jijzeptai 0.1.0rc1

Python client SDK for the JijZept AI Solver HTTP API.

jijzeptai

jijzeptai is the Python SDK for submitting optimization jobs through JijZept AI. It supports CPython 3.11, 3.12, 3.13, and 3.14.

Install

With uv:

uv add jijzeptai

With pip:

python -m pip install jijzeptai

JijModeling and OMMX are included as required dependencies.

Create and configure an API token

In JijZept AI, open the account menu and choose API Tokens. Create a token for the current Organization. The value appears only once, so copy it into your environment with the production JijZept AI URL and do not put it in source code:

export JIJZEPTAI_BASE_URL="https://ai.jijzept.com"
export JIJZEPTAI_API_TOKEN="jzsa_..."

DeveloperClient reads these variables by default. You can also pass base_url="https://ai.jijzept.com" and api_token as constructor arguments when that fits your program better.

Minimal model workflow

This flow deploys a reusable JijModeling model, submits input data, waits for optimization, and downloads the solution. Deploying a model requires an Admin or Developer Organization Role. If your role is User, ask an Admin or Developer to deploy the model and share its name and tag, then call submit_job_by_deployed_model with those values (skip deploy_model).

import jijmodeling as jm

from jijzeptai import DeveloperClient


# Define a reusable knapsack model.
@jm.Problem.define("knapsack", sense=jm.ProblemSense.MAXIMIZE)
def knapsack(problem: jm.DecoratedProblem):
    values = problem.Float(ndim=1)
    item_count = values.len_at(0)
    weights = problem.Float(shape=(item_count,))
    capacity = problem.Float()
    selected = problem.BinaryVar(
        "selected",
        shape=(item_count,),
        description="item selected",
    )
    problem += jm.sum(values * selected)
    problem += problem.Constraint(
        "capacity",
        jm.sum(weights * selected) <= capacity,
    )


# Create the SDK client for JijZept AI.
client = DeveloperClient()
# Publish the model so the Organization can run it with new input later.
model = client.deploy_model("knapsack", "demo", knapsack)
# Start an optimization job with concrete input values.
job = client.submit_job_by_deployed_model(
    model.name,
    model.tag,
    {
        "values": [10.0, 20.0, 15.0, 30.0],
        "weights": [2.0, 3.0, 5.0, 7.0],
        "capacity": 10.0,
    },
)
# Wait until the job succeeds, then download the solution.
finished = client.wait_for_success(job.job_id)
solution = client.download_solution(finished.job_id)
print(solution.extract_decision_variables("selected"))  # selected items
print(solution.objective)  # best objective value
print(solution.feasible)  # True when all constraints hold

Organization roles

• Admin and Developer: deploy, update, archive, and unarchive models; submit a JijModeling problem or an OMMX problem file without deploying a model first; run deployed models.
• User: inspect and run deployed models.

Deployed models belong to the Organization. Jobs, results, and cancellation stay private to the user who submitted the job.

Errors

Catch JijZeptAIError for failures raised by this package. Invalid arguments raise TypeError or ValueError.

from jijzeptai import JijZeptAIError, TransportError

try:
    finished = client.wait_for_success(job.job_id)
except TransportError as error:
    print(f"Network request failed: {error}")
except JijZeptAIError as error:
    print(f"JijZept AI operation failed: {error}")

License

• Apache License 2.0

Apache-2.0 covers the published jijzeptai package. It does not license the JijZept AI hosted service, API tokens, user models or data, service terms, or JIJ Inc. trademarks and the JijZept AI product name.