How to Integrate LlamaIndex for lending with Supabase for RAG

Combining LlamaIndex for lending with Supabase gives you a clean path to build retrieval-augmented generation for credit workflows without bolting together a fragile stack. You use LlamaIndex to ingest and query lending documents, then use Supabase as the durable vector store and metadata layer that your agent can trust in production.

This is useful when your assistant needs to answer questions from loan policies, underwriting guides, borrower documents, and servicing notes. Instead of stuffing everything into prompts, you index the corpus once, store embeddings in Supabase, and let LlamaIndex retrieve the right chunks at query time.

Prerequisites

• •Python 3.10+
• •A Supabase project with:
  • •SUPABASE_URL
  • •SUPABASE_SERVICE_ROLE_KEY or an authenticated key with table access
• •A Postgres database in Supabase with the vector extension enabled
• •LlamaIndex installed with vector store support
• •An embedding model provider configured:
  • •OpenAI, Azure OpenAI, or another LlamaIndex-supported embedder
• •Lending documents ready to ingest:
  • •PDFs, DOCX, HTML, or plain text
• •Environment variables set in .env

Install the packages:

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

Integration Steps

1. •Set up your environment and clients.

import os
from dotenv import load_dotenv
from supabase import create_client

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

load_dotenv()

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

supabase = create_client(SUPABASE_URL, SUPABASE_KEY)

2. •Create or verify the Supabase table for vectors.

LlamaIndex expects a table that can store embeddings, text, and metadata. In practice you create this once in Supabase SQL editor.

create extension if not exists vector;

create table if not exists lending_chunks (
  id bigserial primary key,
  content text,
  metadata jsonb,
  embedding vector(1536)
);

If you are using a different embedding model dimension, match the vector size to that model.

3. •Configure LlamaIndex to write embeddings into Supabase.

vector_store = SupabaseVectorStore(
    postgres_connection_string=f"postgresql://postgres:{os.environ['SUPABASE_DB_PASSWORD']}@{os.environ['SUPABASE_DB_HOST']}:{os.environ.get('SUPABASE_DB_PORT', '5432')}/{os.environ['SUPABASE_DB_NAME']}",
    collection_name="lending_chunks",
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

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

4. •Ingest lending documents into the index.

Point this at your policy folder or loan document directory. LlamaIndex will chunk the files, embed them, and persist them through the Supabase vector store.

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

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

print(f"Indexed {len(documents)} source documents")

5. •Query the index from your lending agent.

This is where RAG starts paying off. The retriever pulls relevant chunks from Supabase and your agent can answer with grounded context.

query_engine = index.as_query_engine(similarity_top_k=3)

response = query_engine.query(
    "What documents are required for a small business term loan application?"
)

print(response)

If you want direct control over retrieval before generation, use the retriever API:

retriever = index.as_retriever(similarity_top_k=5)
nodes = retriever.retrieve("late payment policy for consumer loans")

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

Testing the Integration

Run a focused query against a known policy document and confirm that the returned answer cites relevant chunks from your corpus.

test_query = "What is the minimum credit score required for unsecured personal loans?"
response = query_engine.query(test_query)

print("QUERY:", test_query)
print("ANSWER:", response)

Expected output:

QUERY: What is the minimum credit score required for unsecured personal loans?
ANSWER: Based on the lending policy documents, unsecured personal loans require a minimum credit score of 680...

If you get an empty or irrelevant response:

• •Check that documents were actually loaded from ./lending_docs
• •Verify your embedding dimension matches the Supabase vector(n) column
• •Confirm row-level security is not blocking inserts or reads
• •Make sure your query engine is pointing at the same collection/table you ingested into

Real-World Use Cases

• •

  Loan policy assistant
  Answer internal questions like eligibility criteria, debt-to-income thresholds, collateral rules, and exception handling directly from policy docs.

• •

  Underwriting copilot
  Retrieve borrower-specific supporting evidence from uploaded files and summarize gaps before an underwriter reviews the file.

• •

  Servicing and collections support
  Ground responses in servicing manuals and repayment policies so agents can answer delinquency and hardship-plan questions consistently.

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.