Vector search, RAG, and Azure AI search
ฝัง
- เผยแพร่เมื่อ 23 ม.ค. 2024
- I recorded myself giving the talk I gave at the SF AI tour.
Slides:
speakerdeck.com/pamelafox/vec...
Demos:
github.com/pamelafox/vector-s...
pamelafox.github.io/vectors-c...
github.com/Azure-Samples/azur... - แนวปฏิบัติและการใช้ชีวิต
This is one of best video I have watched so far on RAG. Great explanation in simple terms and to the point. please keep making more videos.
Nice session, please post more such sessions. got lot of clarity on embedding and search
Thank you so much! I especially appreciate the vector similarity lesson in this video. 👏
This is one of the best talks on applied LLMs on youtube. Thank you.
Really enjoyed this explanation! Everything covered to really understand what's happening behind the scenes. Thank you! :)
I've struggled to explain to folks about how vector semantic is different from text search.. finally found somone who explained this so clearly. Thanks, enjoyed this highly informative session.
cone analogy was really good. Thanks.
Excellent session, thanks.
Fantastic. Great work
Very Informative and insightful .. Thanks a lot
Amazing Tutorial,
Thank you
Great Video.. very informative. Thanks
I've become a huge fan of RAG, especially Microsoft's implementation via Copilot and Azure AI Studio.
Amazing tutorial
very helpful thanks
Very helpful
Great Explanation. can you explain about ai enrichments that we can add in ai search especially reading text images from pdfs using ocr
Fantastic
Thanks a lot
Very nice Content on the video a different approach from others. Are you open to questions at any time?
Mam, How to find the api version of Azure OpenAI of gpt-35-turbo? Can tell the steps to find it
so, azure ai search could be better then a simple postgre pgvector for RAG?
Ai search + open ai seems to give different answers for same user prompts. Even the chunks returned for answering seem different based on citations even for same prompt. What causes this? Facing issues with consistency of outputs.
I've written up a guide to debugging answer variance here: github.com/Azure-Samples/azure-search-openai-demo/blob/main/docs/customization.md#improving-answer-quality ...that's specific to that repo but is also a general approach for other RAG apps. Does that help?