Create Your Own ChatGPT with PDF Data in 5 Minutes (LangChain Tutorial)

Creating a Custom Knowledge Chatbot with LangChain

In this video, the speaker demonstrates how to create a custom knowledge chatbot using LangChain that is trained on your own data from your own PDFs. The method provides complete flexibility and customization over how the app works and how the documents are processed.

Brief Explanation of How These Systems Work

• The system created using LangChain takes in documents, chunks them, embeds them, puts them in a vector database, and allows users to query it and get answers back.

• Documents are split into smaller pieces (512 tokens or less) so that when recalling and querying the database, relevant smaller chunks are received instead of massive information.

• Each chunk is embedded one by one using the adder002 model by OpenAI which is one of the best embedding models available right now.

• All embeddings for each chunk are put into a vector database so that they're ready for recall when the user queries.

• Users can query the database by taking in their query, putting it through an embedding model, querying the database based on embeddings of their query, passing it around to a large language model to include it in context, and then sending back an answer.

Building Your Own Custom Knowledge Chatbot

• Install all necessary packages and replace API key with your own.

• Use attention is all you need for chatbot purposes in this video.

• Chunk your document down into however many chunks needed in order to get below 512 tokens per piece.

• Embed each chunk one by one using adder002 model by OpenAI.

• Put all embeddings for each chunk into a vector database so that they're ready for recall when the user queries.

• Allow users to query the database by taking in their query, putting it through an embedding model, querying the database based on embeddings of their query, passing it around to a large language model to include it in context, and then sending back an answer.

Loading and Chunking PDFs with Lang Chain

In this section, the speaker explains how to load and chunk data using Lang Chain. They demonstrate two methods for loading PDFs, including a simple page loader and an advanced method for splitting documents into similar-sized chunks. The speaker also discusses the importance of chunk size in determining the quality of output.

Loading PDFs

• The simple page loader method uses pipe pdfloader to chop the PDF into pages and return them as documents.

• The advanced method involves splitting documents into similar-sized chunks using text track and recursive character text splitter. This allows for customization of chunk size, which can impact output quality.

Creating Chunks

• To create chunks, we need to extract information from the PDF using text track, save it as a text file, count tokens using a GPT2 tokenizer, and use recursive character text splitter to split the document into chunks based on token count.

• We then create chunk objects by passing in the text from our saved file to create documents function. These are stored in a vector database using faiss package.

Querying Chunks

• We can query our vector database by setting up a query (e.g., "who created Transformers") and running a similarity search on the database. LIN docks can be used to visualize distribution of chunks that match query.

• We can combine this functionality with Lang Chain chain chain to retrieve docs based on query input and run a chain that takes in both query and docs as input.

Overall, this section provides an overview of how to load and chunk PDFs using Lang Chain, including both simple and advanced methods. The speaker emphasizes the importance of chunk size in determining output quality and provides detailed steps for creating chunks and querying them.

Creating a Custom Knowledge Chatbot with Lang Chain

In this section, the speaker demonstrates how to create a custom knowledge chatbot using Lang Chain. The chatbot takes in PDFs, chunks them up, embeds them, creates a vector store and then allows you to retrieve those and answer questions based on that information.

Converting Functionality into a Chat Bot

• The speaker shows how to convert the functionality of OpenAI's language models into an actual chat bot.

• They use the conversational retrieval chain component which takes in a language model and uses the database created as a retriever function.

• A little chat bot loop is created that allows users to interact with the knowledge base in a chat format.

Interacting with the Chat Bot

• Users can ask questions such as "who created Transformers?" and receive answers based on the information stored in the database.

• The chat memory is included so that users can ask follow-up questions based on previous interactions.

Availability of Code and Consulting Services

• All code used in this video is available for cloning from the description.

• The speaker offers consulting services for feasibility reports or discussing ideas related to AI development.

• Links are provided for joining their AI entrepreneurship Discord channel and signing up for their AI newsletter.