LangChain

The langchain-blindfold package integrates Blindfold with LangChain, letting you tokenize PII before it reaches your LLM and restore originals in the response. Includes chain-composable Runnables and a DocumentTransformer for RAG pipelines.

Installation

pip install langchain-blindfold

Set your 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")

The LLM only sees <Person_1> and <Email Address_1> — never the real data.

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 for use in chains:

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 and stores the mapping:

Parameter	Type	Default	Description
`api_key`	`str`	`None`	Falls back to `BLINDFOLD_API_KEY` env var
`region`	`str`	`None`	`"eu"` or `"us"` for data residency
`policy`	`str`	`"basic"`	Detection policy
`entities`	`list`	`None`	Specific entity types to detect
`score_threshold`	`float`	`None`	Confidence threshold (0.0–1.0)

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 using the paired tokenizer’s mapping:

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"

This is a client-side operation — no API call is made for detokenization.

`BlindfoldPIITransformer`

A LangChain DocumentTransformer for protecting PII in documents:

Parameter	Type	Default	Description
`api_key`	`str`	`None`	Falls back to `BLINDFOLD_API_KEY` env var
`region`	`str`	`None`	`"eu"` or `"us"` for data residency
`policy`	`str`	`"basic"`	Detection policy
`pii_method`	`str`	`"tokenize"`	How to protect PII
`entities`	`list`	`None`	Specific entity types to detect
`score_threshold`	`float`	`None`	Confidence threshold (0.0–1.0)

When pii_method="tokenize", the mapping is stored in doc.metadata["blindfold_mapping"] so you can restore originals later.

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

See Policies for details.

PII Methods

The pii_method parameter controls how detected PII is protected (applies to BlindfoldPIITransformer):

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`, `jane@example.org`	No
`encrypt`	AES-256 encrypted value	Yes (with key)

Usage Examples

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

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

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

chain = tokenize | prompt | llm | (lambda msg: msg.content) | detokenize
result = chain.invoke("Contact Hans Mueller at hans.mueller@example.de about IBAN DE89370400440532013000")

HIPAA — Redact PHI in Documents

from langchain_blindfold import BlindfoldPIITransformer
from langchain_core.documents import Document

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

docs = [
    Document(page_content="Patient Sarah Jones, SSN 123-45-6789, MRN 4567890"),
    Document(page_content="Dr. Smith prescribed medication on 2024-01-15"),
]
safe_docs = transformer.transform_documents(docs)
# PHI redacted from all documents

Protect RAG Pipeline

from langchain_blindfold import BlindfoldPIITransformer
from langchain_core.documents import Document

# Tokenize documents before storing in vector DB
transformer = BlindfoldPIITransformer(pii_method="tokenize", policy="strict")

docs = [Document(page_content="John Doe's account #12345 has balance $50,000")]
safe_docs = transformer.transform_documents(docs)

# Mapping stored in metadata for later restoration
print(safe_docs[0].metadata["blindfold_mapping"])
# → {"<Person_1>": "John Doe", ...}

Detect Specific Entity Types

tokenize, detokenize = blindfold_protect(
    entities=["Email Address", "Phone Number", "Credit Card Number"],
)

Data Residency

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

Region	Endpoint	Location
`eu`	`eu-api.blindfold.dev`	Frankfurt, Germany
`us`	`us-api.blindfold.dev`	Virginia, US

See Regions for details.

PyPI Package

Install from PyPI

GitHub

Source code and issues

LangChain Docs

LangChain documentation

Cookbook Examples

Working integration examples

Getting Started

Compliance

Essentials

Privacy Methods

SDKs & Tools

Guides

Installation

Quick Start

Protect a LangChain Chain

Transform Documents for RAG

Components

`blindfold_protect()`

`BlindfoldTokenizer`

`BlindfoldDetokenizer`

`BlindfoldPIITransformer`

Policies

PII Methods

Usage Examples

HIPAA — Redact PHI in Documents

Protect RAG Pipeline

Detect Specific Entity Types

Data Residency

Links

PyPI Package

GitHub

LangChain Docs

Cookbook Examples

Getting Started

Compliance

Essentials

Privacy Methods

SDKs & Tools

Guides

​Installation

​Quick Start

​Protect a LangChain Chain

​Transform Documents for RAG

​Components

​blindfold_protect()

​BlindfoldTokenizer

​BlindfoldDetokenizer

​BlindfoldPIITransformer

​Policies

​PII Methods

​Usage Examples

​GDPR Compliance with EU Region

​HIPAA — Redact PHI in Documents

​Protect RAG Pipeline

​Detect Specific Entity Types

​Data Residency

​Links

PyPI Package

GitHub

LangChain Docs

Cookbook Examples

Installation

Quick Start

Protect a LangChain Chain

Transform Documents for RAG

Components

`blindfold_protect()`

`BlindfoldTokenizer`

`BlindfoldDetokenizer`

`BlindfoldPIITransformer`

Policies

PII Methods

Usage Examples

GDPR Compliance with EU Region

HIPAA — Redact PHI in Documents

Protect RAG Pipeline

Detect Specific Entity Types

Data Residency

Links