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indexing【数据与研究】Build, inspect, and export bounded paperai indexes from a paperetl-style SQLite corpus with txtai-compatible vector configuration.
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Use this route when the task is to prepare or inspect the searchable model
behind paperai: validate a corpus, choose a txtai/vector configuration, build
an embeddings index, train optional static word vectors, or export raw section
text. Start with the no-download checker:
python scripts/inspect_corpus.py ./corpus --config ./index.yml --maxsize 1000 --toprank 0
The checker is bundled at scripts/inspect_corpus.py. Run
that command from this sub-skill directory, or replace scripts/ with the
path where this runtime skill is installed.
The ./corpus argument is a user-owned directory containing the source
artifact articles.sqlite; inspect_corpus.py is only a bundled validation
helper and is not a replacement for paperai.index.
maxsize or toprank for an exploratory run; 0 means unbounded in the
source implementation.paperai.index reads
<path>/articles.sqlite and saves txtai artifacts back into the same directory.
Do not delete the database when replacing a failed index. Confirm the output
files before handing the directory to another route.Install paperai==2.6.0 in Python 3.10+ and verify imports before attempting a
model load:
python -m pip install "paperai==2.6.0"
python -c "import paperai, paperai.index, paperai.export; print(paperai.__file__)"
The source module accepts these positional forms:
python -m paperai.index CORPUS_DIR [VECTORS_OR_YML] [MAXSIZE] [TOPRANK]
python -m paperai.export OUTPUT.txt CORPUS_DIR
For exact signatures and output artifacts, use the linked references rather than treating either positional interface as a safe full-corpus default.