Blog Post Generator

这个高级示例演示了如何构建一个复杂的博客文章生成器，该生成器结合了网络研究能力和专业写作技能。该工作流采用多阶段方法：

1. 智能网络研究和来源收集
2. 内容提取和处理
3. 专业博客文章撰写并附带正确引用

主要功能：

• 高级网络研究和来源评估
• 内容抓取和处理
• 专业写作并进行 SEO 优化
• 自动缓存内容以提高效率
• 来源归属和事实核查

尝试以下示例博客主题：

• “通用人工智能的崛起：最新突破”
• “量子计算如何彻底改变网络安全”
• “2024 年可持续生活：减少碳足迹的实用技巧”
• “工作的未来：人工智能与人类协作”
• “太空旅游：从科幻到现实”
• “数字时代的正念与心理健康”
• “电动汽车的演变：现状与未来趋势”

运行 pip install openai duckduckgo-search newspaper4k lxml_html_clean sqlalchemy agno 来安装依赖项。 """

blog_post_generator.py

import json
from textwrap import dedent
from typing import Dict, Iterator, Optional

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.sqlite import SqliteStorage
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.newspaper4k import Newspaper4kTools
from agno.utils.log import logger
from agno.utils.pprint import pprint_run_response
from agno.workflow import RunEvent, RunResponse, Workflow
from pydantic import BaseModel, Field


class NewsArticle(BaseModel):
    title: str = Field(..., description="Title of the article.")
    url: str = Field(..., description="Link to the article.")
    summary: Optional[str] = Field(
        ..., description="Summary of the article if available."
    )


class SearchResults(BaseModel):
    articles: list[NewsArticle]


class ScrapedArticle(BaseModel):
    title: str = Field(..., description="Title of the article.")
    url: str = Field(..., description="Link to the article.")
    summary: Optional[str] = Field(
        ..., description="Summary of the article if available."
    )
    content: Optional[str] = Field(
        ...,
        description="Full article content in markdown format. None if content is unavailable.",
    )


class BlogPostGenerator(Workflow):
    """Advanced workflow for generating professional blog posts with proper research and citations."""

    description: str = dedent("""\
    An intelligent blog post generator that creates engaging, well-researched content.
    This workflow orchestrates multiple AI agents to research, analyze, and craft
    compelling blog posts that combine journalistic rigor with engaging storytelling.
    The system excels at creating content that is both informative and optimized for
    digital consumption.
    """)

    # Search Agent: Handles intelligent web searching and source gathering
    searcher: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[DuckDuckGoTools()],
        description=dedent("""\
        You are BlogResearch-X, an elite research assistant specializing in discovering
        high-quality sources for compelling blog content. Your expertise includes:

        - Finding authoritative and trending sources
        - Evaluating content credibility and relevance
        - Identifying diverse perspectives and expert opinions
        - Discovering unique angles and insights
        - Ensuring comprehensive topic coverage\
        """),
        instructions=dedent("""\
        1. Search Strategy 🔍
           - Find 10-15 relevant sources and select the 5-7 best ones
           - Prioritize recent, authoritative content
           - Look for unique angles and expert insights
        2. Source Evaluation 📊
           - Verify source credibility and expertise
           - Check publication dates for timeliness
           - Assess content depth and uniqueness
        3. Diversity of Perspectives 🌐
           - Include different viewpoints
           - Gather both mainstream and expert opinions
           - Find supporting data and statistics\
        """),
        response_model=SearchResults,
    )

    # Content Scraper: Extracts and processes article content
    article_scraper: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[Newspaper4kTools()],
        description=dedent("""\
        You are ContentBot-X, a specialist in extracting and processing digital content
        for blog creation. Your expertise includes:

        - Efficient content extraction
        - Smart formatting and structuring
        - Key information identification
        - Quote and statistic preservation
        - Maintaining source attribution\
        """),
        instructions=dedent("""\
        1. Content Extraction 📑
           - Extract content from the article
           - Preserve important quotes and statistics
           - Maintain proper attribution
           - Handle paywalls gracefully
        2. Content Processing 🔄
           - Format text in clean markdown
           - Preserve key information
           - Structure content logically
        3. Quality Control ✅
           - Verify content relevance
           - Ensure accurate extraction
           - Maintain readability\
        """),
        response_model=ScrapedArticle,
    )

    # Content Writer Agent: Crafts engaging blog posts from research
    writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        description=dedent("""\
        You are BlogMaster-X, an elite content creator combining journalistic excellence
        with digital marketing expertise. Your strengths include:

