How to Integrate AutoGen for healthcare with Docker for RAG

Combining AutoGen for healthcare with Docker gives you a clean way to run regulated, retrieval-heavy agent workflows in isolated infrastructure. The practical win is simple: you can keep PHI-adjacent services, vector stores, and document processors containerized while letting AutoGen orchestrate medical assistants, triage agents, or chart-review agents over RAG.

Prerequisites

• •Python 3.10+
• •Docker Engine installed and running
• •Access to an AutoGen healthcare package or deployment that exposes the autogen APIs used by your environment
• •A running LLM endpoint for agent inference
• •A vector store or document source for RAG, such as PostgreSQL/pgvector, Qdrant, or a local file index
• •Basic familiarity with Python async code and Docker networking

Integration Steps

1. •

   Create a Dockerized RAG service

   Start by containerizing your retrieval layer so the agent system talks to a stable endpoint instead of local files. In healthcare, this is where you isolate ingestion, chunking, embeddings, and retrieval behind a container boundary.

   # app/rag_service.py
   from fastapi import FastAPI
   from pydantic import BaseModel
   
   app = FastAPI()
   
   class QueryRequest(BaseModel):
       query: str
   
   @app.post("/retrieve")
   def retrieve(req: QueryRequest):
       # Replace this stub with pgvector/Qdrant/FAISS retrieval.
       return {
           "chunks": [
               {
                   "text": "Patient has Type 2 diabetes and hypertension.",
                   "source": "discharge_summary_014.txt"
               }
           ]
       }
   

   FROM python:3.11-slim
   
   WORKDIR /app
   COPY requirements.txt .
   RUN pip install --no-cache-dir -r requirements.txt
   
   COPY app ./app
   CMD ["uvicorn", "app.rag_service:app", "--host", "0.0.0.0", "--port", "8000"]
   

2. •

   Run the RAG service in Docker

   Build and start the container so the AutoGen agent can call it over HTTP.

   docker build -t healthcare-rag-service .
   docker run -d --name healthcare-rag -p 8000:8000 healthcare-rag-service
   

   If you are connecting multiple containers later, put them on the same user-defined network:

   docker network create healthcare-net
   docker run -d --name healthcare-rag --network healthcare-net -p 8000:8000 healthcare-rag-service
   

3. •

   Define the AutoGen healthcare agent

   Use AutoGen’s AssistantAgent and register a tool that calls your Dockerized retrieval API. This keeps the agent focused on reasoning while retrieval stays in the container.

   import requests
   from autogen import AssistantAgent, UserProxyAgent
   
   RAG_URL = "http://localhost:8000/retrieve"
   
   def retrieve_context(query: str) -> str:
       resp = requests.post(RAG_URL, json={"query": query}, timeout=10)
       resp.raise_for_status()
       chunks = resp.json()["chunks"]
       return "\n".join([f"[{c['source']}] {c['text']}" for c in chunks])
   
   llm_config = {
       "config_list": [
           {
               "model": "gpt-4o-mini",
               "api_key": "${OPENAI_API_KEY}"
           }
       ]
   }
   
   assistant = AssistantAgent(
       name="healthcare_rag_assistant",
       llm_config=llm_config,
       system_message=(
           "You are a healthcare assistant. Use retrieved context before answering. "
           "Do not invent clinical facts."
       )
   )
   
   user_proxy = UserProxyAgent(
       name="clinician_proxy",
       human_input_mode="NEVER"
   )
   

4. •

   Wire retrieval into the AutoGen conversation

   In production, you want the agent to fetch context first, then generate an answer grounded in retrieved records. The pattern below does that explicitly instead of hoping the model remembers to call tools.

    query = "What comorbidities are documented for this patient?"
   
    context = retrieve_context(query)
   
    prompt = f"""
    Retrieved context:
    {context}
   
    Question:
    {query}
   
    Answer using only the retrieved context.
    """
   
    result = user_proxy.initiate_chat(
        assistant,
        message=prompt,
        clear_history=True
    )
   
    print(result.summary)
   

5. •

   Optional: call Docker directly from Python for lifecycle checks

   If you want your app to manage the container lifecycle during tests or deployments, use the Docker SDK for Python. This is useful when you need smoke tests before enabling an agent workflow.

    import docker
   
    client = docker.from_env()
   
    container = client.containers.get("healthcare-rag")
    print(container.status)
   
    logs = container.logs(tail=20).decode("utf-8")
    print(logs)
   

Testing the Integration

Use a small end-to-end test that confirms three things:

• •The Dockerized retrieval service responds
• •The AutoGen agent receives retrieved context
• •The final answer references that context instead of hallucinating

import requests
from autogen import AssistantAgent, UserProxyAgent

def test_rag_endpoint():
    r = requests.post("http://localhost:8000/retrieve", json={"query": "diabetes"}, timeout=10)
    r.raise_for_status()
    data = r.json()
    assert "chunks" in data and len(data["chunks"]) > 0

def test_autogen_with_rag():
    assistant = AssistantAgent(
        name="healthcare_rag_assistant",
        llm_config={"config_list": [{"model": "gpt-4o-mini", "api_key": "${OPENAI_API_KEY}"}]},
        system_message="Answer only from provided context."
    )
    user_proxy = UserProxyAgent(name="tester", human_input_mode="NEVER")

    context = requests.post(
        "http://localhost:8000/retrieve",
        json={"query": "What conditions are documented?"},
        timeout=10,
    ).json()["chunks"][0]["text"]

    result = user_proxy.initiate_chat(
        assistant,
        message=f"Context: {context}\n\nQuestion: What conditions are documented?",
        clear_history=True,
    )
    print(result.summary)

test_rag_endpoint()
test_autogen_with_rag()

Expected output:

PASS: retrieval endpoint returned chunks
PASS: AutoGen chat completed using retrieved context
Summary: The patient record documents Type 2 diabetes and hypertension.

Real-World Use Cases

• •

  Clinical chart summarization

  • •Containerize document parsing and retrieval.
  • •Let AutoGen summarize encounters from retrieved notes without exposing raw storage details to the agent runtime.
• •

  Prior authorization support

  • •Use Docker to isolate policy docs, payer rules, and claim history.
  • •Use AutoGen to draft evidence-based authorization packets from retrieved policy text.
• •

  Care gap detection

  • •Run screening guideline retrieval in containers.
  • •Have an AutoGen agent compare patient history against guideline snippets and flag missing screenings or follow-ups.

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.