GLM 5.2 教育场景：辅导、测评与学习应用开发

import openai

client = openai.OpenAI(
    api_key="YOUR_ZHIPU_API_KEY",
    base_url="https://open.bigmodel.cn/api/paas/v4/"
)

TUTOR_SYSTEM_PROMPT = """You are a patient math and science tutor for high school students.
Never give direct answers. Instead:
1. Identify what concept the student is stuck on
2. Ask a guiding question to help them see the next step
3. Celebrate small progress with brief encouragement
4. If the student is completely lost, break the problem into smaller parts

Keep responses under 150 words. Use clear, grade-appropriate language."""

def tutor_session(student_message: str, history: list) -> str:
    messages = [{"role": "system", "content": TUTOR_SYSTEM_PROMPT}]
    messages.extend(history)
    messages.append({"role": "user", "content": student_message})

    response = client.chat.completions.create(
        model="glm-4-plus",
        messages=messages,
        stream=True,
        max_tokens=200,
        temperature=0.7
    )

    result = ""
    for chunk in response:
        if chunk.choices[0].delta.content:
            result += chunk.choices[0].delta.content
            print(chunk.choices[0].delta.content, end="", flush=True)

    return result

# Example usage
history = []
response = tutor_session(
    "I don't understand how to find the derivative of x squared",
    history
)
history.append({"role": "user", "content": "I don't understand how to find the derivative of x squared"})
history.append({"role": "assistant", "content": response})

import json

def assess_essay(rubric: str, student_essay: str) -> dict:
    response = client.chat.completions.create(
        model="glm-4-plus",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are an expert writing teacher. Evaluate essays using this rubric:\n"
                    + rubric
                    + "\n\nReturn a JSON object with keys: thesis_score (1-4), "
                    "evidence_score (1-4), grammar_score (1-4), "
                    "overall_comments (string), specific_suggestions (list of strings)."
                )
            },
            {
                "role": "user",
                "content": "Please assess this essay:\n\n" + student_essay
            }
        ],
        response_format={"type": "json_object"},
        temperature=0.3
    )

    return json.loads(response.choices[0].message.content)

def generate_quiz(source_material: str, num_questions: int = 10, difficulty: str = "medium") -> dict:
    prompt = (
        f"Create {num_questions} {difficulty}-difficulty questions from this material. "
        "Include multiple-choice (4 options, 1 correct) and short-answer questions. "
        "Return JSON with a questions array. Each question has: type, question, "
        "options (if multiple choice), correct_answer, and explanation.\n\n"
        "Source material:\n" + source_material
    )

    response = client.chat.completions.create(
        model="glm-4-plus",
        messages=[
            {
                "role": "system",
                "content": "You are a curriculum designer. Generate quizzes in valid JSON format."
            },
            {"role": "user", "content": prompt}
        ],
        response_format={"type": "json_object"},
        max_tokens=4000
    )

    return json.loads(response.choices[0].message.content)

import base64

def analyze_handwritten_math(image_path: str, grade_level: str = "high school") -> str:
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode("utf-8")

    img_url = "data:image/jpeg;base64," + image_data

    response = client.chat.completions.create(
        model="glm-4-plus",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {"url": img_url}
                    },
                    {
                        "type": "text",
                        "text": (
                            f"This is a {grade_level} student's handwritten math solution. "
                            "Please: 1) Identify what problem they are solving, "
                            "2) Find any errors in their work, "
                            "3) Explain the correct approach in simple terms, "
                            "4) Point out what they did correctly."
                        )
                    }
                ]
            }
        ]
    )

    return response.choices[0].message.content

BILINGUAL_TUTOR_PROMPT = (
    "You are a bilingual English-Chinese tutor. "
    "Detect the student's language from their message and respond in that language. "
    "When explaining English grammar or vocabulary, you may provide Chinese explanations "
    "for clarity. When helping with Chinese writing, you may use English comparisons. "
    "Always be encouraging and patient."
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_student_assignment_history",
            "description": "Retrieve a student's recent assignment submissions and scores",
            "parameters": {
                "type": "object",
                "properties": {
                    "student_id": {
                        "type": "string",
                        "description": "Canvas or LMS student ID"
                    },
                    "course_id": {
                        "type": "string",
                        "description": "Canvas or LMS course ID"
                    },
                    "limit": {
                        "type": "integer",
                        "description": "Number of recent assignments to retrieve",
                        "default": 10
                    }
                },
                "required": ["student_id", "course_id"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {
            "role": "user",
            "content": "Based on my recent assignments, what topics should I focus on this week?"
        }
    ],
    tools=tools
)

K12_SAFETY_PROMPT = (
    "You are an AI tutor for middle and high school students aged 11 to 18. "
    "You may only discuss educational topics related to the student's coursework. "
    "Decline all requests that are not related to academic learning or that are "
    "inappropriate for minors. "
    "If asked an inappropriate question, respond: "
    "I can only help with academic questions. What subject are you working on today?"
)