How to store and restore MCP in a Redis-backed cache?

 

Step 1: Understanding MCP Components

 

• System Instructions: Define what role the model should play.
• User Profile: Capture information about the user such as name, preferences, and goals.
• Document Context: Include any knowledge bases or documents relevant to the task.
• Active Tasks / Goals: Specify the current objectives the model should focus on.
• Tool Access: Outline the external resources the model can use, such as web access or APIs.
• Rules / Constraints: Set boundaries or guidelines for the model's behavior.

 

Step 2: Set Up Redis Environment

 

• Install Redis: Install Redis on your machine or use a cloud-based service.
• Start Redis Server: Start the Redis server to enable data storage.

 

Step 3: Defining MCP Structure in Code

 

• Identify which parts of your data constitute each MCP component.

 

mcp_structure = {
    "system_instructions": "You are a helpful assistant specialized in finance.",
    "user_profile": {
        "name": "Alice",
        "preferences": ["timely responses", "detailed explanations"],
        "goals": ["optimize savings", "understand investments"]
    },
    "document_context": [
        "finance_guide.pdf",
        "investment_tips.docx"
    ],
    "active_tasks": ["budget analysis", "portfolio review"],
    "tool_access": ["web", "Python"],
    "rules_constraints": ["avoid providing medical advice"]
}

 

Step 4: Serialize MCP Data

 

• Convert MCP structure into a JSON string for storage in Redis.

 

import json

mcpjson = json.dumps(mcpstructure)

 

Step 5: Store MCP Data in Redis

 

• Use a Redis client in Python to store the serialized MCP data.

 

import redis

Connect to Redis
r = redis.Redis(host='localhost', port=6379, db=0)

Store MCP data
r.set('mcpcontext', mcpjson)

 

Step 6: Retrieve and Deserialize MCP Data

 

• Fetch the MCP data from Redis and convert it back to a Python dictionary.

 

Retrieve MCP data
retrievedmcpjson = r.get('mcp_context')

Deserialize JSON to dictionary
retrievedmcpstructure = json.loads(retrievedmcpjson)

 

Step 7: Utilize MCP Data in Your Application

 

• Use the deserialized MCP structure to guide the behavior of your AI model.

 

def apply_mcp(context):
    systeminstructions = context['systeminstructions']
    userprofile = context['userprofile']
    documentcontext = context['documentcontext']
    activetasks = context['activetasks']
    toolaccess = context['toolaccess']
    rulesconstraints = context['rulesconstraints']
    
    # Implement the logic to utilize this information
    # in your model's workflow
    pass

applymcp(retrievedmcp_structure)

 

Step 8: Implementing Changes in Real-time (Optional)

 

• Monitor changes in MCP data and update the model context dynamically if needed.

 

def watchforchanges():
    pubsub = r.pubsub()
    pubsub.subscribe('mcp_channel')
    
    for message in pubsub.listen():
        if message['type'] == 'message':
            updatedmcpstructure = json.loads(message['data'])
            applymcp(updatedmcp_structure)

Run in a separate thread or process
watchforchanges()