mirror of https://github.com/mudler/LocalAI.git
115 lines
3.9 KiB
Python
Executable File
115 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Extra gRPC server for HuggingFace AutoModel models.
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"""
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from concurrent import futures
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import argparse
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import signal
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import sys
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import os
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import time
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import backend_pb2
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import backend_pb2_grpc
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import grpc
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from transformers import AutoModel
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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# Implement the BackendServicer class with the service methods
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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"""
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A gRPC servicer for the backend service.
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This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
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"""
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def Health(self, request, context):
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"""
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A gRPC method that returns the health status of the backend service.
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Args:
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request: A HealthRequest object that contains the request parameters.
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context: A grpc.ServicerContext object that provides information about the RPC.
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Returns:
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A Reply object that contains the health status of the backend service.
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"""
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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def LoadModel(self, request, context):
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"""
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A gRPC method that loads a model into memory.
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Args:
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request: A LoadModelRequest object that contains the request parameters.
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context: A grpc.ServicerContext object that provides information about the RPC.
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Returns:
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A Result object that contains the result of the LoadModel operation.
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"""
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model_name = request.Model
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try:
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self.model = AutoModel.from_pretrained(model_name, trust_remote_code=True) # trust_remote_code is needed to use the encode method with embeddings models like jinai-v2
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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# Implement your logic here for the LoadModel service
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# Replace this with your desired response
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return backend_pb2.Result(message="Model loaded successfully", success=True)
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def Embedding(self, request, context):
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"""
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A gRPC method that calculates embeddings for a given sentence.
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Args:
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request: An EmbeddingRequest object that contains the request parameters.
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context: A grpc.ServicerContext object that provides information about the RPC.
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Returns:
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An EmbeddingResult object that contains the calculated embeddings.
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"""
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# Implement your logic here for the Embedding service
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# Replace this with your desired response
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print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
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sentence_embeddings = self.model.encode(request.Embeddings)
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return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings)
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def serve(address):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
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backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
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server.add_insecure_port(address)
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server.start()
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print("Server started. Listening on: " + address, file=sys.stderr)
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# Define the signal handler function
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def signal_handler(sig, frame):
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print("Received termination signal. Shutting down...")
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server.stop(0)
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sys.exit(0)
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# Set the signal handlers for SIGINT and SIGTERM
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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try:
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while True:
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time.sleep(_ONE_DAY_IN_SECONDS)
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except KeyboardInterrupt:
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server.stop(0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run the gRPC server.")
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parser.add_argument(
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"--addr", default="localhost:50051", help="The address to bind the server to."
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)
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args = parser.parse_args()
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serve(args.addr)
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