llama-stack-mirror/tests/integration/fixtures/common.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import inspect
import os
import shlex
import signal
import socket
import subprocess
import tempfile
import time
from urllib.parse import urlparse

import pytest
import requests
import yaml
from llama_stack_client import LlamaStackClient
from openai import OpenAI

from llama_stack import LlamaStackAsLibraryClient
from llama_stack.core.stack import run_config_from_adhoc_config_spec
from llama_stack.env import get_env_or_fail

DEFAULT_PORT = 8321


def is_port_available(port: int, host: str = "localhost") -> bool:
    """Check if a port is available for binding."""
    try:
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
            sock.bind((host, port))
            return True
    except OSError:
        return False


def start_llama_stack_server(config_name: str) -> subprocess.Popen:
    """Start a llama stack server with the given config."""
    cmd = f"uv run --with llama-stack llama stack build --distro {config_name} --image-type venv --run"
    devnull = open(os.devnull, "w")
    process = subprocess.Popen(
        shlex.split(cmd),
        stdout=devnull,  # redirect stdout to devnull to prevent deadlock
        stderr=subprocess.PIPE,  # keep stderr to see errors
        text=True,
        env={**os.environ, "LLAMA_STACK_LOG_FILE": "server.log"},
        # Create new process group so we can kill all child processes
        preexec_fn=os.setsid,
    )
    return process


def wait_for_server_ready(base_url: str, timeout: int = 30, process: subprocess.Popen | None = None) -> bool:
    """Wait for the server to be ready by polling the health endpoint."""
    health_url = f"{base_url}/v1/health"
    start_time = time.time()

    while time.time() - start_time < timeout:
        if process and process.poll() is not None:
            print(f"Server process terminated with return code: {process.returncode}")
            print(f"Server stderr: {process.stderr.read()}")
            return False

        try:
            response = requests.get(health_url, timeout=5)
            if response.status_code == 200:
                return True
        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
            pass

        # Print progress every 5 seconds
        elapsed = time.time() - start_time
        if int(elapsed) % 5 == 0 and elapsed > 0:
            print(f"Waiting for server at {base_url}... ({elapsed:.1f}s elapsed)")

        time.sleep(0.5)

    print(f"Server failed to respond within {timeout} seconds")
    return False


def get_provider_data():
    # TODO: this needs to be generalized so each provider can have a sample provider data just
    # like sample run config on which we can do replace_env_vars()
    keymap = {
        "TAVILY_SEARCH_API_KEY": "tavily_search_api_key",
        "BRAVE_SEARCH_API_KEY": "brave_search_api_key",
        "FIREWORKS_API_KEY": "fireworks_api_key",
        "GEMINI_API_KEY": "gemini_api_key",
        "OPENAI_API_KEY": "openai_api_key",
        "TOGETHER_API_KEY": "together_api_key",
        "ANTHROPIC_API_KEY": "anthropic_api_key",
        "GROQ_API_KEY": "groq_api_key",
        "WOLFRAM_ALPHA_API_KEY": "wolfram_alpha_api_key",
    }
    provider_data = {}
    for key, value in keymap.items():
        if os.environ.get(key):
            provider_data[value] = os.environ[key]
    return provider_data


@pytest.fixture(scope="session")
def inference_provider_type(llama_stack_client):
    providers = llama_stack_client.providers.list()
    inference_providers = [p for p in providers if p.api == "inference"]
    assert len(inference_providers) > 0, "No inference providers found"
    return inference_providers[0].provider_type


@pytest.fixture(scope="session")
def client_with_models(
    llama_stack_client,
    text_model_id,
    vision_model_id,
    embedding_model_id,
    embedding_dimension,
    judge_model_id,
):
    client = llama_stack_client

    providers = [p for p in client.providers.list() if p.api == "inference"]
    assert len(providers) > 0, "No inference providers found"
    inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]

    model_ids = {m.identifier for m in client.models.list()}
    model_ids.update(m.provider_resource_id for m in client.models.list())

    if text_model_id and text_model_id not in model_ids:
        client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
    if vision_model_id and vision_model_id not in model_ids:
        client.models.register(model_id=vision_model_id, provider_id=inference_providers[0])
    if judge_model_id and judge_model_id not in model_ids:
        client.models.register(model_id=judge_model_id, provider_id=inference_providers[0])

    if embedding_model_id and embedding_model_id not in model_ids:
        # try to find a provider that supports embeddings, if sentence-transformers is not available
        selected_provider = None
        for p in providers:
            if p.provider_type == "inline::sentence-transformers":
                selected_provider = p
                break

        selected_provider = selected_provider or providers[0]
        client.models.register(
            model_id=embedding_model_id,
            provider_id=selected_provider.provider_id,
            model_type="embedding",
            metadata={"embedding_dimension": embedding_dimension or 384},
        )
    return client


