LangChain Integration - AmbientMeta

Install

pip install langchain-ambientmeta

Quick Start

from langchain_ambientmeta import PrivacyLLM
from langchain_openai import ChatOpenAI

# Wrap your LLM with privacy protection
safe_llm = PrivacyLLM(
    llm=ChatOpenAI(model="gpt-4"),
    api_key="am_live_xxx",
)

# Use normally — PII is automatically handled
response = safe_llm.invoke("Summarize John Smith's file at john@acme.com")
# OpenAI never sees the real PII

That’s it! The wrapper automatically sanitizes input, calls the LLM with safe text, and rehydrates the response.

How It Works

Your input is sanitized before reaching the LLM
The LLM processes the sanitized text
The response is rehydrated with original entities
You get back the complete response

With Chains (LCEL)

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("Answer this question: {query}")
chain = prompt | safe_llm

result = chain.invoke({"query": "What is John Smith's email?"})

With RAG

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template(
    "Answer the question based on context:\n{context}\n\nQuestion: {input}"
)
combine_chain = create_stuff_documents_chain(safe_llm, prompt)
rag_chain = create_retrieval_chain(your_retriever, combine_chain)

result = rag_chain.invoke({"input": "Find information about employee EMP-123456"})

Configuration

safe_llm = PrivacyLLM(
    llm=ChatOpenAI(model="gpt-4"),
    api_key="am_live_xxx",
    entities=["PERSON", "EMAIL_ADDRESS", "SSN"],  # Optional: specific entities only
    auto_rehydrate=True,   # Automatically restore PII in responses (default: True)
)