How to Integrate LangChain for pension funds with Stripe for RAG

Combining LangChain for pension funds with Stripe gives you a clean pattern for building regulated AI workflows that can answer policy questions and trigger billing or payment events from the same agent. In practice, that means a pension-support assistant can retrieve plan documents, explain contribution rules, and then create an invoice or payment link when a premium service or advisory workflow needs to be paid.

Prerequisites

• •Python 3.10+
• •A LangChain-compatible pension-fund knowledge base indexed for retrieval
• •A Stripe account with API keys
• •OPENAI_API_KEY or another LLM provider key used by LangChain
• •STRIPE_SECRET_KEY
• •Installed packages:
  • •langchain
  • •langchain-openai
  • •langchain-community
  • •stripe
  • •python-dotenv

Install them:

pip install langchain langchain-openai langchain-community stripe python-dotenv

Set your environment variables:

export OPENAI_API_KEY="your-openai-key"
export STRIPE_SECRET_KEY="sk_test_..."

Integration Steps

1) Build the pension-fund retriever

Your agent needs a retrieval layer over pension fund documents: plan rules, fee schedules, withdrawal policies, and member FAQs. Use LangChain’s vector store and retriever APIs so the model answers from source material instead of guessing.

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document

docs = [
    Document(page_content="Early withdrawal is allowed only under hardship conditions."),
    Document(page_content="Employer contributions vest after 3 years."),
    Document(page_content="Monthly advisory fee is $25 for premium members."),
]

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

2) Create a RAG chain for pension answers

Now wire retrieval into a LangChain chain. For production, keep the prompt strict: answer only from retrieved context and return a structured result the rest of your app can consume.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

prompt = ChatPromptTemplate.from_template(
    """
    You are a pension fund support assistant.
    Answer using only the context below.

    Context:
    {context}

    Question:
    {question}

    If the answer implies a paid premium action, say so explicitly.
    """
)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
)

3) Add Stripe payment creation for premium actions

When the RAG answer indicates a paid action, create a Stripe Checkout Session or Payment Link. For agent workflows, Checkout Sessions are usually easier because you can attach metadata and redirect the user immediately.

import os
import stripe

stripe.api_key = os.environ["STRIPE_SECRET_KEY"]

def create_premium_checkout_session(customer_email: str):
    session = stripe.checkout.Session.create(
        mode="payment",
        customer_email=customer_email,
        line_items=[
            {
                "price_data": {
                    "currency": "usd",
                    "product_data": {
                        "name": "Pension Advisory Premium Review",
                    },
                    "unit_amount": 2500,
                },
                "quantity": 1,
            }
        ],
        success_url="https://example.com/success?session_id={CHECKOUT_SESSION_ID}",
        cancel_url="https://example.com/cancel",
        metadata={
            "source": "langchain_pension_agent",
            "service": "premium_review",
        },
    )
    return session.url

4) Orchestrate retrieval and billing in one agent flow

The agent first answers the policy question using RAG. If the response suggests a premium review or paid service, it calls Stripe and returns both the explanation and payment URL.

def handle_member_query(question: str, email: str):
    response = rag_chain.invoke(question)
    answer_text = response.content if hasattr(response, "content") else str(response)

    needs_payment = "paid" in answer_text.lower() or "premium" in answer_text.lower()

    result = {
        "answer": answer_text,
        "payment_required": needs_payment,
        "checkout_url": None,
    }

    if needs_payment:
        result["checkout_url"] = create_premium_checkout_session(email)

    return result


output = handle_member_query(
    "Can you review my withdrawal eligibility and provide a premium assessment?",
    "member@example.com",
)

print(output)

Testing the Integration

Use a known question that should retrieve pension policy text and trigger Stripe only when premium language appears. In test mode, you should see an answer plus a Checkout URL.

test_result = handle_member_query(
    "What are the rules for early withdrawal and do I need a premium review?",
    "test.member@example.com",
)

print("Answer:", test_result["answer"])
print("Payment required:", test_result["payment_required"])
print("Checkout URL:", test_result["checkout_url"])

Expected output:

Answer: ... If you want a premium assessment, this is a paid service.
Payment required: True
Checkout URL: https://checkout.stripe.com/c/pay/cs_test_...

If you get an empty retrieval result, fix your document ingestion before touching Stripe. If Checkout creation fails, verify your secret key and that your account is in test mode.

Real-World Use Cases

• •Member support assistant: Answer pension plan questions from indexed policy docs, then collect payment for human advisor escalation.
• •Premium retirement planning workflow: Retrieve plan rules, estimate eligibility paths, and generate a Stripe Checkout Session for paid financial review.
• •Claims or benefits operations: Use RAG to classify requests against fund documentation, then charge for expedited processing or document certification.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.