How to Integrate Next.js for wealth management with Vercel AI SDK for RAG

Integrating Next.js for wealth management with Vercel AI SDK gives you a clean path to build RAG-backed advisor workflows: client portals, portfolio Q&A, suitability checks, and document-aware assistants. The pattern is simple — Next.js handles the wealth management app surface, while Vercel AI SDK orchestrates retrieval and generation against your internal knowledge base.

Prerequisites

• •Python 3.10+
• •A Next.js for wealth management app already running with authenticated user sessions
• •Vercel AI SDK project configured in your Next.js app
• •Access to your knowledge sources:
  • •policy PDFs
  • •product sheets
  • •investment committee notes
  • •FAQ content
• •An embeddings provider and vector store:
  • •OpenAI, Anthropic, or another supported model provider
  • •Pinecone, pgvector, Weaviate, or similar
• •Environment variables set:
  • •VERCEL_AI_API_KEY
  • •NEXT_PUBLIC_WEALTH_APP_URL
  • •VECTOR_STORE_URL
  • •VECTOR_STORE_API_KEY

Integration Steps

1. •

   Expose wealth-management documents through a retrieval API

   Your Next.js for wealth management app should expose a server endpoint that returns normalized documents for indexing. In practice, this is the bridge between portfolio data, disclosures, and your RAG layer.

   import requests
   
   WEALTH_APP_URL = "https://wealth.example.com"
   API_KEY = "your-service-token"
   
   def fetch_documents():
       resp = requests.get(
           f"{WEALTH_APP_URL}/api/knowledge/documents",
           headers={"Authorization": f"Bearer {API_KEY}"}
       )
       resp.raise_for_status()
       return resp.json()
   
   docs = fetch_documents()
   print(f"Fetched {len(docs)} documents")
   

2. •

   Chunk and embed the documents for retrieval

   The Vercel AI SDK expects your app to retrieve relevant context before generation. In a Python-based ingestion service, chunk the content and create embeddings before storing them in your vector database.

   import os
   import requests
   
   EMBEDDINGS_URL = "https://api.openai.com/v1/embeddings"
   OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
   
   def embed_text(text: str):
       resp = requests.post(
           EMBEDDINGS_URL,
           headers={
               "Authorization": f"Bearer {OPENAI_API_KEY}",
               "Content-Type": "application/json",
           },
           json={
               "model": "text-embedding-3-small",
               "input": text,
           },
       )
       resp.raise_for_status()
       return resp.json()["data"][0]["embedding"]
   
   def chunk_text(text: str, size: int = 800):
       return [text[i:i+size] for i in range(0, len(text), size)]
   
   sample_doc = {
       "id": "fund-factsheet-001",
       "title": "Balanced Growth Fund Factsheet",
       "content": "This fund targets long-term capital appreciation..."
   }
   
   chunks = chunk_text(sample_doc["content"])
   vectors = [embed_text(chunk) for chunk in chunks]
   print(f"Created {len(vectors)} embeddings")
   

3. •

   Store embeddings in your vector database

   Once embedded, persist the chunks with metadata so the assistant can retrieve by client segment, product type, or compliance domain. This is where RAG becomes useful for regulated financial workflows.

   import os
   import requests
   
   VECTOR_STORE_URL = os.environ["VECTOR_STORE_URL"]
   VECTOR_STORE_API_KEY = os.environ["VECTOR_STORE_API_KEY"]
   
   def upsert_vectors(items):
       resp = requests.post(
           f"{VECTOR_STORE_URL}/vectors/upsert",
           headers={
               "Authorization": f"Bearer {VECTOR_STORE_API_KEY}",
               "Content-Type": "application/json",
           },
           json={"vectors": items},
       )
       resp.raise_for_status()
       return resp.json()
   
   payload = [
       {
           "id": f"{sample_doc['id']}-chunk-0",
           "values": vectors[0],
           "metadata": {
               "title": sample_doc["title"],
               "source": "nextjs-wealth-app",
               "doc_type": "factsheet",
               "client_segment": "retail",
           },
       }
   ]
   
   result = upsert_vectors(payload)
   print(result)
   

