创业点子发生器
创作者通常首先要努力的事情就是为他们的服务找到合适的利基创意。最好的方法始终是先找到一个微型 SaaS，它是 SaaS 的一个子集，专注于解决特定问题的小规模、高度专业化的应用程序。
但是你如何发现这些利基创意呢？为什么不让AI为你做这件事呢。在这个实验中，我整理了一个实用的应用程序，它可以帮助挖掘 Reddit、搜索引擎以找到利基创意。
在这篇博文中，我将分享如何构建这个 CrewAI 应用程序，该应用程序利用它来为微型 SaaS 生成利基创意。我们将深入研究应用程序结构、技术堆栈，并提供构建应用程序的分步指南。到最后，你将全面了解如何启动自己的 CrewAI 代理。
1、应用程序结构
首先，让我们看一下应用程序结构。下面是概述我们应用程序关键组件的图表：
为了构建此应用程序，我们将使用各种工具和技术：
- Groq：用于服务大型语言模型 (LLM)。
- 8B Llama3 模型：用于生成想法的核心模型。
- CrewAI：与 LangChain 工具一起使用，用于管理多个代理。
- Praw：用于从 Reddit 抓取数据。
- Pytrends：用于访问 Google Trends 数据。
- DuckDuckGo：用于竞争分析。
2、构建应用程序的分步指南
首先，我们需要安装必要的库和工具。你可以按照以下方法设置开发环境：
pip install groq llama3 crewai praw pytrends duckduckgo streamlit然后设置 LLM 模型
# Set up the customization options
st.sidebar.title('Customization')
model = st.sidebar.selectbox(
'Choose a model',
['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it','llama3-70b-8192']
)
llm = ChatGroq(
temperature=0,
groq_api_key = st.secrets["GROQ_API_KEY"],
model_name=model
)接下来，我们将创建应用程序所需的自定义工具。以下是使用 Praw 抓取 Reddit 的自定义工具示例：
import json
import os
import praw
from datetime import datetime
from helper_tools import remove_emojis
from crewai_tools import BaseTool
import streamlit as st
class RedditTrends(BaseTool):
name: str = "Reddit Trends"
description: str = "Fetches the latest trends from our favorite subreddits."
def _run(self, subreddits=None) -> dict:
"""
Executes the Reddit API to scrape the top posts and their best two comments from specified subreddits.
Parameters:
subreddits (list of str): Optional; a list of subreddit names to scrape. If not provided, the function defaults to
scraping posts from 'selfhosted', 'homelab', 'HomeNetworking', and 'HomeServer'.
A maximum of three subreddits can be specified at a time.
Returns:
dict: A dictionary where each key is a subreddit and the value is a list of the top posts from that subreddit,
each post accompanied by its top two comments.
Notes:
Ensure that the subreddit names are correctly spelled and are existing subreddits on Reddit. The function is
limited to scraping no more than three subreddits at once to maintain performance and adhere to API usage guidelines.
"""
return self.scrape_reddit(subreddits)
def scrape_reddit(self, subreddits=None):
"""
Executes the Reddit API to scrape the top posts and their best two comments from specified subreddits.
Parameters:
subreddits (list of str): Optional; a list of subreddit names to scrape. If not provided, the function defaults to
scraping posts from 'selfhosted', 'homelab', 'HomeNetworking', and 'HomeServer'.
A maximum of three subreddits can be specified at a time.
Returns:
dict: A dictionary where each key is a subreddit and the value is a list of the top posts from that subreddit,
each post accompanied by its top two comments.
Notes:
Ensure that the subreddit names are correctly spelled and are existing subreddits on Reddit. The function is
limited to scraping no more than three subreddits at once to maintain performance and adhere to API usage guidelines.
"""
print("Scraping Reddit for the latest trends...")
# Setup Credentials
reddit = praw.Reddit(
client_id=st.secrets["REDDIT_CLIENT_ID"],
client_secret=st.secrets["REDDIT_CLIENT_SECRET"],
user_agent="aiquill by /u/tuantruong84",
)
# Start up with these subreddits
if subreddits is None:
subreddits = ["Startup_Ideas", "startups", "Entrepreneur"]
if len(subreddits) > 3:
raise Exception("Maximum of 3 subreddits at the time.")
print(f"Scraping Reddit for the latest trends from {subreddits}...")
max_amount_of_posts = 3
scrapped_reddit_data = {}
for subreddit in subreddits:
sub = reddit.subreddit(subreddit)
for post in sub.hot(limit=max_amount_of_posts):
posts = {
"title": remove_emojis(post.title),
"url": post.url,
"score": post.score,
# "description": post.selftext,
"comments": [],
"created": datetime.fromtimestamp(post.created_utc).strftime("%Y-%m-%d %H:%M:%S"),
}
try:
post.comments.replace_more(limit=0)
comments = post.comments.list()[:5]
for comment in comments:
posts["comments"].append(remove_emojis(comment.body))
scrapped_reddit_data.setdefault(sub.display_name, []).append(posts)
except praw.exceptions.APIException as e:
print(f"API exception occurred {e}")
print("scraping done!",scrapped_reddit_data)
return scrapped_reddit_data代理在我们的应用中起着至关重要的作用。以下是使用 CrewAI 创建代理的方法：
niche_analyst = Agent(
role="Niche Analyst",
goal="Find inspiring SASS ideas from specified subreddits ",
backstory="""You are an AI tasked with continuously monitoring specified subreddits to identify trending discussions around SaaS ideas.
