""" Evaluate Medical Tests Classification in LLMS """ ## Setup #### Load the API key and libaries. import json import pandas as pd import os import openai from dotenv import load_dotenv, find_dotenv import argparse import re import subprocess from langchain.prompts import PromptTemplate from langchain.chat_models import ChatOpenAI from langchain.llms import OpenAI from langchain.llms import LlamaCpp from langchain.agents import initialize_agent, Tool from langchain.tools import tool from langchain.agents import AgentType from langchain.agents.react.base import DocstoreExplorer #from langchain_community.tools.pubmed.tool import PubmedQueryRun from langchain.retrievers import PubMedRetriever from langchain.utilities import WikipediaAPIWrapper from langchain.tools import DuckDuckGoSearchRun #from langchain.globals import set_verbose #set_verbose(True) from langchain.output_parsers import ResponseSchema from langchain.output_parsers import StructuredOutputParser from langchain.output_parsers import OutputFixingParser from langchain.chains import LLMChain ### Download LLAMA model: def download_and_rename(url, filename): """Downloads a file from the given URL and renames it to the given new file name. Args: url: The URL of the file to download. new_file_name: The new file name for the downloaded file. """ os.makedirs(os.path.dirname(filename), exist_ok=True) print(f'Downloading the weights of the model: {url} ...') subprocess.run(["wget", "-q", "-O", filename, url]) print(f'Done!') def download_hugging_face_model(model_version='Llama-2-7b'): if model_version not in ['Llama-2-7b', 'Llama-2-13b', 'Llama-2-70b', 'Mistral-7b']: raise ValueError("Options for Llama model should be 7b, 13b or 70b, or Mistral-7b") MODEL_URL = { 'Llama-2-7b': 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q8_0.gguf', 'Llama-2-13b': 'https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q8_0.gguf', 'Llama-2-70b': 'https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF/resolve/main/llama-2-70b-chat.Q5_0.gguf', 'Mistral-7b': 'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q8_0.gguf' } MODEL_URL = MODEL_URL[model_version] model_path = f'Models/{model_version}.gguf' if os.path.exists(model_path): confirmation = input(f"The model file '{model_path}' already exists. Do you want to overwrite it? (yes/no): ").strip().lower() if confirmation != 'yes': print("Model installation aborted.") return model_path download_and_rename(MODEL_URL, model_path) return model_path # Function to validate JSON format def is_valid_json(text): try: json.loads(text) return True except: return False def get_completion_from_messages(messages, model): #try: response = model(messages)['output'] print('response') print(response) if is_valid_json(response): response = json.loads(response) print('Loaded JSON response:') print(response) return response else: if '"response": "a"' in response.lower() or '"response":"a"' in response.lower() or ': "a"' in response.lower() or ':"a"' in response.lower() or '"response": a' in response.lower() or '"response":a' in response.lower() or ': a' in response.lower() or ':a' in response.lower() or "'response': 'a'" in response.lower() or "'response':'a'" in response.lower() or ": 'a'" in response.lower() or ":'a'" in response.lower() or "'response': a" in response.lower() or "'response':a" in response.lower(): response = {'response': 'a'} elif '"response": "b"' in response.lower() or '"response":"b"' in response.lower() or ': "b"' in response.lower() or ':"b"' in response.lower() or '"response": b' in response.lower() or '"response":b' in response.lower() or ': b' in response.lower() or ':b' in response.lower() or "'response': 'b'" in response.lower() or "'response':'b'" in response.lower() or ": 'b'" in response.lower() or ":'b'" in response.lower() or "'response': b" in response.lower() or "'response':b" in response.lower(): response = {'response': 'b'} elif '"response": "c"' in response.lower() or '"response":"c"' in response.lower() or ': "c"' in response.lower() or ':"c"' in response.lower() or '"response": c' in response.lower() or '"response":c' in response.lower() or ': c' in response.lower() or ':c' in response.lower() or "'response': 'c'" in response.lower() or "'response':'c'" in response.lower() or ": 'c'" in response.lower() or ":'c'" in response.lower() or "'response': c" in response.lower() or "'response':c" in response.lower(): response = {'response': 'c'} elif '"response": "d"' in response.lower() or '"response":"d"' in response.lower() or ': "d"' in response.lower() or ':"d"' in response.lower() or '"response": d' in response.lower() or '"response":d' in response.lower() or ': d' in response.lower() or ':d' in response.lower() or "'response': 'd'" in response.lower() or "'response':'d'" in response.lower() or ": 'd'" in response.lower() or ":'d'" in response.lower() or "'response': d" in response.lower() or "'response':d" in response.lower(): response = {'response': 'd'} return response def llm_language_evaluation(path='data/Portuguese.csv', model='gpt-3.5-turbo', temperature=0.0, n_repetitions=1, reasoning=False, languages=['english', 'portuguese'], llm_chain=False): # Load API key if GPT, or Model if LLAMA if 'gpt' in model: _ = load_dotenv(find_dotenv()) # read local .env file openai.api_key = os.environ['OPENAI_API_KEY'] llm = OpenAI(temperature=temperature, model_name=model) elif 'Llama-2' in model or ('Mistral-7b' in model): model_path = download_hugging_face_model(model_version=model) llm = LlamaCpp( model_path=model_path, temperature=temperature, n_ctx=2048, verbose=False, # VERBOSE ) else: print('Model should be a GPT, Llama-2, or Mistral-7b model') return 0 #### Load the Constants PATH = path # 'data/Portuguese.csv' MODEL = model # "gpt-3.5-turbo" TEMPERATURE = temperature # 0.0 N_REPETITIONS = n_repetitions # 1 REASONING = reasoning # False LANGUAGES = languages # ['english', 'portuguese'] ##### RAG: pubmed = PubMedRetriever() wikipedia = WikipediaAPIWrapper() search = DuckDuckGoSearchRun() @tool def json_format(response: str) -> dict: """Given the correct response's letter a, b, c or d; generates the output json. If input is not a, b, c or d, returns an error message.""" if response in ['a', 'b', 'c', 'd']: return {"response": response} else: return "Error: response should be a, b, c or d." ##### Tools: tools = [ Tool( name = "Pubmed search", func=pubmed.run, description="useful for when you need to search for a medical topic, treatment or outcome on pubmed" ), Tool( name = "JSON format", func=json_format, description="Given the correct response's letter a, b, c or d; generates the output json. If input is not a, b, c or d, returns an error message." ), #Tool( # name='Wikipedia', # func= wikipedia.run, # description="Useful for when you need to look up an specific topic, object, or procedure on wikipedia" #), Tool( name='DuckDuckGo Search', func= search.run, description="Useful for when you need to do a search on the internet to find information that another tool can't find. be specific with your input." ) ] ##### Agent: react_agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True, handle_parsing_errors="Check your output and make sure it is a JSON file with the key response and value a letter a, b, c, or d. Make sure you can parse that using Python", max_iterations=10)#AgentType.REACT_DOCSTORE, verbose=True) #Wikipedia: Useful for when you need to look up an specific topic, object, or procedure on wikipedia #Wikipedia prompt = ''' Answer the following questions as best you can. You have access to the following tools: Pubmed search: useful for when you need to search for a medical topic, treatment or outcome on pubmed JSON format: Given the correct response's letter a, b, c or d; generates the output json. If input is not a, b, c or d, returns an error message. DuckDuckGo Search: Useful for when you need to do a search on the internet to find information that another tool can't find. be specific with your input. Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [Pubmed search, JSON format, DuckDuckGo Search]. Don't use the same tool more than 3 times. Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat 4 times maximum, then you should answer the question. Don't iterate more than 4 times. Just provide a response to the question after that in the expected format.) Thought: I now know the final answer, or I reached the limit of iterations. I will provide the final answer now. Final Answer: the final answer to the original input question. The final answer should be a JSON object with the key "response" and the value being the letter a, b, c or d with the correct answer. Begin! Question: {input} Thought:{agent_scratchpad} ''' react_agent.agent.llm_chain.prompt.template = prompt ##### Experiments: if N_REPETITIONS <= 0 or (N_REPETITIONS != int(N_REPETITIONS)): print(f'N_REPETITIONS should be a positive integer, not {N_REPETITIONS}') print('N_REPETITIONS will be set to 1') N_REPETITIONS = 1 ### Questions from a csv file: df = pd.read_csv(PATH) ### Evaluate the model in question answering per language: responses = {} reasoning = {} for language in LANGUAGES: responses[language] = [[] for n in range(N_REPETITIONS)] if REASONING: reasoning[language] = [[] for n in range(N_REPETITIONS)] for row in range(df.shape[0]): print('*'*50) print(f'Question {row+1}: ') for language in LANGUAGES: print(f'Language: {language}') question = df[language][row] print('Question: ') print(question) for n in range(N_REPETITIONS): print(f'Test #{n}: ') response = get_completion_from_messages(question, react_agent) print(type(response)) print(response) # Append to the list: responses[language][n].append(response['response']) if REASONING: reasoning[language][n].append(response['reasoning']) print('*'*50) ### Save the results in a csv file: for language in LANGUAGES: if N_REPETITIONS == 1: df[f'responses_{language}'] = responses[language][0] if REASONING: df[f'reasoning_{language}'] = reasoning[language][0] for n in range(N_REPETITIONS): df[f'responses_{language}_{n}'] = responses[language][n] if REASONING: df[f'reasoning_{language}_{n}'] = reasoning[language][n] if not os.path.exists('responses'): os.makedirs('responses') if N_REPETITIONS == 1: df.to_csv(f"responses/rag_{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv", index=False) else: df.to_csv(f"responses/rag_{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv", index=False) def main(): # Add argparse code to handle command-line arguments parser = argparse.ArgumentParser(description="Evaluate Medical Tests Classification in LLMS") parser.add_argument("--csv_file", default="data/Portuguese.csv", help="Path to the CSV file with the questions") parser.add_argument("--model", default="gpt-3.5-turbo", help="LLM to use e.g: gpt-3.5-turbo, gpt-4, Llama-2-7b, Llama-2-13b, or Llama-2-70b") parser.add_argument("--temperature", type=float, default=0.0, help="Temperature parameter of the model between 0 and 1. Used to modifiy the model's creativity. 0 is deterministic and 1 is the most creative") parser.add_argument("--n_repetitions", type=int, default=1, help="Number of repetitions to run each experiment. Used to measure the model's hallucinations") parser.add_argument("--reasoning", action="store_true", default=False, help="Enable reasoning mode. If set to True, the model will be asked to provide a reasoning for its answer. If set to True the model uses more tokens") parser.add_argument("--languages", nargs='+', default=['english', 'portuguese'], help="List of languages") parser.add_argument("--llm_chain", action="store_true", default=False, help="Enable the use of ") args = parser.parse_args() PATH = args.csv_file MODEL = args.model TEMPERATURE = args.temperature N_REPETITIONS = args.n_repetitions REASONING = args.reasoning LANGUAGES = args.languages llm_chain = args.llm_chain llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES, llm_chain=llm_chain) if __name__ == "__main__": main()