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import time, torch, json
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI, OpenAI
from langchain.schema import HumanMessage
from langchain_core.output_parsers import JsonOutputParser
from langchain.output_parsers import RetryWithErrorOutputParser
from vouchervision.utils_LLM import SystemLoadMonitor, run_tools, count_tokens, save_individual_prompt, sanitize_prompt
from vouchervision.utils_LLM_JSON_validation import validate_and_align_JSON_keys_with_template
class OpenAIHandler:
RETRY_DELAY = 10 # Wait 10 seconds before retrying
MAX_RETRIES = 3 # Maximum number of retries
STARTING_TEMP = 0.5 # 0.5, config_vals_for_permutation
TOKENIZER_NAME = 'gpt-4'
VENDOR = 'openai'
def __init__(self, cfg, logger, model_name, JSON_dict_structure, is_azure, llm_object, config_vals_for_permutation):
self.cfg = cfg
self.tool_WFO = self.cfg['leafmachine']['project']['tool_WFO']
self.tool_GEO = self.cfg['leafmachine']['project']['tool_GEO']
self.tool_wikipedia = self.cfg['leafmachine']['project']['tool_wikipedia']
self.logger = logger
self.model_name = model_name
self.JSON_dict_structure = JSON_dict_structure
self.is_azure = is_azure
self.llm_object = llm_object
self.name_parts = self.model_name.lower().split('-')
self.monitor = SystemLoadMonitor(logger)
self.has_GPU = torch.cuda.is_available()
### Config
self.config_vals_for_permutation = config_vals_for_permutation
# Set up a parser
self.parser = JsonOutputParser()
self.prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": self.parser.get_format_instructions()},
)
self._set_config()
def _can_use_json_mode(self):
if self.is_azure:
return False
# gpt-4-turbo-preview (gpt-4-0125-preview)
if ('0125' in self.name_parts) and ('4' in self.name_parts):
return True
# gpt-3.5-turbo-0125
elif ('0125' in self.name_parts) and ('3.5' in self.name_parts) and ('turbo' in self.name_parts):
return True
else:
return False
def _set_config(self):
if self.config_vals_for_permutation:
self.starting_temp = float(self.config_vals_for_permutation.get('openai').get('temperature'))
self.model_kwargs = {
'max_tokens': self.config_vals_for_permutation.get('openai').get('max_tokens'),
'temperature': self.starting_temp,
# 'seed': self.config_vals_for_permutation.get('openai').get('seed'),
'top_p': self.config_vals_for_permutation.get('openai').get('top_p'),
}
else:
self.starting_temp = float(self.STARTING_TEMP)
self.model_kwargs = {
'max_tokens': 1024,
'temperature': self.starting_temp,
# 'seed': 2023,
'top_p': 1, # Set to 1, change temp only
}
### Not all openai models support json mode
if self._can_use_json_mode():
self.model_kwargs.update({"response_format": {"type": "json_object"}})
self.temp_increment = float(0.2)
self.adjust_temp = self.starting_temp
# Adjusting the LLM settings based on whether Azure is used
if self.is_azure:
self.llm_object.deployment_name = self.model_name
self.llm_object.model_name = self.model_name
else:
self.llm_object = None
self._build_model_chain_parser()
# Define a function to format the input for azure_call
def format_input_for_azure(self, prompt_text):
msg = HumanMessage(content=prompt_text.text)
# self.llm_object.temperature = self.config.get('temperature')
return self.llm_object(messages=[msg])
def _adjust_config(self):
new_temp = self.adjust_temp + self.temp_increment
if self.json_report:
self.json_report.set_text(text_main=f'Incrementing temperature from {self.adjust_temp} to {new_temp}')
self.logger.info(f'Incrementing temperature from {self.adjust_temp} to {new_temp}')
self.adjust_temp += self.temp_increment
self.model_kwargs['temperature'] = self.adjust_temp
def _reset_config(self):
if self.json_report:
self.json_report.set_text(text_main=f'Resetting temperature from {self.adjust_temp} to {self.starting_temp}')
self.logger.info(f'Resetting temperature from {self.adjust_temp} to {self.starting_temp}')
self.adjust_temp = self.starting_temp
self.model_kwargs['temperature'] = self.starting_temp
def _build_model_chain_parser(self):
if not self.is_azure and ('instruct' in self.name_parts):
# Determine the LLM to use based on whether this is an Azure instance
if self.is_azure:
llm_to_use = self.llm_object
else:
llm_to_use = OpenAI(
model=self.model_name,
