💬 Join the Discord Help Server: link.alejandro-ao.com/981ypA ❤ Buy me a coffee (thanks): link.alejandro-ao.com/YR8Fkw ✉ Join the mail list: link.alejandro-ao.com/o6TJUl

I built something similar a while back. You should teach people how to store vectors locally. That would drastically reduce the cost.

I've watched numerous GPT for PDF KZfaq tutorials, but yours stands out for its clarity, conciseness, and thorough explanations. Thanks, Alejandro! I've subscribed and eagerly await more content from you.

I really appreciate the tiny details and explanations like "since we're using langchain you have to set the variable like this" etc. Very helpful in understanding "why" in addition to "how". Cheers

Thank you so much for the very nice tutorial. OpenAI has "gpt-3.5-turbo" available which is much cheaper and should have better performance than its default model here, we just need to specify the model as this: llm = OpenAI(model_name="gpt-3.5-turbo")

Holy smokes - the conciseness of you code given all that it's doing behind the scenes is breathtaking! Impressive!

I stumbled upon this channel by accident - to be honest, I was looking for information. I'm 30 years old and decided to change my career. I started learning the Python language from scratch. Often, I hear that it doesn't make sense at this age, etc., but I have a goal and dreams. Thank you very much for this video and the knowledge you convey in a simple and genuinely transparent way. Your way of explaining is really understandable. You're doing a great job.

You are a really good teacher. Love how you explain things step by step without overwhelming. Keep it up!

Fabulous job! Great use of the diagram to provide the high level view. I like that you kept the code simple and focused on the main objective, and covered each part step-by-step. I also like that you used FAISS, since it is free.

After updating python to the latest version and updating my PyCharm installation, this tutorial ran perfectly. Alejandro is an excellent presenter. Thanks for this terrific tutorial!

Incredible job. Thank you very much for this project. I have no more than 2 weeks learning python and with your help, I was able to complete this project in my free time. Keep the amazing work Sir. Much respect from the Caribbean 🌴

Langchain is a framework that allows you to build apps with LLMs like ChatGPT or GPT4All🔥 Edit: Be careful with adding super long documents as the embedding also has a cost. Pricing is minimal but it can scale up pretty quickly if you embed long texts. Here are OpenAI's embeddings prices: openai.com/pricing#embedding-models Let me know if you want a detailed course on Langchain 💪

Im learning python and i decided to follow along with you here. Amazing! I have less than a few months of experience and I was able to follow along with you. Well done lad!

Perhaps the best tutorial out there. Maximum value 0 crap. Keep it like this. :) Good job.

Hi from France 👋 It's the best tutorial on how to create our own PDF GPT. Thanks a lot.

Never heard about you before Alejandro, but I'm glad you popped up on my KZfaq feed. Got to give it to you---your a natural born teacher. Excellent job and very informative. Liked and subscribed.

I was just thinking about creating a dashboard to visualize $ spent on requests. You are ahead of the game

Thank you, thank you, thank you! Your video solidified everything for me. I knew the different pieces, but not how they all meshed together. Now I can finally continue with the project I was working on. Great content. Subscribed.

This is an incredible tutorial. I'm not very technical and I was able to follow the entire thing! Thanks for creating it.

Great, this is what I needed to get me going. The best guide I have seen so far. I've been working on the code and can now load multiple files of pdf, docx, txt and html at the same time. I also increased the text box size to allow for follow-on questions. Next I want to integrate something like Pinecone with Chat 'memory'.

If you are getting an error that says "ModuleNotFoundError: No module named 'altair.vegalite.v4'" - you have to downgrade your Altair to version 4.1.0 as version 5 is giving the error. You can do this by running the command "pip install altair==4.1.0"

Thanks for the Intro showing the final result. Priceless

Looks super duper good Alejandro !!😍

Some may experience issues doing this as there are a few things I believe Alejandro assumed were already done. This is especially true for getting past the first phase which IMO is launching the initial app in you local environment. 1. You may need to add a .toml file with your open ai keys. You have to add a folder in your project folder titled .streamlit with a file inside it titled secrets.toml 2. You may have to add import openai in your apps file 3. if you have issues with firewall (I did) you'll need to add the port Open the Windows Defender Firewall settings: Go to the Control Panel. Search for "Windows Defender Firewall" and open it. its a bit involved so best to ask GPT how to allow access to the specified port by configuring Windows Defender Firewall after these issues it was a breeze for me Any help needed, just ask me Cheers from Ireland

Very good content. Taking the time to explain what every component of the code is amazing. I never felt I understand code like this. I am subscribing.

