Workflow · Production
Embedding podcast transcripts with Cohere and storing in ApertureDB for semantic search
The problem
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.
Workflow diagram · grounded in source
1
Load transcript text files
trigger
“Dataset: Podcast transcripts (.txt)”
2
Chunk text with LangChain
integration
“the one I used in this project is token text splitting with overlap”
3
Embed chunks with Cohere
ai_action
“Embedding model: Cohere's embed-english-v3.0”
4
Ingest into ApertureDB
integration
“Parallel Loader is a great tool ApertureDB offers that lets you ingest the data into the database in batches”
Reported 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.
Reported stack
CohereApertureDBLangChainGoogle ColabWhispernumpy
Source
https://mlops.community/blog/semantic-search-to-glean-valuable-insights-from-podcast-series-part-2
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
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.
What tools did this team use?
Cohere, ApertureDB, LangChain, Google Colab, Whisper, numpy.
How is this workflow AI workflow structured?
Load transcript text files → Chunk text with LangChain → Embed chunks with Cohere → Ingest into ApertureDB.