temperature=self.model_kwargs.get('temperature'),
top_p=self.model_kwargs.get('top_p'),
max_tokens=self.model_kwargs.get('max_tokens')
)
# Set up the retry parser with 3 retries
self.retry_parser = RetryWithErrorOutputParser.from_llm(
parser=self.parser,
llm=llm_to_use,
max_retries=self.MAX_RETRIES
)
else:
# Determine the LLM to use for non-Azure instances
if self.is_azure:
llm_to_use = self.llm_object
self.llm_object.temperature = self.model_kwargs.get('temperature')
self.llm_object.max_tokens = self.model_kwargs.get('max_tokens')
self.llm_object.model_kwargs = self.model_kwargs
else:
llm_to_use = ChatOpenAI(
model=self.model_name,
temperature=self.model_kwargs.get('temperature'),
top_p=self.model_kwargs.get('top_p'),
max_tokens=self.model_kwargs.get('max_tokens'),
)
# Set up the retry parser with 3 retries for other cases
self.retry_parser = RetryWithErrorOutputParser.from_llm(
parser=self.parser,
llm=llm_to_use,
max_retries=self.MAX_RETRIES
)
# Prepare the chain
if self.is_azure:
chain_llm_to_use = self.format_input_for_azure
else:
if 'instruct' in self.name_parts:
chain_llm_to_use = OpenAI(
model=self.model_name,
temperature=self.model_kwargs.get('temperature'),
top_p=self.model_kwargs.get('top_p'),
max_tokens=self.model_kwargs.get('max_tokens')
)
else:
chain_llm_to_use = ChatOpenAI(
model=self.model_name,
temperature=self.model_kwargs.get('temperature'),
top_p=self.model_kwargs.get('top_p'),
max_tokens=self.model_kwargs.get('max_tokens')
)
self.chain = self.prompt | chain_llm_to_use
def call_llm_api_OpenAI(self, prompt_template, json_report, paths):
_____, ____, _, __, ___, json_file_path_wiki, txt_file_path_ind_prompt = paths
self.json_report = json_report
if self.json_report:
self.json_report.set_text(text_main=f'Sending request to {self.model_name}')
self.monitor.start_monitoring_usage()
nt_in = 0
nt_out = 0
ind = 0
while ind < self.MAX_RETRIES:
ind += 1
try:
self.logger.info(str(self.model_kwargs))
# Invoke the chain to generate prompt text
response = self.chain.invoke(input={"query": prompt_template})#, **self.model_kwargs)# "model_kwargs": self.model_kwargs})
response_text = response.content if not isinstance(response, str) else response
# Use retry_parser to parse the response with retry logic
try:
output = self.retry_parser.parse_with_prompt(response_text, prompt_value=prompt_template)
except:
try:
output = json.loads(response_text)
except:
output = None
if output is None:
self.logger.error(f'[Attempt {ind}] Failed to extract JSON from:\n{response_text}')
self._adjust_config()
else:
nt_in = count_tokens(prompt_template, self.VENDOR, self.TOKENIZER_NAME)
nt_out = count_tokens(response_text, self.VENDOR, self.TOKENIZER_NAME)
output = validate_and_align_JSON_keys_with_template(output, self.JSON_dict_structure)
if output is None:
self.logger.error(f'[Attempt {ind}] Failed to extract JSON from:\n{response_text}')
self._adjust_config()
else:
self.monitor.stop_inference_timer() # Starts tool timer too
if self.json_report:
self.json_report.set_text(text_main=f'Working on WFO, Geolocation, Links')
output_WFO, WFO_record, output_GEO, GEO_record = run_tools(output, self.tool_WFO, self.tool_GEO, self.tool_wikipedia, json_file_path_wiki)
save_individual_prompt(sanitize_prompt(prompt_template), txt_file_path_ind_prompt)
self.logger.info(f"Formatted JSON:\n{json.dumps(output,indent=4)}")
usage_report = self.monitor.stop_monitoring_report_usage()
if self.adjust_temp != self.starting_temp:
self._reset_config()
if self.json_report:
self.json_report.set_text(text_main=f'LLM call successful')
return output, nt_in, nt_out, WFO_record, GEO_record, usage_report
except Exception as e:
self.logger.error(f'{e}')
self._adjust_config()
time.sleep(self.RETRY_DELAY)
self.logger.info(f"Failed to extract valid JSON after [{ind}] attempts")
if self.json_report:
self.json_report.set_text(text_main=f'Failed to extract valid JSON after [{ind}] attempts')
self.monitor.stop_inference_timer() # Starts tool timer too
usage_report = self.monitor.stop_monitoring_report_usage()
self._reset_config()
if self.json_report:
self.json_report.set_text(text_main=f'LLM call failed')
return None, nt_in, nt_out, None, None, usage_report
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