CrewAI RAG - How I Created AI Assistants to Run My News Agency

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AI Summary

• Introduction to RAG for Crew AI
  • RAG stands for Retrievable Augmented Generation.
  • Aim to replicate a news agency’s process using AI.
• System Components
  • News Search Agent: Searches for news articles and stores data in a vector database.
  • Writer Agent: Searches for additional information on topics to create in-depth reports.
• Implementation Steps
  • Install necessary tools: Crew AI, Lang Chain, OpenAI, DuckDuckGo Search, Chroma DB.
  • Export API keys for News API and OpenAI.
  • Create app.py and import required modules.
• Tool Creation
  • Tool 1: Save news articles in Chroma DB.
    • Fetch top five news articles sorted by publish date.
    • Convert content to embeddings and store in Chroma DB.
  • Tool 2: Retrieve stored data from Chroma DB.
  • Tool 3: Search the web for news articles using DuckDuckGo.
• Agent Creation
  • News Search Agent: Searches for latest news and saves in database.
  • Writer Agent: Crafts narratives from complex information using saved data and web searches.
• Task Definition
  • News Search Task: Search for “AI 2024”, create key points.
  • Writer Task: Verify topics, search for more information, write summaries.
• Crew Creation and Execution
  • Create a new crew with assigned agents and tasks.
  • Process tasks sequentially or hierarchically.
  • Run the code and print results.
• Outcome
  • Agents work together to produce a report with in-depth summaries on topics like new AI programs and trends.
• Conclusion
  • Basic implementation of RAG demonstrated.
  • Encouragement to fine-tune and explore further.
  • Invitation to subscribe and follow for more content.