In recent times, the field of Natural Language Processing (NLP) has witnessed significant advancements, especially with the introduction of ChatGPT. This powerful tool, known for its ability to generate text that mimics human language, has sparked numerous innovations across various sectors. Developers, content creators, and customer service professionals have embraced ChatGPT, leveraging its capabilities to enhance their workflows and provide superior experiences for their customers. The widespread adoption of this technology underscores its transformative impact.
Have you ever wondered how to create a chatbot that can read and extract information from images embedded in PDFs? In this guide, we will embark on an exciting journey to build such a chatbot. Our chatbot will use cutting-edge tools like Amazon Textract, Amazon Bedrock, Llama, GPT-3.5, Langchain, and FAISS Vector DB. The goal is to develop a chatbot that not only understands text but also extracts valuable data from images in PDFs, making it a robust NLP tool. Let’s dive into this fascinating fusion of technology to turn your chatbot into an NLP superhero.
Let’s Begin!
To get started, ensure you have the following:
An OpenAI account with an API Key.
An AWS Account with access to bedrock models, such as Llama Chat 2 13b.
Familiarity with Natural Language Processing (NLP) concepts and techniques.
Familiarity with the Langchain Library.
A Jupyter Notebook environment for coding and testing.
Understanding the PDF
For this tutorial, we will use a floor plan PDF of a building. Each page of the PDF contains both text and an image of a floor plan. While tools like PyPDF excel at extracting text, they often overlook embedded images, which are crucial in floor plans. To ensure we don’t miss any data, we’ll convert each PDF page into an image using the pdf2image tool. These images will be stored in a folder for further processing using OCR tools like Amazon Textract and UnstructuredImageLoader.
Converting PDF Pages to Images
The first step is to convert the pages of the PDF into images. This can be achieved using the pdf2image library, which allows us to transform each page of the PDF into a separate image file. These image files will be stored in a designated folder, and their paths will be recorded for easy access during the data extraction process.
Following these steps, each page of the PDF is converted into an individual image, and the file paths of these images are stored for further processing.
Extracting Data from Images
The next crucial step in our NLP journey is to extract and structure data from the images. This step is vital for training Large Language Models (LLMs). Langchain supports two effective methods for data extraction:
- Amazon Textract: Known for its precision, Amazon Textract is ideal for detailed image data extraction.
- UnstructuredImageLoader: This tool offers versatility in handling various image formats.
Both options provide similar quality results. The choice of tool depends on the specific attributes of your images. Testing each method will help you determine the most suitable one for your data.
Using Amazon Textract
Amazon Textract utilizes Optical Character Recognition (OCR) to extract text from images. It is particularly effective for detailed and accurate extraction.
Using Langchain’s Unstructured Image Loader
Langchain’s Unstructured Image Loader is another excellent tool for extracting data from images. It supports various image formats and provides robust data extraction capabilities.
Vector Store vs. Prompting Data
With the data extraction complete, we are ready to progress to the pivotal phase of training our Large Language Model (LLM). There are two main strategies for this training process:
- Vectorization Approach: Convert the extracted data into vectors using embedding models. These vectors are then meticulously stored in a vector database, setting the stage for efficient model training.
- Prompt-Based Approach: Craft prompts from the structured extracted data. These prompts are directly fed into the LLM, providing a more immediate and integrated training approach.
Each method offers unique advantages. The choice depends on the specific requirements and goals of your LLM project. For larger documents, the Vector Database approach is recommended. This strategy helps circumvent token limit errors and minimizes LLM query costs by efficiently managing large volumes of data through vectorization.
Embedding the Extracted Data
To effectively store our extracted data as vectors in a vector database, we need to follow a couple of key steps:
- Embedding the Data: The first step involves embedding the extracted data into vectors. This process transforms our textual data into a numerical vector format, making it suitable for efficient storage and retrieval in vector databases.
- Selecting a Vector Database: Next, we need to choose an appropriate vector database. This database will house our embedded vectors, so it’s important to select one that aligns with our data requirements and offers the desired performance and scalability.
We have several options for embedding our extracted data, such as Amazon Titan Embedding Model, OpenAI’s Text-Embedding-Ada-002, and Cohere-Embed-English-V3. For this project, we’ll use the Amazon Titan Embedding Model via AWS Bedrock. This model efficiently embeds our data, aligning well with our needs. For storing these vectors, we’ll use FAISS, known for its speedy handling of large vector datasets
Embedding Data Using Amazon Titan Embedding Model
Initializing Langchain Chain
Langchain offers various chains suited for different applications. As our goal is to develop a conversational bot, we’re specifically opting for the ConversationalRetrievalChain. This choice aligns perfectly with our objective of creating a responsive and interactive conversational experience.
To integrate an LLM model into our Chain, we turn to Bedrock, which offers a wide array of Large Language Models. For our project, we’ve selected the Llama 13B model. However, Bedrock’s diverse options allow for experimentation, and I encourage exploring different models to find the one that best suits your specific needs.
Interacting with the Conversational Chain
It’s amazing how effortlessly Langchain facilitates the initialization and setup of a Conversational Chain. This streamlined process empowers us to query our documents seamlessly. The flexibility and ease of use provided by Langchain’s Conversational Chain make it a valuable tool in our NLP toolkit.
Enhancing with Agents
To further enhance the effectiveness of our Conversational ChatBot, we can incorporate ‘Agents’ from Langchain. These Agents utilize advanced AI and LLM capabilities to determine the next steps in the conversation and craft intelligent queries.
Conclusion
Congratulations! You’ve successfully built a Conversational ChatBot capable of extracting and interpreting data from images embedded in PDFs. This innovative solution opens up exciting possibilities for creating chatbots that not only process images but also build comprehensive databases from the extracted image data. This project demonstrates the power of integrating advanced NLP tools and techniques to create sophisticated, data-driven chatbots. As you continue to refine and enhance your chatbot, you’ll unlock new levels of performance and capabilities, making your chatbot an invaluable asset in your NLP toolkit.