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
1 maintainer
Metadata
1 maintainer
Release files for gravi-model-client 0.9.0
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