@pytest.fixture(scope="session")
def available_shields(llama_stack_client):
    return [shield.identifier for shield in llama_stack_client.shields.list()]


@pytest.fixture(scope="session")
def model_providers(llama_stack_client):
    return {x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"}


@pytest.fixture(autouse=True)
def skip_if_no_model(request):
    model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id"]
    test_func = request.node.function

    actual_params = inspect.signature(test_func).parameters.keys()
    for fixture in model_fixtures:
        # Only check fixtures that are actually in the test function's signature
        if fixture in actual_params and fixture in request.fixturenames and not request.getfixturevalue(fixture):
            pytest.skip(f"{fixture} empty - skipping test")


@pytest.fixture(scope="session")
def llama_stack_client(request):
    client = request.session._llama_stack_client
    assert client is not None, "llama_stack_client not found in session cache"
    return client


def instantiate_llama_stack_client(session):
    config = session.config.getoption("--stack-config")
    if not config:
        config = get_env_or_fail("LLAMA_STACK_CONFIG")

    if not config:
        raise ValueError("You must specify either --stack-config or LLAMA_STACK_CONFIG")

    # Handle server:<config_name> format or server:<config_name>:<port>
    if config.startswith("server:"):
        parts = config.split(":")
        config_name = parts[1]
        port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
        base_url = f"http://localhost:{port}"

        # Check if port is available
        if is_port_available(port):
            print(f"Starting llama stack server with config '{config_name}' on port {port}...")

            # Start server
            server_process = start_llama_stack_server(config_name)

            # Wait for server to be ready
            if not wait_for_server_ready(base_url, timeout=120, process=server_process):
                print("Server failed to start within timeout")
                server_process.terminate()
                raise RuntimeError(
                    f"Server failed to start within timeout. Check that config '{config_name}' exists and is valid. "
                    f"See server.log for details."
                )

            print(f"Server is ready at {base_url}")

            # Store process for potential cleanup (pytest will handle termination at session end)
            session._llama_stack_server_process = server_process
        else:
            print(f"Port {port} is already in use, assuming server is already running...")

        return LlamaStackClient(
            base_url=base_url,
            provider_data=get_provider_data(),
            timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")),
        )

    # check if this looks like a URL using proper URL parsing
    try:
        parsed_url = urlparse(config)
        if parsed_url.scheme and parsed_url.netloc:
            return LlamaStackClient(
                base_url=config,
                provider_data=get_provider_data(),
            )
    except Exception:
        # If URL parsing fails, treat as non-URL config
        pass

    if "=" in config:
        run_config = run_config_from_adhoc_config_spec(config)
        run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml")
        with open(run_config_file.name, "w") as f:
            yaml.dump(run_config.model_dump(), f)
        config = run_config_file.name

    client = LlamaStackAsLibraryClient(
        config,
        provider_data=get_provider_data(),
        skip_logger_removal=True,
    )
    if not client.initialize():
        raise RuntimeError("Initialization failed")

    return client


@pytest.fixture(scope="session")
def openai_client(client_with_models):
    base_url = f"{client_with_models.base_url}/v1/openai/v1"
    return OpenAI(base_url=base_url, api_key="fake")


@pytest.fixture(params=["openai_client", "client_with_models"])
def compat_client(request, client_with_models):
    if isinstance(client_with_models, LlamaStackAsLibraryClient):
        # OpenAI client expects a server, so unless we also rewrite OpenAI client's requests
        # to go via the Stack library client (which itself rewrites requests to be served inline),
        # we cannot do this.
        #
        # This means when we are using Stack as a library, we will test only via the Llama Stack client.
        # When we are using a server setup, we can exercise both OpenAI and Llama Stack clients.
        pytest.skip("(OpenAI) Compat client cannot be used with Stack library client")

    return request.getfixturevalue(request.param)


@pytest.fixture(scope="session", autouse=True)
def cleanup_server_process(request):
    """Cleanup server process at the end of the test session."""
    yield  # Run tests

    if hasattr(request.session, "_llama_stack_server_process"):
        server_process = request.session._llama_stack_server_process
        if server_process:
            if server_process.poll() is None:
                print("Terminating llama stack server process...")
            else:
                print(f"Server process already terminated with return code: {server_process.returncode}")
                return
            try:
                print(f"Terminating process {server_process.pid} and its group...")
                # Kill the entire process group
                os.killpg(os.getpgid(server_process.pid), signal.SIGTERM)
                server_process.wait(timeout=10)
                print("Server process and children terminated gracefully")
            except subprocess.TimeoutExpired:
                print("Server process did not terminate gracefully, killing it")
                # Force kill the entire process group
                os.killpg(os.getpgid(server_process.pid), signal.SIGKILL)
                server_process.wait()
                print("Server process and children killed")
            except Exception as e:
                print(f"Error during server cleanup: {e}")
        else:
            print("Server process not found - won't be able to cleanup")