        - Crafting viral-worthy headlines
        - Writing engaging introductions
        - Structuring content for digital consumption
        - Incorporating research seamlessly
        - Optimizing for SEO while maintaining quality
        - Creating shareable conclusions\
        """),
        instructions=dedent("""\
        1. Content Strategy 📝
           - Craft attention-grabbing headlines
           - Write compelling introductions
           - Structure content for engagement
           - Include relevant subheadings
        2. Writing Excellence ✍️
           - Balance expertise with accessibility
           - Use clear, engaging language
           - Include relevant examples
           - Incorporate statistics naturally
        3. Source Integration 🔍
           - Cite sources properly
           - Include expert quotes
           - Maintain factual accuracy
        4. Digital Optimization 💻
           - Structure for scanability
           - Include shareable takeaways
           - Optimize for SEO
           - Add engaging subheadings\
        """),
        expected_output=dedent("""\
        # {Viral-Worthy Headline}

        ## Introduction
        {Engaging hook and context}

        ## {Compelling Section 1}
        {Key insights and analysis}
        {Expert quotes and statistics}

        ## {Engaging Section 2}
        {Deeper exploration}
        {Real-world examples}

        ## {Practical Section 3}
        {Actionable insights}
        {Expert recommendations}

        ## Key Takeaways
        - {Shareable insight 1}
        - {Practical takeaway 2}
        - {Notable finding 3}

        ## Sources
        {Properly attributed sources with links}\
        """),
        markdown=True,
    )

    def run(
        self,
        topic: str,
        use_search_cache: bool = True,
        use_scrape_cache: bool = True,
        use_cached_report: bool = True,
    ) -> Iterator[RunResponse]:
        logger.info(f"Generating a blog post on: {topic}")

        # Use the cached blog post if use_cache is True
        if use_cached_report:
            cached_blog_post = self.get_cached_blog_post(topic)
            if cached_blog_post:
                yield RunResponse(
                    content=cached_blog_post, event=RunEvent.workflow_completed
                )
                return

        # Search the web for articles on the topic
        search_results: Optional[SearchResults] = self.get_search_results(
            topic, use_search_cache
        )
        # If no search_results are found for the topic, end the workflow
        if search_results is None or len(search_results.articles) == 0:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content=f"Sorry, could not find any articles on the topic: {topic}",
            )
            return

        # Scrape the search results
        scraped_articles: Dict[str, ScrapedArticle] = self.scrape_articles(
            topic, search_results, use_scrape_cache
        )

        # Prepare the input for the writer
        writer_input = {
            "topic": topic,
            "articles": [v.model_dump() for v in scraped_articles.values()],
        }

        # Run the writer and yield the response
        yield from self.writer.run(json.dumps(writer_input, indent=4), stream=True)

        # Save the blog post in the cache
        self.add_blog_post_to_cache(topic, self.writer.run_response.content)

    def get_cached_blog_post(self, topic: str) -> Optional[str]:
        logger.info("Checking if cached blog post exists")

        return self.session_state.get("blog_posts", {}).get(topic)

    def add_blog_post_to_cache(self, topic: str, blog_post: str):
        logger.info(f"Saving blog post for topic: {topic}")
        self.session_state.setdefault("blog_posts", {})
        self.session_state["blog_posts"][topic] = blog_post

    def get_cached_search_results(self, topic: str) -> Optional[SearchResults]:
        logger.info("Checking if cached search results exist")
        search_results = self.session_state.get("search_results", {}).get(topic)
        return (
            SearchResults.model_validate(search_results)
            if search_results and isinstance(search_results, dict)
            else search_results
        )

    def add_search_results_to_cache(self, topic: str, search_results: SearchResults):
        logger.info(f"Saving search results for topic: {topic}")
        self.session_state.setdefault("search_results", {})
        self.session_state["search_results"][topic] = search_results

    def get_cached_scraped_articles(
        self, topic: str
    ) -> Optional[Dict[str, ScrapedArticle]]:
        logger.info("Checking if cached scraped articles exist")
        scraped_articles = self.session_state.get("scraped_articles", {}).get(topic)
        return (
            ScrapedArticle.model_validate(scraped_articles)
            if scraped_articles and isinstance(scraped_articles, dict)
            else scraped_articles
        )

    def add_scraped_articles_to_cache(
        self, topic: str, scraped_articles: Dict[str, ScrapedArticle]
    ):
        logger.info(f"Saving scraped articles for topic: {topic}")
        self.session_state.setdefault("scraped_articles", {})
        self.session_state["scraped_articles"][topic] = scraped_articles

    def get_search_results(
        self, topic: str, use_search_cache: bool, num_attempts: int = 3
    ) -> Optional[SearchResults]:
        # Get cached search_results from the session state if use_search_cache is True
        if use_search_cache:
            try:
                search_results_from_cache = self.get_cached_search_results(topic)
                if search_results_from_cache is not None:
                    search_results = SearchResults.model_validate(
                        search_results_from_cache
                    )
                    logger.info(
                        f"Found {len(search_results.articles)} articles in cache."
                    )
                    return search_results
            except Exception as e:
                logger.warning(f"Could not read search results from cache: {e}")

