【GPT‑4o】完整教程：LORA微调LLaMA3并结合RAG和Agent技术实现Text2SQL任务

完整教程：LORA微调LLaMA3并结合RAG和Agent技术实现Text2SQL任务

环境准备

首先，安装必要的Python包：

pip install transformers peft datasets torch faiss-cpu
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加载LLaMA3模型

从Hugging Face加载LLaMA3模型和对应的tokenizer：

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
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准备数据集

加载Spider数据集：

from datasets import load_dataset

dataset = load_dataset("spider")
train_data = dataset['train']
valid_data = dataset['validation']
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LORA微调配置

配置LORA参数并应用到模型上：

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.1,
    target_modules=["q_proj", "v_proj"]
)

model = get_peft_model(model, lora_config)
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数据预处理

定义数据预处理函数并处理训练和验证数据：

def preprocess_function(examples):
    inputs = [f"translate English to SQL: {query}" for query in examples["question"]]
    targets = [sql for sql in examples["query"]]
    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
    labels = tokenizer(targets, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

train_dataset = train_data.map(preprocess_function, batched=True)
valid_dataset = valid_data.map(preprocess_function, batched=True)
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自定义训练循环

实现自定义训练循环：

import torch
from torch.utils.data import DataLoader

train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)
valid_dataloader = DataLoader(valid_dataset, batch_size=8)

optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

for epoch in range(3):  # 假设训练3个epoch
    model.train()
    for batch in train_dataloader:
        optimizer.zero_grad()
        outputs = model(input_ids=batch['input_ids'], labels=batch['labels'])
        loss = outputs.loss
        loss.backward()
        optimizer.step()
    
    model.eval()
    eval_loss = 0
    with torch.no_grad():
        for batch in valid_dataloader:
            outputs = model(input_ids=batch['input_ids'], labels=batch['labels'])
            eval_loss += outputs.loss.item()
    print(f"Epoch {epoch+1}, Validation Loss: {eval_loss / len(valid_dataloader)}")
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结合RAG技术

设置FAISS检索器，并结合检索与生成：

import faiss
import numpy as np
from transformers import AutoTokenizer


# 假设我们有一个语料库
corpus = ["Example sentence 1.", "Example sentence 2.", "Example sentence 3."]

# 将语料库句子转换为token IDs
corpus_inputs = tokenizer(corpus, return_tensors='pt', padding=True, truncation=True)

# 使用模型生成语料库句子的embedding
with torch.no_grad():
    corpus_outputs = model(**corpus_inputs)

# 获取最后一层隐藏状态的平均值作为句子的embedding
corpus_embeddings = torch.mean(corpus_outputs.last_hidden_state, dim=1).numpy()

# 构建FAISS索引
index = faiss.IndexFlatL2(corpus_embeddings.shape[1])
index.add(corpus_embeddings)

# 结合RAG技术与检索
def retrieve_and_generate(query, context_size=3, max_length=128, num_return_sequences=1):
    # 使用FAISS检索最相关的文档
    query_inputs = tokenizer(query, return_tensors='pt', padding=True, truncation=True)
    with torch.no_grad():
        query_embedding = model(**query_inputs).last_hidden_state.mean(dim=1).numpy()
    D, I = index.search(query_embedding, k=context_size)
    retrieved_docs = [corpus[i] for i in I[0]]
    context = " ".join(retrieved_docs)
    
    # 将检索到的文档与查询结合
    input_with_context = f"{context} {query}"
    
    # 生成查询的SQL
    inputs = tokenizer(input_with_context, return_tensors="pt", max_length=max_length, truncation=True)
    with torch.no_grad():
        outputs = model.generate(**inputs, num_return_sequences=num_return_sequences)
    
    # 返回生成的SQL查询
    return tokenizer.decode(outputs[0], skip_special_tokens=True)


# 示例
query = "Show all users"
sql_query = retrieve_and_generate(query)
print(sql_query)
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结合Agent技术

实现NLU组件和对话管理：

from transformers import pipeline

nlu = pipeline("ner")

def parse_input(user_input):
    entities = nlu(user_input)
    if "users" in user_input.lower():
        return "SELECT * FROM users"
    else:
        return "Query not recognized"

class Agent:
    def __init__(self):
        self.context = ""
    
    def handle_input(self, user_input):
        self.context += f" {user_input}"
        sql_query = parse_input(self.context)
        return sql_query

agent = Agent()
user_input = "Show all users"
response = agent.handle_input(user_input)
print(response)  # 输出: SELECT * FROM users
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模型保存与部署

保存微调后的模型：

model.save_pretrained("./finetuned_llama3")
tokenizer.save_pretrained("./finetuned_llama3")
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总结

通过以上步骤，我们从头到尾实现了使用LORA微调LLaMA3模型，并结合RAG和Agent技术进行Text2SQL任务。这个流程包括环境准备、数据预处理、自定义训练循环、RAG技术整合、Agent实现，以及最终的模型保存。