文本生成与对话

1from openai import OpenAI
2client = OpenAI()
3
4response = client.responses.create(
5    model="gpt-5.2",
6    input=[
7        {"role": "system", "content": "You are a helpful tutor who explains complex topics in simple terms with analogies."},
8        {"role": "user", "content": "What is machine learning?"}
9    ]
10)
11
12print(response.output_text)

1conversation = [
2    {"role": "system", "content": "You are a helpful assistant."},
3    {"role": "user", "content": "My favorite color is blue."},
4    {"role": "assistant", "content": "That's nice! Blue is a beautiful color. It's often associated with calmness and serenity."},
5    {"role": "user", "content": "What are some things in nature that match my favorite color?"}
6]
7
8response = client.responses.create(
9    model="gpt-5.2",
10    input=conversation
11)
12
13print(response.output_text)

1# Conservative, factual response
2
3print("Conservative,factual response:")
4response_conservative = client.responses.create(
5    model="gpt-4o",
6    input="What is the name of the manga where we have a white haird character as the main protagonist? Only provide two names!",
7    temperature=0.1
8)
9
10print("-" * 100)
11print(response_conservative.output_text)
12print("-" * 100)
13print("Creative, varied response:")
14print("-" * 100)
15# Creative, varied response
16response_creative = client.responses.create(
17    model="gpt-4o",
18    input="What is the name of the manga where we have a white haird character as the main protagonist? Only provide two names!",
19    temperature=0.9
20)
21
22print(response_creative.output_text)
23print("-" * 100)

1# Short response
2print("Short Response:")
3response = client.responses.create(
4    model="gpt-4o",
5    input="Explain artificial intelligence.",
6    max_output_tokens=30 # About 1-2 sentences
7)
8print(response.output_text)
9print("-" * 100)
10
11# Longer response
12print("Longer Response:")
13response = client.responses.create(
14    model="gpt-4o",
15    input="Explain artificial intelligence.",
16    max_output_tokens=200  # About 1 paragraph
17)
18
19print(response.output_text)
20print("-" * 100)

1"""
2GPT-5 Reasoning Effort Demo
3===========================
4
5This script demonstrates how the reasoning_effort parameter controls 
6the quality and depth of GPT-5's thinking process, and measures the 
7performance impact of different reasoning levels.
8"""
9import time
10# Set up OpenAI client
11# Make sure your OPENAI_API_KEY environment variable is set
12def demo_reasoning_effort():
13    """
14    Demonstrates the reasoning_effort parameter.
15    
16    reasoning_effort controls how much computational "thinking" the model does:
17    - minimal: Fastest, for simple deterministic tasks
18    - low: Light reasoning
19    - medium: Default balanced reasoning
20    - high: Most thorough reasoning and analysis
21    """
22    print("=" * 60)
23    print("GPT-5 REASONING EFFORT DEMONSTRATION")
24    print("=" * 60)
25    
26    # Test prompt that benefits from reasoning
27    problem = "If a train leaves Station A at 2:00 PM traveling at 60 mph, and another train leaves Station B at 2:30 PM traveling at 80 mph toward Station A, and the stations are 200 miles apart, when will they meet?"
28    
29    print("Problem:", problem)
30    print("\n" + "-" * 60)
31    
32    # Minimal reasoning effort - fast but simple
33    print("MINIMAL REASONING EFFORT (fastest):")
34    print("-" * 40)
35    start_time = time.time()
36    response_minimal = client.responses.create(
37        model="gpt-5.2",
38        input=problem,
39        reasoning={"effort": "minimal"}
40    )
41    minimal_time = time.time() - start_time
42    print(response_minimal.output_text)
43    print(f"\n⏱️  Time taken: {minimal_time:.2f} seconds")
44    
45    print("\n" + "-" * 60)
46    
47    # High reasoning effort - thorough analysis
48    print("HIGH REASONING EFFORT (most thorough):")
49    print("-" * 40)
50    start_time = time.time()
51    response_high = client.responses.create(
52        model="gpt-5.2", 
53        input=problem,
54        reasoning={"effort": "high"}
55    )
56    high_time = time.time() - start_time
57    print(response_high.output_text)
58    print(f"\n⏱️  Time taken: {high_time:.2f} seconds")
59    print(f"📊 Speed difference: {high_time/minimal_time:.1f}x slower than minimal")
60    
61    print("\n" + "=" * 60)
62
63if __name__ == "__main__":
64    """
65    Run the reasoning effort demonstration.
66    
67    This shows how different reasoning levels affect both the quality
68    of responses and the time taken to generate them.
69    """
70    
71    print("GPT-5 Reasoning Effort Demo")
72    print("This demo shows how reasoning_effort controls the depth of GPT-5's thinking.\n")
73    
74    try:
75        demo_reasoning_effort()
76        
77    except Exception as e:
78        print(f"Error: {e}")
79        print("Make sure your OPENAI_API_KEY environment variable is set correctly.")

1"""
2GPT-5 Verbosity Demo
3====================
4
5This script demonstrates how the verbosity parameter controls 
6the length and depth of GPT-5's responses, and measures the 
7performance impact of different verbosity levels.
8"""
9
10import time
11
12def demo_verbosity():
13    """
14    Demonstrates the verbosity parameter.
15    
16    verbosity controls the natural length and depth of responses:
17    - low: Concise, minimal responses
18    - medium: Default balanced responses  
19    - high: Comprehensive, detailed responses
20    """
21    print("=" * 60)
22    print("GPT-5 VERBOSITY DEMONSTRATION")
23    print("=" * 60)
24    
25    # Topic that can be explained at different depths
26    topic = "Explain how machine learning algorithms learn from data"
27    
28    print("Topic:", topic)
29    print("\n" + "-" * 60)
30    
31    # Low verbosity - concise explanation
32    print("LOW VERBOSITY (concise):")
33    print("-" * 40)
34    start_time = time.time()
35    response_low = client.responses.create(
36        model="gpt-5.2",
37        input=topic,
38        text={"verbosity": "low"}
39    )
40    low_time = time.time() - start_time
41    print(response_low.output_text)
42    print(f"\n⏱️  Time taken: {low_time:.2f} seconds")
43    
44    print("\n" + "-" * 60)
45    
46    # Medium verbosity - comprehensive explanation
47    print("MEDIUM VERBOSITY (comprehensive):")
48    print("-" * 40)
49    start_time = time.time()
50    response_medium = client.responses.create(
51        model="gpt-5.2",
52        input=topic, 
53        text={"verbosity": "medium"}
54    )
55    medium_time = time.time() - start_time
56    print(response_medium.output_text)
57    print(f"\n⏱️  Time taken: {medium_time:.2f} seconds")
58    print(f"📊 Length difference: {medium_time/low_time:.1f}x longer than low verbosity")
59    
60    print("\n" + "=" * 60)
61
62if __name__ == "__main__":
63    """
64    Run the verbosity demonstration.
65    
66    This shows how different verbosity levels affect both the length
67    of responses and the time taken to generate them.
68    """
69    
70    print("GPT-5 Verbosity Demo")
71    print("This demo shows how verbosity controls the length and depth of GPT-5's responses.\n")
72    
73    try:
74        demo_verbosity()
75        
76    except Exception as e:
77        print(f"Error: {e}")
78        print("Make sure your OPENAI_API_KEY environment variable is set correctly.")