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sagemaker streaming
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@ -32,6 +32,12 @@ class Settings(BaseSettings):
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ELASTIC_URL: str = None # url for elasticsearch
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ELASTIC_URL: str = None # url for elasticsearch
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ELASTIC_INDEX: str = "docsgpt" # index name for elasticsearch
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ELASTIC_INDEX: str = "docsgpt" # index name for elasticsearch
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# SageMaker config
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SAGEMAKER_ENDPOINT: str = None # SageMaker endpoint name
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SAGEMAKER_REGION: str = None # SageMaker region name
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SAGEMAKER_ACCESS_KEY: str = None # SageMaker access key
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SAGEMAKER_SECRET_KEY: str = None # SageMaker secret key
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path = Path(__file__).parent.parent.absolute()
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path = Path(__file__).parent.parent.absolute()
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settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")
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settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")
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@ -2,26 +2,140 @@ from application.llm.base import BaseLLM
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from application.core.settings import settings
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from application.core.settings import settings
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import requests
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import requests
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import json
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import json
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import io
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import json
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class LineIterator:
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"""
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A helper class for parsing the byte stream input.
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The output of the model will be in the following format:
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```
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b'{"outputs": [" a"]}\n'
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b'{"outputs": [" challenging"]}\n'
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b'{"outputs": [" problem"]}\n'
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...
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```
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While usually each PayloadPart event from the event stream will contain a byte array
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with a full json, this is not guaranteed and some of the json objects may be split across
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PayloadPart events. For example:
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```
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{'PayloadPart': {'Bytes': b'{"outputs": '}}
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{'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
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```
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This class accounts for this by concatenating bytes written via the 'write' function
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and then exposing a method which will return lines (ending with a '\n' character) within
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the buffer via the 'scan_lines' function. It maintains the position of the last read
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position to ensure that previous bytes are not exposed again.
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"""
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def __init__(self, stream):
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self.byte_iterator = iter(stream)
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self.buffer = io.BytesIO()
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self.read_pos = 0
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def __iter__(self):
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return self
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def __next__(self):
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while True:
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self.buffer.seek(self.read_pos)
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line = self.buffer.readline()
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if line and line[-1] == ord('\n'):
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self.read_pos += len(line)
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return line[:-1]
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try:
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chunk = next(self.byte_iterator)
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except StopIteration:
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if self.read_pos < self.buffer.getbuffer().nbytes:
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continue
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raise
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if 'PayloadPart' not in chunk:
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print('Unknown event type:' + chunk)
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continue
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self.buffer.seek(0, io.SEEK_END)
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self.buffer.write(chunk['PayloadPart']['Bytes'])
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class SagemakerAPILLM(BaseLLM):
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class SagemakerAPILLM(BaseLLM):
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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self.url = settings.SAGEMAKER_API_URL
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import boto3
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runtime = boto3.client(
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'runtime.sagemaker',
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aws_access_key_id=settings.SAGEMAKER_ACCESS_KEY,
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aws_secret_access_key=settings.SAGEMAKER_SECRET_KEY,
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region_name=settings.SAGEMAKER_REGION
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)
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self.endpoint = settings.SAGEMAKER_ENDPOINT
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self.runtime = runtime
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def gen(self, model, engine, messages, stream=False, **kwargs):
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def gen(self, model, engine, messages, stream=False, **kwargs):
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context = messages[0]['content']
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context = messages[0]['content']
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user_question = messages[-1]['content']
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user_question = messages[-1]['content']
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prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
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prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
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response = requests.post(
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url=self.url,
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headers={
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"Content-Type": "application/json; charset=utf-8",
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},
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data=json.dumps({"input": prompt})
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)
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return response.json()['answer']
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# Construct payload for endpoint
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payload = {
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"inputs": prompt,
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"stream": False,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 30,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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body_bytes = json.dumps(payload).encode('utf-8')
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# Invoke the endpoint
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response = self.runtime.invoke_endpoint(EndpointName=self.endpoint,
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ContentType='application/json',
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Body=body_bytes)
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result = json.loads(response['Body'].read().decode())
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import sys
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print(result[0]['generated_text'], file=sys.stderr)
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return result[0]['generated_text'][len(prompt):]
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def gen_stream(self, model, engine, messages, stream=True, **kwargs):
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def gen_stream(self, model, engine, messages, stream=True, **kwargs):
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raise NotImplementedError("Sagemaker does not support streaming")
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context = messages[0]['content']
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user_question = messages[-1]['content']
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prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
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# Construct payload for endpoint
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payload = {
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"inputs": prompt,
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"stream": True,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 512,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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body_bytes = json.dumps(payload).encode('utf-8')
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# Invoke the endpoint
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response = self.runtime.invoke_endpoint_with_response_stream(EndpointName=self.endpoint,
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ContentType='application/json',
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Body=body_bytes)
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#result = json.loads(response['Body'].read().decode())
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event_stream = response['Body']
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start_json = b'{'
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for line in LineIterator(event_stream):
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if line != b'' and start_json in line:
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#print(line)
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data = json.loads(line[line.find(start_json):].decode('utf-8'))
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if data['token']['text'] not in ["</s>", "###"]:
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print(data['token']['text'],end='')
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yield data['token']['text']
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