Eine einfache Möglichkeit, personenbezogene Daten vor dem Senden an LLMs zu entfernen

from gliner import GLiNER
from faker import Faker
from faker.providers import job
import google.generativeai as genai
import re
import warnings
from rapidfuzz import process, utils
warnings.filterwarnings("ignore")

genai.configure(api_key="key")
model_llm = genai.GenerativeModel("gemini-1.5-flash-002")
fake = Faker()
fake.add_provider(job)
model_gliner = GLiNER.from_pretrained("urchade/gliner_small-v2.1")

# let's say we have this prompt along with context that we want to anonymize before sending to LLM
prompt= f"""Given the context, answer the question. \n context: Hi, I am Mayank Laddha.  I lives in India. I love my country. But I would like to go to Singapore once. I am a software developer.\n question: Where does Mayank Laddha want to go?"
"""
# Perform entity prediction
labels = ["Person", "Country", "Profession"]
entities = model_gliner.predict_entities(prompt, labels, threshold=0.4)
print(entities)

# create a replacement dictionary
replacement = {}
for entity in entities: 
    if "Person" in entity["label"] and entity["text"] not in replacement:
        fake_set = {fake.name() for _ in range(3)}
        fake_set.discard(entity["text"])
        new_name = fake_set.pop()
        replacement[entity["text"]] = new_name
    elif "Country" in entity["label"] and entity["text"] not in replacement:
        name_set = {fake.country() for _ in range(10)}
        print(name_set)
        name_set.discard(entity["text"])
        new_name = name_set.pop()
        replacement[entity["text"]] = new_name
    elif "Profession" in entity["label"] and entity["text"] not in replacement:
        name_set = {fake.job() for _ in range(20)}
        name_set = {k for k in name_set if len(k.split())==1}
        print(name_set)
        name_set.discard(entity["text"])
        new_name = name_set.pop()
        replacement[entity["text"]] = new_name

#also create a reverse dictionary
replacement_reversed = {v: k for k, v in replacement.items()}

#perform replacement
for k, v in replacement.items():
    # Split text into a list of words
    words = prompt.split()  
    n = len(k.split()) 
    # so the key appears fully in choices
    choices = [' '.join(words[i:i+n]) for i in range(len(words) - n + 1)] 
    matches = process.extract(k, choices, limit=1, processor=utils.default_process)
    for match in matches:
        if match[1]>80:
            prompt = re.sub(match[0], v, prompt, flags=re.IGNORECASE)

#prompt
response = model_llm.generate_content(prompt)
content = response.text
print("llm response",content)

#perform replacement again
for k, v in replacement_reversed.items():
    words = content.split()  
    n = len(k.split())
    choices = [' '.join(words[i:i+n]) for i in range(len(words) - n + 1)]
    matches = process.extract(k, choices, limit=1, processor=utils.default_process)
    for match in matches:
        if match[1]>80:
            content = re.sub(match[0], v, content, flags=re.IGNORECASE)

print("final result", content)