File size: 13,996 Bytes
10bf19f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
""" 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() |