Workflow · workflow
Embedding podcast transcripts with Cohere and storing in ApertureDB for semantic search
Building semantic search over a podcast series required embedding long-form transcripts and storing them in a vector database before any queries could be run.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Load transcript text files
Podcast transcripts previously generated into text files are loaded as the dataset for embedding.
Tools used
CohereApertureDBLangChainGoogle ColabWhispernumpy
Outcome
The author successfully embedded chunked podcast transcripts using Cohere embed-v3 and stored them in ApertureDB; the semantic search query step is deferred to a subsequent post.
Source
https://mlops.community/blog/semantic-search-to-glean-valuable-insights-from-podcast-series-part-2
Grounding & classification
Source type: technical build writeup
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