        # If there are no cached search_results, use the searcher to find the latest articles
        for attempt in range(num_attempts):
            try:
                searcher_response: RunResponse = self.searcher.run(topic)
                if (
                    searcher_response is not None
                    and searcher_response.content is not None
                    and isinstance(searcher_response.content, SearchResults)
                ):
                    article_count = len(searcher_response.content.articles)
                    logger.info(
                        f"Found {article_count} articles on attempt {attempt + 1}"
                    )
                    # Cache the search results
                    self.add_search_results_to_cache(topic, searcher_response.content)
                    return searcher_response.content
                else:
                    logger.warning(
                        f"Attempt {attempt + 1}/{num_attempts} failed: Invalid response type"
                    )
            except Exception as e:
                logger.warning(f"Attempt {attempt + 1}/{num_attempts} failed: {str(e)}")

        logger.error(f"Failed to get search results after {num_attempts} attempts")
        return None

    def scrape_articles(
        self, topic: str, search_results: SearchResults, use_scrape_cache: bool
    ) -> Dict[str, ScrapedArticle]:
        scraped_articles: Dict[str, ScrapedArticle] = {}

        # Get cached scraped_articles from the session state if use_scrape_cache is True
        if use_scrape_cache:
            try:
                scraped_articles_from_cache = self.get_cached_scraped_articles(topic)
                if scraped_articles_from_cache is not None:
                    scraped_articles = scraped_articles_from_cache
                    logger.info(
                        f"Found {len(scraped_articles)} scraped articles in cache."
                    )
                    return scraped_articles
            except Exception as e:
                logger.warning(f"Could not read scraped articles from cache: {e}")

        # Scrape the articles that are not in the cache
        for article in search_results.articles:
            if article.url in scraped_articles:
                logger.info(f"Found scraped article in cache: {article.url}")
                continue

            article_scraper_response: RunResponse = self.article_scraper.run(
                article.url
            )
            if (
                article_scraper_response is not None
                and article_scraper_response.content is not None
                and isinstance(article_scraper_response.content, ScrapedArticle)
            ):
                scraped_articles[article_scraper_response.content.url] = (
                    article_scraper_response.content
                )
                logger.info(f"Scraped article: {article_scraper_response.content.url}")

        # Save the scraped articles in the session state
        self.add_scraped_articles_to_cache(topic, scraped_articles)
        return scraped_articles


# Run the workflow if the script is executed directly
if __name__ == "__main__":
    import random

    from rich.prompt import Prompt

    # Fun example prompts to showcase the generator's versatility
    example_prompts = [
        "Why Cats Secretly Run the Internet",
        "The Science Behind Why Pizza Tastes Better at 2 AM",
        "Time Travelers' Guide to Modern Social Media",
        "How Rubber Ducks Revolutionized Software Development",
        "The Secret Society of Office Plants: A Survival Guide",
        "Why Dogs Think We're Bad at Smelling Things",
        "The Underground Economy of Coffee Shop WiFi Passwords",
        "A Historical Analysis of Dad Jokes Through the Ages",
    ]

    # Get topic from user
    topic = Prompt.ask(
        "[bold]Enter a blog post topic[/bold] (or press Enter for a random example)\n✨",
        default=random.choice(example_prompts),
    )

    # Convert the topic to a URL-safe string for use in session_id
    url_safe_topic = topic.lower().replace(" ", "-")

    # Initialize the blog post generator workflow
    # - Creates a unique session ID based on the topic
    # - Sets up SQLite storage for caching results
    generate_blog_post = BlogPostGenerator(
        session_id=f"generate-blog-post-on-{url_safe_topic}",
        storage=SqliteStorage(
            table_name="generate_blog_post_workflows",
            db_file="tmp/agno_workflows.db",
        ),
        debug_mode=True,
    )

    # Execute the workflow with caching enabled
    # Returns an iterator of RunResponse objects containing the generated content
    blog_post: Iterator[RunResponse] = generate_blog_post.run(
        topic=topic,
        use_search_cache=True,
        use_scrape_cache=True,
        use_cached_report=True,
    )

    # Print the response
    pprint_run_response(blog_post, markdown=True)

Usage

1. 创建虚拟环境

   打开 Terminal 并创建一个 python 虚拟环境。

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install openai duckduckgo-search newspaper4k lxml_html_clean sqlalchemy agno
   
3. Run the workflow

   python blog_post_generator.py