mirror of
https://github.com/meta-llama/llama-stack.git
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128 lines
4.8 KiB
Python
128 lines
4.8 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 requests
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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JsonSchemaResponseFormat,
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ToolChoice,
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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.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_message_to_openai_dict_new,
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convert_tooldef_to_openai_tool,
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get_sampling_options,
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)
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from .config import SambaNovaImplConfig
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from .models import MODEL_ENTRIES
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logger = get_logger(name=__name__, category="inference")
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class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin):
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_config: SambaNovaImplConfig
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def __init__(self, config: SambaNovaImplConfig):
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self.config = config
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self.environment_available_models = []
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LiteLLMOpenAIMixin.__init__(
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self,
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model_entries=MODEL_ENTRIES,
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litellm_provider_name="sambanova",
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api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
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provider_data_api_key_field="sambanova_api_key",
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)
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def _get_api_key(self) -> str:
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config_api_key = self.config.api_key if self.config.api_key else None
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if config_api_key:
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return config_api_key.get_secret_value()
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else:
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provider_data = self.get_request_provider_data()
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if provider_data is None or not provider_data.sambanova_api_key:
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raise ValueError(
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'Pass Sambanova API Key in the header X-LlamaStack-Provider-Data as { "sambanova_api_key": <your api key> }'
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)
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return provider_data.sambanova_api_key
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async def _get_params(self, request: ChatCompletionRequest) -> dict:
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input_dict = {}
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input_dict["messages"] = [
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await convert_message_to_openai_dict_new(m, download_images=True) for m in request.messages
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]
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if fmt := request.response_format:
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if not isinstance(fmt, JsonSchemaResponseFormat):
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raise ValueError(
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f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
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)
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fmt = fmt.json_schema
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name = fmt["title"]
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del fmt["title"]
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fmt["additionalProperties"] = False
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# Apply additionalProperties: False recursively to all objects
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fmt = self._add_additional_properties_recursive(fmt)
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input_dict["response_format"] = {
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"type": "json_schema",
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"json_schema": {
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"name": name,
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"schema": fmt,
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"strict": False,
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},
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}
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if request.tools:
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input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
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if request.tool_config.tool_choice:
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input_dict["tool_choice"] = (
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request.tool_config.tool_choice.value
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if isinstance(request.tool_config.tool_choice, ToolChoice)
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else request.tool_config.tool_choice
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)
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provider_data = self.get_request_provider_data()
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key_field = self.provider_data_api_key_field
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if provider_data and getattr(provider_data, key_field, None):
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api_key = getattr(provider_data, key_field)
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else:
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api_key = self._get_api_key()
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return {
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"model": request.model,
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"api_key": api_key,
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"api_base": self.config.url,
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**input_dict,
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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async def register_model(self, model: Model) -> Model:
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model_id = self.get_provider_model_id(model.provider_resource_id)
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list_models_url = self.config.url + "/models"
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if len(self.environment_available_models) == 0:
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try:
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response = requests.get(list_models_url)
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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raise RuntimeError(f"Request to {list_models_url} failed") from e
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self.environment_available_models = [model.get("id") for model in response.json().get("data", {})]
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if model_id.split("sambanova/")[-1] not in self.environment_available_models:
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logger.warning(f"Model {model_id} not available in {list_models_url}")
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return model
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async def initialize(self):
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await super().initialize()
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async def shutdown(self):
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await super().shutdown()
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