wehub-resource-sync
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83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
import requests
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import json
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import subprocess
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import logging
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import traceback
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import pandas as pd
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logger = logging.getLogger(__name__)
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def api_completion(messages, model_config, kwargs):
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attempts = 0
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while attempts < 3:
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user_id = kwargs.get('user_id', '1')
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internal_llm_proxy = kwargs.get('internal_llm_proxy', -1)
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job_id = model_config.get('job_id',-1)
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converted_message = convert_input(messages,model_config, user_id)
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payload = json.dumps(converted_message)
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headers = {
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'Content-Type': 'application/json',
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# 'Wd-PCA-Feature-Key':f'your_feature_key, $(whoami)'
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}
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try:
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response = requests.request("POST", internal_llm_proxy, headers=headers, data=payload)
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if model_config.get('log_level','')=='debug':
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logger.info(f'Model response Job ID {job_id} {response.text}')
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if response.status_code!=200:
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# logger.error(f'Error in model response Job ID {job_id}:',str(response.text))
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raise ValueError(str(response.text))
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if response.status_code==200:
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response = response.json()
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if "error" in response:
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raise ValueError(response["error"]["message"])
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else:
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result= response["choices"][0]["message"]["content"]
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response1 = result.replace('\n', '').replace('```json','').replace('```', '').strip()
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try:
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json_data = json.loads(response1)
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df = pd.DataFrame(json_data)
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return(df)
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except json.JSONDecodeError:
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attempts += 1 # Increment attempts if JSON parsing fails
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if attempts == 3:
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raise Exception("Failed to generate a valid response after multiple attempts.")
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except Exception as e:
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raise ValueError(f"{e}")
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def get_username():
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result = subprocess.run(['whoami'], capture_output=True, text=True)
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result = result.stdout
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return result
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def convert_input(messages, model_config, user_id):
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doc_input = {
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"model": model_config.get('model'),
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**model_config,
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"messages": messages,
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"user_id": user_id
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}
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return doc_input
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if __name__=='__main__':
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messages = [
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{
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"role": "system",
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"content": "you are a poet well versed in shakespeare literature"
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},
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{
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"role": "user",
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"content": "write a poem on pirates and penguins"
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}
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]
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kwargs = {"internal_llm_proxy": "http://13.200.11.66:4000/chat/completions", "user_id": 1}
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model_config = {"model": "workday_gateway", "provider":"openai", "max_tokens": 10}
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answer = api_completion(messages, model_config, kwargs)
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print(answer) |