How Does Rag Work? - Vector Database and LLMs

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[@dcpotomac20850]

Wow I got more in this 60 seconds explanation than all the lengthy confusing videos I watched on RAG.

[@24035709]

Simply put. LLMs can't access your company's information directly. RAG lets you share relevant documents with the LLM, so it can answer your questions using your own data.

[@python-programming]

precisely!

[@RamandeepSingh_04]

But is it safe to connect our database to llms?
Can the information get leaked?

[@python-programming]

@@RamandeepSingh_04 It is if you use open-source LLMs that can be hosted locally.

[@RamandeepSingh_04]

@@python-programming okay so it means downloading and installing the llm on my device and then using it?And can all the open source llms be downloaded?

[@stateportSound_wav]

@@RamandeepSingh_04yes, but Small Language Models will typically run smoothly on newer phones or PCs with lower-end GPUs, Large LMs for more VRAM capable systems, or likely the soon-to-come ai chipsets.
Personally my M1 Mac really struggles to run a smaller Dolphin-Llama model, and I’d need to upgrade that one to M3 silicon or newer I think.
I have 16GB VRAM in my PC GPU, so it might run better on there.

[@kunallahiri]

The Vector database is updated accordingly I guess.
Nice explanation ❤

[@lazzybirdflying3225]

Awesome. This 60secs costs a lot!! Thank you for sharing!

[@alextasarov1341]

So it's a fancy way of implementing long term memory, cool

[@Antonio-mn5pq]

Or Real Academia Gallega. An ancient national linguistic institution of Spain.

[@python-programming]

Lol! Love it

[@CodeMaster-w5g]

Great explanation

[@Mari_Selalu_Berbuat_Kebaikan]

Let's always do alot of good ❤

[@visualsbysri]

Does it serve open domain question answering system?

[@Rudra0x01]

Please in next video explain how to create dataset for RAG

[@jatinnandwani6678]

Thanks so much

[@tejasaimaheshchennamsetti3247]

I think you have not explained it completly in this short!, Generative modles provided based on vdb(fixed data set), but when you combaune with Retrieval augmented, now it will provide you with latest data as it communicates and give you the updated and right answer!

[@anuragtekam4588]

Yeah this is one of the major benefits.

[@willyjauregui6541]

Its RAG the same as langchain?

[@python-programming]

You can build a RAG system with langchain but they are two different things. RAG is a workflow while LangChain is a framework.

[@AlexandreMarr-uq8pw]

I don’t need how it works I need the demonstration

[@excusemenoexcusemeno1671]

I didn't understand.

[@anuragtekam4588]

Basically llms are trained on dataset and this dataset may or might not contain relevant information or may contain outdated information. Here in RAG system we store any information in vectorstore and fetch the relevant information when needed. We make sure the vectorstore is updated with updated datasets and allows the model to work without the content in the dataset that the llm was trained on.

[@excusemenoexcusemeno1671]

@@anuragtekam4588 ohhh