FAISS Vector Library with LangChain and OpenAI (Semantic Search)
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- Опубликовано: 26 фев 2024
- In this video, we take a look at the Facebook AI Similarity Search (FAISS) vector library. Through a few examples, we will grab a document, chunk it, set up embeddings, and search through it.
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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
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Brother saw some of your other videos as well , you are doing a superb job here. It feels bad when I see quite a low response from the audience even though your videos are exceptionally knowledgable . keep working hard bro you will grow , best wishes
Thanks man I’m going to continue to push and learn/post videos. Working on getting leetcode out weekly now with a few other vids.
hey, I want to see how embeddings look/is stored in vectorStore. I want to store the embeddings in a CSV format. I am using FAISS only. How to store it in CSV format?
hey man this is _really_ good work!! keep on going!
Thank you!
similarity_search gives 4 most similar texts? because I'm getting from 0th to 3rd index results when i query something.
Thanks, how can we apply it to tables such as csv or even sqlite tables?
When I do date related query with FAISS why FAISS not returning perfect result? Do I need to update query for Vector search?
Great video!
Thanks
Hi bro, Great Video. Hope to see next!
Np see you in the next vid
Just a question: FAISS is now using only CPU not GPU. I have Windows. How can I get FAISS run with my GPU? If not what alternative vectorbases should I use? It makes a large difference in terms of performance doesnt it?
Absolutely brilliant and useful video. You are the REAL rockstar (no pun intended :))!
If you are ever looking for content ideas .... here's one:
Do a video on building a RAG pipeline on AWS Bedrock. The AWS platform is phenomenal but the documentation is often confusing. And they have a ton of really useful features (like Guardrails).
I haven’t used AWS yet. I’m trying to get thrown into a few AWS projects at work but no luck :/ I do want to learn AWS and make vids on it.
I don't understand the difference between query and retriever. Can someone help me understand this?
Small Doubt did you pay for OPENAI api key ? Because I have not paid it .
If we pay only we would able to use the Embedding models ?
I didn’t pay when I made this video. I had to pay last week for gpt vision
@@RyanNolanData thank you so much, learnt a lot from this video.
Great video! Good job. Love Metallica as well!!!
Thanks
Hi Ryan, you have now idea how this video of yours was good for me. I really appreciate it. Do you know how I can replace the OpenAI stuff and Jan AI (Jan is an offline AI thing that you can download to your computer and remove the dependency from cloud providers/service/internet connection. Thanks again! Keep the good stuff coming! btw, who would say Metallica would help learning AI, huh? LOL
Haha glad you like the Metallica. I haven’t heard of jan AI but maybe something to look at in the future. I’m about to crush out a series on dbt so will look after
@@RyanNolanData tagged along! Can't wait to learn more stuff!
Hey bro, this is an amazing tutorial, it would be great if you share the code link
I need to add it on GitHub. I’m just so behind with it