How to Integrate LlamaIndex for wealth management with Supabase for RAG

Combining LlamaIndex for wealth management with Supabase gives you a clean RAG stack for regulated financial workflows. You get LlamaIndex handling document ingestion, chunking, retrieval, and response synthesis, while Supabase gives you Postgres-backed storage, auth, and vector search for portfolio notes, policy docs, research memos, and client statements.

For wealth management teams, this matters because advisors need answers grounded in approved documents, not generic model output. The pattern here is simple: store embeddings and metadata in Supabase, retrieve relevant context through LlamaIndex, and use that context to answer questions about portfolios, suitability rules, product sheets, or market commentary.

Prerequisites

• •Python 3.10+
• •A Supabase project with:
  • •SUPABASE_URL
  • •SUPABASE_SERVICE_ROLE_KEY or anon key for read-only testing
• •A Postgres table ready for vector storage in Supabase
• •Installed packages:
  • •llama-index
  • •llama-index-vector-stores-supabase
  • •supabase
  • •psycopg2-binary or the driver required by your setup
• •An OpenAI API key or another embedding/LLM provider configured for LlamaIndex
• •Wealth management source documents:
  • •investment policy statements
  • •product fact sheets
  • •advisor playbooks
  • •compliance FAQs

Integration Steps

1. •

   Install dependencies and set environment variables

   Start with the packages that connect LlamaIndex to Supabase and provide embeddings for retrieval.

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

   Configure your environment:

   import os
   from dotenv import load_dotenv
   
   load_dotenv()
   
   os.environ["SUPABASE_URL"] = "https://your-project.supabase.co"
   os.environ["SUPABASE_SERVICE_ROLE_KEY"] = "your-service-role-key"
   os.environ["OPENAI_API_KEY"] = "your-openai-key"
   

2. •

   Create the Supabase client and verify the connection

   Use the official Supabase Python client first. This is useful for health checks, metadata lookups, and later operational tasks like syncing access control.

   from supabase import create_client
   
   supabase_url = os.environ["SUPABASE_URL"]
   supabase_key = os.environ["SUPABASE_SERVICE_ROLE_KEY"]
   
   supabase = create_client(supabase_url, supabase_key)
   
   response = supabase.table("documents").select("id").limit(1).execute()
   print(response)
   

   For a production system, keep this table separate from operational app data. In wealth management systems, document metadata should include fields like client_segment, product_type, jurisdiction, and approved_at.

3. •

   Initialize LlamaIndex with a Supabase vector store

   LlamaIndex has a Supabase vector store integration that lets you persist embeddings in Postgres via Supabase. This is the part that turns your document corpus into retrievable knowledge.

   from llama_index.core import VectorStoreIndex, StorageContext
   from llama_index.core.schema import TextNode
   from llama_index.vector_stores.supabase import SupabaseVectorStore
   
   vector_store = SupabaseVectorStore(
       postgres_connection_string="postgresql://postgres:password@db.your-project.supabase.co:5432/postgres",
       collection_name="wealth_docs",
       dimension=1536,
       query_name="match_documents",
   )
   
   storage_context = StorageContext.from_defaults(vector_store=vector_store)
   

   If you are using OpenAI embeddings with the default text-embedding model family, dimension=1536 is the common value. Match this to whatever embedding model you actually deploy.

4. •

   Ingest wealth management documents into Supabase through LlamaIndex

   Load your source files into nodes and build the index. In a real deployment, your ingestion pipeline would run after compliance approval or content publishing.

    from llama_index.core import SimpleDirectoryReader
    from llama_index.core import Settings
    from llama_index.embeddings.openai import OpenAIEmbedding
   
    Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
   
    documents = SimpleDirectoryReader("./wealth_docs").load_data()
   
    index = VectorStoreIndex.from_documents(
        documents,
        storage_context=storage_context,
    )
   

   If you want more control over metadata for filtering by region or product line, attach it before indexing:

   nodes = [
       TextNode(
           text="Model portfolio description...",
           metadata={
               "client_segment": "HNW",
               "jurisdiction": "UK",
               "doc_type": "factsheet",
           },
       )
   ]
   
   index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)
   

5. •

   Build a query engine for RAG responses

   Once indexed, expose a query engine that pulls relevant chunks from Supabase and synthesizes an answer with citations.

   from llama_index.core.query_engine import RetrieverQueryEngine
   
    query_engine = index.as_query_engine(similarity_top_k=3)
   
    response = query_engine.query(
        "What does the approved investment policy say about equity allocation limits for balanced portfolios?"
    )
   
    print(str(response))
   

Testing the Integration

Use a small test corpus first: one IPS document and one product fact sheet. Then run a query that should clearly hit one of those sources.

from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine

query_engine = index.as_query_engine(similarity_top_k=2)

response = query_engine.query(
    "Summarize the risk limits for discretionary portfolios."
)

print("ANSWER:")
print(response)

print("\nSOURCES:")
for source in response.source_nodes:
    print(source.node.metadata)

Expected output:

ANSWER:
The approved risk limits require discretionary portfolios to remain within the stated volatility band...

SOURCES:
{'client_segment': 'HNW', 'jurisdiction': 'UK', 'doc_type': 'ips'}
{'client_segment': 'HNW', 'jurisdiction': 'UK', 'doc_type': 'factsheet'}

If you get empty sources or irrelevant chunks:

• •check that embeddings were created with the correct dimension
• •confirm the table/function names in your Supabase vector setup match what LlamaIndex expects
• •make sure your documents were actually ingested into the same collection name

Real-World Use Cases

• •

  Advisor copilot

  • •Answer questions about portfolio construction rules, product suitability, fee schedules, and approved market commentary using only internal content.
• •

  Compliance-aware client service

  • •Retrieve jurisdiction-specific disclosures and policy language before generating responses to client requests.
• •

  Research assistant for investment teams

  • •Search across house views, research notes, meeting minutes, and asset allocation memos with citations back to source material.

The production pattern here is straightforward: keep authoritative wealth content in controlled documents, index it into Supabase through LlamaIndex, then force every agent answer through retrieval. That gives you traceability, better grounding, and a path to audit what the model used when it answered a client-facing question.

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.