litellm/tests/unit/proxy/decisions_endpoints/test_endpoints.py

from __future__ import annotations

import asyncio
import json
from collections.abc import AsyncGenerator, Iterator, Mapping
from contextlib import asynccontextmanager
from pathlib import Path
from typing import Final

import pytest
import respx
from fastapi import FastAPI
from fastapi.routing import APIRoute
from fastapi.testclient import TestClient
from pydantic import TypeAdapter
from starlette.routing import Match

import litellm
from litellm.litellm_core_utils.decisions_pricing import apply_decisions_pricing
from litellm.proxy._lazy_features import LAZY_FEATURES, LazyFeature, attach_lazy_features
from litellm.proxy.decisions_endpoints.endpoints import decisions, router, systemone
from litellm.proxy.pass_through_endpoints.pass_through_endpoints import SafeRouteAdder
from litellm.proxy.proxy_server import (
    app,
    cleanup_router_config_variables,
    initialize,
)
from litellm.types.decisions import DecisionsRequestBody, OpenAIDecisionRequestBody

_INPUT_TOKENS: Final[int] = 367
_OUTPUT_TOKENS: Final[int] = 3
_RESPONSE: Final[Mapping[str, object]] = {
    "model": "pplx-decider-v1-27b",
    "answers": {
        "is_defect": {"type": "noul", "noul": 0.9},
    },
    "usage": {"input_tokens": _INPUT_TOKENS, "output_tokens": _OUTPUT_TOKENS},
}
_STRANDS_RESPONSE: Final[Mapping[str, object]] = {
    "model": "strands-decider-2B-hobson-v19",
    "answers": {
        "is_defect": {"type": "noul", "noul": 0.9},
    },
    "usage": {"input_tokens": 216, "output_tokens": 3},
    "latency_ms": 3722.17,
}
_REQUEST: Final[Mapping[str, object]] = {
    "model": "decider",
    "state": {"source": "proxy-test"},
    "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
}


@pytest.fixture
def client(monkeypatch: pytest.MonkeyPatch) -> Iterator[TestClient]:
    monkeypatch.setenv("OPENAI_API_KEY", "fake-openai-key")
    monkeypatch.setenv("OPENAI_API_BASE", "https://fake-openai.example")
    monkeypatch.setenv("REDIS_HOST", "localhost")
    cleanup_router_config_variables()
    config_path: Final = Path(__file__).parents[1] / "test_configs" / "test_config_no_auth.yaml"
    asyncio.run(initialize(config=str(config_path), debug=True))
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[
                {
                    "model_name": "decider",
                    "litellm_params": {
                        "model": "perplexity/pplx-decider-v1-27b",
                        "api_key": "test-key",
                    },
                }
            ]
        ),
    )
    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
    litellm.in_memory_llm_clients_cache.flush_cache()
    yield TestClient(app)
    litellm.in_memory_llm_clients_cache.flush_cache()


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/systemone"))
def test_proxy_decisions_route_returns_answers_and_cost(
    client: TestClient,
    respx_mock: respx.MockRouter,
    endpoint: str,
) -> None:
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(endpoint, json=_REQUEST)

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _RESPONSE["answers"]
    assert "_hidden_params" not in response.json()
    perplexity_cost: Final = litellm.model_cost["perplexity/pplx-decider-v1-27b"]
    expected_cost: Final = _INPUT_TOKENS * float(perplexity_cost["input_cost_per_token"]) + _OUTPUT_TOKENS * float(
        perplexity_cost["output_cost_per_token"]
    )

    assert expected_cost > 0
    assert float(response.headers["x-litellm-response-cost"]) == pytest.approx(expected_cost)
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "pplx-decider-v1-27b",
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }
    assert upstream.calls[0].request.headers["authorization"] == "Bearer test-key"


def test_proxy_decisions_dispatches_typesafe_deployment(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "jev",
                "litellm_params": {
                    "model": "typesafe/jev-latest",
                    "api_key": "k",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post("https://api.typesafe.ai/v1/systemone").respond(json=_RESPONSE)

    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "jev",
            "state": {"source": "proxy-test"},
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "jev-latest",
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }


def test_proxy_decisions_sends_the_env_key_to_the_deployment_api_base(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.setenv("PERPLEXITYAI_API_KEY", "server-key")
    monkeypatch.delenv("PERPLEXITY_API_KEY", raising=False)
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "decider",
                "litellm_params": {
                    "model": "perplexity/pplx-decider-v1-27b",
                    "api_base": "https://egress.example/perplexity",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post("https://egress.example/perplexity/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post("/v1/systemone", json=_REQUEST)

    assert response.status_code == 200, response.text
    assert upstream.call_count == 1
    assert upstream.calls[0].request.headers["authorization"] == "Bearer server-key"


def test_proxy_decisions_unknown_model_is_a_client_error(
    client: TestClient,
    respx_mock: respx.MockRouter,
) -> None:
    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "missing-model",
            "state": "review",
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert 400 <= response.status_code < 500, response.text
    assert len(respx_mock.calls) == 0


