Knowledge Extraction in the Age of Information: Leveraging Colab, LangChain, and ChatGPT

“Information is not knowledge.” — Albert Einstein

Einstein’s statement is profoundly relevant in the information age we live in. We have access to more information than ever thanks to the Internet, but that doesn’t necessarily mean we are wiser. In simple terms, information becomes knowledge when it is processed, understood, and effectively applied.

1. The Time Challenge and Information Overload in the Digital Age

In a digital world that generates petabytes of data every day, professionals in technological and financial fields face a paralyzing dilemma: information overload. Time, being our most precious resource, becomes the bottleneck in our quest for meaningful knowledge. In this context, the concept of informational efficiency takes on a new level of urgency. It’s not just about filtering and understanding relevant information; we are in a constant race to do it more quickly, accurately, and usefully for real-time decision-making.

How Can Google Colab, LangChain, and ChatGPT Help?

To tackle these pressing challenges, technologies like Google Colab, LangChain, and AI assistants like ChatGPT emerge as fundamental pieces that accelerate our understanding and management of information:

• Google Colab: is a high-performance cloud computing platform that delivers a Jupyter Notebook environment with access to powerful GPUs and TPUs, along with a wide array of pre-installed data analysis and machine learning libraries. Its ability to handle large data sets and perform complex computations in fractions of the time it would take on a local machine makes it an invaluable tool for real-time data analysis and data-driven decision-making.
• LangChain: provides a comprehensive framework for developing applications powered by Large Language Models (LLMs), offering a full suite of tools for text analysis, language generation, and more. Its capability for real-time semantic and contextual analysis of large volumes of text makes it essential for applications like media monitoring, sentiment analysis, and customer service automation, while offering versatility and scalability.
• ChatGPT: is an advanced artificial intelligence assistant capable of performing a wide range of information processing and text analysis tasks. It can synthesize large volumes of data, generate code, answer complex questions, and more, thereby accelerating the decision-making process and reducing the risk of human errors. Its adaptability to different formats and contexts makes it a flexible tool for a variety of business and academic applications.

Perplexity with Claude 2

In addition to the above-mentioned technologies, we will also explore the capabilities of advanced AI models like Perplexity with Claude 2 in the manual approach to video processing. These models not only offer exceptional text processing capabilities but also introduce a level of precision that complements more automated methods. Stay tuned as we delve into their specific applications later in this article.

Together, these technologies act as levers that enable us to rapidly convert large volumes of raw data into actionable insights, all in real time.

Now that we’ve identified the problem of information overload, let’s examine how artificial intelligence offers effective solutions, particularly in video processing.

2. Deploying Artificial Intelligence in Video Processing: Automated vs. Manual Approaches

In the new informational paradigm, AI stands as a performance multiplier, particularly in video processing and text analysis. The key lies in selecting an optimal strategy that aligns both with your level of technical expertise and the specific demands of the project at hand. Below, two distinct pathways coexist in this scenario are discussed:

Risks and Rewards Analysis

Before diving into the two main approaches, it’s crucial to understand the trade-offs involved:

• Manual Method Risks: Higher susceptibility to human errors, less scalability, and limited customization.
• Automated Method Risks: Possibility of technical glitches, API limitations, or unexpected computational costs.

Decoupled Strategies

• Manual Method: Accessible for the average user, this option involves using a user interface to obtain video transcriptions, which are then fed into a chatbot for text analysis. Its greatest virtue lies in ease of use, but at the expense of scalability and customization.
• Automated Method: This approach relies on the orchestration of a Python script generated by ChatGPT, which operates in conjunction with Google Colaboratory and LangChain to automate text extraction and analysis. Its intrinsic value manifests in the ability to customize, scale, and adapt the process according to needs.

Tool Ecosystem: Colab and LangChain in Symbiosis

For those inclined toward automation, the ecosystem offers open-source resources in a Google Colab Notebook. This cloud environment opens the door to advanced machine learning techniques and data analysis. In contrast, LangChain provides a robust infrastructure for natural language processing, supported by Large Language Models. The combination results in an agile and efficient solution, capable of converting extensive video material into a concise summary in minutes. Link to my Colab here

2.1 Automated Video Processing: Scripting in Python with ChatGPT

One of the most revolutionary aspects of AI assistants like ChatGPT is their ability to generate programming code from a simple command or prompt. In the context of our objective, this means you can get a Python script to transcribe a video just by asking the assistant. Below are the steps and key components to do so.

The Prompt:

The first step is to provide a clear and detailed prompt to the assistant. An example would be: “You are an excellent Python developer, help me create a script that should be able to perform the following operations:”

1. Import the necessary libraries

◦ Tool used: youtube_transcript_api, urllib.parse, langchain.chat_models, langchain.chains.summarize, langchain.schema, langchain.prompts, openai

◦ Description: The script starts by importing all the required libraries.

2. Get the YouTube video ID from the URL

◦ Tool used: Function get_video_id_from_url

◦ Description: This function takes a YouTube URL and extracts the video ID.

3. Obtain the YouTube video transcript

◦ Tool used: Function get_transcript

◦ Description: This function uses the video ID to get the transcript.

