mdc-openai

Text 3: {user_text} Keywords 3:"""

response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": few_shot_prompt}] )

#### 2.1.6 Role/Persona and Constraints

Define the AI's role and explicitly state what it should *not* do.

❌ **BAD**: Only negative constraints
```python
system_message = "DO NOT MENTION PRICES. DO NOT BE OVERLY FORMAL."

✅ GOOD: Clear role, positive instructions, and specific constraints

system_message = """You are a friendly, helpful customer support agent for a SaaS product.
Your goal is to diagnose user issues and suggest solutions.
NEVER ask for Personally Identifiable Information (PII) like passwords or credit card numbers.
Instead, refer users to our secure help portal at `https://support.example.com/security`.
"""

2.2 Agent Design

For building autonomous agents, follow OpenAI's "Practical Guide to Building Agents" framework.

2.2.1 Core Components

Agents require:

1. Model: The LLM for reasoning and decision-making.
2. Tools: External functions/APIs the agent can use.
3. Instructions: Explicit guidelines and guardrails.

2.2.2 Function Calling

Use function calling for agents to reliably interact with external tools and APIs. Define tools with clear schemas.

from openai import OpenAI
import json

client = OpenAI()

def get_current_weather(location: str, unit: str = "fahrenheit") -> str:
    """Get the current weather in a given location"""
    # In a real app, this would call an external weather API
    if "san francisco" in location.lower():
        return json.dumps({"location": location, "temperature": "72", "unit": unit})
    return json.dumps({"location": location, "temperature": "unknown"})

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

def run_agent_with_tool(user_message: str):
    messages = [{"role": "user", "content": user_message}]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
        tool_choice="auto", # Let the model decide if it needs to call a tool
    )
    response_message = response.choices[0].message

    if response_message.tool_calls:
        tool_call = response_message.tool_calls[0]
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        if function_name == "get_current_weather":
            function_response = get_current_weather(**function_args)
            messages.append(response_message) # Extend conversation with assistant's reply
            messages.append(
                {
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": function_response,
                }
            )
            second_response = client.chat.completions.create(
                model="gpt-4o",
                messages=messages,
            )
            return second_response.choices[0].message.content
    return response_message.content

# Example usage: