Extracting Topics from Video/Audio with Language Models (LLMs)

🔑 Topic modeling is the art of extracting groups of information from text or documents.

🌟 Extracting structured data through topic modeling has valuable applications in various fields, such as YouTube videos, podcasts, legal documents, and more.

🔍 The two-pass approach of mapping and reducing followed by retrieval helps in extracting topics and details efficiently.

📂 Using LLMs (Language Models) to extract topics from video/audio transcripts.

🔍 Importing packages and setting up GPT 3.5 Turbo and GPT 4 language models.

📄 Splitting the transcript into chunks for processing and analyzing a subset of the transcript.

📝 The video discusses the process of extracting topic titles and short descriptions from a podcast transcript using LLMs and LangChain.

🔍 By customizing the prompt for the language model, more nuanced topics relevant to the specific domain can be extracted.

💡 Iterating through examples and adding manual inputs helps improve the accuracy of the extracted topics.

🔎 Using a combined prompt, the transcript is analyzed to de-duplicate bullet points and extract key topics.

💡 The load summarize chain and the GPT4 language model are used to identify important topics in the transcript.

📊 The extracted topics are then converted into structured data for further analysis and use.

🔑 Structured data is important for extracting topics from video/audio and can be used to categorize content.

🌟 Expanding topics can be done by generating summaries based on relevant chunks of the transcript.

📚 Using embeddings and similarity search can assist in generating context and expanding on topics.

🔑 Using Pinecone to initialize an index for topic modeling.

📚 Creating a custom prompt to generate a summary of a chosen topic.

🔍 Implementing retrieval-based question-answering with chain type keyword arguments.

🔍 Using LLMs, we can extract structured topics from video/audio transcriptions.

💡 The expanded topics include both the topic name and description, providing a comprehensive understanding.

⏲️ LLMs can also be used to extract time stamps for different chapters in the transcript.

The speaker demonstrates a method for extracting topics from a transcript using LLMs (Language Learning Models).

The speaker explains their approach of using a custom prompt and topic timestamps to identify and organize the topics in the transcript.

The speaker encourages the audience to apply this technique in their own projects and shares their excitement to see what they create.