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import os, time
import vertexai
from vertexai.preview.generative_models import GenerativeModel
from vertexai.generative_models._generative_models import HarmCategory, HarmBlockThreshold
from langchain.output_parsers import RetryWithErrorOutputParser
from langchain.schema import HumanMessage
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_google_genai import ChatGoogleGenerativeAI
from vouchervision.utils_LLM import SystemLoadMonitor, count_tokens
from vouchervision.utils_LLM_JSON_validation import validate_and_align_JSON_keys_with_template
from vouchervision.utils_taxonomy_WFO import validate_taxonomy_WFO
from vouchervision.utils_geolocate_HERE import validate_coordinates_here
class GoogleGeminiHandler:
RETRY_DELAY = 10 # Wait 10 seconds before retrying
MAX_RETRIES = 3 # Maximum number of retries
TOKENIZER_NAME = 'gpt-4'
VENDOR = 'google'
STARTING_TEMP = 0.5
def __init__(self, logger, model_name, JSON_dict_structure):
self.logger = logger
self.model_name = model_name
self.JSON_dict_structure = JSON_dict_structure
self.starting_temp = float(self.STARTING_TEMP)
self.temp_increment = float(0.2)
self.adjust_temp = self.starting_temp
self.monitor = SystemLoadMonitor(logger)
self.parser = JsonOutputParser()
# Define the prompt template
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 _set_config(self):
# os.environ['GOOGLE_API_KEY'] # Must be set too for the retry call, set in VoucherVision class along with other API Keys
# vertexai.init(project=os.environ['PALM_PROJECT_ID'], location=os.environ['PALM_LOCATION'])
self.config = {
"max_output_tokens": 1024,
"temperature": self.starting_temp,
"top_p": 1
}
self.safety_settings = {
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}
self._build_model_chain_parser()
def _adjust_config(self):
new_temp = self.adjust_temp + self.temp_increment
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.config['temperature'] = self.adjust_temp
def _reset_config(self):
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.config['temperature'] = self.starting_temp
def _build_model_chain_parser(self):
# Instantiate the LLM class for Google Gemini
self.llm_model = ChatGoogleGenerativeAI(model='gemini-pro',
max_output_tokens=self.config.get('max_output_tokens'),
top_p=self.config.get('top_p'))
# Set up the retry parser with the runnable
self.retry_parser = RetryWithErrorOutputParser.from_llm(parser=self.parser, llm=self.llm_model, max_retries=self.MAX_RETRIES)
# Prepare the chain
self.chain = self.prompt | self.call_google_gemini
# Define a function to format the input for Google Gemini call
def call_google_gemini(self, prompt_text):
model = GenerativeModel(self.model_name)
response = model.generate_content(prompt_text.text,
generation_config=self.config,
safety_settings=self.safety_settings)
return response.text
def call_llm_api_GoogleGemini(self, prompt_template, json_report):
self.json_report = 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:
model_kwargs = {"temperature": self.adjust_temp}
# Invoke the chain to generate prompt text
response = self.chain.invoke({"query": prompt_template, "model_kwargs": model_kwargs})
# Use retry_parser to parse the response with retry logic
output = self.retry_parser.parse_with_prompt(response, prompt_value=prompt_template)
if output is None:
self.logger.error(f'[Attempt {ind}] Failed to extract JSON from:\n{response}')
self._adjust_config()
else:
nt_in = count_tokens(prompt_template, self.VENDOR, self.TOKENIZER_NAME)
nt_out = count_tokens(response, 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}')
self._adjust_config()
else:
json_report.set_text(text_main=f'Working on WFO and Geolocation')
output, WFO_record = validate_taxonomy_WFO(output, replace_if_success_wfo=False) ###################################### make this configurable
output, GEO_record = validate_coordinates_here(output, replace_if_success_geo=False) ###################################### make this configurable
self.logger.info(f"Formatted JSON: {output}")
self.monitor.stop_monitoring_report_usage()
if self.adjust_temp != self.starting_temp:
self._reset_config()
json_report.set_text(text_main=f'LLM call successful')
return output, nt_in, nt_out, WFO_record, GEO_record
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")
self.json_report.set_text(text_main=f'Failed to extract valid JSON after [{ind}] attempts')
self.monitor.stop_monitoring_report_usage()
self._reset_config()
json_report.set_text(text_main=f'LLM call failed')
return None, nt_in, nt_out, None, None
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