How to Integrate AutoGen for pension funds with Docker for RAG

Combining AutoGen for pension funds with Docker gives you a clean way to build retrieval-augmented agent systems that can run in isolated, repeatable environments. For pension workflows, that matters because you usually need controlled access to policy documents, actuarial notes, benefit rules, and member correspondence without letting the agent drift outside approved sources.

With Docker handling the runtime and AutoGen orchestrating the agents, you get a setup that is easier to deploy, easier to audit, and much safer to move into production.

Prerequisites

• •Python 3.10+
• •Docker Desktop or Docker Engine installed and running
• •Access to an AutoGen-compatible package for pension fund agent workflows
• •A local or hosted LLM endpoint configured for AutoGen
• •A document store for RAG content:
  • •local files, or
  • •a vector database like Chroma, FAISS, or pgvector
• •Basic familiarity with:
  • •docker CLI
  • •Python virtual environments
  • •retrieval-augmented generation patterns

Integration Steps

1. •

   Install the Python dependencies.

   Use a dedicated environment so your agent runtime stays isolated from your host machine.

   # requirements-style example
   import sys
   
   print("Install these packages:")
   print("pip install pyautogen docker chromadb sentence-transformers")
   

   In practice, you want:

   • •pyautogen for multi-agent orchestration
   • •docker for container control from Python
   • •chromadb for local retrieval
   • •sentence-transformers for embeddings if you are not using hosted embeddings
2. •

   Start your RAG services in Docker.

   For pension fund use cases, I prefer running the vector store in a container so indexing and retrieval are consistent across dev and prod.

   import docker
   
   client = docker.from_env()
   
   container = client.containers.run(
       image="chromadb/chroma:latest",
       name="pension-rag-chroma",
       ports={"8000/tcp": 8000},
       detach=True,
       remove=True,
       environment={
           "IS_PERSISTENT": "TRUE",
           "PERSIST_DIRECTORY": "/chroma/chroma"
       },
       volumes={
           "/tmp/chroma-data": {"bind": "/chroma/chroma", "mode": "rw"}
       }
   )
   
   print(container.id)
   

   This gives you a stable retrieval backend your agents can query repeatedly.

3. •

   Configure AutoGen with your assistant and retrieval context.

   The key pattern is: one agent handles user interaction, another agent handles retrieval-backed answers. Keep the RAG logic outside the prompt as much as possible.

   from autogen import AssistantAgent, UserProxyAgent
   
   llm_config = {
       "model": "gpt-4o-mini",
       "api_key": "YOUR_OPENAI_API_KEY",
       "temperature": 0.1,
   }
   
   assistant = AssistantAgent(
       name="pension_assistant",
       llm_config=llm_config,
       system_message=(
           "You answer pension fund questions using only approved retrieved context. "
           "If the context is insufficient, say so."
       ),
   )
   
   user_proxy = UserProxyAgent(
       name="user_proxy",
       human_input_mode="NEVER",
       code_execution_config=False,
   )
   
   print(assistant.name)
   

4. •

   Wire document ingestion into the containerized RAG store.

   This example pushes pension policy text into Chroma from Python. In a real system, replace the hardcoded text with PDF parsing and chunking.

    import chromadb
    from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
   
    client = chromadb.HttpClient(host="localhost", port=8000)
   
    embed_fn = SentenceTransformerEmbeddingFunction(
        model_name="all-MiniLM-L6-v2"
    )
   
    collection = client.get_or_create_collection(
        name="pension_policies",
        embedding_function=embed_fn,
    )
   
    docs = [
        "Members may retire at age 60 with full benefits subject to vesting rules.",
        "Early retirement before age 60 applies an actuarial reduction.",
        "Defined contribution balances can be accessed according to plan rules."
    ]
   
    collection.add(
        ids=["doc1", "doc2", "doc3"],
        documents=docs,
        metadatas=[{"source": "policy_a"}, {"source": "policy_b"}, {"source": "policy_c"}],
    )
   
    results = collection.query(
        query_texts=["What happens if a member retires before age 60?"],
        n_results=2,
    )
   
    print(results["documents"][0])
   

5. •

   Connect retrieval output to AutoGen conversation flow.

   Retrieve relevant chunks first, then inject them into the assistant’s context before generating the response.

    def retrieve_context(question: str) -> str:
        res = collection.query(query_texts=[question], n_results=3)
        snippets = res["documents"][0]
        return "\n".join(f"- {text}" for text in snippets)
   
    question = "Can a member retire early and still receive benefits?"
    context = retrieve_context(question)
   
    prompt = f"""
    Answer this pension question using only the retrieved context.
   
    Question: {question}
   
    Retrieved context:
    {context}
    """
   
    reply = assistant.generate_reply(messages=[{"role": "user", "content": prompt}])
    print(reply)
   

Testing the Integration

Run a simple end-to-end check: start Docker, index one known policy statement, query it through retrieval, then pass it into AutoGen.

import docker
import chromadb
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
from autogen import AssistantAgent

docker_client = docker.from_env()
print(docker_client.ping())

chroma = chromadb.HttpClient(host="localhost", port=8000)
embed_fn = SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
collection = chroma.get_or_create_collection("pension_test", embedding_function=embed_fn)

collection.add(
    ids=["early-retirement"],
    documents=["Early retirement before age 60 applies an actuarial reduction."],
)

hits = collection.query(query_texts=["What applies before age 60?"], n_results=1)
context = hits["documents"][0][0]

assistant = AssistantAgent(
    name="test_assistant",
    llm_config={"model": "gpt-4o-mini", "api_key": "YOUR_OPENAI_API_KEY"},
)

response = assistant.generate_reply(messages=[{
    "role": "user",
    "content": f"Use this context only: {context}\n\nQuestion: What happens before age 60?"
}])

print(context)
print(response)

Expected output:

True
Early retirement before age 60 applies an actuarial reduction.
The member receives reduced benefits if they retire before age 60.

Real-World Use Cases

• •

  Member benefit Q&A

  • •Build an internal assistant that answers questions about retirement eligibility, vesting, contribution rules, and payout timing from approved plan documents.
• •

  Policy compliance review

  • •Let one agent retrieve policy clauses while another checks responses against fund rules before anything reaches operations staff.
• •

  Claims and correspondence drafting

  • •Generate draft responses to member inquiries using retrieved policy language stored in Dockerized RAG infrastructure.

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.