Create Your Own ChatGPT with PDF Data in 5 Minutes (LangChain Tutorial)
TLDRIn this tutorial, viewers learn to create a custom knowledge chatbot using LangChain and their own PDF data. The process is streamlined into simple steps, from chunking documents and embedding them in a vector database to querying the database for relevant information. The result is a flexible and personalized AI tool that can be used for various purposes, with the added bonus of chat memory for context in conversations.
Takeaways
- 🚀 Create a custom knowledge chatbot using LangChain with your own PDF data for business or personal use.
- 📄 The process involves chunking documents into smaller pieces, embedding them, and storing in a vector database for easy retrieval.
- 🧠 Utilize the adder002 model by OpenAI for document embedding, recognized as one of the best models currently available.
- 🔍 Users can query the database to get answers based on the similarity of the query to the embedded documents.
- 💡 The method provides complete flexibility and customization over the app's functionality and document processing.
- 📈 Start by installing necessary packages and importing APIs, replacing the API key with your own.
- 📚 For the PDF 'Attention is all you need', use the Transformers research paper by Google as an example document.
- 📊 Two methods for chunking: a simple page loader and an advanced method for splitting documents into similar-sized chunks.
- 🔢 Use a tokenizer to count the number of tokens and create a function for chunk size distribution visualization.
- 🛠️ Create a vector database with the Faiss package provided by LangChain for efficient document storage and retrieval.
- 🤖 Convert the functionality into a chatbot using the conversational retrieval chain component of LangChain for interactive knowledge base access.
- 🔗 Access the code from the video description to clone the notebook, change the PDF, and customize the chatbot for your specific needs.
Q & A
What is the main topic of the video?
-The main topic of the video is creating a custom knowledge chatbot using LangChain with data from your own PDFs.
What is LangChain?
-LangChain is a tool used for training AI models on your own data, such as PDFs, to create custom knowledge chatbots.
How does the video aim to simplify the process of creating a chatbot?
-The video aims to simplify the process by providing a straightforward, step-by-step guide and code that viewers can copy and paste to quickly build their own custom knowledge tools.
What is the purpose of chunking documents in the process?
-Chunking documents into smaller pieces is done to facilitate the recall and querying process when searching the database for relevant information based on user queries.
Which embedding model is recommended for use in this tutorial?
-The tutorial recommends using the adder002 model by OpenAI as one of the best embedding models available for this purpose.
What is the role of a vector database in this system?
-The vector database stores the embeddings of the document chunks, allowing for efficient retrieval of relevant information when a user query is processed.
How does the chatbot retrieve and combine context from the vector database?
-The chatbot takes the user's query, runs it through the same embedding model, and then performs a similarity search on the vector database to retrieve the most relevant documents, which are then combined with the query and fed into a language model to generate an answer.
What is the significance of the 'Attention is All You Need' paper in this tutorial?
-The 'Attention is All You Need' paper is used as an example PDF document in the tutorial to demonstrate the process of loading and chunking PDFs with LangChain.
How does the tutorial handle the distribution of chunk sizes?
-The tutorial uses a recursive character text splitter to create chunks of a specified size (512 tokens with an overlap of 24) and includes a visualization step to ensure the chunking process is done correctly.
What additional functionality is demonstrated at the end of the tutorial?
-At the end of the tutorial, the functionality is converted into an actual chatbot that can interact with the knowledge base in a chat format, complete with chat memory.
Where can viewers find the code and resources mentioned in the video?
-The code and resources will be available in the video description for viewers to clone and use for their own purposes.
Outlines
🚀 Introducing Custom Knowledge Chatbot Creation
The video begins with the creator expressing their intent to demonstrate a streamlined method for developing a custom knowledge chatbot using Lang chain, specifically trained on personal data from PDFs. They critique existing tutorials for being overly complex and offer a simplified alternative, allowing viewers to quickly replicate their code. The video also mentions a recent AI newsletter launch, encouraging viewers to subscribe for concise and up-to-date AI news delivered directly to their inbox. The creator then provides a brief overview of the system's functionality, emphasizing the flexibility and customization capabilities of the app being developed. The process involves chunking documents, embedding them into a vector database, and enabling user queries to retrieve relevant information. A visualization is presented to illustrate the system's inner workings, from document chunking to query-based retrieval and language model integration for answering user queries.
📚 Detailed Explanation and Practical Application
The second paragraph delves deeper into the technical aspects of creating the custom knowledge chatbot. It outlines the steps for document chunking, embedding, and database querying, providing a clear guide for viewers to follow. The creator introduces the 'attention is all you need' paper as the basis for their chatbot and explains how to upload and integrate a personal PDF into the system. The paragraph also discusses the importance of chunk size in determining output quality and presents an advanced method for splitting documents into evenly-sized chunks. The creator then explains how to visualize the chunk distribution, create a vector database using the Faiss package, and perform similarity searches based on user queries. The paragraph concludes with a demonstration of how to transform the functionality into an interactive chatbot, complete with chat memory, and encourages viewers to use the provided code for their own purposes. The creator also invites viewers to engage with them for further consultation or to join their AI community platforms.
Mindmap
Keywords
💡LangChain
💡Custom Knowledge Chatbot
💡PDF Data
💡Embedding
💡Vector Database
💡Querying
💡Chunking
💡Tokenizer
💡Language Model
💡Chat Memory
💡AI Newsletter
Highlights
The video provides the fastest and easiest way to create a custom knowledge chat GPT using LangChain trained on your own PDF data.
LangChain is used to chunk documents into smaller pieces for efficient querying and recall.
The adder002 model by OpenAI is utilized for embedding each document chunk.
A vector database is used to store the embeddings for quick retrieval during user queries.
The user's query is processed through the same embedding model to find the most relevant document chunks.
A large language model is employed to answer user queries based on the retrieved context.
The process includes a step-by-step guide on how to build a custom knowledge tool for business and personal use.
The video offers a brief explainer on the system's workings and the parts involved in creating the chatbot.
A custom chatbot system can be built using the 'attention is all you need' research paper by Google.
The chunk size is a crucial factor in determining the quality of the chatbot's output.
TextTracker is used to extract information from PDFs and save it for processing.
A function to count tokens is created using the GPT2 tokenizer for chunking the text.
The RecursiveCharacterTextSplitter from LangChain is used to create text chunks of a specified size.
A visualization of chunk distribution ensures the chunking process is done correctly.
The Faiss package by LangChain facilitates the creation of a vector database for storing embeddings.
A similarity search on the vector database returns documents that closely match the user's query.
LangChain's chain functionality combines the query with retrieved documents to generate answers.
The video demonstrates converting the functionality into an interactive chatbot with chat memory.
The custom knowledge chatbot allows users to retrieve and answer questions based on their own PDFs.
The entire process, from installation to creating a chatbot, is detailed in a provided notebook.
The video includes a chat with the consultant for further assistance in building custom AI solutions.