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
91 lines
3.4 KiB
Python
91 lines
3.4 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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Model,
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OpenAICompletion,
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)
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from llama_stack.apis.models import ModelType
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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 = {
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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 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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async def list_models(self) -> list[Model] | None:
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self._model_cache = {} # from OpenAIMixin
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ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async
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endpoints = ws_client.serving_endpoints.list()
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for endpoint in endpoints:
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model = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=endpoint.name,
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identifier=endpoint.name,
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)
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if endpoint.task == "llm/v1/chat":
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model.model_type = ModelType.llm # this is redundant, but informative
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elif endpoint.task == "llm/v1/embeddings":
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if endpoint.name not in self.embedding_model_metadata:
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logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
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continue
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model.model_type = ModelType.embedding
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model.metadata = self.embedding_model_metadata[endpoint.name]
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else:
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logger.warning(f"Unknown model type, skipping: {endpoint}")
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continue
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self._model_cache[endpoint.name] = model
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return list(self._model_cache.values())
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async def should_refresh_models(self) -> bool:
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return False
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