Creating a Chatbot for Conversations with Books and PDF Files

This tutorial focuses on creating a chatbot capable of engaging in conversations with books or PDF files. By leveraging the Blank Chain Nama 2 model and Pinecone as a Vector store, this project aims to enhance user interactions with textual resources while adhering to SEO principles. Let's delve into the key aspects of this tutorial:

1. Architecture Diagram: Understanding the Process Flow

An architecture diagram vividly depicts the project's process flow. It illustrates the user's action of uploading a book or PDF file, followed by data extraction using the PyPDF Python package. The extracted data is then divided into smaller chunks, each comprising 2000 English characters.

2. Embeddings and Vector Store: Storing and Organizing Data

For each text chunk, embeddings or vectors are generated. These embeddings capture the essence and context of the text, facilitating efficient storage and retrieval of information. A knowledge base or Vector store is established using Pinecone, which stores and indexes these embeddings. This Vector store serves as a valuable resource for answering user queries.

3. Advantages of Pinecone: Cloud-based Storage and Querying

Pinecone offers several advantages as an external Vector store. It allows for cloud-based storage of embeddings, making them easily accessible for queries. This enables the chatbot to swiftly search the knowledge base and retrieve the most relevant embeddings to answer user questions.

The tutorial also addresses the input limit of large language models, which typically hovers around 10,000 characters. To overcome this limitation, the data is divided into smaller chunks, and embeddings are created for each chunk. This approach ensures that the chatbot can effectively process and respond to user input.

Step-by-step instructions are provided in the tutorial, guiding readers through the implementation process. This includes setting up the necessary environment, installing required packages, and creating the chatbot using Python.


Q: How does the chatbot interact with books and PDF files?

A: The chatbot utilizes the Blank Chain Nama 2 model and Pinecone as a Vector store to facilitate conversations with books and PDF files. It extracts data from the uploaded files, creates embeddings for text chunks, and searches the knowledge base to provide relevant responses.

Q: What advantages does Pinecone offer as a Vector store?

A: Pinecone provides cloud-based storage for embeddings, ensuring easy accessibility and efficient querying. This enables the chatbot to quickly retrieve relevant information from the knowledge base and deliver accurate responses.

Q: How does the tutorial address the input limit of large language models?

A: The tutorial overcomes the input limit by dividing the data into smaller chunks and creating embeddings for each chunk. This approach allows the chatbot to effectively process user input and provide comprehensive responses.

BARD PDF: Free Online Tool for Conversational PDF Exploration

In addition to the tools mentioned above, BARD PDF is another excellent online tool that allows users to engage in conversational PDF exploration. Completely free to use, BARD PDF offers a user-friendly interface where users can upload their PDF documents and interact with them through natural language queries.Simply visit the BARD PDF website ( and upload your PDF file. Once the file is uploaded, you can start asking questions about the document, and BARD PDF will provide concise and informative answers. You can also ask BARD PDF to summarize the document, extract key points, or translate it into different languages.

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