LangChain Blindfold

LangChain integration for Blindfold PII detection and protection. Tokenize PII before it reaches your LLM, then restore originals in the response.

Installation

pip install langchain-blindfold

Set your Blindfold API key:

export BLINDFOLD_API_KEY=your-api-key

Get a free API key at app.blindfold.dev.

Quick Start

Protect a LangChain Chain

from langchain_blindfold import blindfold_protect
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

tokenize, detokenize = blindfold_protect(policy="basic")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}"),
])
llm = ChatOpenAI(model="gpt-4o-mini")

chain = tokenize | prompt | llm | (lambda msg: msg.content) | detokenize

# PII is tokenized before the LLM sees it, then restored in the response
result = chain.invoke("Write a follow-up email to John Doe at john@example.com")

Transform Documents for RAG

from langchain_blindfold import BlindfoldPIITransformer
from langchain_core.documents import Document

transformer = BlindfoldPIITransformer(pii_method="redact", policy="hipaa_us", region="us")

docs = [Document(page_content="Patient John Smith, SSN 123-45-6789")]
safe_docs = transformer.transform_documents(docs)
# safe_docs[0].page_content → "Patient [REDACTED], SSN [REDACTED]"

Components

blindfold_protect()

Convenience function that returns a paired tokenizer and detokenizer:

tokenize, detokenize = blindfold_protect(
    api_key=None,         # Falls back to BLINDFOLD_API_KEY env var
    region=None,          # "eu" or "us" for data residency
    policy="basic",       # Detection policy
    entities=None,        # Specific entity types to detect
    score_threshold=None, # Confidence threshold (0.0-1.0)
)

BlindfoldTokenizer

A LangChain Runnable that tokenizes PII in text:

from langchain_blindfold import BlindfoldTokenizer

tokenizer = BlindfoldTokenizer(policy="gdpr_eu", region="eu")
safe_text = tokenizer.invoke("Contact Hans at hans@example.de")
# → "Contact <Person_1> at <Email Address_1>"

BlindfoldDetokenizer

A LangChain Runnable that restores original PII from tokenized text:

from langchain_blindfold import BlindfoldTokenizer, BlindfoldDetokenizer

tokenizer = BlindfoldTokenizer(api_key="...")
detokenizer = BlindfoldDetokenizer(tokenizer=tokenizer)

tokenizer.invoke("Hi John")  # stores mapping
result = detokenizer.invoke("Response to <Person_1>")
# → "Response to John"

BlindfoldPIITransformer

A LangChain DocumentTransformer for protecting PII in documents:

from langchain_blindfold import BlindfoldPIITransformer

transformer = BlindfoldPIITransformer(
    api_key=None,         # Falls back to BLINDFOLD_API_KEY env var
    region=None,          # "eu" or "us" for data residency
    policy="basic",       # Detection policy
    pii_method="tokenize",# tokenize, redact, mask, hash, synthesize, encrypt
    entities=None,        # Specific entity types to detect
    score_threshold=None, # Confidence threshold (0.0-1.0)
)

When pii_method="tokenize", the mapping is stored in doc.metadata["blindfold_mapping"].

Policies

PII Methods

Data Residency

Use the region parameter to ensure PII is processed in a specific jurisdiction:

• region="eu" — processed in Frankfurt, Germany
• region="us" — processed in Virginia, US

tokenize, detokenize = blindfold_protect(policy="gdpr_eu", region="eu")

Links

• Blindfold Documentation
• Blindfold Dashboard
• LangChain Documentation
• GitHub