGridConfig

provider = PolarGridAIProvider(
    PolarGridConfig(
        api_key="pg_...",          # from the PolarGrid Console
        model="qwen-3.8-27b",      # default; qwen-3.6-35b-a3b on yul-02
        region=None,               # None = auto-route; pin in production
    )
)

kit = RoomKit()
kit.register_channel(AIChannel("ai", provider=provider))

A full runnable example lives at examples/polargrid_ai.py.

Configuration¶

Field	Default	Notes
`api_key`	(required)	`pg_...` Bearer token from the PolarGrid Console
`model`	`qwen-3.8-27b`	The one LLM on the public fleet. `qwen-3.5-27b` was retired on 2026-08-20; `qwen-3.6-35b-a3b` (vision) is a customer pilot with no public edge. See Models — or call `PolarGridAIProvider.available_models()` / `list_models()`.
`region`	`None`	`toronto` / `vancouver` / `montreal` — or the IDs `yto-01` / `yvr-02` / `yul-01`. `None` auto-routes to an edge already serving the configured model.
`max_tokens`	`None`	API cap is 4096.
`temperature`	`0.7`	0.0-2.0
`top_p`	`0.9`	0.0-1.0
`thinking`	`None`	Toggle qwen reasoning via the `enable_thinking` request flag (sdk 0.8.5+) — `True` on, `False` off, `None` leaves it unset. See Thinking / reasoning.
`timeout`	`30.0`	Seconds.
`connect_timeout`	`5.0`	Seconds, TCP connect alone. `timeout` stays the read budget, so a dead edge is refused in seconds rather than after it. See Connect vs Read Timeout.
`max_retries`	`0`	Defaults to 0 so RoomKit's `RetryPolicy` controls retries.
`debug`	`False`	Verbose SDK logging.

Regions and data residency¶

PolarGrid runs three Canadian edges. Two ways to choose one:

# Auto-routing — on first call, the autorouter picks the nearest edge
# that already has the configured model loaded (polargrid-sdk 0.10.0);
# if no edge serves it, routing falls back to the default edge.
config = PolarGridConfig(api_key="pg_...", region=None)

# Pinned — request always goes to the named edge.
config = PolarGridConfig(api_key="pg_...", region="vancouver")

The auto-routing path is convenient for development but pin a region in production if residency matters. Confirm with PolarGrid whether their auto-routing or failover ever crosses regions before relying on it for compliance.

Region discovery¶

available_regions() is the curated, offline catalog of all PolarGrid edges (id, name, location); connected_region() reports the edge a provider is actually routed to. Both return PolarGridRegion, and location carries the Canada / US split that matters for residency:

from roomkit.providers.polargrid import PolarGridAIProvider

# All edges, filtered to the Canadian ones (Law 25 / PIPEDA).
canadian = [r for r in PolarGridAIProvider.available_regions()
            if (r.location or "").startswith("Canada")]
# → yto-01 Toronto, yul-01 Montreal, yul-02 Montreal 02 (pilot), yvr-02 Vancouver

# Which edge am I actually hitting (esp. under auto-routing)?
here = await provider.connected_region()
print(here.id, here.name, here.location)   # e.g. "yul-01" "Montreal" "Canada East"

Region	Name	Location
`yto-01`	Toronto	Canada Central
`yul-01`	Montreal	Canada East
`yul-02`	Montreal 02	Canada East (customer-pilot edge, no longer in the published list)
`yvr-02`	Vancouver	Canada West
`nyc-01` / `nyc-02` / `was-01` / `mia-01`	New York, Washington DC, Miami	US East
`dfw-01` / `dfw-02` / `chi-01`	Dallas, Chicago	US Central
`sfo-01` / `sfo-03` / `lax-01` / `sea-01` / `phx-01`	San Francisco, Los Angeles, Seattle, Phoenix	US West

available_regions() mirrors the SDK's own routing table (16 edges in polargrid-sdk 0.10.0). PolarGrid's regions guide publishes 15 of them: yul-02 left the public fleet with the qwen-3.6-35b-a3b pilot, but the SDK still routes it and the edge still answers, so pinning it is not refused. There is no live full-region endpoint (/v1/status 404s on edges), so connected_region() reports only the routed edge (its location is backfilled from the catalog). Every edge answered /health on 2026-09-02 except yto-01, down at the time.

Models¶

Like every RoomKit AI provider, PolarGrid exposes two discovery entry points returning ModelInfo:

# Curated, offline catalog — a classmethod, no API key or network.
for m in PolarGridAIProvider.available_models():
    print(m.id, m.display_name, m.capabilities)

# Live query against the connected edge (region-specific).
provider = PolarGridAIProvider(PolarGridConfig(api_key="pg_...", region="yul-01"))
for m in await provider.list_models():
    print(m.id)

available_models() is the curated snapshot of the chat models on the public edges (sourced from PolarGrid's model pages and model availability guide, cross-checked against the autorouter on 2026-09-02); list_models() returns whatever is actually loaded on the connected edge, including the STT/TTS models, and backfills display names, context windows and vision flags from the catalog.

Model	Type	Availability
`qwen-3.8-27b`	chat (tools, thinking), 256K context, $0.20 / $0.75 per 1M tokens	fleet-wide (every public edge)
`qwen-3.6-35b-a3b`	chat (tools, thinking, vision), 8K served context	customer pilot: no public edge; recognised (`supports_vision`, `list_models` backfill) but not advertised
`qwen-3.5-27b`	chat	retired 2026-08-20: `404 model_not_loaded` everywhere, dropped from the catalog
`whisper-large-v3-turbo`	STT	all edges except dfw-02
`cohere-transcribe-03-2026`	STT	all edges except dfw-02
`kokoro-82m`	TTS	all edges except dfw-02
`tada-3b-ml`	TTS	all edges

The autorouter answers GET https://autorouter.polargrid.ai/v1/route?model=<id> with 404 when no edge serves an id, which is how the catalog is checked without a key. The default qwen-3.8-27b reasons via enable_thinking. See examples/list_models.py for a runnable catalog dump across providers.

