llama-stack-mirror/src/llama_stack/providers/remote/inference/vllm/vllm.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.
from collections.abc import AsyncIterator
from urllib.parse import urljoin

import httpx
from pydantic import ConfigDict

from llama_stack.log import get_logger
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack_api import (
    HealthResponse,
    HealthStatus,
    OpenAIChatCompletion,
    OpenAIChatCompletionChunk,
    OpenAIChatCompletionRequestWithExtraBody,
    ToolChoice,
)

from .config import VLLMInferenceAdapterConfig

log = get_logger(name=__name__, category="inference::vllm")


class VLLMInferenceAdapter(OpenAIMixin):
    config: VLLMInferenceAdapterConfig

    model_config = ConfigDict(arbitrary_types_allowed=True)

    provider_data_api_key_field: str = "vllm_api_token"

    def get_api_key(self) -> str | None:
        if self.config.auth_credential:
            return self.config.auth_credential.get_secret_value()
        return "NO KEY REQUIRED"

    def get_base_url(self) -> str:
        """Get the base URL from config."""
        if not self.config.url:
            raise ValueError("No base URL configured")
        return self.config.url

    async def initialize(self) -> None:
        if not self.config.url:
            raise ValueError(
                "You must provide a URL in run.yaml (or via the VLLM_URL environment variable) to use vLLM."
            )

    async def health(self) -> HealthResponse:
        """
        Performs a health check by verifying connectivity to the remote vLLM server.
        This method is used by the Provider API to verify
        that the service is running correctly.
        Uses the unauthenticated /health endpoint.
        Returns:

            HealthResponse: A dictionary containing the health status.
        """
        try:
            base_url = self.get_base_url()
            health_url = urljoin(base_url, "health")

            async with httpx.AsyncClient() as client:
                response = await client.get(health_url)
                response.raise_for_status()
                return HealthResponse(status=HealthStatus.OK)
        except Exception as e:
            return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")

    def get_extra_client_params(self):
        return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)}

    async def check_model_availability(self, model: str) -> bool:
        """
        Skip the check when running without authentication.
        """
        if not self.config.auth_credential:
            model_ids = []
            async for m in self.client.models.list():
                if m.id == model:  # Found exact match
                    return True
                model_ids.append(m.id)
            raise ValueError(f"Model '{model}' not found. Available models: {model_ids}")
        log.warning(f"Not checking model availability for {model} as API token may trigger OAuth workflow")
        return True

    async def openai_chat_completion(
        self,
        params: OpenAIChatCompletionRequestWithExtraBody,
    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
        params = params.model_copy()

        # Apply vLLM-specific defaults
        if params.max_tokens is None and self.config.max_tokens:
            params.max_tokens = self.config.max_tokens

        # This is to be consistent with OpenAI API and support vLLM <= v0.6.3
        # References:
        #   * https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
        #   * https://github.com/vllm-project/vllm/pull/10000
        if not params.tools and params.tool_choice is not None:
            params.tool_choice = ToolChoice.none.value

        return await super().openai_chat_completion(params)