How to Integrate LangChain for pension funds with PostgreSQL for RAG

Why this integration matters

Pension fund workflows are document-heavy and audit-sensitive. If you want an AI agent that can answer policy questions, summarize member communications, or retrieve investment rules with traceable sources, LangChain for pension funds plus PostgreSQL gives you a practical RAG stack: LangChain handles orchestration, PostgreSQL stores your embeddings and metadata, and your agent gets grounded answers instead of free-form guesses.

Prerequisites

• •Python 3.10+
• •A running PostgreSQL instance
• •pgvector enabled in PostgreSQL
• •Access to your LangChain-compatible LLM provider
• •A document set for the pension fund domain:
  • •investment policy statements
  • •retirement benefit guides
  • •contribution rules
  • •compliance memos
• •Python packages:
  • •langchain
  • •langchain-community
  • •langchain-openai or your model provider package
  • •psycopg2-binary
  • •sqlalchemy
• •Environment variables configured:
  • •OPENAI_API_KEY or equivalent
  • •POSTGRES_URL

Integration Steps

1. •Install the dependencies.

pip install langchain langchain-community langchain-openai psycopg2-binary sqlalchemy pgvector

2. •Prepare PostgreSQL for vector storage.

You need a database, the vector extension, and a table managed by LangChain’s PGVector integration.

import os
import psycopg2

conn = psycopg2.connect(os.environ["POSTGRES_URL"])
conn.autocommit = True

with conn.cursor() as cur:
    cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
    cur.execute("""
        CREATE TABLE IF NOT EXISTS pension_docs (
            id bigserial PRIMARY KEY,
            content text,
            metadata jsonb,
            embedding vector(1536)
        );
    """)

conn.close()

If you use LangChain’s built-in PGVector store, you usually do not create the table manually. I’m showing this because pension systems often need explicit DB control for audits and migrations.

3. •Load pension documents, split them, embed them, and write them to PostgreSQL.

This is the core ingestion path. LangChain’s splitter breaks long policy documents into chunks, embeddings convert chunks into vectors, and PGVector persists them in Postgres.

from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores.pgvector import PGVector
from langchain_core.documents import Document

connection_string = os.environ["POSTGRES_URL"]
collection_name = "pension_fund_rag"

docs = [
    Document(
        page_content="The pension fund permits early retirement at age 55 subject to board approval.",
        metadata={"source": "retirement_policy.pdf", "section": "early_retirement"}
    ),
    Document(
        page_content="Employer contributions are matched up to 8% of eligible salary.",
        metadata={"source": "contribution_rules.pdf", "section": "employer_match"}
    ),
]

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)

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

vectorstore = PGVector.from_documents(
    documents=chunks,
    embedding=embeddings,
    collection_name=collection_name,
    connection_string=connection_string,
    pre_delete_collection=True,
)

4. •Build a retriever and wire it into a LangChain RAG chain.

This is where the agent becomes useful. The retriever fetches relevant policy chunks from Postgres, and the chain feeds those chunks into the LLM with a strict prompt.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains.retrieval import create_retrieval_chain

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

prompt = ChatPromptTemplate.from_template(
    """You are a pension fund assistant.
Answer only using the context below.
If the answer is not in the context, say you do not have enough information.

Context:
{context}

Question: {input}
"""
)

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

document_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, document_chain)

response = rag_chain.invoke({"input": "What is the early retirement age?"})
print(response["answer"])

5. •Add source tracing for production use.

For pension funds, you need citations. Return metadata from retrieved documents so compliance teams can verify where each answer came from.

docs = retriever.invoke("What are employer contribution limits?")

for doc in docs:
    print({
        "source": doc.metadata.get("source"),
        "section": doc.metadata.get("section"),
        "content": doc.page_content
    })

Testing the Integration

Run a simple retrieval query against known policy text and confirm the answer comes back grounded in stored documents.

result = rag_chain.invoke({"input": "How much does the employer match?"})
print(result["answer"])

sources = retriever.invoke("How much does the employer match?")
print([doc.metadata for doc in sources])

Expected output:

Employer contributions are matched up to 8% of eligible salary.
[{'source': 'contribution_rules.pdf', 'section': 'employer_match'}]

If you get an empty answer or irrelevant context, check these first:

• •The embeddings model matches the vector dimension in PostgreSQL.
• •The documents were actually inserted into PGVector.
• •Your search_kwargs["k"] is large enough to retrieve relevant chunks.
• •The prompt forces grounded answers instead of general model behavior.

Real-World Use Cases

• •Member support agents that answer questions about retirement age, contribution caps, vesting rules, and benefit eligibility using internal policy documents.
• •Compliance assistants that retrieve source-backed explanations from investment committee notes, actuarial memos, and regulatory updates.
• •Internal operations bots that summarize plan changes from PDFs stored in Postgres and expose them through an authenticated chat interface.

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.