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

AI Integrations

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

​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