@pytest.mark.parametrize(
    "request_body",
    (
        {
            "model": "decider",
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
        {
            "model": "decider",
            "state": {"source": "proxy-test"},
        },
    ),
    ids=("missing_state", "missing_questions"),
)
def test_proxy_decisions_missing_required_field_is_a_client_error(
    client: TestClient,
    respx_mock: respx.MockRouter,
    request_body: Mapping[str, object],
) -> None:
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post("/v1/systemone", json=request_body)

    assert response.status_code == 400, response.text
    assert not upstream.called


def test_proxy_decisions_dispatches_strands_decider(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.delenv("STRANDS_DECIDER_API_BASE", raising=False)
    monkeypatch.delenv("STRANDS_DECIDER_API_KEY", raising=False)
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "strands",
                "litellm_params": {
                    "model": "strands_decider/strands-decider-2B-hobson-v19",
                    "api_base": "https://strands.example",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post("https://strands.example/v1/systemone").respond(json=_STRANDS_RESPONSE)

    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "strands",
            "state": {"source": "proxy-test"},
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _STRANDS_RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "strands-decider-2B-hobson-v19",
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }
    assert "authorization" not in upstream.calls[0].request.headers


@pytest.mark.parametrize(
    ("model", "env_base", "env_key"),
    (
        ("laya/english", "LAYA_API_BASE", "LAYA_API_KEY"),
        ("bespoke/nimble-latest", "BESPOKE_API_BASE", "BESPOKE_API_KEY"),
    ),
    ids=("laya", "bespoke"),
)
def test_proxy_decisions_dispatches_oss_classifier_deployment(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    model: str,
    env_base: str,
    env_key: str,
) -> None:
    monkeypatch.delenv(env_base, raising=False)
    monkeypatch.delenv(env_key, raising=False)
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "oss-classifier",
                "litellm_params": {
                    "model": model,
                    "api_base": "https://oss-decider.example",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post("https://oss-decider.example/v1/systemone").respond(json=_STRANDS_RESPONSE)

    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "oss-classifier",
            "state": {"source": "proxy-test"},
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _STRANDS_RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "model": model.partition("/")[2],
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }
    assert "authorization" not in upstream.calls[0].request.headers


def test_proxy_systemone_dispatches_databricks_serving_endpoint(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.delenv("DATABRICKS_API_BASE", raising=False)
    monkeypatch.delenv("DATABRICKS_API_KEY", raising=False)
    monkeypatch.delenv("DATABRICKS_TOKEN", raising=False)
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "databricks-decider",
                "litellm_params": {
                    "model": "databricks/databricks-openjev-qwen35-4b",
                    "api_base": "https://workspace.example/serving-endpoints",
                    "api_key": "dapi-deployment",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post(
        "https://workspace.example/serving-endpoints/databricks-openjev-qwen35-4b/invocations"
    ).respond(json={**_RESPONSE, "model": "/mosaicml/local_model"})

    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "databricks-decider",
            "state": {"source": "proxy-test"},
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "databricks-openjev-qwen35-4b",
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }
    assert upstream.calls[0].request.headers["authorization"] == "Bearer dapi-deployment"


def test_proxy_systemone_dispatches_databricks_ai_decide(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.delenv("DATABRICKS_API_BASE", raising=False)
    monkeypatch.delenv("DATABRICKS_API_KEY", raising=False)
    monkeypatch.delenv("DATABRICKS_TOKEN", raising=False)
    router: Final = litellm.Router(
        model_list=[
            {
                "model_name": "databricks-decider",
                "litellm_params": {
                    "model": "databricks/ai_decide",
                    "api_base": "https://workspace.example/serving-endpoints",
                    "api_key": "dapi-deployment",
                },
            }
        ]
    )
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router)
    upstream: Final = respx_mock.post("https://workspace.example/api/2.0/ai-functions/ai-decide").respond(
        json={
            "response": {"answers": {"is_defect": {"type": "noul", "probability": 0.9}}},
            "metadata": {"version": "1.0"},
        }
    )

    response: Final = client.post(
        "/v1/systemone",
        json={
            "model": "databricks-decider",
            "state": {"source": "proxy-test"},
            "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        },
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content) == {
        "state": {"source": "proxy-test"},
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }
    assert upstream.calls[0].request.headers["authorization"] == "Bearer dapi-deployment"


def test_proxy_decisions_without_model_uses_the_proxy_default_model(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.setattr(litellm.proxy.proxy_server, "user_model", "decider")
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(
        "/v1/systemone", json={key: value for key, value in _REQUEST.items() if key != "model"}
    )

