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Overview

redact_for_llm() combines detection and masking into a single call optimized for LLM input preparation. 
from flexorch_audit import redact_for_llm

text = """
From: maria.garcia@company.es
Subject: Invoice INV-2024-0042

Please process the payment of €8,500 to IBAN ES91 2100 0418 4502 0005 1332.
"""

clean_text, summary = redact_for_llm(text)

print(clean_text)
# From: [MASKED_EMAIL]
# Subject: Invoice INV-2024-0042
#
# Please process the payment of €8,500 to IBAN [MASKED_IBAN_ES].

print(summary)
# {"count": 2, "types": ["email", "iban_es"]}

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With locale filtering

# Only detect Turkish and universal types
clean_text, summary = redact_for_llm(text, locales=["universal", "tr"])

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Token estimation

After redacting, estimate token count before sending to the LLM: 
from flexorch_audit import redact_for_llm, estimate_tokens

clean_text, _ = redact_for_llm(text)
tokens = estimate_tokens(clean_text)
print(f"{tokens} tokens")

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LangChain integration

Use redact_for_llm() as a pre-processing step in a LangChain chain: 
from langchain_core.runnables import RunnableLambda
from flexorch_audit import redact_for_llm

def safe_input(text: str) -> str:
    clean, _ = redact_for_llm(text)
    return clean

chain = RunnableLambda(safe_input) | your_llm_chain
Or use the ready-made AuditedLoader for document loading.