自动化搜集信息

开发AI微型SaaS往往需要搜集网上的信息，下面介绍一些比较标志性的，平时会很容易需要用到的搜集信息（爬虫）的方法，参考代码有：

1, AI-Content-Ideas-Generator-Prototype

自动化打开浏览器，读取文章主要部分，AI进行总结

https://github.com/hassancs91/AI-Content-Ideas-Generator-Prototype

    chrome_options = Options()
    chrome_options.add_argument("--headless")
    chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
    chrome_options.add_experimental_option("useAutomationExtension", False)
    driver = webdriver.Chrome(options=chrome_options)

    stealth(
        driver,
        languages=["en-US", "en"],
        vendor="Google Inc.",
        platform="Win32",
        webgl_vendor="Intel Inc.",
        renderer="Intel Iris OpenGL Engine",
        fix_hairline=True,
    )

“Selenium Stealth” 是一个用于 Selenium 框架的工具或技术，旨在帮助用户更隐秘地自动化浏览器操作，以避免被网站检测到并阻止。在网络爬取、自动化测试和其他需要模拟用户操作的情况下，有时候需要避免被网站检测到使用自动化工具而采取措施限制访问。

def get_article_from_url(url):
    try:
        # Scrape the web page for content using newspaper
        article = newspaper.Article(url)
        # Download the article's content with a timeout of 10 seconds
        article.download()
        # Check if the download was successful before parsing the article
        if article.download_state == 2:
            article.parse()
            # Get the main text content of the article
            article_text = article.text
            return article_text
        else:
            print("Error: Unable to download article from URL:", url)
            return None
    except Exception as e:
        print("An error occurred while processing the URL:", url)
        print(str(e))
        return None

newspaper3k 是一个流行的 Python 库，用于从新闻网站和文章中提取内容，如文章文本、标题、作者、发布日期、图片等。这个库可以让用户轻松地从在线新闻源中提取信息，方便进行数据分析、自然语言处理等任务。

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2, auto_jobfindchatgpt__rpa

RPA 自动化打开浏览器，执行动作，获得信息推送给 gpt Assistants api ,自动化获得见解和操作处理

https://github.com/Frrrrrrrrank/auto_job__find__chatgpt__rpa

driver = finding_jobs.get_driver()

            # 更改下拉列表选项
            finding_jobs.select_dropdown_option(driver, label)
            # 调用 finding_jobs.py 中的函数来获取描述
            job_description = finding_jobs.get_job_description_by_index(job_index)
            if job_description:
                element = driver.find_element(By.CSS_SELECTOR, '.op-btn.op-btn-chat').text
                print(element)
                if element == '立即沟通':
                    # 发送描述到聊天并打印响应
                    if should_use_langchain():
                        response = generate_letter(vectorstore, job_description)
                    else:
                        response = chat(job_description, assistant_id)
                    print(response)
                    time.sleep(1)
                    # 点击沟通按钮

                    contact_button = driver.find_element(By.XPATH, "//*[@id='wrap']/div[2]/div[2]/div/div/div[2]/div/div[1]/div[2]/a[2]")
                    
                    contact_button.click()

                    # 等待回复框出现
                    xpath_locator_chat_box = "//*[@id='chat-input']"
                    chat_box = WebDriverWait(driver, 50).until(
                        EC.presence_of_element_located((By.XPATH, xpath_locator_chat_box))
                    )

                    # 调用函数发送响应
                    send_response_and_go_back(driver, response)

RPA调动浏览器，获得信息和操作动作。

 file = client.files.create(file=open("my_cover.pdf", "rb"),
                               purpose='assistants')

    assistant = client.beta.assistants.create(
        # Getting assistant prompt from "prompts.py" file, edit on left panel if you want to change the prompt
        instructions=assistant_instructions,
        model="gpt-3.5-turbo-1106",
        tools=[
            {
                "type": "retrieval"  # This adds the knowledge base as a tool
            },
        ],
        file_ids=[file.id])

利用gpt的 “retrieval”，向量数据库存储和索引搜索内容。

client.beta.threads.messages.create(
            thread_id=thread_id,
            role="user",
            content=user_input
        )

        # Start the Assistant Run
        run = client.beta.threads.runs.create(
            thread_id=thread_id,
            assistant_id=assistant_id
        )

利用gpt的 assistant api 可以作为“慢思考”，深度思考和自动分析，获得更好的分析结果。