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == _RESPONSE["answers"]
    assert upstream.called
    assert json.loads(upstream.calls[0].request.content)["model"] == "pplx-decider-v1-27b"


def test_a_request_body_cannot_turn_on_box_questions_for_a_proxy_without_a_router(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", None)
    monkeypatch.setattr(litellm.proxy.proxy_server, "user_model", "openai/gpt-6-luna")
    upstream: Final = respx_mock.post("https://fake-openai.example/v1/decisions").respond(json={})

    response: Final = client.post(
        "/v1/decisions",
        json={
            "model": "openai/gpt-6-luna",
            "input": "A phone screen with a diagonal crack.",
            "questions": [{"type": "box", "name": "crack", "instructions": "Where is the crack?"}],
            "model_info": {"supports_box_questions": True},
        },
    )

    assert response.status_code == 400, response.text
    assert "['box questions']" in response.json()["error"]["message"]
    assert not upstream.called


_OPENAI_FORMAT_REQUEST: Final[Mapping[str, object]] = {
    "model": "decider",
    "input": [
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": "The package arrived with a broken screen."},
                {"type": "input_text", "text": "I want a refund."},
            ],
        },
        {"role": "user", "content": "Order 1234."},
    ],
    "questions": [
        {"type": "predicate", "name": "damaged", "instructions": "Does the customer report a damaged item?"},
        {
            "type": "choice",
            "instructions": "Should we refund?",
            "choices": [{"value": True, "description": "Refund now"}, {"value": "escalate"}],
        },
        {
            "type": "score",
            "name": "severity",
            "instructions": "How severe is the issue?",
            "levels": [{"label": "minor"}, {"label": "major", "description": "Product unusable"}],
        },
        {"type": "predicate", "name": "fraud", "instructions": "Is this fraud?"},
    ],
    "safety_identifier": "end-user-1",
}
_SYSTEMONE_ANSWERS_FOR_OPENAI_REQUEST: Final[Mapping[str, object]] = {
    "model": "pplx-decider-v1-27b",
    "answers": {
        "0": {"type": "noul", "noul": 0.95},
        "1": {"type": "choice", "choice": "true", "confidence": 0.8, "probabilities": {"true": 0.9, "escalate": 0.1}},
        "2": {
            "type": "score",
            "score": 0.7,
            "confidence": 0.6,
            "legend": {"0": "minor", "1": "major: Product unusable"},
            "probabilities": {"0": 0.3, "1": 0.7},
        },
    },
    "usage": {"input_tokens": _INPUT_TOKENS, "output_tokens": _OUTPUT_TOKENS},
}

_OPENAI_FORMAT_ANSWERS: Final = [
    {"type": "predicate", "name": "damaged", "probability": 0.95},
    {
        "type": "choice",
        "name": None,
        "choice": True,
        "probabilities": [{"value": True, "probability": 0.9}, {"value": "escalate", "probability": 0.1}],
        "confidence": 0.8,
    },
    {
        "type": "score",
        "name": "severity",
        "score": 0.7,
        "probabilities": [
            {"value": 0, "label": "minor", "probability": 0.3},
            {"value": 1, "label": "major", "probability": 0.7},
        ],
        "confidence": 0.6,
    },
    {"type": "refusal", "name": "fraud"},
]


@pytest.mark.parametrize("endpoint", ("/v1/decisions", "/decisions"))
def test_openai_format_decisions_translate_through_systemone(
    client: TestClient,
    respx_mock: respx.MockRouter,
    endpoint: str,
) -> None:
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(
        json=_SYSTEMONE_ANSWERS_FOR_OPENAI_REQUEST
    )

    response: Final = client.post(endpoint, json=_OPENAI_FORMAT_REQUEST)

    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "pplx-decider-v1-27b",
        "state": "The package arrived with a broken screen.\n\nI want a refund.\n\nOrder 1234.",
        "questions": {
            "0": {"type": "noul", "instructions": "Does the customer report a damaged item?"},
            "1": {
                "type": "choice",
                "instructions": "Should we refund?",
                "criteria": {"true": "Refund now", "escalate": None},
            },
            "2": {
                "type": "score",
                "instructions": "How severe is the issue?",
                "criteria": ["minor", "major: Product unusable"],
            },
            "3": {"type": "noul", "instructions": "Is this fraud?"},
        },
    }
    body: Final = response.json()
    assert body["model"] == _SYSTEMONE_ANSWERS_FOR_OPENAI_REQUEST["model"]
    assert body["answers"] == _OPENAI_FORMAT_ANSWERS
    assert body["usage"] == {
        "input_tokens": _INPUT_TOKENS,
        "input_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0},
        "output_tokens": _OUTPUT_TOKENS,
        "output_tokens_details": {"reasoning_tokens": 0},
        "total_tokens": _INPUT_TOKENS + _OUTPUT_TOKENS,
    }
    assert float(response.headers["x-litellm-response-cost"]) > 0


