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Major update. Support for 15 LLMs, World Flora Online taxonomy validation, geolocation, 2 OCR methods, significant UI changes, stability improvements, consistent JSON parsing
9d06861
| 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, count_tokens, save_individual_prompt | |
| 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 | |
| from vouchervision.tool_wikipedia import WikipediaLinks | |
| class OpenAIHandler: | |
| RETRY_DELAY = 10 # Wait 10 seconds before retrying | |
| MAX_RETRIES = 3 # Maximum number of retries | |
| STARTING_TEMP = 0.5 | |
| TOKENIZER_NAME = 'gpt-4' | |
| VENDOR = 'openai' | |
| def __init__(self, logger, model_name, JSON_dict_structure, is_azure, llm_object): | |
| 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.split('-') | |
| self.monitor = SystemLoadMonitor(logger) | |
| self.has_GPU = torch.cuda.is_available() | |
| self.starting_temp = float(self.STARTING_TEMP) | |
| self.temp_increment = float(0.2) | |
| self.adjust_temp = self.starting_temp | |
| # 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 _set_config(self): | |
| self.config = {'max_new_tokens': 1024, | |
| 'temperature': self.starting_temp, | |
| 'random_seed': 2023, | |
| 'top_p': 1, | |
| } | |
| # 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 | |
| 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): | |
| if not self.is_azure and ('instruct' in self.name_parts): | |
| # Set up the retry parser with 3 retries | |
| self.retry_parser = RetryWithErrorOutputParser.from_llm( | |
| # parser=self.parser, llm=self.llm_object if self.is_azure else OpenAI(temperature=self.config.get('temperature'), model=self.model_name), max_retries=self.MAX_RETRIES | |
| parser=self.parser, llm=self.llm_object if self.is_azure else OpenAI(model=self.model_name), max_retries=self.MAX_RETRIES | |
| ) | |
| else: | |
| # Set up the retry parser with 3 retries | |
| self.retry_parser = RetryWithErrorOutputParser.from_llm( | |
| # parser=self.parser, llm=self.llm_object if self.is_azure else ChatOpenAI(temperature=self.config.get('temperature'), model=self.model_name), max_retries=self.MAX_RETRIES | |
| parser=self.parser, llm=self.llm_object if self.is_azure else ChatOpenAI(model=self.model_name), max_retries=self.MAX_RETRIES | |
| ) | |
| # Prepare the chain | |
| if not self.is_azure and ('instruct' in self.name_parts): | |
| # self.chain = self.prompt | (self.format_input_for_azure if self.is_azure else OpenAI(temperature=self.config.get('temperature'), model=self.model_name)) | |
| self.chain = self.prompt | (self.format_input_for_azure if self.is_azure else OpenAI(model=self.model_name)) | |
| else: | |
| # self.chain = self.prompt | (self.format_input_for_azure if self.is_azure else ChatOpenAI(temperature=self.config.get('temperature'), model=self.model_name)) | |
| self.chain = self.prompt | (self.format_input_for_azure if self.is_azure else ChatOpenAI(model=self.model_name)) | |
| 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 | |
| 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}) | |
| response_text = response.content if not isinstance(response, str) else response | |
| # Use retry_parser to parse the response with retry logic | |
| output = self.retry_parser.parse_with_prompt(response_text, prompt_value=prompt_template) | |
| 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 | |
| json_report.set_text(text_main=f'Working on WFO, Geolocation, Links') | |
| 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 | |
| Wiki = WikipediaLinks(json_file_path_wiki) | |
| Wiki.gather_wikipedia_results(output) | |
| save_individual_prompt(Wiki.sanitize(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() | |
| 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") | |
| self.json_report.set_text(text_main=f'Failed to extract valid JSON after [{ind}] attempts') | |
| usage_report = 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, usage_report | |