mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-13 21:22:42 +00:00
fd06717d87
- 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 - enhance the list models error handling w/ new tests - update cerebras (remove cerebras-cloud-sdk) and anthropic (custom model listing) inference adapters - parametrized test_inference_client_caching - remove cerebras, databricks, fireworks, together from blanket mypy exclude
71 lines
2.4 KiB
Python
71 lines
2.4 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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from typing import Any
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from databricks.sdk import WorkspaceClient
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from llama_stack.apis.inference import (
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OpenAICompletion,
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)
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from .config import DatabricksImplConfig
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logger = get_logger(name=__name__, category="inference::databricks")
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class DatabricksInferenceAdapter(OpenAIMixin):
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config: DatabricksImplConfig
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# source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
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embedding_model_metadata: dict[str, dict[str, int]] = {
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"databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192},
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"databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
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}
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def get_api_key(self) -> str:
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return self.config.api_token.get_secret_value()
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def get_base_url(self) -> str:
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return f"{self.config.url}/serving-endpoints"
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async def get_models(self) -> list[str] | None:
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return [
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endpoint.name
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for endpoint in WorkspaceClient(
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host=self.config.url, token=self.get_api_key()
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).serving_endpoints.list() # TODO: this is not async
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]
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async def should_refresh_models(self) -> bool:
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return False
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async def openai_completion(
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self,
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model: str,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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) -> OpenAICompletion:
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raise NotImplementedError()
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