In previous posts, I covered how to download podcasts, transcribe them, and store them in a vector database using embeddings. For more on downloading podcasts, check out my previous post: The Great Podcast Download: Building the Foundation of My AI. Now, it’s time to demonstrate how these elements come together to create a powerful search engine that allows you to query your podcast library using natural language.

In this post, I’ll walk through five different search examples that showcase how embeddings can retrieve podcast episodes based on themes, topics, or specific phrases, even when those exact words don’t appear in the transcription.

What is Embedding Search?

Embeddings allow us to convert text into a numerical format that captures the semantic meaning. For a more detailed explanation of embeddings, check out my previous post: The Magic of Embeddings: Transforming Data for AI. By storing these embeddings in a vector database, we can quickly and accurately search across thousands of podcast episodes based on the meaning of the search query—not just the exact words. For more on vector databases and how they work, see my post: Unlocking AI Potential: Vector Databases and Embeddings.

For example, searching for “AI ethics” might bring up episodes discussing “machine learning fairness” or “responsible AI” because embeddings capture the similarity in meaning, even if the exact phrase isn’t mentioned.

Example 1: Search for “Historical Revolutions”

To demonstrate the power of embeddings and vector search, I ran a query for “Historical Revolutions”. The system retrieved episodes from the Revolutions podcast that cover events from both the Russian and French revolutions.

Search Query:

python src/chroma_search.py --query "Historical revolutions"

Results:

• Relevant Episode: Revolutions, Episode: Relaunch-and-Recap.mp3
  Transcription Snippet: “This movement led to the infamous going to the people of 1874, where those idealistic students flocked to the countryside to enlighten the people and teach them how to be free…”
• Relevant Episode: Revolutions, Episode: The-Russian-Colony.mp3
  Transcription Snippet: “By early 1876, Axelrod was back in Switzerland, where he found the Russian colony splitting between the still faithful Bakuninists, the slow and steady Lavrovists, and Kachov’s Jacobin militancy…”

Analysis:

The system retrieved episodes that discuss key revolutionary movements, even though the exact phrase “Historical Revolutions” was not used. This highlights how embeddings allow for thematic searches that go beyond simple keyword matching.

Example 2: Search for “The Economy and Innovation”

This query explored how embedding-based search can surface episodes discussing the intersection of economic growth and technological innovation.

Search Query:

python src/chroma_search.py --query "The economy and innovation"

Results:

• Relevant Episode: Planet Money, Episode: Patent-racism-(classic).mp3
  Transcription Snippet: “In the mid-90s, there was this big new economic theory that was all the rage. It was an idea for how countries can produce unlimited economic growth…”
• Relevant Episode: Freakonomics Radio, Episode: 399-Honey,-I-Grew-the-Economy.mp3
  Transcription Snippet: “And it turns out that the countries where families prize obedient children, those countries are low in innovation…”

Analysis:

This search brought up episodes from Planet Money and Freakonomics Radio discussing theories of economic growth and innovation, showing how the system connects broad themes across different podcasts.

Example 3: Search for “Myths and Legends of Ancient Rome”

For this example, I ran a query to find content related to Roman mythology and folklore, and the system retrieved relevant episodes from Myths and Legends.

Search Query:

python src/chroma_search.py --query "Myths and legends of ancient Rome"

Results:

• Relevant Episode: Myths and Legends, Episode: 142A-Rome-Glory.mp3
  Transcription Snippet: “Two brothers with an interesting past. We’ll hear all about their origin and learn why my four-year-old is right. Sometimes a bath is not a good idea…”
• Relevant Episode: Myths and Legends, Episode: 211-Aeneid-Troy-Story.mp3
  Transcription Snippet: “This week, we’re back in Greek and Roman mythology for the Aeneid…”

Analysis:

The system successfully pulled up episodes on the stories of Romulus and Remus, as well as the Aeneid. This demonstrates how embeddings can capture the meaning of mythological themes, even when the exact words aren’t used in the transcription.

Example 4: Search for “Ethics in Science and Technology”

Next, I queried for “Ethics in Science and Technology”, and the system pulled up episodes discussing ethical issues in gene patents and philosophical debates on the role of science.

Search Query:

python src/chroma_search.py --query "Ethics in science and technology"

Results:

• Relevant Episode: Stuff You Should Know, Episode: How-Gene-Patents-Work.mp3
  Transcription Snippet: “This is where it gets hot… That’s the standard for what’s going on in the US right now as far as gene patents go.”
• Relevant Episode: Philosophize This, Episode: Episode-051-David-Hume-pt-1.mp3
  Transcription Snippet: “Science is fantastic at doing certain things. It’s fantastic at telling us about what the universe is…”

Analysis:

The search brought up discussions from both practical and philosophical podcasts, demonstrating the range of ethical questions raised in science and technology.

Example 5: Search for “Philosophy of Language”

Finally, I explored the “Philosophy of Language”, and the system pulled up episodes from Lexicon Valley and Philosophize This, which delve into linguistic theories and philosophical discussions about language.

Search Query:

python src/chroma_search.py --query "Philosophy of language"

Results:

• Relevant Episode: Lexicon Valley, Episode: That’s-Not-What-Irony-Means,-Alanis.mp3
  Transcription Snippet: “Language is a mess too. I recommend a book. It’s Nick Enfield’s book, Language vs. Reality…”
• Relevant Episode: Philosophize This, Episode: Episode-097-Wittgenstein-ep-1.mp3
  Transcription Snippet: “Just think for a second how massively important language is, whether you’re Aristotle, Francis Bacon, Karl Popper…”

Analysis:

This search highlighted episodes discussing the philosophical and linguistic complexities of language, showing how embeddings can capture abstract concepts and pull relevant content from different sources.

How to Try This Yourself

If you’d like to try this out, check out the Podcast Rag repository on GitHub for all the tools you need to build your own podcast search engine. You can also find all posts related to the Podcast Rag project on my site: Podcast Rag Series.

Final Thoughts

These examples illustrate the power of using embeddings for semantic search across a diverse podcast library. By converting both queries and podcast transcriptions into embeddings, the system can:

• Understand Context: Grasp the underlying meaning of queries and match them with relevant content, even if specific keywords aren’t present.
• Handle Diversity: Work across a wide range of topics—from historical events and economic theories to mythology and abstract philosophy.
• Enhance Discovery: Help you uncover episodes and discussions you might have missed with traditional keyword searches.

In future posts, I’ll explore additional functionality you can build into your system, such as:

• Summarization: Automatically generating concise summaries for podcast episodes based on their transcriptions.
• Recommendations: Building a personalized recommendation system that suggests episodes based on listening habits.

Stay tuned for more deep dives into building AI-powered tools with your own data!