Agent 开发实战

# prompt_engineering.py - Prompt 设计模式

from openai import OpenAI

client = OpenAI(api_key="your-api-key")

# 1. 零样本提示 (Zero-shot)
def zero_shot(question):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{
            "role": "user",
            "content": question
        }]
    )
    return response.choices[0].message.content

# 2. 少样本提示 (Few-shot)
def few_shot(question):
    prompt = """
    你是一个图书管理助手。请根据以下示例回答问题。

    示例1:
    用户: 有哪些计算机类的书？
    助手: 图书馆有3本计算机类书籍：《深入理解计算机系统》、《算法导论》、《Python编程》

    示例2:
    用户: 借阅期限是多久？
    助手: 图书借阅期限为30天，到期前请按时归还。

    请回答:
    """ + question

    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# 3. 思维链提示 (Chain of Thought)
def chain_of_thought(question):
    prompt = f"""
    请一步一步思考以下问题：

    问题：{question}

    让我们逐步分析：
    1. 首先，理解问题的核心...
    2. 然后，分析相关因素...
    3. 最后，得出结论...

    请按此格式回答：
    """

    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# rag_system.py - RAG 系统实现
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# 1. 加载文档
loader = TextLoader("library_books.txt")
documents = loader.load()

# 2. 文本分块
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)
chunks = text_splitter.split_documents(documents)

# 3. 向量化并存储
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(chunks, embeddings)

# 4. 创建检索器
retriever = vectorstore.as_retriever(
    search_kwargs={"k": 3}
)

# 5. 创建 QA 链
llm = OpenAI(model="gpt-3.5-turbo")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever
)

# 6. 提问
question = "图书馆有哪些计算机类的书？"
answer = qa_chain.run(question)
print(answer)

# lora_finetune.py - LoRA 微调实现
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType

# 1. 加载基座模型
model_name = "Qwen/Qwen2-7B"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 2. 配置 LoRA
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=8,                    # 低秩矩阵的秩
    lora_alpha=32,           # 缩放因子
    lora_dropout=0.1,        # Dropout 比例
    target_modules=["q_proj", "v_proj"],  # 目标模块
)

# 3. 应用 LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

# 输出: trainable params: 4,194,304 || all params: 7,615,616,000 || 0.05%

# 4. 准备训练数据
train_data = [
    {"input": "图书馆有哪些书？", "output": "图书馆有8本图书..."},
    {"input": "如何借书？", "output": "选择图书后点击借阅..."},
]

# 5. 训练配置
from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./lora_output",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    learning_rate=2e-4,
    warmup_steps=100,
    logging_steps=10,
    save_strategy="epoch",
)

# dify_workflow.yaml - Dify 工作流配置
app:
  name: "智能图书馆助手"
  mode: "workflow"

workflow:
  nodes:
    - id: "start"
      type: "start"
      data:
        variables:
          - name: "user_question"
            type: "string"
            required: true

    - id: "llm_node"
      type: "llm"
      data:
        model: "gpt-3.5-turbo"
        prompt: |
          你是一个专业的图书馆助手。
          用户问题: {{user_question}}
          请根据知识库内容回答。

    - id: "knowledge_retrieval"
      type: "knowledge_retrieval"
      data:
        knowledge_base: "图书馆书籍库"
        query: "{{user_question}}"
        top_k: 3

    - id: "condition"
      type: "if_else"
      data:
        condition: "{{relevance_score}} > 0.7"

    - id: "output"
      type: "end"
      data:
        output: "{{llm_response}}"

  connections:
    - source: "start"
      target: "llm_node"
    - source: "llm_node"
      target: "knowledge_retrieval"
    - source: "knowledge_retrieval"
      target: "condition"
    - source: "condition"
      target: "output"

# coze_bot.json - Coze Bot 配置
{
  "bot_name": "图书馆智能助手",
  "description": "帮你查询图书、借阅、归还",
  "model": "gpt-3.5-turbo",
  "system_prompt": "你是图书馆的智能助手...",
  "plugins": [
    {
      "name": "知识库",
      "type": "knowledge_base",
      "config": {
        "dataset_ids": ["books_db"]
      }
    },
    {
      "name": "API调用",
      "type": "api_call",
      "config": {
        "url": "http://oywangzhan.cn/library/api.php"
      }
    }
  ]
}

# 1. 安装依赖
pip install langchain openai faiss-cpu

# 2. 设置 API Key
import os
os.environ["OPENAI_API_KEY"] = "your-key"

# 3. 最简单的 RAG
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator

loader = TextLoader("your_doc.txt")
index = VectorstoreIndexCreator().from_loaders([loader])
query = index.query("你的问题")
print(query)