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Install

pip install llamaindex-ambientmeta

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Quick Start

from llama_index.core import VectorStoreIndex, Settings
from llama_index.llms.openai import OpenAI
from llamaindex_ambientmeta import PrivacyLLM

# Wrap your LLM with privacy protection
Settings.llm = PrivacyLLM(
    llm=OpenAI(),
    api_key="am_live_xxx",
)

# Build your index as normal
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Queries are automatically sanitized
response = query_engine.query("What's in John Smith's contract?")
# LLM never sees real names

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How It Works

  1. Your query is sanitized before processing
  2. RAG retrieval happens with sanitized text
  3. The LLM generates a response with placeholders
  4. Response is rehydrated with original entities

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With Chat Engine

chat_engine = index.as_chat_engine()

# Multi-turn conversations stay private
response = chat_engine.chat("Tell me about employee EMP-123456")
response = chat_engine.chat("What's their email?")

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Configuration

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

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With Any LLM

Works with any LlamaIndex-supported LLM: 
from llama_index.llms.anthropic import Anthropic

Settings.llm = PrivacyLLM(
    llm=Anthropic(model="claude-sonnet-4-20250514"),
    api_key="am_live_xxx",
)