forked from phoenix-oss/llama-stack-mirror
53 lines
1.7 KiB
Python
53 lines
1.7 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 logging
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from typing import List, Optional
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from llama_stack.apis.inference import (
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EmbeddingsResponse,
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EmbeddingTaskType,
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InterleavedContentItem,
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ModelStore,
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TextTruncation,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
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EMBEDDING_MODELS = {}
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log = logging.getLogger(__name__)
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class SentenceTransformerEmbeddingMixin:
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model_store: ModelStore
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async def embeddings(
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self,
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model_id: str,
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contents: List[str] | List[InterleavedContentItem],
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text_truncation: Optional[TextTruncation] = TextTruncation.none,
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output_dimension: Optional[int] = None,
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task_type: Optional[EmbeddingTaskType] = None,
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) -> EmbeddingsResponse:
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model = await self.model_store.get_model(model_id)
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embedding_model = self._load_sentence_transformer_model(model.provider_resource_id)
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embeddings = embedding_model.encode([interleaved_content_as_str(content) for content in contents])
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return EmbeddingsResponse(embeddings=embeddings)
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def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":
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global EMBEDDING_MODELS
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loaded_model = EMBEDDING_MODELS.get(model)
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if loaded_model is not None:
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return loaded_model
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log.info(f"Loading sentence transformer for {model}...")
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from sentence_transformers import SentenceTransformer
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loaded_model = SentenceTransformer(model)
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EMBEDDING_MODELS[model] = loaded_model
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return loaded_model
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