How to Integrate LangChain for investment banking with PostgreSQL for RAG

Combining LangChain with PostgreSQL gives you a practical RAG stack for investment banking workflows: store deal documents, research notes, and compliance policies in Postgres, then let an agent retrieve the right context before answering. That means faster analyst support, better auditability, and fewer hallucinations when users ask about mandates, comps, or transaction history.

Prerequisites

• •Python 3.10+
• •PostgreSQL 14+ running locally or in your VPC
• •A PostgreSQL database created for embeddings and metadata
• •An OpenAI API key or another embedding/LLM provider supported by LangChain
• •Installed packages:
  • •langchain
  • •langchain-openai
  • •langchain-postgres
  • •psycopg[binary]
  • •sqlalchemy
• •A corpus of banking documents ready to ingest:
  • •pitch decks
  • •CIMs
  • •earnings notes
  • •internal policy docs
• •Optional but useful:
  • •pgvector extension enabled in PostgreSQL

Integration Steps

1) Install dependencies and enable pgvector

Start by installing the Python packages and enabling vector support in Postgres. For RAG, you want embeddings stored close to your relational metadata so filtering by deal team, sector, or date is cheap.

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

CREATE EXTENSION IF NOT EXISTS vector;

If you are using managed Postgres and cannot install extensions yourself, confirm that pgvector is available in the service plan.

2) Connect LangChain to PostgreSQL

Use LangChain’s PostgreSQL vector store to persist document chunks and embeddings. This is the core bridge between your banking content and retrieval layer.

from langchain_openai import OpenAIEmbeddings
from langchain_postgres.vectorstores import PGVector

DB_CONNECTION = "postgresql+psycopg://bank_user:bank_pass@localhost:5432/investment_banking"

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

vectorstore = PGVector(
    embeddings=embeddings,
    collection_name="ib_rag_docs",
    connection=DB_CONNECTION,
    use_jsonb=True,
)

That PGVector instance is what LangChain uses to call methods like add_documents() and similarity_search(). In production, keep the collection name stable per domain, for example mna_docs, credit_research, or compliance_policy.

3) Load investment banking documents into Postgres

Chunk your documents before storage. Banking docs are dense, so chunking by section headings or page ranges works better than naive fixed-size splits.

from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter

raw_docs = [
    Document(
        page_content="Company X reported FY24 EBITDA of $420M. Management guided to $450M next year.",
        metadata={"source": "earnings_note_q4", "sector": "industrials", "deal_team": "north_america"}
    ),
    Document(
        page_content="The acquisition target has recurring revenue exposure above 80% and low customer concentration.",
        metadata={"source": "cim_target_co", "sector": "software", "deal_team": "mna"}
    ),
]

splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
chunks = splitter.split_documents(raw_docs)

vectorstore.add_documents(chunks)

This writes both embeddings and metadata into PostgreSQL. The metadata is important because investment banking users usually want retrieval constrained by sector, client, region, or transaction type.

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

Once the vectors are stored, expose them through a retriever. Then connect that retriever to a LangChain chain so user questions get grounded in Postgres-backed context.

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

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

retriever = vectorstore.as_retriever(
    search_kwargs={
        "k": 4,
        "filter": {"sector": "software"}
    }
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an investment banking analyst assistant. Use only retrieved context."),
    ("human", "{input}\n\nContext:\n{context}")
])

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

This pattern is stable for production agents. The retriever handles context selection; the LLM handles synthesis; PostgreSQL remains your durable source of truth for searchable knowledge.

5) Query the system from your agent layer

Now call the chain from your app or agent runtime. This is where analysts get answers on comps, deal summaries, or policy interpretation without searching five different systems.

response = rag_chain.invoke({
    "input": "What metrics suggest the software target is attractive for acquisition?"
})

print(response["answer"])

If you want stronger control in an agent system, wrap this chain as one tool and keep other tools separate for SQL lookup, market data fetches, or CRM access.

Testing the Integration

Run a quick smoke test after ingestion. You want to verify three things: documents were stored, retrieval works, and the model answers from retrieved context rather than guessing.

results = vectorstore.similarity_search(
    "What was the EBITDA guidance?",
    k=2,
)

for doc in results:
    print(doc.page_content)
    print(doc.metadata)

Expected output should look like this:

Company X reported FY24 EBITDA of $420M. Management guided to $450M next year.
{'source': 'earnings_note_q4', 'sector': 'industrials', 'deal_team': 'north_america'}

If you get empty results:

• •confirm embeddings were created successfully
• •check your Postgres connection string
• •verify the collection name matches across ingestion and retrieval
• •make sure your filter values match stored metadata exactly

Real-World Use Cases

• •

  Deal desk assistant

  • •Retrieve prior CIMs, teaser language, diligence notes, and comparable transactions for bankers preparing new materials.
• •

  Compliance Q&A

  • •Ground responses in internal policy docs so analysts can ask whether a statement complies with house rules before sending it externally.
• •

  Research copilot

  • •Let users query earnings notes and sector reports with filters like sector, region, or coverage team to produce tighter summaries.

The clean pattern here is simple: LangChain orchestrates retrieval and generation; PostgreSQL stores vectors plus metadata; your agent sits on top as the interface. For investment banking teams that need traceable answers and controlled access to sensitive content, that’s the right architecture.

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.