How to pass MCP into OpenAI Assistants API session state?

 

Step 1: Understand the Components of MCP

 

To effectively pass MCP (Model Context Protocol) into OpenAI Assistant's API session states, it's crucial to understand MCP's components:

• System Instructions: These are directives that guide the model in a specific task, such as “You are a helpful assistant specialized in finance.”
• User Profile: This includes details like the user's name, preferences, and goals.
• Document Context: Refers to the knowledge base or recent document uploads that the model needs to reference.
• Active Tasks/Goals: Current objectives or to-dos that the model is working on.
• Tool Access: Defines which external tools the model is permitted to use, like web access or database querying.
• Rules/Constraints: Specific guidelines such as avoiding certain topics or staying within a domain (e.g., no medical diagnoses).

 

Step 2: Set Up Your Development Environment

 

You need to make sure your development environment is ready to handle API calls to OpenAI's assistant. This involves installing necessary libraries, setting up API keys, and ensuring you have a way to manage session state.

• Use Python as it's a common language for such integrations.
• Install requests or http.client if using a low-level API interaction.

pip install openai
pip install requests

• Obtain API keys from OpenAI and set up your environment variables to securely manage these keys.

import os
os.environ["OPENAIAPIKEY"] = "your-api-key-here"

 

Step 3: Define MCP Structure in Code

 

Create a structure in your code that mirrors the MCP attributes you defined earlier. This structure will be used to maintain a session state.

mcp_context = {
    "system_instructions": "You are a helpful assistant specialized in finance.",
    "user_profile": {
        "name": "John Doe",
        "preferences": ["short responses", "focus on finance"],
        "goals": ["learn investing strategies"]
    },
    "document_context": [
        "Document1.pdf",
        "Investing_Guide.docx"
    ],
    "active_tasks": [
        "research stock market trends"
    ],
    "tool_access": ["web", "database"],
    "rules_constraints": [
        "avoid medical diagnoses",
        "stay within financial topics"
    ]
}

 

Step 4: Create a Function to Pass MCP to OpenAI API Session

 

Develop a function to format and pass the defined MCP context to OpenAI's API when initiating each session.

import openai

def initiatesessionwith_mcp(mcp):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": mcp["system_instructions"]},
            {"role": "user", "content": "Hello, I need assistance with " + mcp["user_profile"]["goals"][0]}
        ],
        temperature=0.7,
        max_tokens=150
    )
    return response.choices[0].message["content"]

responsefromai = initiatesessionwithmcp(mcpcontext)
print(responsefromai)

 

Step 5: Session Management and State Maintenance

 

Ensure that the session state is maintained across multiple API calls. This involves updating the MCP structure based on user interactions or new data.

def updateusergoal(mcp, new_goal):
    mcp["userprofile"]["goals"].append(newgoal)

Simulating adding a new goal
updateusergoal(mcp_context, "understand savings strategies")
responsefromaiupdated = initiatesessionwithmcp(mcp_context)
print(responsefromai_updated)

 

Step 6: Implement Error Handling and Constraints

 

Implement logic to ensure constraints are respected and manage errors gracefully.

def initiatesessionwith_constraints(mcp):
    try:
        response = initiatesessionwith_mcp(mcp)
        if "medical" in response:
            raise ValueError("Response violates MCP rules: mentions medical content.")
        return response
    except ValueError as e:
        print("Error:", e)
        return "Response contained restricted content."

responsehandled = initiatesessionwithconstraints(mcp_context)
print(response_handled)

 

Step 7: Testing and Iteration

 

Continuously test your implementation with varying contexts and adjust the MCP structure and logic based on feedback or detected issues. This ensures robustness and adaptability to different use cases.

 

By following these detailed steps and understanding how to structure and pass an MCP into an OpenAI Assistant's API session, you can leverage the full potential of structured context for predictable and efficient model behavior.