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Dbctx

Compile a PostgreSQL database into compact, queryable context

Details

External ID
49258397
Source
HN
Company
—
Product
Dbctx
Website domain
github.com
Launched
Aug. 11, 2026
Cohort
—
Upvotes
5
Upvotes percentile
0.12634408602150538
Tags
—
Fetched at
Sept. 10, 2026, 5:32 a.m.
Updated at
Sept. 10, 2026, 5:32 a.m.

Description

Hi HN, I've been finding increasing need for helping our users chat with the underlying application data in postgresql directly using LLMs, visualize results and so on.A major obstacle to this is -- traditional sql schema doesn't contain enough information for an LLM to easily query things in one shot.For example, traditional schema doesn't say what is the expected/allowed value set in a state field, or what are the paths available embedded in a jsonb field.Also -- given a user query -- only a small fraction of the whole schema is relevant for an LLM to construct SQL queries. So filtering parts of schema relevant to construct a prompt is important.dbctx solves all these by inspecting a postgresql database and turning the results into an sqlite backed ".dtx" file. You can pass your natural language query to dtx, and get relevant tables in a compressed schema format along with field/json intelligence embedded in it. This leads to much better responses from the LLM from the getgo.I have a CLI, library and tiny UI embedded in a single go binary.Do give it a go, and any feedback welcome.

Enrichment

Theme
database infrastructure and developer tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
convert postgres database to ai context
Manually corrected
False

Could you build this?

Yes It is a developer utility that inspects PostgreSQL information schema catalogs, foreign keys, and statistics to generate compact, token-optimized context prompts for LLMs.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 154 launches clear the similarity bar, closest 8 shown.

Attention rank: #137 of 155 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 286 days after the earliest competitor.

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