langchain-blindfold 0.1.0

LangChain integration for Blindfold PII detection and protection

LangChain Blindfold

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

Developed by	Blindfold
License	MIT
Input/Output	String, Document

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 [e-mail]")

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 [e-mail]")
# → "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

Policy	Entities	Best For
basic	Names, emails, phones, locations	General PII protection
gdpr_eu	EU-specific: IBANs, addresses, dates of birth	GDPR compliance
hipaa_us	PHI: SSNs, MRNs, medical terms	HIPAA compliance
pci_dss	Card numbers, CVVs, expiry dates	PCI DSS compliance
strict	All entity types, lower threshold	Maximum detection

PII Methods

Method	Output	Reversible
tokenize	<Person_1>, <Email Address_1>	Yes
redact	PII removed entirely	No
mask	J****oe, j****om	No
hash	HASH_abc123	No
synthesize	Jane Smith, [e-mail]	No
encrypt	AES-256 encrypted value	Yes (with key)

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