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import logging
from injector import inject, singleton
from llama_index import MockEmbedding
from llama_index.embeddings.base import BaseEmbedding
from private_gpt.paths import models_cache_path
from private_gpt.settings.settings import Settings
import os
logger = logging.getLogger(__name__)
@singleton
class EmbeddingComponent:
embedding_model: BaseEmbedding
@inject
def __init__(self, settings: Settings) -> None:
embedding_mode = settings.embedding.mode
logger.info("Initializing the embedding model in mode=%s", embedding_mode)
match embedding_mode:
case "local":
from llama_index.embeddings import HuggingFaceEmbedding
self.embedding_model = HuggingFaceEmbedding(
model_name=settings.local.embedding_hf_model_name,
cache_folder=str(models_cache_path),
)
case "sagemaker":
from private_gpt.components.embedding.custom.sagemaker import (
SagemakerEmbedding,
)
self.embedding_model = SagemakerEmbedding(
endpoint_name=settings.sagemaker.embedding_endpoint_name,
)
case "openai":
from llama_index import OpenAIEmbedding
openai_settings = os.environ.get("OPENAI_API_KEY")
self.embedding_model = OpenAIEmbedding(api_key=openai_settings)
case "mock":
# Not a random number, is the dimensionality used by
# the default embedding model
self.embedding_model = MockEmbedding(384)
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