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https://github.com/trustgraph-ai/trustgraph.git
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f2ae0e8623
- Put scores in all responses - Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
79 lines
2 KiB
Python
Executable file
79 lines
2 KiB
Python
Executable file
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"""
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Embeddings service, applies an embeddings model using fastembed
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Input is text, output is embeddings vector.
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"""
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import logging
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from ... base import EmbeddingsService
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from fastembed import TextEmbedding
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "embeddings"
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default_model="sentence-transformers/all-MiniLM-L6-v2"
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class Processor(EmbeddingsService):
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def __init__(self, **params):
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model = params.get("model", default_model)
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super(Processor, self).__init__(
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**params | { "model": model }
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)
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self.default_model = model
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# Cache for currently loaded model
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self.cached_model_name = None
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self.embeddings = None
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# Load the default model
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self._load_model(model)
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def _load_model(self, model_name):
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"""Load a model, caching it for reuse"""
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if self.cached_model_name != model_name:
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logger.info(f"Loading FastEmbed model: {model_name}")
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self.embeddings = TextEmbedding(model_name=model_name)
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self.cached_model_name = model_name
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logger.info(f"FastEmbed model {model_name} loaded successfully")
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else:
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logger.debug(f"Using cached model: {model_name}")
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async def on_embeddings(self, texts, model=None):
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if not texts:
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return []
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use_model = model or self.default_model
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# Reload model if it has changed
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self._load_model(use_model)
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# FastEmbed processes the full batch efficiently
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vecs = list(self.embeddings.embed(texts))
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# Return list of vectors, one per input text
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return [v.tolist() for v in vecs]
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@staticmethod
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def add_args(parser):
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EmbeddingsService.add_args(parser)
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parser.add_argument(
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'-m', '--model',
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default=default_model,
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help=f'Embeddings model (default: {default_model})'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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