Thank you Alejandro for all the efforts and amazing videos you are creating A big thanks from my bottom of my heart ♥

Alejandro thanks for this. I’m getting a little more familiar with Python from every video. I believe there is a big opportunity for someone like you to help ‘no coders’ like me get the most from LLM’s. It would be helpful to know how multiple documents could be uploaded and what the limits of the LLM are before the responses begin to fail completely or degrade in quality.

Thank you man, I had this project in mind for a while, never had time for it.

I love this content.. thanks for sharing

Hey there! Let me know what you want to see next 👇

Awesome and thank you for not using colab or jyupter. Very few people doing actual LLM coding, mostly skims thru colab notebook.

Deeply appreciate for your sharing !!

One of the best tutorials on the subject! Great job!

This video is amazing, I get the most from it.

Very nice tutorial, thanks! Wish your channel quick growth🤞

Streamlit seems lit 🔥Thanks bro ;)

The best step by step explanation. Please keep it up! I suscribed expecting to learn much more. Thank you

great tutorial. Thanks soo much. Really easy to follow for a non-programmer.

Excellent tutorial, thank you for sharing. Let's learn more about Fais and modularizing code 😃

Thanks buddy for this awesome content waiting for more videos on langchain and keep explaining everything like you did so that it helps all to understand in detail.

Having watched several GPT for PDF KZfaq tutorials, I must say that yours stands out due to its clear, concise, and comprehensive explanations. Thank you, Alejandro! I have subscribed to your channel and eagerly look forward to your future content. By the way, I'm in the process of developing my own Langchain app by replicating the code and incorporating additional features. Could you please guide me on replacing the Open API Key in your .env.example file?

Very well explained. Thank you!

I have subscribed to you buddy. This is what my employer assign me to do this, You make my job easier. 🤩

Excellent content, and it works smoothly n correct for me. I also tested your other Repo about ask-multiple-pdf's. GREAT, thank you!

I learned new frameworks and technology today. Thanks

Thanks for the great tutorial. I'm glad I found you ! $0.02 per request might not be high for an individual, but that's something to consider for any commercial application. At scale, it might go up fast. I guess prices can only go down and OpenAI is currently getting the first mover bonus.

You have done an excellent job of explaining complex material in a simple manner. I was wondering if you could provide information on loading multiple PDFs or direct me to additional tutorials on the topic?

Nice tutorial video, and thanks for the shout out to my work!

Great job tks from mexico keep going man

Well done excellent tutorial best I’ve seen so far

I've watched several videos on knowledge bases, but this one stands out! The way they explain OpenAI is so clear and concise. Highly recommended! @Alejandro how can I make a chatbot which understand my previous chat and give batter answer it my pleasure if you answer me,.

I have seen a video and it is also very clear that you can that program is based on GPT-3.0. I tried to make adjustments on that program but failed. I hope I can get some inspiration from you here. Thanks!

HA! My Bass instructor just assigned your intro song for me to practice ; )

I really appreciate your videos, great walkthrough. Thanks.

🎯 Key Takeaways for quick navigation: 00:16 📋 The tutorial demonstrates how to build a Python application with a graphical user interface for PDF text analysis. 01:24 🛠️ The environment setup includes using a .env file for secret keys, and it's essential to name the OpenAI API key environment variable correctly as "openai-api-key." 03:16 📦 The necessary dependencies for the project include Langchain, pdf2, python-dotenv, and Streamlit. 06:46 📄 The tutorial utilizes Streamlit to create a user interface for the PDF application, allowing users to upload PDFs. 12:54 🧩 The text from the PDF is divided into manageable chunks to work with language models efficiently. 20:24 📃 The tutorial explains how to convert the text chunks into embeddings for semantic search. 26:19 💬 An input field is added for users to ask questions about the uploaded PDF. 27:27 📚 The video discusses the process of using a knowledge base and similarity search to find chunks of information relevant to a user's question. 31:44 💬 The Langchain framework provides a convenient "load QA chain" for question answering using language models like OpenAI's. It allows you to use various language models. 34:51 💰 To monitor spending on questions answered by the language model, Langchain offers a callback function that tracks the cost per operation, especially useful when working with OpenAI. 38:09 🧾 You can track the cost of generating answers to questions using Langchain's monitoring function, which shows the cost in tokens and currency for each question answered. Made with HARPA AI

Awesome explanation every single code steps

Thanks for this video, it was the most well explained video I have found on KZfaq till now. One question, how is this knowledge base created using FAISS performs against vector databases like Pinecone for semantic searches?

Thanks! This is what I was looking for!

