gravi-model-client

Python client for Gravitate's demand and supply optimization models.

Installation

pip install gravi-model-client

Usage

from gravi_model_client import GraviModelClient, ModelRunRequest, OptimizationInput

async with GraviModelClient(api_key="your-api-key", base_url="https://...") as client:
    # Health check
    await client.health_check()

    # Run demand model (synchronous)
    result = await client.run_demand_model(request)

    # Run TSD (supply) model (synchronous)
    result = await client.run_tsd_model(optimization)

Async (queue-based) models: VRP and OSA

VRP (tank wagon turn) and OSA (order-based supply) runs are queued and processed by a worker. run_vrp_model / run_osa_model handle the whole queue → poll → fetch lifecycle and block until the result is ready:

from gravi_model_client import GraviModelClient, VrpModelRunRequest, OsaInput

async with GraviModelClient(api_key="your-api-key") as client:
    vrp_result = await client.run_vrp_model(vrp_request)   # -> VrpModelResultResponse
    osa_result = await client.run_osa_model(osa_input)     # -> OsaAPIResponse

They raise ModelRunError if the run fails or is cancelled, and ModelRunTimeout if it doesn't finish within timeout (default 300s; tune poll_interval too).

For fire-and-forget / webhook workflows, use the primitives instead: queue_vrp_model returns a job id, get_job_status / get_osa_job_status return a JobStatus, and get_vrp_model_run_response / get_osa_model_run_response fetch the finished result.

Models

This package is the single source of truth for all pydantic models used by both the client and the server. Import models directly:

from gravi_model_client.demand import ModelRunRequest, StoreRequest, TankRequest, VolumeRange
from gravi_model_client.tsd import OptimizationInput, Directive, Site, Tank

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