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Describe the retrieval that runs, not the one that is designed #14

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Merged
DenisDrobyshev merged 1 commit into from
Aug 27, 2026
Merged

Describe the retrieval that runs, not the one that is designed #14

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9 changes: 5 additions & 4 deletions index.html
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Expand Up @@ -330,11 +330,12 @@ <h2>What these are built on, and why</h2>
</article>

<article class="card">
<div class="card-top"><h3>FastAPI · Postgres · pgvector</h3><span class="tag">praxis</span></div>
<div class="card-top"><h3>FastAPI · NumPy</h3><span class="tag">praxis</span></div>
<p>
One API core serves every client. pgvector keeps the dense index in the same database
as the corpus, so there is no second system to keep in sync and no separate backup
story for the part that makes answers.
One API core serves every client. Dense retrieval is a matrix-vector product over
vectors held in memory and cached to disk, with a pure standard-library fallback so
the offline image and CI run without NumPy at all. A Postgres index with pgvector is
designed and not yet wired — the schema is in the repository, the retriever is not.
</p>
</article>

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9 changes: 5 additions & 4 deletions ru/index.html
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Expand Up @@ -328,11 +328,12 @@ <h2>На чём это построено и почему</h2>
</article>

<article class="card">
<div class="card-top"><h3>FastAPI · Postgres · pgvector</h3><span class="tag">praxis</span></div>
<div class="card-top"><h3>FastAPI · NumPy</h3><span class="tag">praxis</span></div>
<p>
Одно API-ядро обслуживает всех клиентов. pgvector держит плотный индекс в той же базе,
что и корпус: нет второй системы, которую надо синхронизировать, и отдельной истории с
бэкапами для той части, что делает ответы.
Одно API-ядро обслуживает всех клиентов. Плотный поиск — умножение матрицы на вектор
по векторам в памяти с кэшем на диск, плюс запасной путь на чистой стандартной
библиотеке, чтобы офлайн-образ и CI работали вообще без NumPy. Индекс на Postgres с
pgvector спроектирован, но не подключён: схема в репозитории есть, ретривера нет.
</p>
</article>

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