@pytest.mark.parametrize(
    ("endpoint", "request_body", "upstream_response"),
    (
        ("/v1/systemone", _REQUEST, _RESPONSE),
        ("/v1/decisions", _OPENAI_FORMAT_REQUEST, _SYSTEMONE_ANSWERS_FOR_OPENAI_REQUEST),
    ),
    ids=("systemone", "openai_format"),
)
def test_decisions_return_guardrail_information_when_requested(
    client: TestClient,
    respx_mock: respx.MockRouter,
    endpoint: str,
    request_body: Mapping[str, object],
    upstream_response: Mapping[str, object],
) -> None:
    respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=upstream_response)

    response: Final = client.post(endpoint, json={**request_body, "include_guardrail_response": True})

    assert response.status_code == 200, response.text
    assert response.json()["guardrail_information"] == []


@pytest.mark.parametrize(
    "request_body",
    (
        _REQUEST,
        {
            "model": "decider",
            "input": "review",
            "questions": [
                {
                    "type": "choice",
                    "instructions": "Pick one",
                    "choices": [{"value": True}, {"value": "true"}],
                }
            ],
        },
    ),
    ids=("systemone_body", "colliding_choice_values"),
)
def test_openai_format_decisions_rejects_bodies_it_cannot_translate(
    client: TestClient,
    respx_mock: respx.MockRouter,
    request_body: Mapping[str, object],
) -> None:
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post("/v1/decisions", json=request_body)

    assert response.status_code == 400, response.text
    assert not upstream.called


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/v1/decisions"))
@pytest.mark.parametrize("raw_body", (b"", b"{not json"), ids=("empty", "malformed"))
def test_a_body_that_is_not_json_is_a_client_error(
    client: TestClient,
    respx_mock: respx.MockRouter,
    endpoint: str,
    raw_body: bytes,
) -> None:
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(endpoint, content=raw_body, headers={"Content-Type": "application/json"})

    assert response.status_code == 400, response.text
    assert not upstream.called


def test_openai_format_decisions_reach_an_openai_deployment_unchanged_including_images(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "gpt-6-luna",
        {
            **litellm.model_cost["gpt-6-luna"],
            "input_cost_per_token_decisions": 1e-07,
            "cache_read_input_token_cost_decisions": 1e-07,
            "output_cost_per_token_decisions": 0.0,
        },
    )
    monkeypatch.setattr(litellm, "api_base", None)
    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
    monkeypatch.delenv("OPENAI_API_BASE", raising=False)
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[{"model_name": "decider", "litellm_params": {"model": "openai/gpt-6-luna", "api_key": "k"}}]
        ),
    )
    image_message: Final = {
        "type": "message",
        "role": "user",
        "content": [{"type": "input_image", "image_url": "data:image/png;base64,AA==", "detail": "low"}],
    }
    request_body: Final = {**_OPENAI_FORMAT_REQUEST, "input": [*_OPENAI_FORMAT_REQUEST["input"], image_message]}
    cached_tokens: Final = 128
    upstream_usage: Final = {
        "input_tokens": _INPUT_TOKENS,
        "input_tokens_details": {"cached_tokens": cached_tokens, "cache_write_tokens": 0},
        "output_tokens": _OUTPUT_TOKENS,
        "output_tokens_details": {"reasoning_tokens": 0},
        "total_tokens": _INPUT_TOKENS + _OUTPUT_TOKENS,
    }
    upstream: Final = respx_mock.post("https://api.openai.com/v1/decisions").respond(
        json={"model": "gpt-6-luna", "answers": _OPENAI_FORMAT_ANSWERS, "usage": upstream_usage}
    )

    response: Final = client.post("/v1/decisions", json=request_body)

    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "gpt-6-luna",
        "input": [
            {
                "type": "message",
                "role": "user",
                "content": [
                    {"type": "input_text", "text": "The package arrived with a broken screen."},
                    {"type": "input_text", "text": "I want a refund."},
                ],
            },
            {"type": "message", "role": "user", "content": "Order 1234."},
            image_message,
        ],
        "questions": _OPENAI_FORMAT_REQUEST["questions"],
        "safety_identifier": "end-user-1",
    }
    body: Final = response.json()
    assert body["answers"] == _OPENAI_FORMAT_ANSWERS
    assert body["usage"] == upstream_usage
    luna_decisions_cost: Final = apply_decisions_pricing(litellm.model_cost["gpt-6-luna"])
    expected_cost: Final = _INPUT_TOKENS * float(luna_decisions_cost["input_cost_per_token"]) + _OUTPUT_TOKENS * float(
        luna_decisions_cost["output_cost_per_token"]
    )
    assert expected_cost > 0
    assert float(response.headers["x-litellm-response-cost"]) == pytest.approx(expected_cost)


