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Applies the same pattern from https://github.com/llamastack/llama-stack/pull/3777 to embeddings and vector_stores.create() endpoints. This should _not_ be a breaking change since (a) our tests were already using the `extra_body` parameter when passing in to the backend (b) but the backend probably wasn't extracting the parameters correctly. This PR will fix that. Updated APIs: `openai_embeddings(), openai_create_vector_store(), openai_create_vector_store_file_batch()`
85 lines
3 KiB
Python
85 lines
3 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 collections.abc import Iterable
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from huggingface_hub import AsyncInferenceClient, HfApi
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from pydantic import SecretStr
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from llama_stack.apis.inference import (
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OpenAIEmbeddingsRequestWithExtraBody,
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OpenAIEmbeddingsResponse,
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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 InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
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log = get_logger(name=__name__, category="inference::tgi")
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class _HfAdapter(OpenAIMixin):
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url: str
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api_key: SecretStr
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hf_client: AsyncInferenceClient
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max_tokens: int
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model_id: str
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overwrite_completion_id = True # TGI always returns id=""
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def get_api_key(self):
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return "NO KEY REQUIRED"
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def get_base_url(self):
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return self.url
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async def list_provider_model_ids(self) -> Iterable[str]:
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return [self.model_id]
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async def openai_embeddings(
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self,
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params: OpenAIEmbeddingsRequestWithExtraBody,
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) -> OpenAIEmbeddingsResponse:
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raise NotImplementedError()
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class TGIAdapter(_HfAdapter):
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async def initialize(self, config: TGIImplConfig) -> None:
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if not config.url:
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raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
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log.info(f"Initializing TGI client with url={config.url}")
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self.hf_client = AsyncInferenceClient(model=config.url, provider="hf-inference")
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endpoint_info = await self.hf_client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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self.url = f"{config.url.rstrip('/')}/v1"
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self.api_key = SecretStr("NO_KEY")
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class InferenceAPIAdapter(_HfAdapter):
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async def initialize(self, config: InferenceAPIImplConfig) -> None:
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self.hf_client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
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endpoint_info = await self.hf_client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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# TODO: how do we set url for this?
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class InferenceEndpointAdapter(_HfAdapter):
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async def initialize(self, config: InferenceEndpointImplConfig) -> None:
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# Get the inference endpoint details
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api = HfApi(token=config.api_token.get_secret_value())
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endpoint = api.get_inference_endpoint(config.endpoint_name)
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# Wait for the endpoint to be ready (if not already)
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endpoint.wait(timeout=60)
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# Initialize the adapter
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self.hf_client = endpoint.async_client
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self.model_id = endpoint.repository
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self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])
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# TODO: how do we set url for this?
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