Master PDF Chat with LangChain - Your essential guide to queries on documents

In this video I go through how to chat and query PDF files using LangChain and creator a FAISS vector store.

My Links:

Github:

[ChatGPT-efsr]

It’s very generous of you, giving us the source code, and explaining everything clearly. This is the kind of channels that deserve a subscription and follow.

[is]

Thanks a lot, your efforts are much appreciated

[arjunbaidya]

Amazing tutorial video! The pace is just perfect for learning. 👍Thanks!

[RafiDude]

Thank you Sam, for your amazing explaination on how and why of Q&A on PDFs using LangChain. Looking forward to more such developer oriented educational videos.

[RanchoTexano]

Outstanding video. Well done showing such a powerful technique.

[Renozilla]

This is amazing, thanks for taking the time to do this

[Davidkiania]

This is amazing and very well done. This channel has become my go to every morning. Thank you.

[gabijazza]

Brilliant stuff. Really fascinating explanation on how to customise your own AI.

[adityahpatel]

This is brilliant content. Thanks Sam.

[waleed]

thanks, i understood it . a really fantastic video

[sammiller]

Fantastic video! I appreciate the inclusion of a Colab project for us to experiment with. It would be amazing to see a similar tutorial on loading multiple PDFs from a Google Drive folder (e.g., "data"), recursively into a Colab project, enabling interaction for creating outlines, glossaries, taxonomies, and more from multiple pdf sources. I'm interested in an approach resembling ChatGPT, where you can input a long passage of text and generate new content from it, going beyond semantic searches and summaries.

[ys]

Fantastic stuff. You've got such a knack for describing this stuff. I hope the AIs spare you when they take over.

[vjGoogle]

Sam - Thank you for this great conceptual explainer on the basic building blocks of leveraging LLMs with Langchain for our own content corpus. One question on the specific use of the PromptTemplate around 12:00 minutes into the video - Prompt has 2 dynamic variables in there named {context} and {question}. However, in the chain.run command, the variables being used are "input_documents" and "question". Where does the variable {context} get defined for the template to use and elaborate in its response?

[jddoerr]

*New Subscriber* Great video! I am interested in learning more on how to load in multiple PDF’s. Thanks

[LucianoPerezzini]

Hey Sam, love your lectures! Any resources about free alternatives of OpenAI embeddings? Would be really useful! Thanks!

[asermauricio]

🎉 thanks for the great explanation, can you explain the process with open assistant and a free vectores store, and fine tuning

[ankitjain]

Thanks Sam for this fantastic video. I am trying to read a complex pdf for example annual result pdf of a company containing all details with financial details in tabular format. Any suggestions how to preprocess and create embedding.

[johnholdsworth]

Brilliant video thanks Sam. Do you know if the LangChain Text Splitter would take titles into consideration when splitting the text? Titles often provide important contextual information, and preserving their relationship with the subsequent text is crucial for maintaining context and meaning.

[Jasonknash]

Great vidos Sam a lot of people jumping on the bandwagon with LLM's, langChain etc but your is clear and well constructed FAB I would love to see how you could use pinecone as a replacement for the vector store you used as i was unable to make it work with the one in the video.

[kesavanr]

Awesome, I have a query, 15:47 can we make the LLM to be focused only on the document information not the external world information.