🧾 Prompt Registry Microservice with MongoDB¶

This README provides setup guides and all the necessary information about the Prompt Registry microservice with MongoDB database.

Setup Environment Variables¶

export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export MONGO_HOST=${MONGO_HOST}
export MONGO_HOST=27017
export DB_NAME=${DB_NAME}
export COLLECTION_NAME=${COLLECTION_NAME}
export ENABLE_MCP=false  # Set to true to enable MCP support

🚀 Start Microservice with Docker (Option 1)¶

Build Docker Image¶

cd ~/GenAIComps
docker build -t opea/promptregistry-mongo:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/prompt_registry/src/Dockerfile .

Run Docker with CLI¶

Run MongoDB image container

docker run -d -p 27017:27017 --name=mongo mongo:latest

Run Prompt Registry microservice

docker run -d --name="promptregistry-mongo-server" -p 6018:6018 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MONGO_HOST=${MONGO_HOST} -e MONGO_PORT=${MONGO_PORT} -e DB_NAME=${DB_NAME} -e COLLECTION_NAME=${COLLECTION_NAME} -e ENABLE_MCP=${ENABLE_MCP} opea/promptregistry-mongo:latest

🚀 Start Microservice with Docker Compose (Option 2)¶

docker compose -f ../deployment/docker_compose/compose.yaml up -d

✅ Invoke Microservice¶

The Prompt Registry microservice exposes the following API endpoints:

Save prompt

curl -X 'POST' \
  http://${host_ip}:6018/v1/prompt/create \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt_text": "test prompt", "user": "test"
}'

Retrieve prompt from database by user

curl -X 'POST' \
  http://${host_ip}:6018/v1/prompt/get \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "user": "test"}'

Retrieve prompt from database by prompt_id

curl -X 'POST' \
  http://${host_ip}:6018/v1/prompt/get \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "user": "test", "prompt_id":"{prompt_id returned from save prompt route above}"}'

Retrieve relevant prompt by keyword

curl -X 'POST' \
  http://${host_ip}:6018/v1/prompt/get \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "user": "test", "prompt_text": "{keyword to search}"}'

Delete prompt by prompt_id

curl -X 'POST' \
  http://${host_ip}:6018/v1/prompt/delete \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "user": "test", "prompt_id":"{prompt_id to be deleted}"}'

🤖 MCP (Model Context Protocol) Usage¶

When MCP is enabled (ENABLE_MCP=true), the service operates in MCP-only mode. Note: Regular HTTP endpoints are not available when MCP is enabled. AI agents can discover and use the prompt registry service through the OPEA MCP framework:

Available MCP Tools¶

create_prompt: Store prompts with user association
get_prompt: Retrieve prompts by various criteria
delete_prompt: Remove prompts from the database

Example MCP Client Usage¶

from mcp.client.session import ClientSession
from mcp.client.sse import sse_client


async def use_prompt_registry():
    async with sse_client("http://localhost:6018/sse") as streams:
        async with ClientSession(*streams) as session:
            # Initialize connection
            await session.initialize()

            # Create a prompt
            result = await session.call_tool(
                "create_prompt",
                {"request": {"prompt_text": "Explain quantum computing in simple terms", "user": "ai_agent_001"}},
            )

            # Retrieve prompts
            prompts = await session.call_tool("get_prompt", {"request": {"user": "ai_agent_001"}})

Benefits for AI Agents¶

Dynamic Prompt Management: Agents can build prompt libraries on-the-fly
Context Persistence: Store and retrieve prompts across conversations
Collaborative Learning: Share effective prompts between agents
Personalization: Create user-specific prompt repositories