Hey there,

Joris here.

Do you recall that exciting moment when generative AI first emerged on the scene?

It was a game-changer!

Among the many remarkable capabilities it introduced, one that truly captured everyone's attention was its ability to engage in conversations with PDF files. This innovation not only sparked our imagination but also opened up a world of possibilities for significant time savings.

Today, I'm excited to delve into a topic that's both intriguing and practical: the importance of breaking down information into smaller, manageable chunks when working with vector databases. This insight is key to harnessing the full potential of AI, and I'm here to guide you through it.

Today's Unchained Insights

🔍 LangChain Code Snippet: Unveiling the Q&A Bot for Your Files.

💡 Project Idea: Elevate Your Capabilities - Comparing Multiple PDF Files with a Q&A Bot.

📚 AI Research: PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents

Introduction

At first glance, this project might appear straightforward, but it's a fantastic learning opportunity that delves into the fundamental aspects of working with large language models, and particularly LangChain. It's a chance to grasp essential concepts like text splitters, document vectorization, and similarity search.

In the concise code snippet that follows, we'll construct a Question and Answer bot with a mere 8 lines of code, showcasing the remarkable capabilities of this technology.

But that's just the beginning. As we explore the project idea, we'll take things up a notch by demonstrating how to leverage multiple PDF files and enable them to be compared with one another. This extension adds a layer of complexity and usefulness to our AI-powered toolkit.

LangChain Code Snippet

GitHub Repo

Grab the code from the Github Repo.

6 Steps to Question Answer over PDF’s

1. Import dependencies

2. Load PDF

3. Split PDF

4. Setup Large Language Model

5. Setup Vector Database Retriever

6. Setup Retrieval Question Answer Chain

All wrapped up in just 8 lines of code.

Peek Under The Hood

Let's take a closer look at the inner workings of this process.

We kick things off by configuring the environment, requiring just one essential component: your OpenAI API Key.

Next, we employ the PyPDFLoader, a crucial tool within the LangChain arsenal. This loader initializes the PDFLoader, enabling us to seamlessly extract data from your PDF files. The process is as simple as invoking the .load() method on the loader.

Once we have the PDF data in our hands, our next step is to divide it into smaller, more manageable chunks. While OpenAI's GPT-3.5-turbo boasts an impressive input window of 16,000 tokens, it's astute to keep these sections compact for several compelling reasons:

1. Semantic Precision: Smaller chunks enhance the precision of semantic meaning. If a piece of text covers a wide array of topics, pinpointing the relevant information becomes more challenging.

2. Speed: Each query to the vector database, facilitated by the RetrievalQA Chain, prompts a call to the Large Language Model. In this interaction, the model processes a specific number of chunks to formulate responses. Each token the LLM has to process, increases the amount of time it takes.

3. Cost Optimization: API costs are linked to token usage. Therefore, fewer tokens translate to lower expenses.

These text chunks then undergo transformation into embeddings, numerical representations of their semantic significance.

Before diving into action, you'll configure your preferred large language model with a lower temperature setting. This adjustment ensures that the model remains closely aligned with the content it retrieves from the PDF, avoiding the generation of extraneous information.

With these preparations complete, you have all the components necessary to establish the Retrieval Question Answer Chain. This powerful tool combines the Large Language Model with a retriever. When queried with a question, it conducts a semantic similarity search within the vector database to retrieve the most relevant documents.

To complete the process, a straightforward while loop is employed, enabling you to continuously pose questions to your PDF file.

With these steps, you'll have your Question and Answer bot up and running efficiently.

Unchained Project Idea: Compare Multiple PDF Files.

While the provided code equips you to engage a single PDF file in Q&A, what if your needs extend to comparing multiple PDF files?

Imagine a scenario where you possess several financial reports, each hailing from a different company, and you aim to assess their performance during a specific quarter. Enter LangChain, your versatile AI companion for such tasks.

In fact, I've previously shared insights on this very topic in a comprehensive Twitter Thread.

To elevate your project and unlock even greater potential, consider integrating the following functionalities:

1. Create an Agile Agent: Develop an intelligent agent capable of harnessing multiple Vector Databases. This enhancement broadens your system's capacity to handle diverse datasets and inquiries effectively.

2. Implement Multiple RetrievalQA Chains: Equip your agent with the capability to wield multiple RetrievalQA Chains. Each chain serves as a specialized tool, enhancing the agent's adaptability and responsiveness to varied demands.

3. Craft a User-Friendly Interface with Streamlit: Enhance user experience by building a sleek and intuitive Chatbot UI using Streamlit. This interface empowers users to effortlessly interact with your AI system, expanding its accessibility and utility.

Please share your project with me on Twitter and the best one will feature in next week’s AI Unchained.

AI Research

Title: PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents

Short summary: PEARL is a prompting framework that improves reasoning over long documents using large language models (LLMs). It decomposes questions into actions and executes them over the document to obtain answers, outperforming other prompting methods on complex reasoning tasks.

Interesting insight: PEARL leverages LLMs to reason over long documents, demonstrating the potential of these models in tackling complex tasks.

Read Article

Rounding off

Your feedback fuels AI Unchained's evolution. Just hit reply or catch me on Twitter with your thoughts.

Until next week,

Joris