Unlocking the Power of Podcast Transcripts: How I Made Information Accessible

Keluar Sekejap has accumulated a vast amount of text from their podcast transcripts. While this represents a rich source of information, it also presents a significant challenge: How can we quickly and accurately find relevant answers within this mountain of data?

1. Data Cleaning and Organization:
   • I started by cleaning and organizing the raw transcript data.
   • Instead of treating each transcript as one long document, I grouped text segments that discuss the same subtopic within each episode.
   • Why it matters: This organization helps to maintain context and improves the accuracy of our search process.
2. Creating Semantic Embeddings:
   • I converted each grouped text segment into what's called an "embedding."
   • Think of embeddings as a way to represent text in a multidimensional space where similar meanings are close together.
   • Why it matters: This allows us to capture the essence and meaning of the text, not just individual words.
3. Implementing Dual Search Techniques:
   • a) Semantic Search:
     • When a user asks a question, I convert it into an embedding and find the top 5 most similar text segments from our organized data.
     • This helps us find content that's conceptually related to the question.
   • b) Keyword-Based Search (BM25):
     • I also use a more traditional search method called BM25.
     • This looks for keyword matches and considers how frequently terms appear.
     • Why use both? It balances finding conceptually similar content with directly relevant keyword matches.
4. Combining Search Results:
   • I give equal weight (50-50) to both the semantic and keyword-based search results.
   • This combination helps us capture both the meaning and the specific terms in the user's question.
5. Advanced Reranking:
   • After getting our initial combined results, I apply a reranking algorithm.
   • This further refines the results to ensure the most relevant information rises to the top.
6. Leveraging AI for Final Answers:
   • I feed the reranked results along with the original question into a Large Language Model (LLM).
   • The LLM then processes this information to generate a final, coherent answer.

1. Contextual Understanding: By grouping related content, we maintain the context of discussions, leading to more accurate results.
2. Semantic Intelligence: Embeddings allow us to understand the meaning behind words, not just exact matches.
3. Balanced Search: Combining semantic and keyword searches ensures we don't miss important information.
4. Continuous Refinement: Multiple stages of ranking and reranking help prioritize the most relevant information.
5. AI-Powered Answers: The use of an LLM helps synthesize information into clear, concise responses.

This sophisticated process allows users to quickly find relevant information from Keluar Sekejap's extensive podcast archives. Whether you're looking for a specific fact, a broader topic discussion, or insights on a particular theme, the system can efficiently navigate the vast sea of information to provide accurate and contextually relevant answers.

The method isn't limited to podcast transcripts. This powerful approach to information retrieval and analysis has numerous real-world applications. Here are some examples:

1. Legal Document Analysis:
   • Evaluating complex legal contracts to quickly identify important clauses or potential risks.
   • Detecting unfair or unusual terms in agreements by comparing them to standard industry practices.
   • Assisting lawyers in case research by finding relevant precedents from vast legal databases.
2. Medical Research:
   • Analyzing large volumes of medical literature to find relevant studies for specific conditions or treatments.
   • Assisting in diagnosis by quickly retrieving information about rare diseases or unusual symptom combinations.
3. Customer Support:
   • Creating intelligent chatbots that can accurately answer customer queries by searching through product manuals and support documentation.
   • Analyzing customer feedback to identify recurring issues or improvement opportunities.
4. Academic Research:
   • Helping researchers quickly find relevant papers and studies in their field from vast academic databases.
   • Assisting in literature reviews by summarizing key findings from multiple sources.
5. Financial Analysis:
   • Analyzing company reports and financial news to identify market trends or potential investment opportunities.
   • Assisting in due diligence processes by quickly identifying potential risks or red flags in company documentation.

These examples demonstrate how this approach to information retrieval and analysis can be adapted to various fields, making it a versatile tool for navigating and extracting insights from large volumes of text data across different industries and applications.