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https://github.com/meta-llama/llama-stack.git
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d266c59c2a
# What does this PR do? remove unused chat_completion implementations vllm features ported - - requires max_tokens be set, use config value - set tool_choice to none if no tools provided ## Test Plan ci
220 lines
8 KiB
Python
220 lines
8 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 typing import Any
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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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ChatCompletionRequest,
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GrammarResponseFormat,
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InferenceProvider,
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JsonSchemaResponseFormat,
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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.models.llama.sku_types import CoreModelId
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from llama_stack.providers.datatypes import (
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HealthResponse,
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HealthStatus,
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ModelsProtocolPrivate,
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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.model_registry import (
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ModelRegistryHelper,
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build_hf_repo_model_entry,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_options,
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)
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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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chat_completion_request_to_prompt,
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convert_image_content_to_url,
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request_has_media,
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)
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logger = get_logger(name=__name__, category="inference::ollama")
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class OllamaInferenceAdapter(
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OpenAIMixin,
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ModelRegistryHelper,
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InferenceProvider,
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ModelsProtocolPrivate,
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):
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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 = {
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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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def __init__(self, config: OllamaImplConfig) -> None:
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# TODO: remove ModelRegistryHelper.__init__ when completion and
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# chat_completion are. this exists to satisfy the input /
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# output processing for llama models. specifically,
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# tool_calling is handled by raw template processing,
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# instead of using the /api/chat endpoint w/ tools=...
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ModelRegistryHelper.__init__(
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self,
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model_entries=[
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build_hf_repo_model_entry(
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"llama3.2:3b-instruct-fp16",
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CoreModelId.llama3_2_3b_instruct.value,
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),
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build_hf_repo_model_entry(
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"llama-guard3:1b",
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CoreModelId.llama_guard_3_1b.value,
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),
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],
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)
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self.config = config
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# Ollama does not support image urls, so we need to download the image and convert it to base64
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self.download_images = True
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self._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 _get_model(self, model_id: str) -> Model:
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if not self.model_store:
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raise ValueError("Model store not set")
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return await self.model_store.get_model(model_id)
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async def _get_params(self, request: ChatCompletionRequest) -> dict:
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sampling_options = get_sampling_options(request.sampling_params)
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# This is needed since the Ollama API expects num_predict to be set
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# for early truncation instead of max_tokens.
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if sampling_options.get("max_tokens") is not None:
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sampling_options["num_predict"] = sampling_options["max_tokens"]
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input_dict: dict[str, Any] = {}
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media_present = request_has_media(request)
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llama_model = self.get_llama_model(request.model)
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if media_present or not llama_model:
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contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages]
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# flatten the list of lists
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input_dict["messages"] = [item for sublist in contents for item in sublist]
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else:
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input_dict["raw"] = True
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input_dict["prompt"] = await chat_completion_request_to_prompt(
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request,
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llama_model,
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)
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if fmt := request.response_format:
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if isinstance(fmt, JsonSchemaResponseFormat):
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input_dict["format"] = fmt.json_schema
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elif isinstance(fmt, GrammarResponseFormat):
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raise NotImplementedError("Grammar response format is not supported")
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else:
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raise ValueError(f"Unknown response format type: {fmt.type}")
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params = {
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"model": request.model,
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**input_dict,
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"options": sampling_options,
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"stream": request.stream,
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}
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logger.debug(f"params to ollama: {params}")
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return params
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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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