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Sun, 01 Mar 2026 23:25:37 -0800 (PST) MIME-Version: 1.0 From: Blessy Thomas Date: Mon, 2 Mar 2026 12:55:26 +0530 X-Gm-Features: AaiRm51a_niijn4Q8iuq3hae8GWtBRMM_3E1ZQg2vrlR1TfD1rV7NlrK06niM20 Message-ID: Subject: Extension - multilingual_fuzzy_match : Multilingual phonetic matching extension for PostgreSQL To: pgsql-hackers@postgresql.org Content-Type: multipart/mixed; boundary="0000000000004b724e064c057eba" List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Archived-At: Precedence: bulk --0000000000004b724e064c057eba Content-Type: multipart/alternative; boundary="0000000000004b724d064c057eb8" --0000000000004b724d064c057eb8 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hello PostgreSQL Community, I would like to introduce a PostgreSQL extension called multilingual_fuzzy_match. This extension enables multilingual name normalization, transliteration, and fuzzy phonetic matching directly inside PostgreSQL at query time. 1. What Problem It Solves: In multilingual datasets (especially Indian language datasets), the same name may appear in: - Different scripts - Different transliterations - Slight spelling variations - Multiple languages For example: =E0=A4=B0=E0=A4=BE=E0=A4=AE =E2=89=88 Raam =E2=89=88 =D8=B1=D9=8E=D8=A7=D9= =85 =E2=89=88 =E0=AE=B0=E0=AE=BE=E0=AE=AE=E0=AF=8D Traditional equality or LIKE queries fail in such cases. Even trigram matching doesn=E2=80=99t fully address cross-script phonetic similarity. 2. What This Extension Does - Detects the script of the input text - Performs transliteration and normalization - Generates a phonetic key - Uses Levenshtein distance (via python-Levenshtein) - Returns similarity-scored results All of this happens inside PostgreSQL using PL/Python (plpython3u). 3. Key Features - No schema changes required - Query-level matching - Supports 11 major Indian scripts: Devanagari, Tamil, Telugu, Bengali, Urdu, Malayalam, Kannada, Odia, Gujarati, Punjabi - Works on existing tables 4. Requirements - PostgreSQL 17 (compiled with Python support) - Python 3.12+ - plpython3u - Python packages: pip install indic-transliteration python-Levenshtein 5. Example Usage ---------------------------------------------------------------------------= -------------------------------------------------- postgres=3D# SELECT * FROM fuzzy_match('names_native_dist', 'name', 'Rahul') WHERE distance <=3D 1; id | name | translit | normalized | fuzzy | distance ----+-------+----------+------------+-------+---------- 1 | =E0=A4=B0=E0=A4=BE=E0=A4=B9=E0=A5=81=E0=A4=B2 | rAhula | rahul = | rahul | 0 2 | =E0=A6=B0=E0=A6=BE=E0=A6=B9=E0=A7=81=E0=A6=B2 | rAhula | rahul = | rahul | 0 4 | =E0=B2=B0=E0=B2=BE=E0=B2=B9=E0=B3=81=E0=B2=B2=E0=B3=8D | rAhul | r= ahul | rahul | 0 5 | Rahul | Rahul | rahul | rahul | 0 (4 rows) ---------------------------------------------------------------------------= ----------------------------------------------------- 6. Feedback Requested I would really appreciate feedback from the community on: - Extension design approach - Performance considerations - Suitability for PGXN submission I would love suggestions, improvements, and any guidance on making this production-ready. I=E2=80=99m sharing this not just as a project, but as a = starting point for discussion about multilingual data handling inside PostgreSQL. Looking forward to your thoughts and critiques. Thank you! Regards Blessy Thomas --0000000000004b724d064c057eb8 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hello PostgreSQL Community,

I would like to introdu= ce a PostgreSQL extension called multilingual_fuzzy_match. This extension e= nables multilingual name normalization, transliteration, and fuzzy phonetic= matching directly inside PostgreSQL at query time.

1. What Problem = It Solves:
In multilingual datasets (especially Indian language datasets= ), the same name may appear in:
- Different scripts
- Different trans= literations
- Slight spelling variations
- Multiple languages

= For example:
=E0=A4=B0=E0=A4=BE=E0=A4=AE =E2=89=88 Raam =E2=89=88 =D8=B1= =D9=8E=D8=A7=D9=85 =E2=89=88 =E0=AE=B0=E0=AE=BE=E0=AE=AE=E0=AF=8D
Tradit= ional equality or LIKE queries fail in such cases. Even trigram matching do= esn=E2=80=99t fully address cross-script phonetic similarity.

2. Wha= t This Extension Does

- Detects the script of the input text
- Pe= rforms transliteration and normalization
- Generates a phonetic key
-= Uses Levenshtein distance (via python-Levenshtein)
- Returns similarity= -scored results
All of this happens inside PostgreSQL using PL/Python (p= lpython3u).

3. Key Features
- No schema changes required
- Que= ry-level matching
- Supports 11 major Indian scripts:
Devanagari, Tam= il, Telugu, Bengali, Urdu, Malayalam, Kannada, Odia, Gujarati, Punjabi
-= Works on existing tables

4. Requirements
- PostgreSQL 17 (compil= ed with Python support)
- Python 3.12+
- plpython3u
- Python packa= ges:
=C2=A0 =C2=A0pip install indic-transliteration python-Levenshtein
5. Example Usage
-------------------------------------------------= ---------------------------------------------------------------------------= -
postgres=3D#
SELECT * FROM fuzzy_match('names_native_dist',= 'name', 'Rahul')
WHERE distance <=3D 1;
=C2=A0id = | name =C2=A0| translit | normalized | fuzzy | distance
----+-------+---= -------+------------+-------+----------
=C2=A0 1 | =E0=A4=B0=E0=A4=BE=E0= =A4=B9=E0=A5=81=E0=A4=B2 =C2=A0| rAhula =C2=A0 | rahul =C2=A0 =C2=A0 =C2=A0= | rahul | =C2=A0 =C2=A0 =C2=A0 =C2=A00
=C2=A0 2 | =E0=A6=B0=E0=A6=BE=E0= =A6=B9=E0=A7=81=E0=A6=B2 =C2=A0| rAhula =C2=A0 | rahul =C2=A0 =C2=A0 =C2=A0= | rahul | =C2=A0 =C2=A0 =C2=A0 =C2=A00
=C2=A0 4 | =E0=B2=B0=E0=B2=BE=E0= =B2=B9=E0=B3=81=E0=B2=B2=E0=B3=8D | rAhul =C2=A0 =C2=A0| rahul =C2=A0 =C2= =A0 =C2=A0| rahul | =C2=A0 =C2=A0 =C2=A0 =C2=A00
=C2=A0 5 | Rahul | Rahu= l =C2=A0 =C2=A0| rahul =C2=A0 =C2=A0 =C2=A0| rahul | =C2=A0 =C2=A0 =C2=A0 = =C2=A00
(4 rows)
----------------------------------------------------= ---------------------------------------------------------------------------= -

6. Feedback Requested

I would really appreciate feedback fr= om the community on:
- Extension design approach
- Performance consid= erations
- Suitability for PGXN submission
I would love suggestions, = improvements, and any guidance on making this production-ready. I=E2=80=99m= sharing this not just as a project, but as a starting point for discussion= about multilingual data handling inside PostgreSQL.

Looking forward= to your thoughts and critiques.
Thank you!

Regards
Blessy Tho= mas
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