使用DeepSeek+本地知识库，尝试从0到1搭建高度定制化工作流（数据分析篇）

7.3. 数据监控与生成本地知识库

• 目的：监控新生成的小红书文案，记录每一次生成的小红书文案风格。后续根据输入topic，检索与某一topic有关的文案，可以根据先前的文案风格，生成类似风格的文案。
• 实现思路：
  • 1.要实现文件监控功能，需要使用watchdog库。watchdog是一个Python库，用于监控文件系统的变化。它提供了多种事件类型，如文件创建、修改、删除等，可以用来监控文件的变化。启动一个线程，实时监控xiaohongshu_drafts目录下的文件变化，当有新文件生成时，调用process_new_file(file_path)函数生成知识库。
  • 2.process_new_file(file_path)函数读取新文件中的内容，并调用generate_knowledge_base()函数对新生成的文案进行文本分割、对象转换、向量化等一系列操作来生成知识库。
• 代码实现：

'''
Author: yeffky
Date: 2025-02-12 13:29:31
LastEditTime: 2025-02-17 14:28:11
'''
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from transformers import AutoTokenizer
from langchain.schema import Document
from text2vec import SentenceModel
import time
import osclass NewFileHandler(FileSystemEventHandler):def on_created(self, event):if not event.is_directory:file_path = event.src_pathprocess_new_file(file_path)  # 处理新文件print(f"新增文件已加载: {file_path}")def process_new_file(file_path):# 加载文档（假设为txt格式）loader = TextLoader(file_path, encoding="utf-8")documents = loader.load()print(type(documents[0]))# 从 Hugging Face 加载一个标记器tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(tokenizer,chunk_size=256,chunk_overlap=0,separators=['---'])# 进行文本分块text_chunks = text_splitter.split_text(documents[0].page_content)# 将分块结果转换为文档对象列表chunk_docs = [Document(page_content=chunk) for chunk in text_chunks]# 向量化并更新索引embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-zh")vector_store_dir = "./vector_store"if os.path.exists(vector_store_dir):vector_store = FAISS.load_local(vector_store_dir, embeddings, allow_dangerous_deserialization=True)vector_store.add_documents(chunk_docs)else:vector_store = FAISS.from_documents(chunk_docs, embeddings)vector_store.save_local(vector_store_dir)def start_observer():observer = Observer()observer.schedule(NewFileHandler(), path="./xiaohongshu_drafts", recursive=False)observer.start()try:while True:# 每隔一小时检查一次time.sleep(1)except KeyboardInterrupt:# 当用户按下 Ctrl+C 时，停止观察者observer.stop()# 等待观察者线程结束observer.join()if __name__ == "__main__":start_observer()

7.4. 数据分析

• 目的：根据先前爬取数据，与本地知识库联动，调用deepseek api完成小红书文案生成。
• 实现思路：
  • 要通过deepseek生成文案首先需要构建prompt和提问词，首先从文件中加载prompt和提问词模板，然后将前期爬取的商品数据以及本地知识库中的文案数据作为输入，构建prompt和提问词。
• 代码实现：

'''
Author: yeffky
Date: 2025-02-11 11:17:04
LastEditTime: 2025-02-17 15:35:13
'''
import json
import os
import requests
from datetime import datetime
import random
from langchain import FAISS
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from text2vec import SentenceModelos.environ['HF_ENDPOINT'] = 'hf-mirror.com'# 获取今天的日期
today_date = datetime.now().strftime('%Y-%m-%d')
topic = "手机推荐"# 1. 读取JSON文件
def read_json_file(filename):with open(f'data/{filename}', 'r', encoding='utf-8') as f:return json.load(f)# 2. 构造分析提示词
def build_prompt(item):with open('./docs/prompt.txt', 'r', encoding='utf-8') as f:prompt = f.read()# 创建文本嵌入对象embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-zh")# 检索知识库（扩大检索范围）vector_store = FAISS.load_local("./vector_store", embeddings, allow_dangerous_deserialization=True)retrieved_docs = vector_store.similarity_search(topic, k=5)  # 检索Top 5# 随机选择3个不同风格的参考文案random.shuffle(retrieved_docs)selected_docs = retrieved_docs[:3]return f"""{prompt}, 
{json.dumps(item, ensure_ascii=False, indent=2)} 
**根据以下文案风格，做出创新**：{selected_docs}**注意**：- 在结尾加入提示，数据截至当前日期：{today_date}- 每一段内容使用 --- 进行分割
"""def build_preset():with open('./docs/preset.txt', 'r', encoding='utf-8') as f:preset = f.read()# 创建文本嵌入对象embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-zh")print("embeddings加载完毕")# 检索知识库（扩大检索范围）vector_store = FAISS.load_local("./vector_store", embeddings, allow_dangerous_deserialization=True)retrieved_docs = vector_store.similarity_search(topic, k=5)  # 检索Top 5# 随机选择3个不同风格的参考文案random.shuffle(retrieved_docs)selected_docs = retrieved_docs[:3]preset += f"""\n **主题**：{topic}**创新要求**：- 使用{random.choice(["轻松幽默", "专业严谨", "犀利吐槽"])}的语气- 加入{["emoji表情", "热门梗", "互动提问"]}元素"""print(preset)return preset# 3. 调用Deepseek API
def get_deepseek_response(preset, prompt, api_key):url = "https://api.deepseek.com/chat/completions"headers = {"Authorization": f"Bearer {api_key}",'Content-Type': 'application/json','Accept': 'application/json',}payload = json.dumps({"messages": [{"content": preset, "role": "system"},{"content": prompt,"role": "user"}],"model": "deepseek-reasoner","frequency_penalty": 0,"max_tokens": 2048,"presence_penalty": 0,"response_format": {"type": "text"},"stop": None,"stream": False,"stream_options": None,"temperature": 1,"top_p": 1,"tools": None,"tool_choice": "none","logprobs": False,"top_logprobs": None})response = Nonewhile not response:try:response = requests.post(url, data=payload, headers=headers, timeout=100)response.raise_for_status()if not response.json():response = Noneprint("没有收到响应，重试中...")else:print("收到响应，内容为：\n" + response.json()['choices'][0]['message']['content'])except requests.exceptions.RequestException as e:print(f"请求失败：{str(e)}")response = Nonereturn response.json()['choices'][0]['message']['content']# 4. 保存文案文件
def save_copywriting(content):base_path = f'./xiaohongshu_drafts/'filename = f"小红书_推广文案_千战系列" + today_date + ".txt"print(content)with open(base_path + filename, 'w', encoding='utf-8') as f:f.write(content)print(f"文案已保存至：{filename}")# 主流程
def analysis_data():# 配置参数API_KEY = os.getenv("DEEPSEEK_API_KEY")  # 从环境变量获取API密钥# 在文件名后加上今天的日期JSON_FILE = f'goods_{today_date}.json'# 读取数据items = read_json_file(JSON_FILE)print(f"正在处理：{JSON_FILE}")# 构造提示词prompt = build_prompt(items)preset = build_preset()# 获取AI响应try:response = get_deepseek_response(preset, prompt, API_KEY)save_copywriting(response)except Exception as e:print(f"处理失败：{str(e)}")if __name__ == "__main__":analysis_data()