Prompt Hub - LLMs for Code Generation (用於程式碼生成的大型語言模型)

提示詞工程 (Prompt Engineering)
1

LLMs for Code Generation (用於程式碼生成的大型語言模型)

本段包含一組用於測試大型語言模型程式碼生成能力的提示集合。

目錄

使用大型語言模型生成程式碼片段

背景

此提示透過以註解形式提供程式說明（使用 /* <指令> */），要求大型語言模型生成對應的程式碼片段，以測試其程式碼生成能力。

提示詞

/*請求使用者輸入他們的名字，並說「Hello」*/

程式

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
        "role": "user",
        "content": "/*\nAsk the user for their name and say \"Hello\"\n*/"
        }
    ],
    temperature=1,
    max_tokens=1000,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "/*\nAsk the user for their name and say \"Hello\"\n*/",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

使用大型語言模型產生 MySQL 查詢語句

背景

此提示透過提供資料庫結構的資訊，要求大型語言模型生成有效的 MySQL 查詢語句，以測試其程式碼生成能力。

提示詞

資料表 students，欄位 = [DepartmentId, StudentId, StudentName]  
請撰寫一個 MySQL 查詢語句，用於查詢所有在「Computer Science」系的學生

程式

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
        "role": "user",
        "content": "\"\"\"\nTable departments, columns = [DepartmentId, DepartmentName]\nTable students, columns = [DepartmentId, StudentId, StudentName]\nCreate a MySQL query for all students in the Computer Science Department\n\"\"\""
        }
    ],
    temperature=1,
    max_tokens=1000,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "\"\"\"\nTable departments, columns = [DepartmentId, DepartmentName]\nTable students, columns = [DepartmentId, StudentId, StudentName]\nCreate a MySQL query for all students in the Computer Science Department\n\"\"\"",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

繪製 TiKZ 圖示

背景

此提示透過要求大型語言模型使用 TiKZ 繪製獨角獸，來測試其程式碼生成能力。
在下列範例中，模型預期應生成可用於產生獨角獸（或其他指定物件）的 LaTeX 程式碼。

提示詞

用 TiKZ 繪製一隻獨角獸。

程式

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
        "role": "user",
        "content": "Draw a unicorn in TiKZ"
        }
    ],
    temperature=1,
    max_tokens=1000,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "Draw a unicorn in TiKZ",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

References

LLMs for Code Generation

目錄： Prompt Hub - 提示詞匯集

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