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import os, time, json, typing
# import vertexai
from vertexai.language_models import TextGenerationModel
from vertexai.generative_models._generative_models import HarmCategory, HarmBlockThreshold
from vertexai.language_models import TextGenerationModel
# from vertexai.preview.generative_models import GenerativeModel
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 langchain_google_vertexai import VertexAI
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_core.prompt_values import PromptValue as BasePromptValue
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
#https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk
#pip install --upgrade google-cloud-aiplatform
# from google.cloud import aiplatform
#### have to authenticate gcloud
# gcloud auth login
# gcloud config set project XXXXXXXXX
# https://cloud.google.com/docs/authentication
class GooglePalm2Handler:
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, cfg, logger, model_name, JSON_dict_structure, 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.config_vals_for_permutation = config_vals_for_permutation
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):
# vertexai.init(project=os.environ['PALM_PROJECT_ID'], location=os.environ['PALM_LOCATION'])
if self.config_vals_for_permutation:
self.starting_temp = float(self.config_vals_for_permutation.get('google').get('temperature'))
self.config = {
'max_output_tokens': self.config_vals_for_permutation.get('google').get('max_output_tokens'),
'temperature': self.starting_temp,
'top_k': self.config_vals_for_permutation.get('google').get('top_k'),
'top_p': self.config_vals_for_permutation.get('google').get('top_p'),
}
else:
self.starting_temp = float(self.STARTING_TEMP)
self.config = {
"max_output_tokens": 1024,
"temperature": self.starting_temp,
"top_k": 1,
"top_p": 1.0,
}
self.temp_increment = float(0.2)
self.adjust_temp = self.starting_temp
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
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.config['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.config['temperature'] = self.starting_temp
def _build_model_chain_parser(self):
# Instantiate the parser and the retry parser
# self.llm_model = ChatGoogleGenerativeAI(model=self.model_name)
self.llm_model = VertexAI(model=self.model_name,
max_output_tokens=self.config.get('max_output_tokens'),
temperature=self.config.get('temperature'),
top_k=self.config.get('top_k'),
top_p=self.config.get('top_p'))
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_palm2
# Define a function to format the input for Google PaLM call
# https://cloud.google.com/vertex-ai/docs/generative-ai/migrate/migrate-palm-to-gemini?_ga=2.225326234.-1652490527.1705461451&_gac=1.186295771.1706291573.CjwKCAiAzc2tBhA6EiwArv-i6QCpx7xTP0yrBy9KKSwno3QXOWUe14mbp9RGZO0ShcbtFqyXii2PnRoCywgQAvD_BwE
def call_google_palm2(self, prompt_text):
model = TextGenerationModel.from_pretrained(self.model_name)
response = model.predict(prompt_text.text,
max_output_tokens=self.config.get('max_output_tokens'),
temperature=self.config.get('temperature'),
top_k=self.config.get('top_k'),
top_p=self.config.get('top_p'))
# model = GenerativeModel(self.model_name)
# response = model.generate_content(prompt_text.text,generation_config=self.config, safety_settings=self.safety_settings, stream=False)
return response.text
def call_llm_api_GooglePalm2(self, prompt_template, json_report, paths):
_____, ____, _, __, ___, json_file_path_wiki, txt_file_path_ind_prompt = paths
self.json_report = json_report
if 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
try:
output = self.retry_parser.parse_with_prompt(response, prompt_value=PromptValue(prompt_template))
except:
try:
output = self.retry_parser.parse_with_prompt(response, prompt_value=prompt_template)
except:
try:
output = json.loads(response)
except Exception as e:
print(e)
output = None
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:
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
class PromptValue(BasePromptValue):
prompt_str: str
def to_string(self) -> str:
return self.prompt_str |