100天跟着CP学PostgreSQL+AI,第18天 : 代码生成神器：基于 Embedding 的 PostgreSQL

csv
code,label
"SELECT user_id, COUNT(*) AS order_count FROM orders WHERE create_time > '2024-01-01' GROUP BY user_id","补全GROUP BY后的HAVING子句"
"CREATE FUNCTION calculate_discount(amount NUMERIC, tier INT) RETURNS NUMERIC AS $$ ... $$ LANGUAGE plpgsql;","补全函数参数提示"

步骤 1：安装依赖
bash
pip install transformers torch pandas scikit-learn
步骤 2：数据预处理（关键代码）
python
运行
from transformers import AutoTokenizer, BertForSequenceClassification
import pandas as pd
import torch
# 加载CodeBERT的分词器
tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
def preprocess_data(file_path):
    df = pd.read_csv(file_path)
    # 将代码和标签转换为模型输入格式
    inputs = tokenizer(
        df["code"].tolist(),
        padding="max_length",
        truncation=True,
        max_length=128,
        return_tensors="pt"
    )
    # 标签编码（这里简化为二分类，实际可扩展）
    labels = torch.tensor(df["label"].factorize()[0])
    return inputs, labels
步骤 3：模型训练（核心代码）
python
运行
from transformers import TrainingArguments, Trainer
# 加载预训练模型
model = BertForSequenceClassification.from_pretrained("microsoft/codebert-base")
# 训练参数配置
training_args = TrainingArguments(
    output_dir="./codebert-sql-finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    logging_dir="./logs",
    logging_steps=10,
    save_strategy="epoch"
)
# 定义训练器
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=preprocess_data("sql_corpus.csv")
)
# 开始训练
trainer.train()

typescript
// extension.ts（VS Code插件主文件）
import * as vscode from 'vscode';
import { CodeBERTEmbedding } from './codebert';
export function activate(context: vscode.ExtensionContext) {
    // 注册SQL补全提供者
    const provider = vscode.languages.registerCompletionItemProvider(
        'sql',
        {
            provideCompletionItems(document: vscode.TextDocument, position: vscode.Position) {
                // 获取当前输入的代码片段（前50个字符）
                const line = document.lineAt(position).text;
                const currentCode = line.substring(0, position.character);
                // 生成代码Embedding
                const embedding = CodeBERTEmbedding.encode(currentCode);
                // 查询相似补全建议（伪代码，实际需连接向量数据库）
                const suggestions = querySimilarSuggestions(embedding);
                // 转换为VS Code补全项
                return suggestions.map(suggest => new vscode.CompletionItem(suggest));
            }
        },
        ...[' ', '(', '='] // 触发补全的字符
    );
    context.subscriptions.push(provider);
}

from transformers import AutoTokenizer, BertForSequenceClassification, TrainingArguments, Trainer
import pandas as pd
import torch

# 加载CodeBERT分词器和预训练模型
tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
model = BertForSequenceClassification.from_pretrained("microsoft/codebert-base")

def preprocess_data(file_path):
    df = pd.read_csv(file_path)
    inputs = tokenizer(
        df["code"].tolist(),
        padding="max_length",
        truncation=True,
        max_length=128,
        return_tensors="pt"
    )
    labels = torch.tensor(pd.factorize(df["label"])[0])
    return inputs, labels

# 配置训练参数
training_args = TrainingArguments(
    output_dir="./codebert-sql-finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    logging_dir="./logs",
    logging_steps=10,
    save_strategy="epoch"
)

# 定义训练器并启动训练
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=preprocess_data("sql_corpus.csv")
)
trainer.train()