RAG with PII Redaction

pip install promptguard-sdk openai chromadb

import promptguard
from openai import OpenAI
import chromadb

# Initialize PromptGuard with response scanning
promptguard.init(
    api_key="pg_live_xxxxxxxx",
    mode="enforce",
    scan_responses=True,
)

# Initialize clients
openai_client = OpenAI()
chroma_client = chromadb.Client()
collection = chroma_client.get_or_create_collection("documents")

def secure_rag_query(user_query: str) -> str:
    # Step 1: User query is auto-scanned by promptguard.init()

    # Step 2: Retrieve relevant documents
    results = collection.query(
        query_texts=[user_query],
        n_results=5
    )

    # Step 3: Build context from retrieved docs
    context = "\n\n".join(results["documents"][0])

    # Step 4: LLM call - auto-scanned by PromptGuard
    response = openai_client.chat.completions.create(
        model="gpt-5-nano",
        messages=[
            {
                "role": "system",
                "content": f"Answer based on this context:\n\n{context}"
            },
            {
                "role": "user",
                "content": user_query
            }
        ]
    )

    return response.choices[0].message.content

# Example usage
answer = secure_rag_query("What's the salary for employee John Smith?")
print(answer)

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.chains import RetrievalQA
from promptguard.integrations.langchain import PromptGuardCallbackHandler

pg_handler = PromptGuardCallbackHandler(
    api_key="pg_live_xxxxxxxx",
    scan_responses=True,
)

llm = ChatOpenAI(model="gpt-5-nano", callbacks=[pg_handler])
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

answer = qa_chain.invoke("What benefits does Jane Doe have?")