How to Integrate Anthropic for payments with pgvector for RAG

Combining Anthropic for payments with pgvector gives you a clean pattern for building AI agents that can both reason over private knowledge and trigger monetized actions. In practice, that means your agent can retrieve the right policy, contract clause, or product rule from vector search, then use Anthropic to generate the response and decide when a payment-related workflow should be executed.

Prerequisites

• •Python 3.10+
• •A PostgreSQL database with the pgvector extension enabled
• •An Anthropic API key
• •Access to your payments stack or sandbox environment
• •Installed packages:
  • •anthropic
  • •psycopg[binary]
  • •pgvector
  • •sqlalchemy
• •A running app server or script entrypoint for your agent

Integration Steps

1. •Install dependencies and enable pgvector

pip install anthropic psycopg[binary] pgvector sqlalchemy

CREATE EXTENSION IF NOT EXISTS vector;

If you are using SQLAlchemy models, define the embedding column with the correct dimension for your embedding model.

from sqlalchemy import create_engine, Column, Integer, Text
from sqlalchemy.orm import declarative_base
from pgvector.sqlalchemy import Vector

Base = declarative_base()

class KnowledgeChunk(Base):
    __tablename__ = "knowledge_chunks"
    id = Column(Integer, primary_key=True)
    content = Column(Text, nullable=False)
    embedding = Column(Vector(1536))  # match your embedding size

2. •Create the database schema and insert embedded documents

Use your embedding provider to generate vectors for each chunk before storing them. The important part is that retrieval is done by similarity search in PostgreSQL, not by scanning text.

import os
import psycopg
from anthropic import Anthropic

client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

def embed_text(text: str) -> list[float]:
    # Replace this with your actual embedding model/provider.
    # Anthropic does not expose embeddings in the core Messages API.
    raise NotImplementedError("Use your embeddings provider here")

conn = psycopg.connect(os.environ["DATABASE_URL"])
with conn.cursor() as cur:
    cur.execute("""
        CREATE TABLE IF NOT EXISTS knowledge_chunks (
            id SERIAL PRIMARY KEY,
            content TEXT NOT NULL,
            embedding vector(1536) NOT NULL
        )
    """)
    conn.commit()

Insert chunks after generating embeddings:

chunks = [
    "Refunds are processed within 5 business days.",
    "Chargebacks require manual review and evidence collection."
]

with conn.cursor() as cur:
    for chunk in chunks:
        vec = embed_text(chunk)
        cur.execute(
            "INSERT INTO knowledge_chunks (content, embedding) VALUES (%s, %s)",
            (chunk, vec),
        )
    conn.commit()

3. •Implement pgvector similarity search for RAG retrieval

When a user asks a question, embed the query and retrieve the nearest chunks using <-> distance search. This is the RAG part: your agent gets grounded context before it calls Anthropic.

def retrieve_context(query: str, limit: int = 3) -> list[str]:
    query_vec = embed_text(query)

    with conn.cursor() as cur:
        cur.execute(
            """
            SELECT content
            FROM knowledge_chunks
            ORDER BY embedding <-> %s
            LIMIT %s
            """,
            (query_vec, limit),
        )
        rows = cur.fetchall()

    return [row[0] for row in rows]

4. •Call Anthropic with retrieved context and payment intent

Use client.messages.create(...) to pass retrieved context into the prompt. If the user request involves a payment action, keep that decision explicit in your application logic instead of burying it inside free-form text generation.

def answer_user(question: str) -> str:
    context = retrieve_context(question)

    prompt = f"""
You are a support agent for payment operations.

Context:
{chr(10).join(f"- {c}" for c in context)}

User question:
{question}

If this requires a payment action, return a concise JSON object with:
- action: one of [none, refund, chargeback_review]
- reason: short explanation
Otherwise answer normally.
"""

    resp = client.messages.create(
        model="claude-3-5-sonnet-latest",
        max_tokens=300,
        messages=[
            {"role": "user", "content": prompt}
        ],
    )

    return resp.content[0].text

print(answer_user("Can I refund this failed subscription payment?"))

5. •Wire the output into your payments workflow

Treat Anthropic’s output as decision support. Parse structured responses and route them to Stripe, Adyen, or your internal ledger only after validation.

import json

def handle_request(question: str):
    result = answer_user(question)

    if result.strip().startswith("{"):
        payload = json.loads(result)
        action = payload.get("action")

        if action == "refund":
            # call your payments service here
            print("Trigger refund workflow")
        elif action == "chargeback_review":
            print("Trigger chargeback review workflow")
        else:
            print("No payment action required")
    else:
        print(result)

Testing the Integration

Run a simple end-to-end check: store one known policy chunk, query it, and confirm Anthropic responds using that retrieved context.

test_question = "What happens when a customer requests a refund?"
print(handle_request(test_question))

Expected output:

Refunds are processed within 5 business days.
No payment action required

If you test with an actionable request like “refund invoice 1234,” you should see either structured JSON or a routed workflow message depending on how you format the prompt and parser.

Real-World Use Cases

• •Payment support agents
  • •Retrieve policy clauses from pgvector and have Anthropic draft accurate customer responses.
• •Dispute handling assistants
  • •Pull chargeback procedures from your knowledge base and route cases into review queues.
• •Billing operations copilots
  • •Combine internal docs with payment status data to recommend refunds, retries, or escalations.

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.