mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-03 19:57:35 +00:00
f754e1b65b
vllm - - requires max_tokens be set, use config value - set tool_choice to none if no tools provided
102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# 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 typing import Any
|
|
|
|
from databricks.sdk import WorkspaceClient
|
|
|
|
from llama_stack.apis.inference import (
|
|
Inference,
|
|
Model,
|
|
OpenAICompletion,
|
|
)
|
|
from llama_stack.apis.models import ModelType
|
|
from llama_stack.log import get_logger
|
|
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
|
|
|
from .config import DatabricksImplConfig
|
|
|
|
logger = get_logger(name=__name__, category="inference::databricks")
|
|
|
|
|
|
class DatabricksInferenceAdapter(
|
|
OpenAIMixin,
|
|
Inference,
|
|
):
|
|
# source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
|
|
embedding_model_metadata = {
|
|
"databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192},
|
|
"databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
|
|
}
|
|
|
|
def __init__(self, config: DatabricksImplConfig) -> None:
|
|
self.config = config
|
|
|
|
def get_api_key(self) -> str:
|
|
return self.config.api_token.get_secret_value()
|
|
|
|
def get_base_url(self) -> str:
|
|
return f"{self.config.url}/serving-endpoints"
|
|
|
|
async def initialize(self) -> None:
|
|
return
|
|
|
|
async def shutdown(self) -> None:
|
|
pass
|
|
|
|
async def openai_completion(
|
|
self,
|
|
model: str,
|
|
prompt: str | list[str] | list[int] | list[list[int]],
|
|
best_of: int | None = None,
|
|
echo: bool | None = None,
|
|
frequency_penalty: float | None = None,
|
|
logit_bias: dict[str, float] | None = None,
|
|
logprobs: bool | None = None,
|
|
max_tokens: int | None = None,
|
|
n: int | None = None,
|
|
presence_penalty: float | None = None,
|
|
seed: int | None = None,
|
|
stop: str | list[str] | None = None,
|
|
stream: bool | None = None,
|
|
stream_options: dict[str, Any] | None = None,
|
|
temperature: float | None = None,
|
|
top_p: float | None = None,
|
|
user: str | None = None,
|
|
guided_choice: list[str] | None = None,
|
|
prompt_logprobs: int | None = None,
|
|
suffix: str | None = None,
|
|
) -> OpenAICompletion:
|
|
raise NotImplementedError()
|
|
|
|
async def list_models(self) -> list[Model] | None:
|
|
self._model_cache = {} # from OpenAIMixin
|
|
ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async
|
|
endpoints = ws_client.serving_endpoints.list()
|
|
for endpoint in endpoints:
|
|
model = Model(
|
|
provider_id=self.__provider_id__,
|
|
provider_resource_id=endpoint.name,
|
|
identifier=endpoint.name,
|
|
)
|
|
if endpoint.task == "llm/v1/chat":
|
|
model.model_type = ModelType.llm # this is redundant, but informative
|
|
elif endpoint.task == "llm/v1/embeddings":
|
|
if endpoint.name not in self.embedding_model_metadata:
|
|
logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
|
|
continue
|
|
model.model_type = ModelType.embedding
|
|
model.metadata = self.embedding_model_metadata[endpoint.name]
|
|
else:
|
|
logger.warning(f"Unknown model type, skipping: {endpoint}")
|
|
continue
|
|
|
|
self._model_cache[endpoint.name] = model
|
|
|
|
return list(self._model_cache.values())
|
|
|
|
async def should_refresh_models(self) -> bool:
|
|
return False
|