Using Spring AI's Output Parsers to structure the response from LLMs
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- Опубликовано: 1 июн 2024
- In this tutorial we will discuss the Output Parsers in Spring AI. The Output Parsers will help us get what we want from the LLM and turn them into a desired response.
🔗Resources & Links mentioned in this video:
Spring AI Introduction: • Spring AI Introduction...
GitHub Repository: github.com/danvega/spring-int...
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Please RAG video next. Also please teach us how to set up vector db
Stuffing the prompt next and then a RAG example is on its way.
@@DanVega Sounds exciting!!! Can't wait for your next video. Thank you Dan
Excellent stuff! Please do something where we do RAG with a Weaviate DB. I'm looking to build an app where I have message arriving via AWS SQS that I would like to pump into the vector DB and then pass into the LLM locally so I don't leak company confidential information.
I have Ollama running and this works wellish so far on my MacbookPRO (old Intel). Understand the local GPU problem , but can live with that for dev/evaluation.
SpringAI really makes talking to these LLM's a breeze - great work to the Spring Teams!
Can you please create a video on how to Summerize large document using Spring AI ?
That sounds like a great idea. What type of documents are you trying to summarize?
@@DanVega mostly PDF documents
👍👍👍
next video about RAG please
It's coming soon!
Dan, great video as always, but I'm sorry to say that this one seems a bit too niche with its focus on AI and some marginal technical aspects. Perhaps consider going back to your regular topics, which have a wider appeal and tend to capture more attention. Thanks!
Utilizing AI in our apps is the hot topic using Spring AI. I enjoy the niche topic.
AI is a big topic right now and being able to build intelligent applications with Spring is something I get asked about a lot. I have 5 more videos coming on the topic and then I will be working on some htmx content.