This is the best explanation of RAG ever given by anyone - Detailed + Beginner to Advanced. One suggestion, we need a playlist for Gen AI and Agentic AI to follow in sequence, currently it's hard to navigate over youtube channel !!
I really missed your videos, and now you have come back with a comprehensive and wonderful video that solved a lot of my problems, so thank you and I hope you continue.
Great video! Could you please create one on combining fine-tuning with Retrieval-Augmented Generation (RAG) for chatbots? Fine-tuning can be costly for certain use cases, but applying it selectively to establish the tone or behavior of RAG models could be highly efficient. This would be useful for instances where we want the model to follow a specific conversational style without extensive, full-scale fine-tuning
Excellent work brother. Very lucid explanation. It will even help the season developer to refine their understanding of the concepts. Looking forward for more such videos like these. You have earned my respect and a subscriber :).
Hello Pradip, could you make a video on how can we split based on the ranking of the employee in an organization? For example executives would have access to financials but a junior would have access to a guide documentation
Thanks a lot for such a wonderful content. Could u please list down all step required to get code in local and make it running 1. Clone code using git clone 2. Replace api key 3. Run command for Backend 4 run command for fronted This will help to play around with this code
Its like deploying any api check this ruclips.net/video/7FVPn25mmEQ/видео.htmlsi=k9IiN8XS13hn_O48 ruclips.net/video/904cW9lJ7LQ/видео.htmlsi=aydaAzyj-nyJPDke
In newer version of langchain v0.3, these chains has been deprecated, in favor of the more flexible and powerful frameworks of LCEL and LangGraph. Then, why do you prefer to use these chain??
I've also demonstrated in the video how to achieve similar functionality without this specific chain, using LCEL instead. Additionally, I don’t believe these chains have been fully deprecated; even the 0.3 documentation still includes them. You can check it out here: python.langchain.com/docs/tutorials/qa_chat_history/
my langsmith account is showing: Failed to execute 'getReader' on 'ReadableStream': ReadableStreamDefaultReader constructor can only accept readable streams that are not yet locked to a reader || how to resolve this?
Planning open source models in next video. I work with multiple clients and they still prefer open ai models rather spending money hosting open source models
@@FutureSmartAI Thank you for the reply. Interesting to know and company which I work for want to host open source models due to privacy and security and no need to worry about vendor lock in. Can you please guide or have any plans using litellm to make the code model agnostic?
The context consists of relevant documents retrieved from a vector database. These documents are processed to extract only the page content, excluding metadata, and all content is combined into a single long string, with each document separated by two newline characters.
This is the best explanation of RAG ever given by anyone - Detailed + Beginner to Advanced. One suggestion, we need a playlist for Gen AI and Agentic AI to follow in sequence, currently it's hard to navigate over youtube channel !!
Great suggestion!
@ Can you help me with the sequence which I can follow as of now ?
I really missed your videos, and now you have come back with a comprehensive and wonderful video that solved a lot of my problems, so thank you and I hope you continue.
Great to hear!
Please keep uploading like this exquisite content. Thank you!
Great video! Could you please create one on combining fine-tuning with Retrieval-Augmented Generation (RAG) for chatbots? Fine-tuning can be costly for certain use cases, but applying it selectively to establish the tone or behavior of RAG models could be highly efficient. This would be useful for instances where we want the model to follow a specific conversational style without extensive, full-scale fine-tuning
Excellent work brother. Very lucid explanation. It will even help the season developer to refine their understanding of the concepts. Looking forward for more such videos like these. You have earned my respect and a subscriber :).
Glad it was helpful!
Nice series. Please keep uploading.
bhai, what an explaination! just wow
Hello Pradip, could you make a video on how can we split based on the ranking of the employee in an organization? For example executives would have access to financials but a junior would have access to a guide documentation
Excellent
very important and great videos please keep posting , how can we store unstructured data like table and image in vector db
Thanks a lot for such a wonderful content.
Could u please list down all step required to get code in local and make it running
1. Clone code using git clone
2. Replace api key
3. Run command for Backend
4 run command for fronted
This will help to play around with this code
How to run both application
I m new to python
please make video on all topics in same way
it was such a great course. We can deploy this application on azure or aws also ? not only streamlit right?
Yes you can deploy this app anywhere
Awesome sir thanks a lot
I have a request sir please make a vedio on integrating diagram generation feature also in this chatbot sir it helps alot sir
Timestamp
RAG: 31:27
Embedding: 38:07
Great explanation! Where can I find the code/notebook present in the video?
link In the description
❤❤
thank you!!
Thanks
Thank you for this great tuto, I have question though : The app you developed is on localhost, but how we can deploy so that it's available online ?
Its like deploying any api check this
ruclips.net/video/7FVPn25mmEQ/видео.htmlsi=k9IiN8XS13hn_O48
ruclips.net/video/904cW9lJ7LQ/видео.htmlsi=aydaAzyj-nyJPDke
@FutureSmartAI thank you
this is one amazing video sir ! I have a question, weaviate seems to give a tough competition to chromaDB so how to choose between vector DBs
Let me see if I could create video on that
wonderful...you are doing excellent.. can we get this code file?
Yes. code and explanation link in description
Nice one! please, the blog post seems not to be opening.
Finally
great video! can you share the code used?
In the description
In newer version of langchain v0.3, these chains has been deprecated, in favor of the more flexible and powerful frameworks of LCEL and LangGraph. Then, why do you prefer to use these chain??
I've also demonstrated in the video how to achieve similar functionality without this specific chain, using LCEL instead. Additionally, I don’t believe these chains have been fully deprecated; even the 0.3 documentation still includes them. You can check it out here:
python.langchain.com/docs/tutorials/qa_chat_history/
Hi Pradip. Is it still worth it to pursue this space/market on upwork? is there still demand for it ?
Yes
my langsmith account is showing: Failed to execute 'getReader' on 'ReadableStream': ReadableStreamDefaultReader constructor can only accept readable streams that are not yet locked to a reader || how to resolve this?
How can be save tokens. every time we hit LLM or open AI. we consume some tokens. and token are pricey right? how do we save them ?
open ai api key limit exceed when ever i have used that it shows the same
😮💨
Please teach Huggingaface or ollama open source models instead of Open AI LLMs
Planning open source models in next video. I work with multiple clients and they still prefer open ai models rather spending money hosting open source models
@@FutureSmartAI Thank you for the reply. Interesting to know and company which I work for want to host open source models due to privacy and security and no need to worry about vendor lock in. Can you please guide or have any plans using litellm to make the code model agnostic?
One thing i can't understand is : what is context ? In the example 52:00 you put all the document as context.
The context consists of relevant documents retrieved from a vector database. These documents are processed to extract only the page content, excluding metadata, and all content is combined into a single long string, with each document separated by two newline characters.
Can i use gemini pro instead of open ai ?
Yes
1:15:05
27:36
I think It would be better to revise
Thanks for suggestions
lottery lag gai
Hi @pradip