Build a RAG Based LLM App in 20 Minutes! | Full Langflow Tutorial
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Top Comments (10)
Tim please please more more on AI, LLMs, LangChain, .... free APIs
Please make one dedicated playlist Tim, which will contain RAG, AI agent, vector db-related concepts
If you're watching this after the recent update, Astra DB Search and Source nodes have been unified. You will now need a Parse Data node connected to your Astra DB node's Search Results output. Connect the Parse Data node's Text output to the Prompt context.
Hello, LongFlow has been updated and changed a lot. [Text Input] component not longer support text type output, only message type output. You need to create "Custom Component" to convert "Message to Text" and this connect with "Session ID" with [Chat Memory] component.
Honestly I've been waiting for an intuitive tutorial on RAG and it's really nice that you have posted this video. Thanks @TechWithTim
Tim, I was trying to understand the RAG topic from the codes, but I was having difficulty understanding what the sequence was. It was a very clear and great video. Please don't deprive us of your knowledge. Thank you very much.
Any chance of a video update, as per other comments many things have changed since this video was made.
Great video and was very easy to follow. Please consider a similar tutorial using 100% local LLM and local VectorDB setup.
i was literally watching a 1hr course of Langchain but the video is 11months ago then i saw your video😂
It was damn helpful. Please keep posting videos like this....
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Top Comments (10)
Tim please please more more on AI, LLMs, LangChain, .... free APIs
Please make one dedicated playlist Tim, which will contain RAG, AI agent, vector db-related concepts
If you're watching this after the recent update, Astra DB Search and Source nodes have been unified. You will now need a Parse Data node connected to your Astra DB node's Search Results output. Connect the Parse Data node's Text output to the Prompt context.
Hello, LongFlow has been updated and changed a lot. [Text Input] component not longer support text type output, only message type output. You need to create "Custom Component" to convert "Message to Text" and this connect with "Session ID" with [Chat Memory] component.
Honestly I've been waiting for an intuitive tutorial on RAG and it's really nice that you have posted this video. Thanks @TechWithTim
Tim, I was trying to understand the RAG topic from the codes, but I was having difficulty understanding what the sequence was. It was a very clear and great video. Please don't deprive us of your knowledge. Thank you very much.
Any chance of a video update, as per other comments many things have changed since this video was made.
Great video and was very easy to follow. Please consider a similar tutorial using 100% local LLM and local VectorDB setup.
i was literally watching a 1hr course of Langchain but the video is 11months ago then i saw your video😂
It was damn helpful. Please keep posting videos like this....