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Mon, 13 Apr 2026 03:26:30 -0700 (PDT) MIME-Version: 1.0 From: Susmitha S Date: Mon, 13 Apr 2026 15:56:19 +0530 X-Gm-Features: AQROBzCjEL8oLwXvupu2x-HJcL0RcjeTC_BxuCHt1UyRe1n2H557b3UwJfCECCI Message-ID: Subject: =?UTF-8?Q?audio=5Fsimilarity_=E2=80=93_simple_SELECT_query_for_audio_s?= =?UTF-8?Q?imilarity_search?= To: pgsql-general@lists.postgresql.org Content-Type: multipart/mixed; boundary="00000000000089b054064f54ea6f" List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Archived-At: Precedence: bulk --00000000000089b054064f54ea6f Content-Type: multipart/alternative; boundary="00000000000089b052064f54ea6d" --00000000000089b052064f54ea6d Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hello PostgreSQL community, I have been working on a small extension that adds audio similarity search to PostgreSQL. I would like to ask for your suggestions on the approach and the SQL interface. IDEA: Once audio embeddings are generated, users can find similar audio with a simple SELECT query: SELECT * FROM similar_audio(42, 5); This returns the 5 most similar audio files to the file with id 42, including similarity score, speaker name, and filename. How it works - Audio files are processed using MFCC (librosa) to produce 26=E2=80=91dimensional embeddings. - Embeddings are stored using `pgvector`. - Similarity is cosine distance with an HNSW index for speed. What I would like your feedback on - Is the SELECT=E2=80=91based interface intuitive enough for end users? - Are there better patterns for similarity search in PostgreSQL? - Any performance or design pitfalls I should be aware of? The extension currently provides these functions: - similar_audio(id, limit)=E2=80=93 search by audio ID - similar_audio_by_filename(filename, limit) =E2=80=93 search by filename - search_similar(file_path, limit) =E2=80=93 search by full path Requirements - PostgreSQL 18+ (or 17/16) - plpython3u and pgvector - Python 3.11 with librosa, soundfile, numpy I have tested it on 7,595 audio files (total ~10 hours) with good performance. I would greatly appreciate any suggestions on improving the query interface, indexing strategy, or embedding pipeline. Thank you for your time. --00000000000089b052064f54ea6d Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hello PostgreSQL community,

I have been working on = a small extension that adds audio similarity
search to PostgreSQL. I wou= ld like to ask for your suggestions on the
approach and the SQL interfac= e.

IDEA:

Once audio embeddings are generated, users can find = similar audio with
a simple SELECT query:


SELECT * FROM simil= ar_audio(42, 5);


This returns the 5 most similar audio files to = the file with id 42,
including similarity score, speaker name, and filen= ame.

=C2=A0How it works

- Audio files are processed using MFC= C (librosa) to produce
26=E2=80=91dimensional embeddings.
- Embedding= s are stored using `pgvector`.
- Similarity is cosine distance with an H= NSW index for speed.

What I would like your feedback on

- Is = the SELECT=E2=80=91based interface intuitive enough for end users?
- Are= there better patterns for similarity search in PostgreSQL?
- Any perfor= mance or design pitfalls I should be aware of?

The extension current= ly provides these functions:

- similar_audio(id, limit)=E2=80=93 sea= rch by audio ID
- similar_audio_by_filename(filename, limit) =E2=80=93 s= earch by filename
- search_similar(file_path, limit) =E2=80=93 search by= full path

Requirements

- PostgreSQL 18+ (or 17/16)
- plpy= thon3u and pgvector
- Python 3.11 with librosa, soundfile, numpy

= I have tested it on 7,595 audio files (total ~10 hours) with good performan= ce.

I would greatly appreciate any suggestions on improving the quer= y
interface, indexing strategy, or embedding pipeline.

Thank you = for your time.
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