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Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

Ashpreet Bedi

19,986 subscribers

45,035 Aufrufe • vor 2 Jahren •via X (Twitter)

Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

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9 Kommentare

Profilbild von Joel Burigo

Joel Burigovor 2 Jahren

Nice work

Profilbild von Ashpreet Bedi

Ashpreet Bedivor 2 Jahren

thank you

Profilbild von Shashank Shekhar

Shashank Shekharvor 2 Jahren

What do you use for illustrations - these are incredibly clean and beautiful looking!

Profilbild von Ryan Lisse

Ryan Lissevor 2 Jahren

@memdotai mem it

Profilbild von Mem

Memvor 2 Jahren

@ashpreetbedi Saved! Here's the compiled thread:

Profilbild von Nasrin Tohidi

Nasrin Tohidivor 2 Jahren

Brilliant! That's exactly what I needed!

Profilbild von Power Of AI

Power Of AIvor 2 Jahren

That is awesome👍🏻

Profilbild von Mamba S6

Mamba S6vor 2 Jahren

Is it possible to run this with ollama as well? Thanks ahead :)

Profilbild von Koma

Komavor 2 Jahren

what is video recorder he was use?

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Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

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grok-build-0.1 for interface design. Pretty impressed with the agentic capabilities and multi tool calling. good model.

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