Unlocking the Power of Podcast Transcripts: How We Make Information Accessible (and Fun!)

Keluar Sekejap has hoarded a ginormous pile of text from their podcast transcripts. Seriously, it's like the digital version of hoarding! While this is a treasure trove of info, it also poses a big question: How can we the nuggets of wisdom in this mountain of words without losing our sanity?

1. Data Cleaning and Organization:
   • First, we give the raw transcript data a good scrub and a nice comb-over.
   • Instead of treating each transcript like one endless monologue, we break it down into bite-sized chunks that talk about the same stuff.
   • Why it matters: Think of it like sorting your laundry – it keeps things neat and tidy, and helps us find what we need faster.
2. Creating Semantic Embeddings:
   • We turn each text chunk into something called an "embedding."
   • Imagine embeddings as a way to map out text in a multi-dimensional playground where similar ideas hang out together.
   • Why it matters: This lets us capture the vibe and meaning of the text, not just the words themselves. It's like understanding the plot of a movie without memorizing every line.
3. Implementing Dual Search Techniques:
   • a) Semantic Search:
     • When someone asks a question, we translate it into an embedding and find the top 5 best-matching text chunks from our organized chaos.
     • It’s like playing matchmaker for text and questions!
   • b) Keyword-Based Search (BM25):
     • We also use a trusty old-school search method called BM25.
     • This one’s all about finding keyword matches and counting how often terms show up.
     • Why use both? It’s like having both a metal detector and a treasure map. Double the chances of finding the good stuff!
4. Combining Search Results:
   • We mix the results from our two search buddies in a nice 50-50 blend.
   • This combo helps us catch both the meaning and the exact words from the user's question, like a perfect smoothie blend.
5. Advanced Reranking:
   • We then give our combined results a final polish with a reranking algorithm.
   • This ensures the crème de la crème of information bubbles up to the top.
6. Leveraging AI for Final Answers:
   • We throw the reranked results and the original question into the brain of a Large Language Model (LLM).
   • The LLM works its magic and spits out a final, coherent answer. Voila!

1. Contextual Savvy: By grouping related chatter, we keep the convo context intact, leading to more accurate results. No out-of-context bloopers here!
2. Semantic Brains: Embeddings help us grasp the text's meaning, not just the surface words. It's like having a mind reader on board.
3. Balanced Search: By combining semantic and keyword searches, we make sure we don’t miss any juicy bits.
4. Continuous Polish: Multiple ranking rounds ensure the best info shines through like a diamond.
5. AI-Powered Brilliance: Our LLM synthesizes info into clear, snappy answers. No more head-scratching required!

This sophisticated, yet totally fun process lets users quickly unearth relevant gems from Keluar Sekejap's massive podcast vault. Whether you're hunting for a specific fact, a deep dive on a topic, or some juicy insights, the system navigates the info jungle to deliver accurate, context-rich answers with flair.