Your discoveries will lay the groundwork for further market analysis and MVP feature recommendations.""",
tools=[reddit_trends], # Assuming reddit_trends is an object of RedditTrends
verbose=True,
allow_delegation=False,
max_iter = 3,
llm=llm,
)
# Competitor Analysis Agent for identifying similar SaaS products
competitor_analyst = Agent(
role="Competitor Analyst",
goal="Identify existing competitors for the trending SaaS ideas, and analyze their strengths and weaknesses",
backstory="""You dive deep into the web to find existing SaaS solutions that match the ideas found. Your research helps in understanding the competitive landscape, highlighting the potentials.""",
tools=[search_tool], # Assuming competitor_analysis is an object of CompetitorAnalysis
llm=llm,
)
# Feature Analyst Agent for MVP feature suggestions
feature_analyst = Agent(
role="Feature Analyst",
goal="Suggest potential features for MVP based on the compiled analysis",
backstory="""With the insights provided by the Market and Competitor Analysts, you suggest a possible feature set for the MVP. Your goal is to craft a compelling value proposition for future development.""",
llm=llm,
verbose=True,
allow_delegation=True,
)
# Task for Trend Analyst to scrape trending SaaS ideas from specified subreddits
niche_analysis_task = Task(
description=f""" Based on these subreddit : {subreddit}.
Scrape specified subreddits for trending discussions around SaaS ideas. Focus on identifying emerging trends, popular discussions, and the most engaging content related to SaaS products.
""",
expected_output=f"""
Maxium of 10 SASS ideas containing the specific idea, including the problem, solution and a brief overview of the discussion around idea.
This list will serve as the foundation for further analysis, list are concise and easy to follow. List should be concise and easy to follow.
""",
agent=niche_analyst,
async_execution=False,
)
# Task for Competitor Analyst to conduct an in-depth analysis of existing solutions
competitor_analysis_task = Task(
description="""
Conduct a detailed analysis of existing competitors for the sass ideas.
""",
expected_output="""
A concise competitor analysis for each idea, listing major competitors, their key features, pricing. Highlight any gaps or opportunities for innovation, or problems to solve.
""",
agent=competitor_analyst,
async_execution=False,
context=[niche_analysis_task],
)任务对于CrewAI组织工作流程至关重要。以下是创建任务的示例：
# Task for Trend Analyst to scrape trending SaaS ideas from specified subreddits
niche_analysis_task = Task(
description=f""" Based on these subreddit : {subreddit}.
Scrape specified subreddits for trending discussions around SaaS ideas. Focus on identifying emerging trends, popular discussions, and the most engaging content related to SaaS products.
""",
expected_output=f"""
Maxium of 10 SASS ideas containing the specific idea, including the problem, solution and a brief overview of the discussion around idea.
This list will serve as the foundation for further analysis, list are concise and easy to follow. List should be concise and easy to follow.
""",
agent=niche_analyst,
async_execution=False,
)
# Task for Competitor Analyst to conduct an in-depth analysis of existing solutions
competitor_analysis_task = Task(
description="""
Conduct a detailed analysis of existing competitors for the sass ideas.
""",
expected_output="""
A concise competitor analysis for each idea, listing major competitors, their key features, pricing. Highlight any gaps or opportunities for innovation, or problems to solve.
""",
agent=competitor_analyst,
async_execution=False,
context=[niche_analysis_task],
)
# Task for Feature Analyst to outline potential MVP features
mvp_feature_suggestion_task = Task(
description="""
Based on the comprehensive analysis provided by the trend and competitor analysis, suggest potential features for each idea. Focus on unique selling points and core functionalities written in concise format.
""",
expected_output="""
A report on top SASS ideas with brief description and market potential, and suggested MVP features for each idea. Report should be in formatted markdown.
""",
agent=feature_analyst,
async_execution=False,
context=[competitor_analysis_task],
)现在，让我们将自定义工具、代理和任务整合到 AI 中
crew = Crew(
agents=[niche_analyst, competitor_analyst,feature_analyst],
tasks=[niche_analysis_task, competitor_analysis_task, mvp_feature_suggestion_task],
verbose=2,
process=Process.sequential,
full_output=True,
)启动 AI Crew：
result = ""
result_container = st.empty()
for delta in crew.kickoff():
result += delta # Assuming delta is a string, if not, convert it appropriately
result_container.markdown(result)2、示例场景和用例
我们想到如下两个应用场景：
- 场景 1：发现生产力工具的利基创意。
- 场景 2：确定教育应用市场的空白。
运行应用程序后，你会注意到根据收集的数据生成的利基创意列表。这些创意可以作为您的 Micro SaaS 项目的起点。
最令人惊讶的结果之一是确定了为远程工作者量身定制的心理健康应用的利基市场。这一见解为开发迎合这一不断增长的人口群体的专用应用程序开辟了新的可能性。
3、结束语
在这篇博文中，我们探讨了如何使用 CrewAI 和 Llama3 为 Micro SaaS 生成利基创意。从设置环境到集成各种组件，我们涵盖了你入门所需的一切。该应用程序的潜力巨大，我们鼓励你深入研究、探索并为这个令人兴奋的开源项目做出贡献：代码，在线应用。
关于未来改进的潜力和想法如下：
- 增强数据处理：实施更先进的数据处理技术来提高生成创意的质量。
- 用户反馈循环：结合用户反馈来改进和改善创意生成过程。
原文链接：Discovering Niche Ideas for Micro SaaS using CrewAI and Groq with Llama3
汇智网翻译整理，转载请标明出处