Building a RAG Application from Scratch Using Python, Langchain, and OpenAI API.

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Welcome to our groundbreaking tutorial where we unveil the magic of implementing Retrieval-Augmented Generation (RAG) using Langchain! In this video, we'll guide you step-by-step through the process of connecting a Language Model (LLM) to your PDF data and transforming it into an intelligent question-answering system.
By leveraging the power of Langchain, we'll demonstrate how to seamlessly integrate your PDF documents with an LLM, enabling it to retrieve relevant information and generate accurate answers to your questions-straight from your PDFs!
Here's what you'll discover:
Setting up Langchain and connecting it to your PDF data.
Configuring your LLM to perform question answering on your PDF documents.
Real-time demonstrations of Langchain and the RAG model in action, answering questions from your PDFs with precision.
Tips and tricks for enhancing the capabilities of your RAG system and maximizing its utility.
Whether you're a researcher, student, or knowledge enthusiast, this tutorial will empower you to unlock the wealth of information hidden within your PDF documents. Get ready to revolutionize your document processing workflow and unleash the full potential of RAG with Langchain-let's dive in and transform the way you interact with your data!
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Опубликовано:

22 апр 2024

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Комментарии : 16

@aiforyounow 2 месяца назад

Finally, I can build my own RAG-based AI Applications. Great tutorial, excellent explanation thanks for this video.

@Watttechs 2 месяца назад

Great tutorial. it's very helpful to me.

@Robinxander Месяц назад

You are my hero. Can you make a guide on how to use multiple pdfs?

@tech_watt Месяц назад

Okay I will

@ShadowD2C Месяц назад

Hell bro, Im running a similar code on langchain but with different model, vectordb and embedding model because i tried to make everything opensource, but I run into the same issue of of the retriever doesnt manage to get the right chunks to be passed to the llm, how to mitigate that? similar to your question about the authors which it wasnt able to answer

@tech_watt Месяц назад

So the rule of thumb is to tweak chunk size parameter in your Character splitter. You can also tweak the chunk overlap to see how the retrieval responds.

@mrd6869 Месяц назад

What the difference between this and just uploading a pdf into gpt4o & using prompt engineering. This more accurate?

@tech_watt Месяц назад

This have numerous advantages for example you only need a specific information from your pdf file but you ended up exposing the whole data to GPT 4.0. 1. You will have to pay more for uploading such a huge file. 2. It’s not a good practice to expose let’s say company data to GPT models.

@adebayoadenekan8106 2 месяца назад

Very helpful tutorial. Considering it's even recent. I'm having an issue with the OPENAI_API_KEY I set in the .env file. I'm unable to load it. When i changed it to OPENAI_KEY it worked. Do you have an idea why this would happen?!

@tech_watt 2 месяца назад

Sorry to hear that but make sure the name in the .env file is same as the name in the is.getevn() parentheses.