How to Integrate AutoGen for lending with Docker for RAG

Combining AutoGen for lending with Docker gives you a clean way to run lending-specific agent workflows against isolated, reproducible retrieval environments. In practice, that means your loan policy docs, underwriting rules, and product knowledge can live in a containerized RAG service while AutoGen coordinates the conversation, tool calls, and decision flow.

Prerequisites

• •Python 3.10+
• •Docker Desktop or Docker Engine installed and running
• •A working AutoGen setup for lending
  • •pyautogen or your lending-specific AutoGen package
  • •API key/config for the model backend you plan to use
• •A vector store or retrieval backend running in Docker
  • •Example: Chroma, Qdrant, or a custom FastAPI retrieval service
• •Basic familiarity with:
  • •Python virtual environments
  • •Docker CLI
  • •REST APIs

Integration Steps

1. •

   Start a Dockerized retrieval service for RAG

   The cleanest pattern is to keep retrieval inside a container and expose it over HTTP. That lets AutoGen call it as a tool without caring about how embeddings or storage are implemented.

   import docker
   
   client = docker.from_env()
   
   container = client.containers.run(
       image="qdrant/qdrant:latest",
       name="lending-rag-qdrant",
       detach=True,
       ports={"6333/tcp": 6333},
       volumes={
           "qdrant_storage": {"bind": "/qdrant/storage", "mode": "rw"}
       }
   )
   
   print(container.id)
   

   If you already have a retrieval API container, swap the image for your own service. The important part is that your agent can reach it over http://localhost:<port>.

2. •

   Load lending documents into the containerized RAG store

   For lending use cases, your corpus usually includes underwriting policy PDFs, rate sheets, KYC rules, and exception handling docs. Keep ingestion outside the agent loop so retrieval stays deterministic and cheap.

   import requests
   
   docs = [
       {
           "id": "policy-001",
           "text": "Debt-to-income ratio must be below 43% unless manual review approves an exception."
       },
       {
           "id": "policy-002",
           "text": "Minimum credit score for standard personal loans is 680."
       }
   ]
   
   resp = requests.post(
       "http://localhost:6333/collections/lending_docs/points?wait=true",
       json={
           "points": [
               {
                   "id": d["id"],
                   "vector": [0.1] * 1536,
                   "payload": {"text": d["text"]}
               } for d in docs
           ]
       },
       timeout=30
   )
   
   print(resp.status_code)
   print(resp.text)
   

   In production, replace the dummy vector with real embeddings from your embedding model. If you’re using a FastAPI retrieval layer in Docker, call that API instead of talking directly to Qdrant.

3. •

   Create an AutoGen assistant that can call the RAG endpoint

   AutoGen works well when you expose retrieval as a tool/function. The assistant asks questions; the tool fetches supporting context from the container; then the model answers using that context.

   import requests
   from autogen import AssistantAgent, UserProxyAgent
   
   def retrieve_lending_context(query: str) -> str:
       response = requests.post(
           "http://localhost:8000/retrieve",
           json={"query": query, "top_k": 3},
           timeout=20
       )
       response.raise_for_status()
       return response.json()["context"]
   
   llm_config = {
       "model": "gpt-4o-mini",
       "api_key": "YOUR_OPENAI_API_KEY"
   }
   
   assistant = AssistantAgent(
       name="lending_assistant",
       llm_config=llm_config,
       system_message=(
           "You are a lending operations assistant. "
           "Use retrieved policy context before answering."
       )
   )
   
   user_proxy = UserProxyAgent(
       name="user_proxy",
       human_input_mode="NEVER"
   )
   

4. •

   Register the retrieval function as an AutoGen tool

   This is the bridge between orchestration and RAG. AutoGen will call your function when it needs policy context.

    from autogen import register_function
   
    register_function(
        retrieve_lending_context,
        caller=assistant,
        executor=user_proxy,
        name="retrieve_lending_context",
        description="Fetch relevant lending policy context from the Dockerized RAG service."
    )
   
    task = (
        "Can we approve an applicant with DTI of 46% and credit score of 710? "
        "Check policy and explain."
    )
   
    user_proxy.initiate_chat(
        assistant,
        message=task,
        max_turns=3
    )
   

5. •

   Wrap Docker lifecycle management around your agent workflow

   In real systems, you want reproducible startup and teardown so tests and deployments behave the same way every time.

   import docker
   
   client = docker.from_env()
   
   try:
       container = client.containers.get("lending-rag-qdrant")
       print("RAG container is running:", container.status)
       
       logs = container.logs(tail=20).decode("utf-8")
       print(logs)
   
   finally:
       # Use this in test environments only.
       # container.stop()
       # container.remove()
       pass
   

Testing the Integration

Run one end-to-end check: start the containerized retriever, ask a lending question, and confirm AutoGen pulls back policy context before answering.

import requests

query = "What is the minimum credit score for a standard personal loan?"
resp = requests.post(
    "http://localhost:8000/retrieve",
    json={"query": query, "top_k": 2},
    timeout=20
)
resp.raise_for_status()

context = resp.json()["context"]
print("Retrieved context:")
print(context)

assert "680" in context

Expected output:

Retrieved context:
Minimum credit score for standard personal loans is 680.
Debt-to-income ratio must be below 43% unless manual review approves an exception.

If you want to test through AutoGen too, ask a question like:

user_proxy.initiate_chat(
    assistant,
    message="Does our policy allow a standard loan at 675 credit score?",
    max_turns=2
)

Expected behavior:

• •The assistant calls retrieve_lending_context
• •The returned policy text includes the minimum score rule
• •The final answer states that 675 does not meet standard policy

Real-World Use Cases

• •

  Loan policy copilot

  • •Let underwriters ask natural-language questions about eligibility rules while Docker hosts the controlled RAG layer.
• •

  Exception review assistant

  • •Combine retrieved policy evidence with AutoGen’s multi-agent reasoning to draft exception memos for borderline applications.
• •

  Customer support for lending products

  • •Answer product questions about rates, terms, and required documents using versioned docs inside containers so support stays aligned with current policy.

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.