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122 lines
3.6 KiB
Python
Executable file
122 lines
3.6 KiB
Python
Executable file
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"""
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Simple LLM service, performs text prompt completion using AWS Bedrock.
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Input is prompt, output is response. Mistral is default.
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"""
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import boto3
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import json
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from .... schema import TextCompletionRequest, TextCompletionResponse
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from .... schema import text_completion_request_queue
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from .... schema import text_completion_response_queue
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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 = text_completion_request_queue
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default_output_queue = text_completion_response_queue
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default_subscriber = module
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default_model = 'mistral.mistral-large-2407-v1:0'
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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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api_key = params.get("api_key")
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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": TextCompletionRequest,
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"output_schema": TextCompletionResponse,
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"model": model,
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}
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)
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self.model = model
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self.bedrock = boto3.client(service_name='bedrock-runtime', region_name="us-west-2")
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print("Initialised", flush=True)
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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 prompt {id}...", flush=True)
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prompt = v.prompt
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promptbody = json.dumps({
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"prompt": prompt,
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"max_tokens": 8192,
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"temperature": 0.0,
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"top_p": 0.99,
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"top_k": 40
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})
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accept = 'application/json'
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contentType = 'application/json'
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response = self.bedrock.invoke_model(body=promptbody, modelId=self.model, accept=accept, contentType=contentType)
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# Mistral Response Structure
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if self.model.startswith("mistral"):
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response_body = json.loads(response.get("body").read())
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outputtext = response_body['outputs'][0]['text']
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# Claude Response Structure
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elif self.model.startswith("anthropic"):
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model_response = json.loads(response["body"].read())
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outputtext = model_response['content'][0]['text']
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# Llama 3.1 Response Structure
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elif self.model.startswith("meta"):
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model_response = json.loads(response["body"].read())
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outputtext = model_response["generation"]
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# Use Mistral as default
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else:
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response_body = json.loads(response.get("body").read())
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outputtext = response_body['outputs'][0]['text']
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resp = outputtext
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print(resp, flush=True)
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print("Send response...", flush=True)
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r = TextCompletionResponse(response=resp)
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self.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="mistral.mistral-large-2407-v1:0",
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help=f'Bedrock model (default: Mistral-Large-2407)'
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)
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def run():
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Processor.start(module, __doc__)
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