How to Integrate FastAPI for wealth management with LangChain for RAG

Combining FastAPI for wealth management with LangChain gives you a clean way to expose regulated financial workflows through HTTP while adding retrieval-augmented generation on top of your internal knowledge base. In practice, this means you can build an agent that answers portfolio questions, retrieves policy docs, and routes client requests through a proper API layer instead of hardcoding logic into the model.

Prerequisites

• •Python 3.10+
• •A running FastAPI application for wealth management
• •LangChain installed with your chosen LLM provider
• •A vector store for RAG, such as FAISS, Chroma, or Pinecone
• •Access to your internal documents:
  • •investment policy statements
  • •product fact sheets
  • •compliance notes
  • •advisor playbooks
• •Basic familiarity with:
  • •FastAPI
  • •pydantic
  • •LangChain retrievers and chains
• •Environment variables configured for your model provider, for example:
  • •OPENAI_API_KEY
  • •or equivalent keys for Anthropic / Azure OpenAI

Integration Steps

1. •

   Create the FastAPI service boundary for wealth queries

   Start by defining request and response models. This keeps your wealth management API explicit and makes it easier to attach LangChain later without leaking implementation details into the route layer.

   from fastapi import FastAPI
   from pydantic import BaseModel
   
   app = FastAPI(title="Wealth Management RAG API")
   
   class WealthQueryRequest(BaseModel):
       customer_id: str
       question: str
   
   class WealthQueryResponse(BaseModel):
       answer: str
       sources: list[str]
   

   Add a route that will eventually call your RAG pipeline:

   @app.post("/wealth/query", response_model=WealthQueryResponse)
   async def query_wealth(request: WealthQueryRequest):
       return WealthQueryResponse(
           answer="placeholder",
           sources=[]
       )
   

2. •

   Load documents and build the LangChain retriever

   The RAG side starts with ingestion. Use LangChain loaders, splitters, embeddings, and a vector store to make your wealth content searchable.

   from langchain_community.document_loaders import PyPDFLoader
   from langchain_text_splitters import RecursiveCharacterTextSplitter
   from langchain_openai import OpenAIEmbeddings
   from langchain_community.vectorstores import FAISS
   
   loader = PyPDFLoader("docs/wealth_policy.pdf")
   docs = loader.load()
   
   splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=120)
   chunks = splitter.split_documents(docs)
   
   embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
   vectorstore = FAISS.from_documents(chunks, embeddings)
   
   retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
   

   For production, persist the index instead of rebuilding it on every boot.

3. •

   Build the RAG chain with LangChain

   Use a prompt that keeps the model grounded in retrieved context. For wealth management, this matters because you want answers tied to policy text, not free-form speculation.

   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
   
   llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
   
   prompt = ChatPromptTemplate.from_messages([
       ("system", "You are a wealth management assistant. Answer only using the provided context."),
       ("human", "Question: {input}\n\nContext:\n{context}")
   ])
   
   doc_chain = create_stuff_documents_chain(llm, prompt)
   rag_chain = create_retrieval_chain(retriever, doc_chain)
   

4. •

   Wire LangChain into the FastAPI endpoint

   Now connect the route to the chain. You can pass customer metadata into retrieval filters later if your vector store supports it.

    @app.post("/wealth/query", response_model=WealthQueryResponse)
    async def query_wealth(request: WealthQueryRequest):
        result = rag_chain.invoke({"input": request.question})
   
        answer = result["answer"]
        source_docs = result.get("context", [])
   
        sources = []
        for doc in source_docs:
            src = doc.metadata.get("source", "unknown")
            sources.append(src)
   
        return WealthQueryResponse(answer=answer, sources=sources)
   

5. •

   Add basic guardrails for finance-specific behavior

   Don’t let the agent answer outside its lane. In wealth systems, you usually want policy grounding plus explicit refusal paths for advice that requires human review.

   def is_advice_request(question: str) -> bool:
       keywords = ["should i buy", "guaranteed return", "best stock", "recommend portfolio"]
       q = question.lower()
       return any(k in q for k in keywords)
   
   
    @app.post("/wealth/query-safe", response_model=WealthQueryResponse)
    async def query_wealth_safe(request: WealthQueryRequest):
        if is_advice_request(request.question):
            return WealthQueryResponse(
                answer="This request requires advisor review before making a recommendation.",
                sources=[]
            )
   
        result = rag_chain.invoke({"input": request.question})
        return WealthQueryResponse(
            answer=result["answer"],
            sources=[doc.metadata.get("source", "unknown") for doc in result.get("context", [])]
        )
   

Testing the Integration

Run FastAPI locally:

uvicorn main:app --reload

Then test the endpoint with curl or Python:

import requests

payload = {
    "customer_id": "CUST-10021",
    "question": "What does our policy say about equity allocation limits for conservative portfolios?"
}

response = requests.post("http://127.0.0.1:8000/wealth/query-safe", json=payload)
print(response.status_code)
print(response.json())

Expected output:

{
  "answer": "According to the policy document, conservative portfolios should maintain equity exposure within the approved range...",
  "sources": [
    "docs/wealth_policy.pdf"
  ]
}

If you get "placeholder" back or empty sources, check these first:

• •the retriever is pointing at populated documents
• •embeddings were created successfully
• •your prompt includes retrieved context
• •your route is calling rag_chain.invoke(...), not returning static data

Real-World Use Cases

• •

  Advisor copilot

  • •Answer questions like “What’s our IPS stance on alternatives?” using internal policy docs.
  • •Keep responses traceable with source citations.
• •

  Client servicing workflow

  • •Let relationship managers ask natural-language questions about account rules, fee schedules, or product constraints.
  • •Route sensitive requests to human advisors when needed.
• •

  Compliance-aware knowledge assistant

  • •Retrieve approved language for disclosures and suitability guidance.
  • •Reduce time spent searching PDFs and internal wikis during client calls.

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.