How to Integrate LangChain for wealth management with PostgreSQL for RAG

Combining LangChain for wealth management with PostgreSQL gives you a practical RAG stack for client-facing and advisor-facing workflows. You can store portfolio notes, policy documents, suitability rules, and meeting transcripts in Postgres, then retrieve the right context at query time to ground responses from your agent.

For wealth management teams, this matters because the model should not guess. It should answer from approved internal knowledge, client records, and current product documentation.

Prerequisites

• •Python 3.10+
• •PostgreSQL 14+ running locally or in your cloud environment
• •A PostgreSQL database and user with write access
• •OPENAI_API_KEY or your preferred LLM provider key
• •langchain, langchain-openai, langchain-postgres, and psycopg installed
• •A document source for wealth management content:
  • •PDF policy docs
  • •advisor notes
  • •product sheets
  • •compliance playbooks

Install the packages:

pip install langchain langchain-openai langchain-postgres psycopg[binary] sqlalchemy

Integration Steps

1) Connect to PostgreSQL and prepare a vector table

Use PostgreSQL as the persistence layer for your RAG corpus. In production, keep embeddings in a dedicated schema so you can manage access control cleanly.

import os
from sqlalchemy import create_engine, text

POSTGRES_URL = os.getenv(
    "POSTGRES_URL",
    "postgresql+psycopg://rag_user:rag_pass@localhost:5432/wealth_rag"
)

engine = create_engine(POSTGRES_URL)

with engine.begin() as conn:
    conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
    conn.execute(text("""
        CREATE TABLE IF NOT EXISTS wealth_docs (
            id SERIAL PRIMARY KEY,
            content TEXT NOT NULL,
            metadata JSONB DEFAULT '{}'::jsonb
        )
    """))

print("PostgreSQL is ready")

If you are using pgvector, make sure the extension is installed on the server. Without it, you do not get efficient similarity search.

2) Load wealth management documents into LangChain

Use LangChain loaders to ingest policy docs or advisor notes. For structured wealth workflows, keep metadata like client segment, jurisdiction, and document type.

from langchain_core.documents import Document

docs = [
    Document(
        page_content="Client risk profile must be reviewed every 12 months.",
        metadata={"source": "compliance_playbook", "jurisdiction": "US", "type": "policy"}
    ),
    Document(
        page_content="Recommended allocation for balanced portfolios: 60% equity, 30% fixed income, 10% cash.",
        metadata={"source": "investment_policy", "jurisdiction": "US", "type": "guideline"}
    ),
]

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

In a real system, replace this with PDF parsing or a document ingestion job. The important part is that every chunk carries metadata your agent can filter on later.

3) Create embeddings and store them in PostgreSQL

LangChain’s PostgreSQL vector store handles embedding persistence and retrieval. This is the core of your RAG setup.

import os
from langchain_openai import OpenAIEmbeddings
from langchain_postgres import PGVector

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

vector_store = PGVector(
    embeddings=embeddings,
    collection_name="wealth_management_kb",
    connection=POSTGRES_URL,
    use_jsonb=True,
)

vector_store.add_documents(docs)
print("Documents embedded and stored in PostgreSQL")

This gives you durable retrieval across agent runs. For regulated environments, that matters because you want repeatable indexing and auditable storage behavior.

4) Build a retriever and wire it into a LangChain RAG chain

Now retrieve relevant context from Postgres and feed it into an LLM chain. This is where LangChain turns your database into a knowledge layer.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

retriever = vector_store.as_retriever(search_kwargs={"k": 3})

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

prompt = ChatPromptTemplate.from_template("""
You are an assistant for wealth management operations.
Answer only using the provided context.

Context:
{context}

Question:
{question}
""")

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

question = "How often should client risk profiles be reviewed?"
context_docs = retriever.invoke(question)

chain = prompt | llm | StrOutputParser()
response = chain.invoke({
    "context": format_docs(context_docs),
    "question": question
})

print(response)

For production agents, add filters on metadata so one client’s documents never bleed into another client’s retrieval scope.

5) Add metadata filtering for advisor-specific queries

Wealth systems usually need jurisdiction-aware retrieval. Use metadata filters when querying so the agent only sees approved content.

retriever_us = vector_store.as_retriever(
    search_kwargs={
        "k": 5,
        "filter": {"jurisdiction": {"$eq": "US"}}
    }
)

docs_us = retriever_us.invoke("What is the balanced portfolio allocation?")
for d in docs_us:
    print(d.metadata, d.page_content)

This pattern is what keeps your RAG system usable in regulated workflows. Without filtering, retrieval quality may look fine in testing and fail in production governance reviews.

Testing the Integration

Run a simple retrieval test against a known query. You want to confirm both storage and semantic search are working before wiring this into an agent loop.

query = "How often must risk profiles be reviewed?"
results = retriever.invoke(query)

print(f"Retrieved {len(results)} docs")
for i, doc in enumerate(results, start=1):
    print(f"{i}. {doc.page_content}")

Expected output:

Retrieved 1 docs
1. Client risk profile must be reviewed every 12 months.

If you get zero results, check these first:

• •Embeddings model key is set correctly
• •PostgreSQL vector extension exists
• •Your connection string points to the right database
• •Documents were actually inserted into the collection

Real-World Use Cases

• •Advisor copilot that answers policy questions from internal compliance docs before sending recommendations to clients.
• •Client servicing agent that retrieves portfolio notes, KYC history, and product factsheets from PostgreSQL-backed RAG.
• •Suitability review assistant that checks whether an investment recommendation aligns with stored risk profile rules and jurisdiction constraints.

This stack works well when you need traceable answers backed by internal data. LangChain handles orchestration; PostgreSQL gives you durable storage and retrieval control; together they give you a clean path to production RAG in wealth management.

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.