_CLEF_OMNI_MEDIA: Final[Mapping[str, object]] = {
    "audio": ["data:audio/wav;base64,YQ==", {"content_type": "audio/webm;codecs=opus", "base64": "Yg=="}],
    "videos": ["data:video/mp4;base64,Yw=="],
}
_CLEF_OMNI_RESPONSE: Final[Mapping[str, object]] = {
    "success": True,
    "result": {
        "model": "clef-omni",
        "answers": {"heard": {"type": "noul", "noul": 0.9}},
        "usage": {"input_tokens": 100, "output_tokens": 0},
    },
}
_CLEF_OMNI_UPSTREAM: Final = "https://cf.example/ai/run/@cf/cloudflare/clef-omni"


def _route_to_clef_omni(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[
                {
                    "model_name": "clef-omni",
                    "litellm_params": {
                        "model": "cloudflare/clef-omni",
                        "api_base": "https://cf.example",
                        "api_key": "cf-key",
                    },
                },
                {
                    "model_name": "decider",
                    "litellm_params": {"model": "perplexity/pplx-decider-v1-27b", "api_key": "k"},
                },
            ]
        ),
    )


def _clef_omni_request(endpoint: str) -> Mapping[str, object]:
    if endpoint.endswith("systemone"):
        return {
            "model": "clef-omni",
            "state": "Evaluate the clips",
            "questions": {"heard": {"type": "noul", "instructions": "Is there speech?"}},
            **_CLEF_OMNI_MEDIA,
        }
    return {
        "model": "clef-omni",
        "input": "Evaluate the clips",
        "questions": [{"type": "predicate", "name": "heard", "instructions": "Is there speech?"}],
        **_CLEF_OMNI_MEDIA,
    }


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/systemone", "/v1/decisions", "/decisions"))
def test_proxy_forwards_clef_omni_media_on_every_decisions_route(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
) -> None:
    _route_to_clef_omni(monkeypatch)
    upstream: Final = respx_mock.post(_CLEF_OMNI_UPSTREAM).respond(json=_CLEF_OMNI_RESPONSE)

    response: Final = client.post(endpoint, json=_clef_omni_request(endpoint))

    assert response.status_code == 200, response.text
    assert response.json()["answers"] == (
        {"heard": {"type": "noul", "noul": 0.9}}
        if endpoint.endswith("systemone")
        else [{"type": "predicate", "name": "heard", "probability": 0.9}]
    )
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef-omni",
        "state": "Evaluate the clips",
        "questions": {"heard": {"type": "noul", "instructions": "Is there speech?"}},
        **_CLEF_OMNI_MEDIA,
    }


@pytest.mark.parametrize(
    ("endpoint", "request_body"),
    (
        ("/v1/systemone", {**_REQUEST, **_CLEF_OMNI_MEDIA}),
        ("/v1/decisions", {**_clef_omni_request("/v1/decisions"), "audio": [{"content_type": "audio/wav"}]}),
        ("/systemone", {**_clef_omni_request("/systemone"), "audio": ["data:image/png;base64,AA=="]}),
        ("/decisions", {**_clef_omni_request("/decisions"), "videos": ["data:video/mp4;base64,Yw=="] * 3}),
    ),
    ids=("model_without_media_support", "clip_missing_base64", "audio_slot_holding_an_image", "too_many_videos"),
)
def test_proxy_refuses_decisions_media_it_cannot_send(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    request_body: Mapping[str, object],
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    _route_to_clef_omni(monkeypatch)
    clef_upstream: Final = respx_mock.post(_CLEF_OMNI_UPSTREAM).respond(json=_CLEF_OMNI_RESPONSE)
    perplexity_upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(endpoint, json=request_body)

    assert response.status_code == 400, response.text
    assert not clef_upstream.called
    assert not perplexity_upstream.called


@pytest.mark.parametrize("safety_identifier", (7, ["end-user-1"]), ids=("numeric", "list"))
@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
def test_a_non_string_safety_identifier_is_refused_unless_the_deployment_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    safety_identifier: object,
    deployment_drops_params: bool,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    monkeypatch.setattr(litellm, "api_base", None)
    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
    monkeypatch.delenv("OPENAI_API_BASE", raising=False)
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[
                {
                    "model_name": "decider",
                    "litellm_params": {
                        "model": "openai/gpt-6-luna",
                        "api_key": "k",
                        "drop_params": deployment_drops_params,
                    },
                }
            ]
        ),
    )
    upstream: Final = respx_mock.post("https://api.openai.com/v1/decisions").respond(
        json={
            "model": "gpt-6-luna",
            "answers": _OPENAI_FORMAT_ANSWERS[:1],
            "usage": {
                "input_tokens": _INPUT_TOKENS,
                "output_tokens": _OUTPUT_TOKENS,
                "total_tokens": _INPUT_TOKENS + _OUTPUT_TOKENS,
            },
        }
    )
    request_body: Final = {
        "model": "decider",
        "input": "The package arrived with a broken screen.",
        "questions": _OPENAI_FORMAT_REQUEST["questions"][:1],
        "safety_identifier": safety_identifier,
    }