Streaming¶

PolarGrid streams via OpenAI-shaped chunked SSE. RoomKit's provider exposes both plain text deltas and structured events.

async for delta in provider.generate_stream(context):
    print(delta, end="", flush=True)

async for event in provider.generate_structured_stream(context):
    # StreamThinkingDelta | StreamTextDelta | StreamToolCall | StreamDone
    ...

Thinking / reasoning¶

qwen surfaces its reasoning inline as <think>...</think> tags in the message content (the same convention vLLM / Ollama reasoning models use; PolarGrid has no separate reasoning_content field). The provider parses those tags out so:

non-streaming generate() returns the reasoning on AIResponse.thinking and a clean AIResponse.content;
streaming generate_structured_stream() emits the reasoning as StreamThinkingDelta (handling tags split across chunks) and the answer as StreamTextDelta.

generate_stream() (plain text) filters thinking out entirely.

Reasoning is off by default on the edge. polargrid-sdk 0.8.5+ exposes an enable_thinking request flag, which the provider sets from the thinking config:

PolarGridConfig(api_key="pg_...", thinking=True)   # enable_thinking=true  → reasoning on
PolarGridConfig(api_key="pg_...", thinking=False)  # enable_thinking=false → reasoning off
PolarGridConfig(api_key="pg_...")                  # thinking=None         → flag unset (model default)

Thinking responses are larger and slower (the reasoning counts toward latency and max_tokens), so raise timeout and max_tokens when enabling it. To display reasoning in a CLI, construct the channel with CLIChannel("cli", show_thinking=True).

Tool / function calling¶

PolarGrid's chat-completions endpoint supports tool / function calling as of polargrid-sdk>=0.8.5. The provider forwards context.tools (OpenAI-shaped) and surfaces tool calls back:

Non-streaming — generate() returns them on AIResponse.tool_calls.
Streaming — generate_structured_stream() emits a StreamToolCall per call after the text deltas, accumulating the SDK's fragmented delta.tool_calls.

PolarGrid sends tool arguments as a JSON string; the provider parses them into a dict for RoomKit (malformed payloads are preserved under a raw key). For multi-turn tool loops, assistant tool calls and tool results are rendered back into structured messages (role="assistant" with tool_calls, role="tool" with tool_call_id) rather than flattened to text.

from roomkit.providers.ai.base import AITool

context.tools = [
    AITool(
        name="get_weather",
        description="Get current weather for a city.",
        parameters={
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    )
]

tool_choice is not exposed by AIContext, so it is left unset and the backend defaults to auto. Forcing a specific tool is steered, not hard-guaranteed, on PolarGrid's backend — design tool loops to tolerate the model answering directly instead of calling the tool.

examples/polargrid_ai.py wires a web_search tool end-to-end: it passes tools=[WebSearchTool()] to the AIChannel, which runs the whole loop (model → web_search → grounded answer). Ask it "What is the speed of light?" to see the model search and answer from the result. The tool works key-free (Wikipedia search + summary, which handles the natural-language queries models generate); set TAVILY_API_KEY for real web search that also finds niche companies and current info.

Vision¶

PolarGrid added multimodal chat in polargrid-sdk>=0.9.0: Message.content now accepts OpenAI-shaped image_url parts. The provider renders an AIImagePart (in a user turn or split off an image tool result) as an image_url block — the URL may be a remote https:// URL or a base64 data: URI.

supports_vision is model-driven: it reads the configured model's flag from the curated catalog, pilot models included. Only qwen-3.6-35b-a3b actually reads images — verified live on yul-02 while that edge was public. The default qwen-3.8-27b refuses image input outright (ValidationError: "does not support image input"), so it — and any unknown model id — is treated as text-only. Vision is the deployed model's capability, not the SDK's, and as of 2026-09-02 no public edge serves a vision-capable model: qwen-3.6-35b-a3b is a customer pilot.

With pilot access, pin the vision model and the pilot edge you were given:

PolarGridConfig(api_key="pg_...", model="qwen-3.6-35b-a3b", region="yul-02")

examples/polargrid_ai.py accepts a /image <path> [question] command: it embeds the local file as a base64 data: URI and sends it as an image_url part (defaulting to "Analyse this image." when no question is given). Run it with POLARGRID_MODEL=qwen-3.6-35b-a3b POLARGRID_REGION=<pilot edge> for vision.

Error handling¶

The provider maps the PolarGrid SDK's exception hierarchy onto RoomKit's ProviderError:

PolarGrid exception	`retryable`
`AuthenticationError`	`False`
`ValidationError`	`False`
`NotFoundError`	`False`
`RateLimitError`	`True`
`NetworkError`	`True`
`TimeoutError`	`True`
`ServerError`	`True`
unknown	`True` (let `RetryPolicy` decide)

Roadmap¶

PolarGrid also exposes Speech-to-Text (whisper-large-v3-turbo) and Text-to-Speech (kokoro-82m) endpoints. RoomKit STT/TTS provider wrappers are planned. There is no speech-to-speech / realtime duplex endpoint today, so RealtimeVoiceChannel is out of scope for this provider.