How to Build Your Own ChatGPT with PDF Data: A 5-Minute LangChain Tutorial
Are you interested in building your own custom knowledge chat GPT using LangChain? Look no further! In this 5-minute tutorial, I'll show you the fastest and easiest way to create a chat GPT that's trained on your own PDF data. Forget about complicated tutorials, because I'm here to provide you with a simple and straightforward method that you can quickly implement in your projects. With this tutorial, you'll have complete flexibility and customization over your app's functionality and how your documents are processed. Let's get started!
The Basics: How it All Works
Before jumping into the code, let's go over the basics of how these systems work. Essentially, the system we are creating using LangChain takes in your documents, chunks them, embeds them, and then allows users to query and receive relevant answers. Here's a step-by-step breakdown:
Step 1: Chunking
The first step is to take a document and split it into smaller, more manageable chunks. We do this because when we query the database, we want to receive smaller chunks that are directly relevant to the user's query, rather than the entire document. In this tutorial, we'll be chunking our documents into pieces of 512 tokens or less.
Step 2: Embedding
Once we have our chunks, we need to embed each one of them. We'll be using the adder002 model by OpenAI, one of the best embedding models available. By embedding each chunk, we capture the semantic information and create a representation for the text.
Step 3: Vector Database
Next, we'll take all the embeddings for each chunk and store them in a vector database. This database will be used for recall when a user queries the system. The vector database allows us to efficiently retrieve relevant documents based on a user's query.
Step 4: Querying
The final step is to allow users to query the database. Users can input their query, and it will be embedded using the same model we used earlier. Then, we run a similarity search on the database to retrieve the most relevant documents. We can also pass the query and match documents to a large language model to generate answers based on the context.
Now that you have a basic understanding of how this system works, let's dive into the code!
Using LangChain to Build Your Own ChatGPT
To follow along with this tutorial, make sure you have the necessary packages installed. You can find the installation commands in the code cells below. Once you have everything set up, you're ready to get started!
Loading and Chunking PDFs
The first step is to load your PDFs and chunk the data using LangChain. I'll show you two methods, a simple one using the pipe.pdfloader function and a more advanced one that allows you to customize the chunk size.
If you want a quick test, you can use the simple method by running the provided code. In this case, LangChain will chop your PDF into pages, and each page will be treated as a separate document.
If you want more control over the chunking process, you can split your documents into smaller, similar-sized chunks using the advanced method. There are some factors to consider, such as the chunk size and overlap, that can affect the output quality. The code provided allows you to set the chunk size, and it will split your document accordingly.
Creating a Vector Database
Once you have your chunks, you can create a vector database using the embeddings. LangChain makes this process simple with the faiss package. The code provided will embed your chunks using the chosen model and store them in the vector database.
Querying and Answering Questions
Now that you have your vector database set up, you can start querying and answering questions. The code provided shows you how to query the database, retrieve the relevant documents, and generate answers using a language model. You can experiment with different queries and see the system in action!
From Functionality to Chat Bot
If you're interested in going beyond the basic functionality and turning this into a chat bot, I have a little extra for you! I'll show you how to convert the functionality into an actual chat bot using the conversational_retrieval_chain component in LangChain.
This component takes a language model and uses the vector database as a retriever function. The code provided sets up a simple chat bot loop that allows you to interact with the knowledge base in a chat format. You can ask questions and receive answers just like you would with a chat bot!
Try it Out!
Now that you have all the code and knowledge to build your own custom knowledge chat GPT, it's time to give it a try! You can find the complete code in the description below. Simply clone the notebook, replace the PDF with your own, and start using it for your business or personal use. Have fun exploring and customizing your own chat GPT!
FAQs
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Can I use this tutorial with my own PDF data?
Absolutely! The code provided in this tutorial is designed to be easily customizable for your own PDF data. Simply replace the PDF with your own and run the code accordingly.
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What is the advantage of using LangChain for building a chat bot?
LangChain offers a simple and efficient solution for building chat bots that are trained on your own data. It allows you to have complete flexibility and customization over the functionality and processing of your documents. With LangChain, you can create a chat bot that meets your specific needs and requirements.
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Can I use a different embedding model?
Yes, you can use a different embedding model if you prefer. The code provided in this tutorial uses the adder002 model by OpenAI, but you can replace it with any other model that suits your needs.
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How can I improve the performance of my chat bot?
There are several ways to improve the performance of your chat bot. One approach is to experiment with different chunk sizes and overlap values to find the optimal settings for your data. Additionally, you can fine-tune the language model to generate more accurate and relevant answers. Continuous refinements and iterations will help you improve the performance over time.
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Is it possible to integrate this chat bot into my existing application?
Yes, you can integrate this chat bot into your existing application. The code provided in this tutorial can be easily adapted and integrated into your project. Simply follow the steps and customize the code to fit your application's requirements.
That's all for this tutorial on building your own chat GPT with PDF data using LangChain. I hope you found this tutorial helpful and informative. If you have any questions or need further assistance, feel free to reach out to me. Enjoy building your own chat GPT and exploring the possibilities it offers!
