mirror of
https://github.com/meta-llama/llama-stack.git
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60f0056cbc
- implement get_api_key instead of relying on LiteLLMOpenAIMixin.get_api_key - remove use of LiteLLMOpenAIMixin - add default initialize/shutdown methods to OpenAIMixin - remove __init__s to allow proper pydantic construction - remove dead code from vllm adapter and associated / duplicate unit tests - update vllm adapter to use openaimixin for model registration - remove ModelRegistryHelper from fireworks & together adapters - remove Inference from nvidia adapter - complete type hints on embedding_model_metadata - allow extra fields on OpenAIMixin, for model_store, __provider_id__, etc - new recordings for ollama
136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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from ollama import AsyncClient as AsyncOllamaClient
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from llama_stack.apis.common.content_types import (
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ImageContentItem,
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TextContentItem,
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)
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from llama_stack.apis.common.errors import UnsupportedModelError
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from llama_stack.apis.inference import (
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Message,
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)
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from llama_stack.apis.models import Model
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from llama_stack.log import get_logger
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from llama_stack.providers.datatypes import (
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HealthResponse,
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HealthStatus,
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)
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from llama_stack.providers.remote.inference.ollama.config import OllamaImplConfig
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from llama_stack.providers.utils.inference.prompt_adapter import (
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convert_image_content_to_url,
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)
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logger = get_logger(name=__name__, category="inference::ollama")
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class OllamaInferenceAdapter(OpenAIMixin):
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config: OllamaImplConfig
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# automatically set by the resolver when instantiating the provider
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__provider_id__: str
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embedding_model_metadata: dict[str, dict[str, int]] = {
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"all-minilm:l6-v2": {
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"embedding_dimension": 384,
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"context_length": 512,
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},
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"nomic-embed-text:latest": {
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"embedding_dimension": 768,
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"context_length": 8192,
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},
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"nomic-embed-text:v1.5": {
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"embedding_dimension": 768,
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"context_length": 8192,
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},
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"nomic-embed-text:137m-v1.5-fp16": {
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"embedding_dimension": 768,
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"context_length": 8192,
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},
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}
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download_images: bool = True
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_clients: dict[asyncio.AbstractEventLoop, AsyncOllamaClient] = {}
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@property
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def ollama_client(self) -> AsyncOllamaClient:
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# ollama client attaches itself to the current event loop (sadly?)
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loop = asyncio.get_running_loop()
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if loop not in self._clients:
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self._clients[loop] = AsyncOllamaClient(host=self.config.url)
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return self._clients[loop]
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def get_api_key(self):
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return "NO_KEY"
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def get_base_url(self):
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return self.config.url.rstrip("/") + "/v1"
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async def initialize(self) -> None:
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logger.info(f"checking connectivity to Ollama at `{self.config.url}`...")
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r = await self.health()
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if r["status"] == HealthStatus.ERROR:
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logger.warning(
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f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal"
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)
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async def should_refresh_models(self) -> bool:
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return self.config.refresh_models
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async def health(self) -> HealthResponse:
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"""
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Performs a health check by verifying connectivity to the Ollama server.
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This method is used by initialize() and the Provider API to verify that the service is running
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correctly.
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Returns:
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HealthResponse: A dictionary containing the health status.
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"""
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try:
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await self.ollama_client.ps()
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return HealthResponse(status=HealthStatus.OK)
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except Exception as e:
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return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
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async def shutdown(self) -> None:
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self._clients.clear()
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async def register_model(self, model: Model) -> Model:
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if await self.check_model_availability(model.provider_model_id):
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return model
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elif await self.check_model_availability(f"{model.provider_model_id}:latest"):
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model.provider_resource_id = f"{model.provider_model_id}:latest"
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logger.warning(
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f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_model_id}'"
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)
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return model
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raise UnsupportedModelError(model.provider_model_id, list(self._model_cache.keys()))
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async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
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async def _convert_content(content) -> dict:
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if isinstance(content, ImageContentItem):
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return {
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"role": message.role,
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"images": [await convert_image_content_to_url(content, download=True, include_format=False)],
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}
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else:
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text = content.text if isinstance(content, TextContentItem) else content
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assert isinstance(text, str)
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return {
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"role": message.role,
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"content": text,
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}
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if isinstance(message.content, list):
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return [await _convert_content(c) for c in message.content]
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else:
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return [await _convert_content(message.content)]
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