4. Analyze the text through the OpenAI API.

◦ Example Code: Insert this code in this Function.

def analyze_text(text):
    llm = ChatOpenAI(openai_api_key='', temperature=0, model_name="gpt-3.5-turbo-16k")
    prompt_template = """
    Summary in English of the text.\
    Organize the summary by separating key ideas and concepts with dashes.\
    Make sure to include relevant data, numbers, and names.\
    The result will follow a similar format to the following:\
    [* Key Idea/Concept 1 (relevant data or number, related name)]\
    [* Key Idea/Concept 2 (relevant data or number, related name)]\
    [* Key Idea/Concept 3 (relevant data or number, related name)]\

    text :
        {text}    
    """
    prompt = PromptTemplate.from_template(prompt_template)
    chain = load_summarize_chain(llm, chain_type="stuff")
    doc = Document(page_content=text)
    summary = chain.run({"input_documents": [doc], "prompt": prompt})
    return summary

5. Save the summary in a TXT file.

◦ Tool used: Function save_summary

◦ Description: This function saves the generated summary in a TXT file.

6. Execute the main script.

◦ Tool used: Block if name == “main”

◦ Description: This block runs when the script is launched as a standalone program.

Prompt Adaptability:

It’s crucial to understand that the above prompt is just an example. You can adapt its parameters and specifications to fit your personal or business needs. This prompt acts as the input for the AI model, which then generates code based on the requested specifications.

Complete Integration

In the end, you can combine these snippets to form a complete script that takes a YouTube URL, extracts the transcript, and generates an incredible summary. Using an AI assistant like ChatGPT to generate this code saves you significant time and avoids common mistakes, especially if programming is not your forte. Moreover, the result is a flexible script that can be adapted and expanded to meet specific needs.

ChatGPT Code Output:

2.2 Manual Video Processing: Leveraging Third-Party Tools for Text Extraction

General Overview

The advantage of using third-party tools to download YouTube subtitles lies in their simplicity. They provide a ‘plug-and-play’solution for users without technical experience or those who simply prefer not to engage with YouTube’s API or website directly.

Popular Tools and Their Features

• 4K Video Downloader: This is a desktop software that allows you to download videos, playlists, and subtitles from YouTube. It’s quite versatile but does require installation on your system.
• DownSub: This is a web app that merely requires you to input the YouTube video URL to download subtitles. Its simplicity and ease-of-use make it a popular choice.
• YTD Video Downloader: Similar to 4K Video Downloader but with a slightly different interface and additional functionalities.

Typical Process

1. Video URL: Start by copying the YouTube video URL from which you wish to download subtitles.
2. Input: Open your chosen third-party tool and paste the URL into the designated field.
3. Download: Choose the format you want for the subtitles and click ‘Download’ or its equivalent.

2.2.1 Choosing DownSub for Its Simplicity

Among the aforementioned tools, DownSub stands out for its sheerease of use and uncluttered interface. For those who prioritize straightforwardness and are perhaps not looking for extensive features, DownSub is an excellent choice.

How DownSub Works

1. Visit DownSub: Navigate to the DownSub website.
2. Enter YouTube URL: There is a prominent input field where you can paste the YouTube video URL from which you wish to download subtitles.
3. Download: Once the URL is processed, you’ll be presented with various download options. Choose the subtitle format you prefer and click ‘Download.’

2.3 Transition from Download to Analysis: Claude 2

Once the text is obtained through automatic or manual means, Claude 2 becomes a valuable resource, especially when dealing with longer texts.

Why Claude 2?

• Longer Responses: With a 100,000 token limit, Claude 2 becomes an invaluable tool when it comes to processing extensive documents or even entire books.
• Ongoing Updates: Claude 2’s ability to train with more current data gives it an edge in terms of accuracy and contextualization.
• • Better Coding Performance: Claude 2 has shown notable superiority in tests like Codex HumanEval, giving it an advantage in code generation.
• • Limited Availability: It’s worth mentioning that Claude 2 is currently restricted to the U.S. and the U.K., which could limit its global application, but you can use Perplexity to bypass this limitation.

Case Study: “How The Economic Machine Works by Ray Dalio” We will be transcribing the video “How The Economic Machine Works by Ray Dalio”. This video provides an in-depth look at economic principles and will serve as a real-world test case for our exploration of Claude 2’s capabilities.

Post-Processing with Perplexity and Claude 2 In the settings of your Perplexity account, select the Claude 2 AI model. Copy the transcribed text into the chat interface and ask the model to summarize it for you. This allows you to gain a concise and potentially more insightful version of the video’s content, taking full advantage of Claude 2’s capabilities.

Claude 2 Code Output:

3. Conclusion

The confluence of technologies like Google Colaboratory, LangChain, and ChatGPT represents a synergy that addresses a fundamental problem of our time: information overload. They not only facilitate the processing and analysis of large volumes of data, but they also do so in a highly scalable and customizable manner. This is not mere incremental optimization; we’re talking about improvements that can be orders of magnitude more efficient.

Ultimately, this set of technologies represents more than the sum of its parts. They function as levers that allow us to extract more value from the time and resources we invest in the consumption and processing of information. This synergy, therefore, not only maximizes our information efficiency but also redefines what is possible in modern information management.

The train is in the station and you can’t miss it, so join us on this journey. We are an open community and if you want to collaborate with us, please send us an email: andres@smartlever.tech