4. •

   Create a retrieval endpoint that Vercel AI SDK can call

   Your Next.js route should accept a user query, retrieve top matches from the vector store, and pass those results into the generation flow. The Vercel AI SDK’s streamText pattern works well here because it keeps responses responsive while context is assembled server-side.

    import os
    import requests
   
    VECTOR_STORE_URL = os.environ["VECTOR_STORE_URL"]
    VECTOR_STORE_API_KEY = os.environ["VECTOR_STORE_API_KEY"]
   
    def search_vectors(query_embedding, top_k=5):
        resp = requests.post(
            f"{VECTOR_STORE_URL}/vectors/query",
            headers={
                "Authorization": f"Bearer {VECTOR_STORE_API_KEY}",
                "Content-Type": "application/json",
            },
            json={
                "vector": query_embedding,
                "topK": top_k,
                "includeMetadata": True,
            },
        )
        resp.raise_for_status()
        return resp.json()["matches"]
   
    def build_context(matches):
        return "\n\n".join(
            f"[{m['metadata']['title']}] {m['metadata'].get('excerpt', '')}"
            for m in matches
        )
   
    # In production this query embedding comes from the same embedding model used at ingest time.
    query_embedding = vectors[0]
    matches = search_vectors(query_embedding)
    context = build_context(matches)
   
    print(context)
   

5. •

   Call Vercel AI SDK from your app layer using the retrieved context

   In the app route or server action that powers chat inside your wealth management UI, pass retrieved context into Vercel AI SDK’s generation call. This keeps model answers grounded in approved content instead of free-form guessing.

import os import requests

VERCEL_AI_ENDPOINT = os.environ["VERCEL_AI_ENDPOINT"] VERCEL_AI_API_KEY = os.environ["VERCEL_AI_API_KEY"]

def ask_assistant(user প্রশ্ন=None): payload = { "model": "gpt-4o-mini", "messages": [ { "role": "system", "content": ( "You are a wealth management assistant. " "Answer only from provided context." ), }, { "role": "user", # replace with real query + retrieved context in production # e.g. from streamText() inputs on the Next.js side # using retrieved RAG chunks from the vector store # tied to this client session. # # The exact Vercel AI SDK integration lives in your Next.js route. # # This Python call is used to verify transport and orchestration. # # Keep responses concise and cite source titles. # # user question: # # what is my portfolio exposure to equities?

            # placeholder to keep this example concrete
            
            
        },
    ],
}

resp = requests.post(
    VERCEL_AI_ENDPOINT,
    headers={
        "Authorization": f"Bearer {VERCEL_AI_API_KEY}",
        "Content-Type": "application/json",
    },
    json=payload,
)
resp.raise_for_status()
return resp.json()

print("Assistant wired")

## Testing the Integration

Use a known document and a controlled query. You want to verify three things: retrieval returns relevant chunks, context is passed through, and the assistant answer cites the expected source.

```python
import requests

def test_rag_flow():
    query = {"question": "What does the Balanced Growth Fund invest in?"}

    resp = requests.post(
        "https://wealth.example.com/api/assistant/rag-test",
        json=query,
        headers={"Content-Type": "application/json"}
    )
    resp.raise_for_status()
    return resp.json()

result = test_rag_flow()
print(result)

Expected output:

{
  "answer": "The Balanced Growth Fund invests primarily in diversified equities and fixed income instruments...",
  "sources": [
    {
      "title": "Balanced Growth Fund Factsheet",
      "_score": 0.92
    }
  ]
}

Real-World Use Cases

• •Client-facing portfolio assistant that answers questions from approved fund factsheets, account statements, and market commentary.
• •Advisor copilot that drafts suitability summaries by retrieving KYC notes, risk profiles, and product disclosures.
• •Compliance-aware Q&A system that routes policy questions through vetted internal documents before generating any response.

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.