How to use OpenAI function calling with an MCP-influenced schema?

 

Step 1: Understanding the MCP Schema

 

To use OpenAI function calling with an MCP-influenced schema, first you need to have a clear understanding of how the MCP framework structures and organizes context for language models. MCP helps in defining:

• What the model knows

  : Includes long-term memory, established rules, and instructions.

• What the model is supposed to do

  : Defines goals, tasks, and personas.

• Active context

  : Current user profile, ongoing conversation history, and active documents.

• Guardrails or constraints

  : Instructions about avoiding specific outputs or staying within a domain.

 

Step 2: Setting Up System Instructions

 

System Instructions guide the LLM's behavior. Here’s how you could set up your system instructions in Python:

system_instructions = {
    "role": "assistant",
    "specialty": "finance",
    "purpose": "You are a helpful assistant specialized in finance."
}

 

Step 3: Configuring the User Profile

 

Next, define the user's profile that the LLM will utilize for personalized interactions:

user_profile = {
    "name": "Alex",
    "preferences": {
        "communication_style": "formal",
        "content_focus": "investment strategies"
    },
    "goals": ["learn about stock market", "optimize retirement savings"]
}

 

Step 4: Structuring Document Context

 

Incorporate documents or knowledge bases that the LLM can access:

document_context = {
    "knowledge_base": [
        "investment_guide.pdf",
        "latestmarkettrends.docx"
    ],
    "recent_uploads": [],
    "conversation_history": []
}

 

Step 5: Defining Active Tasks and Goals

 

Outline any tasks or goals the model should focus on:

active_tasks = {
    "objectives": [
        "analyze user portfolio",
        "provide market insights"
    ],
    "to_dos": []
}

 

Step 6: Specifying Tool Access

 

Determine which tools the model has access to, shaping its functionality scope:

tool_access = {
    "allowedtools": ["webscraping", "python", "SQL database"]
}

 

Step 7: Setting Rules and Constraints

 

Establish guardrails to maintain safety and domain relevance:

rules_constraints = {
    "prohibited_actions": ["suggest medical diagnoses"],
    "domain_constraints": ["stay within finance domain"]
}

 

Step 8: Implementing the Complete MCP Schema

 

Combine all components into a comprehensive MCP schema that can be passed to the LLM:

mcp_schema = {
    "systeminstructions": systeminstructions,
    "userprofile": userprofile,
    "documentcontext": documentcontext,
    "activetasks": activetasks,
    "toolaccess": toolaccess,
    "rulesconstraints": rulesconstraints
}

 

Step 9: Integrating with OpenAI Function Calling

 

Finally, use your constructed MCP schema with OpenAI’s function calling feature to leverage these structured contexts effectively. Here is a simple illustration:

import openai

Your OpenAI API key
apikey = "yourapi_key"

Integrate MCP Schema into OpenAI API Request
response = openai.ChatCompletion.create(
  model="gpt-4",
  messages=[
    {"role": "system", "content": mcpschema["systeminstructions"]["purpose"]},
    {"role": "user", "content": "How should I adjust my portfolio?"}
  ]
)

print(response['choices'][0]['message']['content'])

By following these steps, you establish an efficient way to use MCP-influenced schema for structured operation of language models using OpenAI's function calling.