    response: Final = client.post("/v1/decisions", json=request_body)

    if not deployment_drops_params:
        assert response.status_code == 400, response.text
        assert "safety_identifier" in response.json()["error"]["message"]
        assert not upstream.called
        return
    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "gpt-6-luna",
        "input": "The package arrived with a broken screen.",
        "questions": _OPENAI_FORMAT_REQUEST["questions"][:1],
    }


_IMAGE_TEXT: Final = "Is the screen cracked?"
_PNG_DATA_URL: Final = "data:image/png;base64,AA=="
_IMAGE_ENDPOINTS: Final = ("/v1/decisions", "/v1/systemone")
_IMAGE_REQUEST_BODIES: Final[tuple[Mapping[str, object], ...]] = (
    {
        "model": "decider",
        "input": [
            {
                "role": "user",
                "content": [
                    {"type": "input_text", "text": _IMAGE_TEXT},
                    {"type": "input_image", "image_url": _PNG_DATA_URL},
                ],
            }
        ],
        "questions": [{"type": "predicate", "name": "is_defect", "instructions": "Is this a defect?"}],
    },
    {
        "model": "decider",
        "state": _IMAGE_TEXT,
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        "images": [_PNG_DATA_URL],
    },
)
_IMAGE_REQUESTS: Final = tuple(zip(_IMAGE_ENDPOINTS, _IMAGE_REQUEST_BODIES, strict=True))
_IMAGE_REQUEST_IDS: Final = ("openai_format", "systemone_format")


def _route_decider_to(monkeypatch: pytest.MonkeyPatch, litellm_params: Mapping[str, object]) -> None:
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(model_list=[{"model_name": "decider", "litellm_params": dict(litellm_params)}]),
    )


@pytest.mark.parametrize(("endpoint", "request_body"), _IMAGE_REQUESTS, ids=_IMAGE_REQUEST_IDS)
def test_images_reach_a_cloudflare_clef_deployment_as_its_images_field(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    request_body: Mapping[str, object],
) -> None:
    _route_decider_to(
        monkeypatch,
        {
            "model": "cloudflare/clef",
            "api_key": "cloudflare-key",
            "api_base": "https://api.cloudflare.com/client/v4/accounts/acct/ai/run",
        },
    )
    upstream: Final = respx_mock.post(
        "https://api.cloudflare.com/client/v4/accounts/acct/ai/run/@cf/cloudflare/clef"
    ).respond(json=_RESPONSE)

    response: Final = client.post(endpoint, json=request_body)

    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef",
        "state": _IMAGE_TEXT,
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
        "images": [_PNG_DATA_URL],
    }


@pytest.mark.parametrize(
    ("endpoint", "request_body", "param"),
    tuple(zip(_IMAGE_ENDPOINTS, _IMAGE_REQUEST_BODIES, ("input_image", "images"), strict=True)),
    ids=_IMAGE_REQUEST_IDS,
)
def test_images_to_a_text_only_deployment_are_refused_without_drop_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    request_body: Mapping[str, object],
    param: str,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(endpoint, json=request_body)

    assert response.status_code == 400, response.text
    assert f"['{param}']" in response.json()["error"]["message"]
    assert not upstream.called


@pytest.mark.parametrize(("endpoint", "request_body"), _IMAGE_REQUESTS, ids=_IMAGE_REQUEST_IDS)
def test_images_to_a_text_only_deployment_are_dropped_when_it_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    request_body: Mapping[str, object],
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    _route_decider_to(
        monkeypatch, {"model": "perplexity/pplx-decider-v1-27b", "api_key": "test-key", "drop_params": True}
    )
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(endpoint, json=request_body)

    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "pplx-decider-v1-27b",
        "state": _IMAGE_TEXT,
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }


@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
def test_malformed_images_are_refused_unless_the_deployment_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    deployment_drops_params: bool,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    _route_decider_to(
        monkeypatch,
        {
            "model": "cloudflare/clef",
            "api_key": "cloudflare-key",
            "api_base": "https://api.cloudflare.com/client/v4/accounts/acct/ai/run",
            "drop_params": deployment_drops_params,
        },
    )
    upstream: Final = respx_mock.post(
        "https://api.cloudflare.com/client/v4/accounts/acct/ai/run/@cf/cloudflare/clef"
    ).respond(json=_RESPONSE)

    response: Final = client.post(
        "/v1/systemone", json={**_IMAGE_REQUEST_BODIES[1], "images": [{"url": _PNG_DATA_URL}]}
    )

    if not deployment_drops_params:
        assert response.status_code == 400, response.text
        assert "images" in response.json()["error"]["message"]
        assert not upstream.called
        return
    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef",
        "state": _IMAGE_TEXT,
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }


