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76 lines
2 KiB
Python
Executable file
76 lines
2 KiB
Python
Executable file
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"""
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Embeddings service, applies an embeddings model selected from HuggingFace.
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Input is text, output is embeddings vector.
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"""
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from langchain_huggingface import HuggingFaceEmbeddings
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from ... schema import EmbeddingsRequest, EmbeddingsResponse
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from ... log_level import LogLevel
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from ... base import ConsumerProducer
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = 'embeddings'
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default_output_queue = 'embeddings-response'
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default_subscriber = module
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default_model="all-MiniLM-L6-v2"
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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model = params.get("model", default_model)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"input_schema": EmbeddingsRequest,
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"output_schema": EmbeddingsResponse,
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}
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)
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self.embeddings = HuggingFaceEmbeddings(model_name=model)
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def handle(self, msg):
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling input {id}...", flush=True)
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text = v.text
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embeds = self.embeddings.embed_documents([text])
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print("Send response...", flush=True)
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r = EmbeddingsResponse(vectors=embeds)
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self.producer.send(r, properties={"id": id})
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print("Done.", flush=True)
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@staticmethod
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def add_args(parser):
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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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parser.add_argument(
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'-m', '--model',
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default="all-MiniLM-L6-v2",
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help=f'LLM model (default: all-MiniLM-L6-v2)'
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)
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def run():
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Processor.start(module, __doc__)
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