I. Moving Beyond Simple Search: The RAG Challenge
If you've built applications using Large Language Models (LLMs), you've likely used Re...
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This is an incredible deep dive — Cognee feels like a genuine step forward from traditional RAG setups. The triple-store architecture (Vector + Graph + Relational) makes perfect sense for achieving both semantic recall and structural reasoning. The .memify() feature in particular stands out — it’s exactly what’s needed for AI agents to move from retrieval to real contextual understanding. Definitely going to explore this on GitHub.
Thanks Sir, Glad you liked it!!! ❤️
Thanks, Om! 🙌 Really appreciate the detailed write-up — the .memify() pipeline idea was especially insightful. Excited to see how Cognee evolves, this approach could set a new standard for AI memory systems 🔥
I'm excited tooooo Sir ❤️❤️❤️
Haha awesome, Om! 😄 Can’t wait to see where you take this — really promising work 🚀
Thanks Sir, all credit goes to Cognee's team ❤️.
How interesting, thanks for sharing it 👍🏼
Thanks Sir, Glad you liked it!!!
Nice Article Om!, I love the idea of using the Knowledge Graph for structural memory. Loved the visualization part.
Thanks Ma'am, Glad you liked it!!!
Loved it, but how does Cognee’s hybrid search differ from traditional RAG pipelines in practice?
Thanks ma'am!!!
Cognee's hybrid search combines vector similarity (semantic context) with knowledge graph traversal (structural relationships) to enable complex, multi-hop reasoning that traditional RAG cannot perform.
great work Om, thank you for sharing!
Thanks ma'am, Glad you liked it!!!
Love it!
Thanks Sir, Glad you liked it!
Loved how Simple and detailed it is!
Thanks Sir, Glad you liked it!
Insightful ❤️
Thanks Sir, Glad you liked it!
That’s a solid approach. The combination of vector search with a knowledge graph feels like the missing piece for most RAG setups. I’ve seen similar issues where context gets lost between related documents or previous user sessions.
In Vezlo, we’re testing something similar for SaaS knowledge bases — trying to link context across multiple data sources instead of just retrieving static chunks. Curious how Cognee handles updates when the underlying data changes — does the graph rebuild incrementally or from scratch?
This is really awesome and opens many more ways to improve our AI agents. Very nice work!
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How interesting, thanks for sharing it 👍🏼