How to Integrate AutoGen for wealth management with Docker for RAG

Combining AutoGen for wealth management with Docker gives you a clean way to run retrieval-augmented agents in isolated, reproducible environments. In practice, that means you can spin up document stores, vector search services, and agent workers in containers while AutoGen coordinates the workflow across portfolio analysis, client Q&A, and compliance checks.

Prerequisites

• •Python 3.10+
• •Docker Engine installed and running
• •Access to an AutoGen-compatible wealth management SDK or package
• •A local or remote RAG backend:
  • •PostgreSQL + pgvector, or
  • •Chroma, or
  • •FAISS inside a container
• •API keys or credentials for:
  • •your LLM provider
  • •any document ingestion source
• •Basic familiarity with:
  • •docker CLI
  • •Python virtual environments
  • •agent orchestration patterns

Integration Steps

1. •

   Create a Dockerized RAG service

   Start by running your retriever in Docker so the agent layer stays separate from storage and indexing concerns. For wealth management use cases, this keeps client documents, policy PDFs, and market research isolated from the orchestration code.

   import docker
   
   client = docker.from_env()
   
   container = client.containers.run(
       image="chromadb/chroma:latest",
       name="wealth-rag-store",
       ports={"8000/tcp": 8000},
       detach=True,
       remove=True,
   )
   
   print(f"Started container: {container.id[:12]}")
   

2. •

   Connect AutoGen agents to the retrieval layer

   Use AutoGen to define the assistant that will answer wealth management questions. The key is to point the agent at a retrieval tool that queries the Docker-hosted service.

   import os
   from autogen import AssistantAgent, UserProxyAgent
   
   llm_config = {
       "config_list": [
           {
               "model": "gpt-4o-mini",
               "api_key": os.environ["OPENAI_API_KEY"],
           }
       ],
       "temperature": 0,
   }
   
   assistant = AssistantAgent(
       name="wealth_advisor",
       llm_config=llm_config,
       system_message=(
           "You are a wealth management assistant. "
           "Use retrieved context before answering."
       ),
   )
   
   user_proxy = UserProxyAgent(
       name="client_proxy",
       human_input_mode="NEVER",
       code_execution_config=False,
   )
   

3. •

   Expose Docker-hosted retrieval as a Python tool

   In production, I prefer keeping retrieval as a normal Python function so AutoGen can call it deterministically. This example queries a containerized endpoint over HTTP; swap the URL for your own vector store API.

   import requests
   
   def retrieve_client_context(query: str) -> str:
       resp = requests.post(
           "http://localhost:8000/query",
           json={"query": query, "top_k": 5},
           timeout=30,
       )
       resp.raise_for_status()
       data = resp.json()
       return "\n".join([item["text"] for item in data["matches"]])
   
   context = retrieve_client_context("What is the client's risk tolerance?")
   print(context[:500])
   

4. •

   Register the retriever with AutoGen and route messages through it

   This is where the integration becomes useful. The agent can call retrieval first, then draft an answer grounded in client documents and investment policy statements.

   from autogen import register_function
   
   def rag_lookup(query: str) -> str:
       return retrieve_client_context(query)
   
   register_function(
       rag_lookup,
       caller=assistant,
       executor=user_proxy,
       name="rag_lookup",
       description="Retrieve relevant wealth management context from the Docker-hosted RAG store.",
   )
   
   chat_result = user_proxy.initiate_chat(
       assistant,
       message=(
           "Use rag_lookup to answer: "
           "Can this client increase equity allocation without violating policy?"
       ),
   )
   
   print(chat_result.summary)
   

5. •

   Add container lifecycle control from Python

   Don’t leave containers hanging around during tests or ephemeral runs. Manage them explicitly so your agent stack starts cleanly and tears down predictably.

   import docker
   
   client = docker.from_env()
   container = client.containers.get("wealth-rag-store")
   
   try:
       logs = container.logs(tail=20).decode("utf-8")
       print(logs)
   finally:
       container.stop()
       print("Stopped RAG container")
   

Testing the Integration

Run a smoke test that checks three things:

• •Docker container is reachable
• •Retrieval returns text
• •AutoGen produces an answer using retrieved context

def test_wealth_rag_flow():
    query = "Summarize client's asset allocation constraints."
    context = retrieve_client_context(query)

    assert isinstance(context, str)
    assert len(context) > 0

    result = user_proxy.initiate_chat(
        assistant,
        message=f"Using retrieved context only, answer: {query}",
    )

    print("Context OK:", context[:120])
    print("Answer OK:", result.summary)

test_wealth_rag_flow()

Expected output:

Context OK: Client IPS states equity exposure must remain below 60%...
Answer OK: The client's policy limits equity exposure to below 60%, so any increase must stay within that cap...

Real-World Use Cases

• •

  Client portfolio Q&A

  • •Answer questions like “Can I retire at 62?” using uploaded statements, IPS documents, and historical contribution data stored in containers.
• •

  Advisor copilot for compliance

  • •Check recommendations against suitability rules, concentration limits, and firm policies before they go out to clients.
• •

  Research-grounded meeting prep

  • •Pull recent market notes, fund factsheets, and household data into an AutoGen workflow that drafts meeting summaries for advisors.

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.