What "semantic search over audio" actually requires

Three things in order. (1) A transcript that preserves where each subject starts and stops, because flat text loses too much for production retrieval. (2) Chunking that respects that structure, because character-count chunking on transcripts produces incoherent embeddings. (3) Metadata that survives ingestion (section heading, timestamp, source recording) so retrieval can filter and synthesis can cite.

Markdown with topic headings provides all three by construction. The conversion gives you structured text. Header-aware chunking gives you coherent units. Heading metadata survives any ingestion pipeline. Pinecone, Chroma, Weaviate, and Qdrant all handle the resulting vectors equally well.

Recommended schema

Per-chunk metadata to store: section (string, indexed), timestamp (HH:MM:SS, indexed), source_file (the original audio filename), source_date (when the recording was made), topic (optional, from ### subheadings). Indexing section and timestamp lets you scope retrieval to specific subjects or time ranges; indexing source_date lets you query "what was said about X in Q1".

Vector DB choice

For audio corpora specifically: Pinecone if you want managed and don't want to think about ops; Chroma for local development and small archives; Weaviate when hybrid retrieval (keyword + vector) matters because exact phrase matches happen often in transcripts; Qdrant when filter-heavy queries (per-topic, per-time-range) dominate your access patterns. Pair with PDF and web sources via PDF for Vector DBs and URL for Vector DBs for unified retrieval.