Your contents are great 👍, also I like your teaching style with visual flow of codes. Please make more videos on streamlit, langchain and OpenAI. Thanks in advanced

Great Tutorial!!! Thanks a lot for sharing with us!

nice video! appreciate the step-by-step details.

I appreciate your hard work and great video. Thank you.

Just a note on cost: The embedding also costs money and for a large enough PDF, you might end up with most of your cost coming from this. Especially if you are loading multiple pdfs and not just chatting with one in one session. I'm sure you can wrap that embedding step in that callback function (I have never tried so I'm not positive).

Amazing tutorial! Thank you! 😃

I made it. I feel like data scientist now

First of all thank you so much for making this tutorial. This is one of the best tutorials I've seen on this topic. So I've edited your code to be able to use Google's flan-t5-xl model using Hugging Face API, but I always encountered "ValueError: Error raised by inference API: Model google/flan-t5-xl time out" time out error. Do you have any idea on how I can fix this? I'm still new in coding too so It'll be great if you can help with this issue. Thanks again!

Nice tutorial! I got two questions here. 1. Can I upload 2 or more pdf files? 2. When I got answer from this program, is it possible to show me the source of the answer? Such as page number? Paragraph? Which will be much better for us to locate it or double check.

Absolutely brilliant! Subscribed

Excellent. Very well done!!

That's very interesting thanks for share

Pretty wild! So cool - thanks

Using FAISS part was very good. I would like to see the alternatives and different approaches also. You might consider making different advanced level videos also. Thank you.

Great work. Like and Subscribed. Thank you again for sharing this project. I am looking forward to seeing more of your videos. Perhaps, others may have already asked these, but I would like to know if: - For PDF files that are non-English languages, would FAISS embedding and the model work as effectively? - In the case of multiple pdf files or a large pdf file, is there a way to reduce the embedding cost? (like using non-open AI models, or other embedding methods) Thank you for your time.

Brilliant. Useful. Thank you.

Amazing explication, thank you so much

Quick question, what PDF data goes out to the servers versus what data is processed within your local machine? By the way, your explanation is the best on this content within KZfaq hands down... Keep up the good work and thanks from the bottom of my heart for producing such content

amazing video, thank you for sharing.

Awesome explanation, video, and idea

Thanks for the video, its been very helpful. However I keep getting the following error 'AttributeError: module 'openai' has no attribute 'error'' especially when i run these scripts: chain = load_qa_chain(llm=llm, chain_type="stuff") response = chain.run(input_documents=docs, question=user_question). Not sure if i'm missing something. Thanks!

Love it! Thanks!!

Very good tutorial, thanks.

I saw several of you video trainings and I like the details and the easy and clear explanations. I wonder if instead to send the request for the embeddings each time, is is possible to be saved in a local db and in that case only needed if there is a new pdf file uploaded. Hope this makes sense. Definitely I will subscribe to your channel 👏👏👏

Can I use the information in the knowledgebase to write a paragraph or summarize information rather than "question and answer"? And how can i do that? Thank you.

This is really great content thank you 🎉

wats the IDE u used? it would be helpful if you had spent a minute to explain how you set up your project. Great videp

very good, thanks man! I'd love to see it integrated with pinecone so that we could upload a bunch of pdfs and have it stateful.

very useful thank you bro

Superb!

Good tutorial. Very helpful. Thanks

Hey!, keep these videos man!

Great tutorial, very thorough. I'm getting an error however, I wonder if anyone else has encountered it? "ImportError: cannot import name 'LLM' from partially initialized module 'langchain.llms.base' (most likely due to a circular import)"

Are the embeddings computed locally when the FAISS object is initialized or are the embeddings done using the OpenAI API? (e.g. you upload your chunks and then get back the embeddings)

If I am getting charged by using my openai key then I automatically lose interest since I can’t scale this without worrying about cost.😢

Thanks for the tutorial! This one stands out for me because you have a great teaching style. I was wondering if it's possible to pass the pdf variable a url of a pdf file for it to read instead of drag and dropping and uploading one that way?

Thank you very much!!!!!!!

Nice video - FYI - you forgot to include the example .env in the repo.

thank you very much Alejandro

Thank you for sharing and explaining.

Very good and comprehensive tutorial Alejandro - Do you have an idea how I can use multiple JSON file instead PDFs?

Really well done tutorial Alejandro - thank you! Have to ask - here the LLM is relying completely on the knowledge base for it's response? For example, any pre-trained knowledge it has of the constitution is not drawn from - only the knowledge in the chunk provided - correct? So you're essentially building a custom domain into the knowledge base and using the LLM to interpret your query, identify the answer based on the chunk provided, and respond in natural language...is that right thinking?