@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
def test_malformed_assets_for_a_deployment_without_asset_support_are_dropped_only_when_it_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    deployment_drops_params: bool,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    _route_decider_to(
        monkeypatch,
        {"model": "perplexity/pplx-decider-v1-27b", "api_key": "test-key", "drop_params": deployment_drops_params},
    )
    upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)

    response: Final = client.post(
        "/v1/systemone",
        json={**_IMAGE_REQUEST_BODIES[1], "assets": {"photo-1": {"type": "image", "part": "file1"}}},
    )

    if not deployment_drops_params:
        assert response.status_code == 400, response.text
        assert "assets" in response.json()["error"]["message"]
        assert not upstream.called
        return
    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "pplx-decider-v1-27b",
        "state": _IMAGE_TEXT,
        "questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
    }


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/v1/decisions"))
@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
def test_clips_for_a_model_without_media_support_are_refused_unless_the_deployment_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    deployment_drops_params: bool,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[
                {
                    "model_name": "clef-flash",
                    "litellm_params": {
                        "model": "cloudflare/clef-flash",
                        "api_base": "https://cf.example",
                        "api_key": "cf-key",
                        "drop_params": deployment_drops_params,
                    },
                }
            ]
        ),
    )
    upstream: Final = respx_mock.post("https://cf.example/ai/run/@cf/cloudflare/clef-flash").respond(
        json=_CLEF_OMNI_RESPONSE
    )

    response: Final = client.post(endpoint, json={**_clef_omni_request(endpoint), "model": "clef-flash"})

    if not deployment_drops_params:
        assert response.status_code == 400, response.text
        assert "['audio', 'videos']" in response.json()["error"]["message"]
        assert not upstream.called
        return
    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef-flash",
        "state": "Evaluate the clips",
        "questions": {"heard": {"type": "noul", "instructions": "Is there speech?"}},
    }


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/systemone", "/v1/decisions", "/decisions"))
def test_null_media_on_every_decisions_route_means_no_clips(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
) -> None:
    _route_to_clef_omni(monkeypatch)
    upstream: Final = respx_mock.post(_CLEF_OMNI_UPSTREAM).respond(json=_CLEF_OMNI_RESPONSE)

    response: Final = client.post(endpoint, json={**_clef_omni_request(endpoint), "audio": None, "videos": None})

    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef-omni",
        "state": "Evaluate the clips",
        "questions": {"heard": {"type": "noul", "instructions": "Is there speech?"}},
    }


@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/v1/decisions"))
@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
def test_a_malformed_clip_for_a_model_without_media_support_is_dropped_only_when_the_deployment_drops_params(
    client: TestClient,
    monkeypatch: pytest.MonkeyPatch,
    respx_mock: respx.MockRouter,
    endpoint: str,
    deployment_drops_params: bool,
) -> None:
    monkeypatch.setattr(litellm, "drop_params", False)
    monkeypatch.setattr(
        litellm.proxy.proxy_server,
        "llm_router",
        litellm.Router(
            model_list=[
                {
                    "model_name": "clef-flash",
                    "litellm_params": {
                        "model": "cloudflare/clef-flash",
                        "api_base": "https://cf.example",
                        "api_key": "cf-key",
                        "drop_params": deployment_drops_params,
                    },
                }
            ]
        ),
    )
    upstream: Final = respx_mock.post("https://cf.example/ai/run/@cf/cloudflare/clef-flash").respond(
        json=_CLEF_OMNI_RESPONSE
    )
    malformed: Final = {"audio": ["https://example.com/clip.wav"], "videos": [{"content_type": "video/mp4"}]}

    response: Final = client.post(endpoint, json={**_clef_omni_request(endpoint), "model": "clef-flash", **malformed})

    if not deployment_drops_params:
        assert response.status_code == 400, response.text
        assert "Invalid Decisions request" in response.json()["error"]["message"]
        assert not upstream.called
        return
    assert response.status_code == 200, response.text
    assert json.loads(upstream.calls[0].request.content) == {
        "model": "clef-flash",
        "state": "Evaluate the clips",
        "questions": {"heard": {"type": "noul", "instructions": "Is there speech?"}},
    }


def _decisions_feature() -> LazyFeature:
    return next(feature for feature in LAZY_FEATURES if feature.name == "decisions")


