Advanced RAG 01 - Self Querying Retrieval

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00:00 Intro
00:47 Self Querying Diagram
01:55 Code Time

#langchain #openai #llm

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I implemented RNG to submit together queries to make my potty mouth AI bots even more toxic. It's almost like jailbreaking them. I will remember this video for more professional things tho. Very cool!

alexanderroodt

I am always curious to see what Langchain does under the hood. This is a super useful video, and I will dig a little deeper to see how the actual query and filters are determined. Thanks 🙏

alchemication

this + an additional chain that takes user input and does some kind of automatic entity extraction and inserts them back into the vector database = best second brain software ever

knoopx

This was a fantastic walk through and explanation thanks

thehve

Great insight. Content like this helps a lot to keep the costs low. Thanks 🎉

miikalewandowski

This is just amazing! This could potentially change the way how we can utilize langchain. Great video 👏. Kudos!

nazmulalamnazim

Nice feature of langchain, but it works only if the metadata are not in the scattered in the documents itself.... Thanks for the video👍

henkhbit

this is a great video and a very interesting Colab: please continue with tutorials on this retrieval topic

aa-xnhc

Thanks a lot Sam for this new tutorial. I really like the way the documents are prepared and the precision of the RAG. Willl definitley give it a try very soon

AdrienSales

Great video Sam. Keen to uncover more of the hidden gems in langchain

bwilliams

Another way of putting it is that you should only be using LLM as a classifier, in order to resolve ambiguity of NL queries, then translate them into normal database queries (which you can also understand and debug, unlike all the magic LLM output). While it is clearly taking a step back from the ridiculous attempts at placing AI in the driver seat, this approach actually works for real applications and brings added value... just like OCR, voice, or image recognition brings added value to tasks that require such capabilities. And when you need reasoning, idk, maybe use a FOL theorem prover or something.

clray

Exactly what I was looking for, thanks 🎉

sergiosajche

I am building a pilot of Q&A chatbot for physicians based on RAG - thank you for video.

micbab-vgmu

love it! I fed the list of wine description and metadata to my local llm, gave it two examples of metadata_field_info and asked it to complete that info. it did so with flying colors. what a glorious feeling.

mbottambotta

Waiting for next video with open source model and with csv data

tousifahamed

Hi Sam, how would you implement semantic search with data reference when retrieving results over a large number of different documents?

mizz

Hi Sam, great tutorial! :D
I have 3 questions:
1. Why do you use in the attribute description for "color", that it can be a list of strings? Is it possible to have a metadata field that is a list?
2. Do you know if it's possible to make the self query retriever use more fields than just page_content for semantic search?
3. Why do you use name as metadata field? Cause as far as i understand, the LLM doesn't know the exact names, so it wouldn't find the relevant document if the name is slightly missspelled from the user. Or are string metadata fields searched by semantic search and not by their exact value?

Would really appreciate some help! I hope the questions are clear, if not, feel free to ask! :D

quengelbeard

Thank you very much for sharing great videos.

ersineser

Can we use Self Query Retrievers with Open Source LLMs such as Llama-2? If yes then how?
I'm getting error on this statement
are some red wines')

TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not StringPromptValue

Tommyshelby

In the last example, llm automatically capitalized the first letter of country names. What if, in the dataset, they are not capitalized but are lowercase as you inputted?

xinxunzeng

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