Deploy and promote

from trajectory import Client

client = Client()
deployable = [
    c for c in client.training.checkpoints.list("YOUR_TRAINING_RUN_ID")
    if c.is_deployable
]
if not deployable:
    raise RuntimeError("No deployable checkpoint is available for this training run")
checkpoint = max(deployable, key=lambda c: c.step_index)
deployment = client.deployments.create(
    checkpoint_id=checkpoint.checkpoint_id,
    test_model_slug="my-agent-test",
)
print(deployment.deployment_id)
print(client.deployments.retrieve(deployment.deployment_id).status)

import os
import time
from contextlib import suppress

from openai import OpenAI
from trajectory import Client

trajectory = Client()
deadline = time.monotonic() + 900
while True:
    state = trajectory.deployments.retrieve(deployment.deployment_id)
    if state.status == "DEPLOYED":
        break
    if state.status in {"FAILED", "CANCELLED"}:
        raise RuntimeError(f"Deployment {state.status}: {state.failure}")
    if time.monotonic() >= deadline:
        raise TimeoutError(f"Deployment still {state.status} after 15 minutes")
    time.sleep(10)

tid = trajectory.trajectories.create().tid
api_origin = os.environ.get("TRAJECTORY_BASE_URL", "https://api.trajectory.ai").rstrip("/")
inference = OpenAI(
    api_key=os.environ["TRAJECTORY_API_KEY"],
    base_url=f"{api_origin}/api/v1/deploy/{deployment.deployment_id}",
)
try:
    response = inference.chat.completions.create(
        model=state.test_model_slug,
        messages=[{"role": "user", "content": "Hello!"}],
        extra_headers={"X-Trajectory-Id": tid},
    )
    choice = response.choices[0]
    answer = (choice.message.content or "").strip()
except Exception:
    with suppress(Exception):
        trajectory.trajectories.complete(tid, termination_reason="ERROR")
    raise
reason = (
    "TRUNCATION" if choice.finish_reason in ("length", "max_tokens")
    else "ENV_DONE" if choice.finish_reason == "stop" and answer
    else "ERROR"
)
trajectory.trajectories.complete(tid, termination_reason=reason)
if reason != "ENV_DONE":
    raise RuntimeError(f"No complete answer: {choice.finish_reason}")
print(answer)

from trajectory import Client

client = Client()
state = client.deployments.retrieve(deployment.deployment_id)
if state.production_model_slug is None:
    client.model_slugs.create_production(state.agent_id, production_model_slug="my-agent")
# Pass the current production deployment ID instead of None when replacing one.
client.deployments.promote(state.deployment_id, expected_deployment_id=None)

from contextlib import suppress

from trajectory import Client

client = Client()
tid = client.trajectories.create().tid
try:
    response = client.chat.completions.create(
        model="my-agent",
        messages=[{"role": "user", "content": "Hello!"}],
        x_trajectory_id=tid,
    )
    choice = response.choices[0]
    answer = (choice.message.content or "").strip()
except Exception:
    with suppress(Exception):
        client.trajectories.complete(tid, termination_reason="ERROR")
    raise
reason = (
    "TRUNCATION" if choice.finish_reason in ("length", "max_tokens")
    else "ENV_DONE" if choice.finish_reason == "stop" and answer
    else "ERROR"
)
client.trajectories.complete(tid, termination_reason=reason)
if reason != "ENV_DONE":
    raise RuntimeError(f"No complete answer: {choice.finish_reason}")
print(answer)