def _serving_endpoint(bare: FastAPI, path: str) -> object:
    scope: Final = {"type": "http", "method": "POST", "path": path, "root_path": "", "query_string": b"", "headers": ()}
    return next(
        route.endpoint for route in bare.routes if isinstance(route, APIRoute) and route.matches(scope)[0] is Match.FULL
    )


def test_a_config_pass_through_at_v1_decisions_keeps_its_route_and_the_native_api_serves_decisions(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.delenv("LITELLM_DISABLE_LAZY_ROUTES", raising=False)

    async def pass_through() -> dict[str, str]:
        return {"served_by": "pass-through"}

    bare: Final = FastAPI()
    attach_lazy_features(bare, (_decisions_feature(),))
    SafeRouteAdder.add_api_route_if_not_exists(bare, "/v1/decisions", pass_through, ["POST"])
    with TestClient(bare) as client:
        assert client.post("/v1/decisions", json={"model": "gpt-6-luna"}).json() == {"served_by": "pass-through"}
    assert _serving_endpoint(bare, "/v1/decisions") is pass_through
    assert _serving_endpoint(bare, "/decisions") is decisions
    assert _serving_endpoint(bare, "/v1/systemone") is systemone
    assert _serving_endpoint(bare, "/systemone") is systemone


def test_with_lazy_routes_disabled_a_config_pass_through_at_v1_decisions_still_wins(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setenv("LITELLM_DISABLE_LAZY_ROUTES", "true")

    async def pass_through() -> dict[str, str]:
        return {"served_by": "pass-through"}

    @asynccontextmanager
    async def loads_the_config(app_: FastAPI) -> AsyncGenerator[None]:
        assert SafeRouteAdder.add_api_route_if_not_exists(app_, "/v1/decisions", pass_through, ["POST"]), (
            "the native route registered at startup must not block the config pass-through"
        )
        yield

    bare: Final = FastAPI(lifespan=loads_the_config)
    attach_lazy_features(bare, (_decisions_feature(),))
    with TestClient(bare) as client:
        assert client.post("/v1/decisions", json={"model": "gpt-6-luna"}).json() == {"served_by": "pass-through"}
    assert _serving_endpoint(bare, "/v1/decisions") is pass_through
    assert _serving_endpoint(bare, "/decisions") is decisions


@pytest.fixture(scope="module")
def decisions_openapi() -> dict[str, object]:
    app = FastAPI()
    app.include_router(router)
    return app.openapi()


@pytest.mark.parametrize(
    ("path", "body_adapter", "response_schema", "required"),
    (
        ("/v1/systemone", TypeAdapter(DecisionsRequestBody), "DecisionsResponse", {"model", "state", "questions"}),
        ("/systemone", TypeAdapter(DecisionsRequestBody), "DecisionsResponse", {"model", "state", "questions"}),
        (
            "/v1/decisions",
            TypeAdapter(OpenAIDecisionRequestBody),
            "OpenAIDecisionResponse",
            {"model", "input", "questions"},
        ),
        (
            "/decisions",
            TypeAdapter(OpenAIDecisionRequestBody),
            "OpenAIDecisionResponse",
            {"model", "input", "questions"},
        ),
    ),
)
def test_swagger_documents_the_request_format_and_response_of_each_decisions_route(
    decisions_openapi: dict[str, object],
    path: str,
    body_adapter: TypeAdapter[DecisionsRequestBody] | TypeAdapter[OpenAIDecisionRequestBody],
    response_schema: str,
    required: set[str],
) -> None:
    operation = decisions_openapi["paths"][path]["post"]
    media = operation["requestBody"]["content"]["application/json"]

    assert "Decisions" in operation["summary"] or "System One" in operation["summary"]
    assert "docs.litellm.ai/docs/decisions" in operation["description"]
    assert required <= set(media["schema"]["required"])
    assert required <= set(media["schema"]["properties"])
    assert "$ref" not in json.dumps(media["schema"])
    assert media["schema"]["properties"]["model"]["description"]

    example = {key: value for key, value in media["example"].items() if key != "model"}
    assert len(body_adapter.validate_python(example).questions) == 3

    response_ref = operation["responses"]["200"]["content"]["application/json"]["schema"]["$ref"]
    assert response_ref == f"#/components/schemas/{response_schema}"
    assert "answers" in decisions_openapi["components"]["schemas"][response_schema]["properties"]


def test_the_two_decisions_routes_document_different_request_formats(decisions_openapi: dict[str, object]) -> None:
    def properties(path: str) -> set[str]:
        operation = decisions_openapi["paths"][path]["post"]
        return set(operation["requestBody"]["content"]["application/json"]["schema"]["properties"])

    assert "state" in properties("/v1/systemone") and "state" not in properties("/v1/decisions")
    assert "input" in properties("/v1/decisions") and "input" not in properties("/v1/systemone")