👋🏼 Check out the article version of the video here: www.pinecone.io/learn/sagemaker-rag/
@mr.daniish Жыл бұрын
James can teach a 9 year old what a RAG is!
@jamesbriggs Жыл бұрын
I try my best haha
@noneofyourbusiness8625 Жыл бұрын
This channel provides so much valuable information for free and I really appreciate it!
@jamesbriggs Жыл бұрын
glad to hear :)
@shashwatkumar555611 ай бұрын
I want to thank you for this walkthrough. This was very informative. And I know it must have taken quite a lot of time and effort to make it. So thank you!!
@Yikina76 ай бұрын
Amazing video, thank you very much! It's obvious there was a lot of work involved to make it in such a well structure way. Very easy to follow, you know how to teach :)
@sandeeprawat498110 ай бұрын
Thank you so much.. really appreciate...love from India
@RezaA Жыл бұрын
Thank you for the well described demo. The recommended vector db for this stack is probably opensearch which does the same as pinecone but you have more control and you own it and its more expensive.
@jamesbriggs Жыл бұрын
meh, opensearch doesn't scale beyond 1M vecs well and their vec search implementation is nothing special - if you want open source I'd recommend qdrant (also rust like Pinecone) or weaviate
@arikupe2 Жыл бұрын
@jamesbriggs Thanks for the video James! I was wondering what issues you've experienced with scaling OpenSearch? We're considering it for our large-scale business use case and had thought it would be a good fit for larger-scale use
@e_hossam969 ай бұрын
Thank you for your great effort 🤗
@megamehdi89 Жыл бұрын
awesome content, thank you so much. very good explanation. i love watching your videos. i try to follow them and learn 😊
@jamesbriggs Жыл бұрын
happy to hear that! :)
@SolidBuildersInc3 ай бұрын
Thank you for your presentation. I clicked the Subscribe button, although I didn't delve into the video content. During your talk, I recall you mentioning the open-source LLM and discussing AWS pricing. This led me to prioritize a cost-effective solution that allows for scalability. Have you considered running an ollama model locally and setting up a tunnel with a port endpoint for a public URL? I appreciate any feedback you can provide." 😊
@VaibhavPatil-rx7pc Жыл бұрын
Excellent
@barkingchicken11 ай бұрын
Great video
@shalabhgarg8225 Жыл бұрын
Well just too good
@energyexecs5 ай бұрын
James -Great video and I like how you referred by to your flow chart diagram. My task is I am working on the "Corppus" of publicly available engineer technical standards documents that are only available in PDF or Word documents. I want to encode the words (tokens) in those document into a vector database and then take through LLM Bing GPT Transformation Architecture and then using RAG to focus only on the tokens (words) for that "corpus" of engineering standards. Why? This because right now I do a “Control F Search” which takes forever with my clients to find the standards which includes both words and diagrams, pictures (different modality) -- so instead of spending hours on "Control F" my plan is to convert those documents to the vector database and enable a "generative search" in "natural language" instead of "Control F search". Does this make sense? Your video is giving me the pathway to success.
@user-yu4kt5ie4r Жыл бұрын
will you be a video on deployment? Great video btw.
@energyexecs5 ай бұрын
Great video and I like how you referred to your flow chart diagram. I am working on the "Corpus" of publicly available engineer technical standards documents that are only available in PDF or Word documents. I want to encode the words (tokens) in those document into a vector database and then take through LLM Bing GPT Transformation Architecture and then using RAG to focus only on the tokens (words) for that "corpus" of engineering standards. Why? This because right now I do a “Control F Search” which takes forever with my clients to find the standards which includes both words and diagrams, pictures (different modality) -- so instead of spending hours on "Control F" my plan is to convert those documents to the vector database and enable a "generative search" in "natural language" instead of "Control F search". Does this make sense? Your video is giving me the pathway to success.
@VenkatesanVenkat-fd4hg Жыл бұрын
Thanks for your valuable videos as always. Can you discuss fine tuning llama 2 7b or 13b using dataset & deploy in sagemaker.....
@serkansandkcoglu304810 ай бұрын
Thank you! this is very informative! when we put our embeddings into pinecone vector db, is our data going outside? I would be ok to push my sensitive data to aws s3 bucket, but where does that pinecone db resides in?
@sergioquintero46249 ай бұрын
@jamesbriggs Hi james, thank you for the amazing video, I have a question.. it's possible to deploy models (embedding and LLM) in the same endpoint ? Just for save monye considering that in the RAG pipelines the embedding step and the retrieval are sequencial steps
@AaronChan-x2d23 күн бұрын
You need to define your llm in step 2 of asking the model directly.... llm = HuggingFacePredictor( endpoint_name="flan-t5-demo" # Use the name of your deployed endpoint )
@riyaz80728 ай бұрын
how to create vector vector database for pdf documents ?
@brianrowe1152 Жыл бұрын
Neat but why? Is sagemaker just langchain hosted at Aws?
@jamesbriggs Жыл бұрын
no it's more like Colab + ML infra, you can also use langchain with sagemaker - the why is for the infra component, hosting open source LLMs is super easy
@pantherg4236 Жыл бұрын
What is the best way to learn deep learning fundamentals via implementation (let's say pick a trivial problem of build a recommendation system for movies) using pytorch in Aug 26, 2023?
@rociotesla2 ай бұрын
tu código no corre una mierda bro
@sndrstpnv84195 ай бұрын
you use in article wrong LLM 'HF_MODEL_ID':'meta-llama/Llama-2-7b' but it suppose to be MiniLM