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