> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flexorch.com/llms.txt
> Use this file to discover all available pages before exploring further.

# redact_for_llm

> One-liner PII removal before sending text to an LLM.

## Overview

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

<CodeGroup>
  ```python Python theme={null}
  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"]}
  ```

  ```javascript JavaScript theme={null}
  import { redactForLlm } from '@flexorch/audit';

  const { text: cleanText, summary } = await redactForLlm(text);

  console.log(cleanText);
  // From: [MASKED_EMAIL]
  // ...

  console.log(summary);
  // { count: 2, types: ['email', 'iban_es'] }
  ```
</CodeGroup>

***

## With locale filtering

<CodeGroup>
  ```python Python theme={null}
  # Only detect Turkish and universal types
  clean_text, summary = redact_for_llm(text, locales=["universal", "tr"])
  ```

  ```javascript JavaScript theme={null}
  const { text: cleanText, summary } = await redactForLlm(text, {
    locales: ['universal', 'tr']
  });
  ```
</CodeGroup>

***

## Token estimation

After redacting, estimate token count before sending to the LLM:

<CodeGroup>
  ```python Python theme={null}
  from flexorch_audit import redact_for_llm, estimate_tokens

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

  ```javascript JavaScript theme={null}
  import { redactForLlm, estimateTokens } from '@flexorch/audit';

  const { text: cleanText } = await redactForLlm(text);
  const tokens = estimateTokens(cleanText);
  console.log(`${tokens} tokens`);
  ```
</CodeGroup>

***

## LangChain integration

Use `redact_for_llm()` as a pre-processing step in a LangChain chain:

```python theme={null}
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](/open-source/langchain) for document loading.