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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 次观看 • 2 年前 •via X (Twitter)

教育科学技术

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9 条评论

Joel Burigo 的头像

Joel Burigo2 年前

Nice work

Ashpreet Bedi 的头像

Ashpreet Bedi2 年前

thank you

Shashank Shekhar 的头像

Shashank Shekhar2 年前

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

Ryan Lisse 的头像

Ryan Lisse2 年前

@memdotai mem it

Mem 的头像

Mem2 年前

@ashpreetbedi Saved! Here's the compiled thread:

Nasrin Tohidi 的头像

Nasrin Tohidi2 年前

Brilliant! That's exactly what I needed!

Power Of AI 的头像

Power Of AI2 年前

That is awesome👍🏻

Mamba S6 的头像

Mamba S62 年前

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

Koma 的头像

Koma2 年前

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:

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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