# Produce MySQL Queries using LLMs import { Tabs, Tab } from 'nextra/components' ## Background This prompt tests an LLM's code generation capabilities by prompting it to generate a valid MySQL query by providing information about the database schema. ## Prompt ```markdown """ Table departments, columns = [DepartmentId, DepartmentName] Table students, columns = [DepartmentId, StudentId, StudentName] Create a MySQL query for all students in the Computer Science Department """ ``` ## Code / API ```python 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 ) ``` ```python import fireworks.client fireworks.client.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 ) ``` ## Reference - [Prompt Engineering Guide](https://www.promptingguide.ai/introduction/examples#code-generation) (16 March 2023)