"Vector DBs: Agent Zero Never Forgets—Unlike Me When My Wife Says 'Are you listening ...'"
Ever wondered how AI agents—like in Agent Zero—can "remember" lots of information the LLM's produce and the data held in documents we feed it, grab the right info instantly, and skip bloating the model's context? Checkout vector databases and semantic search with embeddings. Basically, it gives your AI agents a fast, meaning-based memory that actually works. I just watched this clear Computerphile vid that explains it well (with examples and code): https://youtu.be/YDdKiQNw80c?si=8XWOyWkBJZDhm60h I had no clue about vector databases before looking into agent zero and this video really helped me understand how storage and retrieval in agent zero works since Agent Zero uses **FAISS** for quick, local similarity searches across main memories, conversation chunks, and proven solutions. Worth a look—short, no fluff. Anyone messing with vector DBs like **Chroma**, **Pinecone**, or **FAISS** in their agents? Curious what you're running! 🚀