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can you please share the code? it will help me in my FYP.

github link note working. Can you share the functional link again?

Enroll to learn complete RAG hands-on and fundamental concepts www.udemy.com/course/genai_rag Coupon code link- www.udemy.com/course/genai_rag/?couponCode=F6EB2A903481F19DA8CA

I was looking for python code to integrate with Tradingview. Thanks for sharing. Will wait for upcoming videos.

Thanks for sharing code to integrate TradingView with Python. Looking for upcoming videos on Generative AI integration.

Enroll to learn complete RAG hands-on and fundamental concepts www.udemy.com/course/genai_rag Coupon code link- www.udemy.com/course/genai_rag/?couponCode=F6EB2A903481F19DA8CA

Enroll to learn complete RAG hands-on and fundamental concepts www.udemy.com/course/genai_rag Coupon code for next 5 days - B2351FC4326171856AD3

Enroll to learn complete RAG hands-on and fundamental concepts www.udemy.com/course/genai_rag Coupon code link www.udemy.com/course/genai_rag/?couponCode=F6EB2A903481F19DA8CA

Enroll to learn complete RAG hands-on and fundamental concepts and full code - www.udemy.com/course/genai_rag Coupon code few days - F6EB2A903481F19DA8CA

Enroll to learn complete RAG hands-on and fundamental concepts and full code www.udemy.com/course/genai_rag Coupon code for next 5 days - F6EB2A903481F19DA8CA

Enroll to learn complete RAG hands-on and fundamental concepts www.udemy.com/course/genai_rag Coupon code link www.udemy.com/course/genai_rag/?couponCode=F6EB2A903481F19DA8CA

It is very helpful any GitHub link

thank you sir for the explanation of RAG , can you give me your github link so that i can check the code,i'm planning to inculde this in my final year project please help

Thank you for the good tutorial! Could you share the code as well please?

The problem is that this process significantly increases costs and response times, imagine if the question is generic or outside the context of application for the system.

Thanks for the great video. @TechKnow_WithMe the changes that are covered very quickly at 17:55 seem to have broken the code if there is no existing vector database (i.e if you are using a new neo4j instance). I can make it work with the old code for create_vector_index(). I am not expert enough to know the full reason for this. Any thoughts?

I really love this. I am a bit speechless as this is such a powerful demo of a groundbreaking application. Thank you and keep making more. It is these little TH-cam accounts that are actually the most valuable!

If my data is structured (sql) can I still create a knowledge graph? I have used dynamic fewshots for optimal retrieval but nothing close to your amazing graph driven hybrid search. Appreciate suggestions

Great video! But how to break a loop after a few trials if the model gets stuck into an infinite loop during Hallucinations check or answer relevance?

Can this be used for json files

Thanks for sharing the detailed tutorial of Graph RAG.

Hybrid search RAG technique is very advance. Thanks for sharing.

Please share the code.

Link ,

provide github link

Very good explanation. Thanks for sharing.

Very informative. Thank you.

Very informative. Thank you

Sir if you have uploaded the code to GitHub please add that link to the description 😊

Can you please share the code also 🙏

Hi, Pls guide me Traceback (most recent call last): File "d:\Curr Task\PDF Rag App_LM Studio.py", line 25, in <module> db = FAISS.from_documents(texts, embeddings) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\AppData\Local\Programs\Python\Python312\Lib\site-packages\langchain_core\vectorstores.py", line 550, in from_documents return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\\AppData\Local\Programs\Python\Python312\Lib\site-packages\langchain_community\vectorstores\faiss.py", line 930, in from_texts embeddings = embedding.embed_documents(texts) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\AppData\Local\Programs\Python\Python312\Lib\site-packages\langchain_community\embeddings\huggingface.py", line 98, in embed_documents embeddings = self.client.encode( ^^^^^^^^^^^^^^^^^^^ File "C:\Users\AppData\Local\Programs\Python\Python312\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 564, in encode if all_embeddings[0].dtype == torch.bfloat16: ~~~~~~~~~~~~~~^^^ IndexError: list index out of range

when i update my langchain i can't use ConversationalRetrievalChain and i adjust specific langchain version i can't use HuggingFaceBgeEmbeddings can you tell me what is the best version langchain i can use this script?

Hi, I am getting error: index = faiss.IndexFlatL2(len(embeddings[0])) ~~~~~~~~~~^^^ IndexError: list index out of range please help me resolve it

i want download your codes but there are not download link. Tanks

good explanation, looking forward for more tech videos like this.

I really appreciate your videos and hence asking my queries to you, please help me in understanding a few things: for me It takes approx 2 mins for answering the question My laptop has 16GB ram, is it because of that or model? for this its mandatory to keep the LM Studio up and running all the time? also, each and every time I make a fresh execution, do I need to start the server? What safety measures can be taken to make it more secure?

It worked, Thank you for this :)

how to setup local LLM please share some resources? llama-2-7b-chat.ggmlv3.q8_0.bin