How to Integrate LlamaIndex for fintech with Supabase for RAG

Combining LlamaIndex for fintech with Supabase gives you a practical RAG stack for regulated data: ingest financial documents, chunk and index them, store embeddings in Postgres, and retrieve grounded answers with access control. This is the pattern I use when teams need an AI agent that can answer questions from policy docs, KYC files, product terms, or internal research without copying data into a separate vector database.

The value is simple: LlamaIndex handles document parsing, indexing, and retrieval orchestration, while Supabase gives you Postgres, auth, and pgvector in one place. For fintech teams, that means fewer moving parts and a cleaner story for auditability, tenancy, and operational control.

Prerequisites

• •Python 3.10+
• •A Supabase project with:
  • •pgvector enabled
  • •a table for vectors or permission to let LlamaIndex create it
• •API keys:
  • •SUPABASE_URL
  • •SUPABASE_SERVICE_ROLE_KEY or an authenticated anon key for limited access
  • •an embedding model key if you use OpenAI or another hosted embedder
• •Installed packages:
  • •llama-index
  • •llama-index-vector-stores-supabase
  • •supabase
  • •your embedding provider package, if required
• •A folder of source docs:
  • •PDFs
  • •markdown files
  • •CSV exports
  • •policy text files

Integration Steps

1. •Install the dependencies.

pip install llama-index llama-index-vector-stores-supabase supabase openai python-dotenv

2. •Load your environment variables and initialize the Supabase client.

Use the service role key for backend ingestion jobs. Do not ship that key to browsers or mobile clients.

import os
from dotenv import load_dotenv
from supabase import create_client

load_dotenv()

SUPABASE_URL = os.environ["SUPABASE_URL"]
SUPABASE_SERVICE_ROLE_KEY = os.environ["SUPABASE_SERVICE_ROLE_KEY"]

supabase = create_client(SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY)
print("Supabase connected:", supabase is not None)

3. •Load fintech documents into LlamaIndex.

This example uses local files. In production, you can replace this with S3, SharePoint, or a document API.

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader(
    input_dir="./data/fintech_docs",
    recursive=True,
).load_data()

print(f"Loaded {len(documents)} documents")

4. •Create embeddings and connect LlamaIndex to Supabase as the vector store.

This is the core integration point. LlamaIndex writes embeddings into Supabase/Postgres through the Supabase vector store adapter.

import os
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.supabase import SupabaseVectorStore

embed_model = OpenAIEmbedding(model="text-embedding-3-small")

vector_store = SupabaseVectorStore(
    postgres_connection_string=os.environ["SUPABASE_POSTGRES_CONNECTION_STRING"],
    collection_name="fintech_rag",
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
    embed_model=embed_model,
)

print("Index built in Supabase")

5. •Build a retriever/query engine and run a grounded question.

For fintech workflows, keep retrieval narrow and deterministic. You want answers backed by source chunks, not free-form speculation.

query_engine = index.as_query_engine(similarity_top_k=3)

response = query_engine.query(
    "What is the refund policy for wire transfer disputes?"
)

print(response)

If you want more control over retrieval in an agent pipeline, use the retriever directly:

retriever = index.as_retriever(similarity_top_k=5)
nodes = retriever.retrieve("AML escalation rules for suspicious activity")

for node in nodes:
    print(node.score)
    print(node.node.get_text()[:300])
    print("---")

Testing the Integration

Run a smoke test against one known document and one known query. The goal is to confirm three things:

• •documents were ingested
• •embeddings were stored in Supabase
• •retrieval returns source-backed text

test_query = "What does the customer onboarding policy say about identity verification?"
response = query_engine.query(test_query)

print("ANSWER:")
print(response)

print("\nSOURCE NODES:")
for node in response.source_nodes:
    print(node.score)
    print(node.node.get_text()[:200])
    print("---")

Expected output should look like this:

ANSWER:
Customers must complete identity verification before account activation...

SOURCE NODES:
0.89
Identity verification is required before accounts are activated...
---
0.84
KYC checks include government-issued ID and address validation...
---

If you get empty results:

• •check that your docs were actually loaded
• •confirm the Postgres connection string points to the right project
• •verify pgvector is enabled in Supabase
• •make sure your embedding model key is valid

Real-World Use Cases

• •

  Policy Q&A for compliance teams

  • •Ask questions over KYC, AML, sanctions screening, underwriting rules, or internal controls.
  • •Return citations from stored source docs so reviewers can trace every answer.
• •

  Customer support copilots

  • •Ground responses in product terms, fee schedules, dispute procedures, and loan servicing policies.
  • •Keep answers consistent across chat agents and human support tooling.
• •

  Analyst assistants for financial research

  • •Index earnings notes, market commentary, risk memos, or portfolio reports.
  • •Let analysts query across internal research without exposing raw files to every tool in the stack.

The pattern holds up well in regulated environments because it keeps storage simple and retrieval auditable. LlamaIndex manages document intelligence; Supabase stores vectors close to your app data; your agent gets fast RAG with fewer systems to maintain.

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.