Tema: search
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El filtrado por defecto de ripgrep acorta las búsquedas de código de los agentes frente a grep -r
Hipótesis: los agentes de codificación que buscan en repositorios con las reglas de exclusión por defecto de ripgrep (se omiten los archivos ignorados por git, los ocultos y los binarios) necesitan menos llamadas de búsqueda y leen menos resultado irrelevante por tarea que los agentes que usan grep -r sin exclusiones, porque no aparecen coincidencias en artefactos de compilación ni en dependencias; no se reporta ninguna medición.
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Búsqueda de texto completo en PostgreSQL con tsvector
PostgreSQL convierte el texto en un tsvector de lexemas normalizados mediante una configuración de idioma, lo compara con un tsquery, lo puntúa con ts_rank y lo indexa con GIN; gestiona la derivación morfológica (stemming) y las palabras vacías, pero no las erratas ni los sinónimos de forma nativa.
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grep and ripgrep for code search: recursion, filters and exit codes
GNU grep searches recursively with -r, narrows with --include and --exclude-dir and exits 0, 1 or 2 for match, no match or error; ripgrep does the same by default while skipping git-ignored, hidden and binary files. Know what each tool skips before trusting an empty result.
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Document search over a corpus walk-through: indexing pipeline, permissions and reindexing
A design walk-through for search over documents held in a system of record: a derived, rebuildable index fed by change events, ACL keys indexed as fields so filtering happens before ranking, versioned indexes switched by alias, a reconciler that finds drift, and a list of what to defer.
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Tries for prefix lookups: autocomplete and longest-prefix matching
A trie stores strings with one node per common prefix, so lookup costs the key length regardless of how many keys exist, all keys with a prefix form one subtree, and the longest stored prefix of a query is found in one walk; use it for autocomplete and routing, and compare against a sorted array first.
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Embeddings as a data type: fixed-length vectors, a distance function and what a column of them needs
An embedding is a fixed-length float vector produced by a specific model, meaningful only under the distance that model was trained for and only next to vectors from the same model version; storing it requires the dimension, the model identifier and the distance to be recorded, and querying it means nearest-neighbour search, exact or approximate.
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Retrieval basics for LLM applications: chunking, passage identifiers and citing what was retrieved
Retrieval-augmented generation feeds retrieved passages to the model; the decisions that matter are how documents are split, what context each chunk carries, and how the answer points back to a specific passage so that a reader can check it.
Legible por máquina: JSON