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document-indexing

document-indexing

Guides Semantra text and PDF preprocessing, window configuration, cache artifact inspection, and no-server indexing workflows.

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name: document-indexing description: "Guides Semantra text and PDF preprocessing, window configuration, cache artifact inspection, and no-server indexing workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Semantra Document Indexing

Use this sub-skill when the task is to prepare local text or PDF files for Semantra, choose chunk windows, control the Semantra cache directory, inspect cached artifacts, or troubleshoot preprocessing before the interactive web UI is used.

Read first

  • workflows.md for practical indexing command patterns, cache-directory decisions, validation steps, and safe no-server workflows.
  • cache-and-data-formats.md for the Semantra artifact filenames, JSON fields, embedding binaries, Annoy indexes, PDF conversion artifacts, and window tuple semantics.
  • troubleshooting.md for filename, encoding, PDF extraction, stale-cache, corrupt-artifact, and window-format failures.
  • Run create_tiny_corpus.py when you need a tiny local text fixture to construct or smoke-test Semantra commands.
  • Run inspect_semantra_cache.py when you need a read-only summary of an existing Semantra cache directory.

Route here when

  • The user asks how to run semantra over one file, many files, *.txt, or a PDF collection.
  • The user wants to preprocess documents without starting the browser UI; use --no-server.
  • The user asks what --windows, --encoding, --force, --semantra-dir, or --show-semantra-dir means.
  • The user needs to understand why Semantra reprocessed a document or which files appeared in the cache directory.
  • The user has PDF text extraction or PDF viewer-position problems.

Route model/backend questions to models-and-embeddings. Route query syntax, result interpretation, preference tags, web API, or local server behavior to interactive-search.

Core indexing workflow

  1. Confirm that Semantra is installed and the CLI is visible. From a normal user environment, prefer:

    semantra --help
    semantra --list-models
    semantra --show-semantra-dir
    
  2. Decide where processed artifacts should live. If the user does not care, let Semantra use its application cache directory. For reproducible tasks, pass an explicit cache directory:

    semantra --semantra-dir ./semantra-cache --no-server documents/*.txt
    
  3. Choose documents. Semantra accepts one or more existing filenames. It reads .pdf files through its PDF path and treats other filenames as text using the selected encoding.

  4. Choose a model with the sibling models-and-embeddings sub-skill before launching a large job. Local transformer models may download model files the first time; OpenAI mode requires credentials and sends text to an external API.

  5. Use --no-server for preprocessing-only jobs. Omit it when the next step is interactive search in the local browser UI.

  6. Validate the result without relying on the source checkout:

    python path/to/inspect_semantra_cache.py --cache-dir ./semantra-cache
    

    Expect at least a config JSON, token JSON, embedding binary, and usually an Annoy index for each processed document/config/window combination.

Window decisions

Semantra's default --windows 128_0_16 means windows of 128 tokens, initial offset 0, and a rewind/overlap of 16 tokens. The CLI accepts comma-separated window specs:

  • 128 -> (size=128, offset=0, rewind=0).
  • 128_0_16 -> (size=128, offset=0, rewind=16).
  • 64_8 -> (size=64, offset=8, rewind=0).

Only the first configured window is used by the query routes in this Semantra version; extra windows can still be processed and cached. Use smaller windows for short, precise excerpts and larger windows for more context. Increase rewind when important passages are often split across window boundaries.

Cache and force rules

Semantra hashes both document content and model/window configuration. A document can have multiple cache groups if you change model, tokens, windows, Annoy tree count, encoding, or Semantra version. Use --force when the cache exists but must be rebuilt. Before deleting artifacts, inspect the cache and identify the matching document/config group in cache-and-data-formats.md.

PDF-specific notes

For PDFs, Semantra extracts text and stores page-position metadata used by the web viewer. If the PDF opens but navigation/highlighting is wrong, distinguish between:

  • text extraction problems in the converted text artifact;
  • page-position JSON problems;
  • web API or browser rendering problems, which route to interactive-search.

Do not

  • Do not tell future agents to open or run original repository docs, examples, or tests. Use the bundled references and scripts here instead.
  • Do not run default local transformer indexing on sensitive or huge document sets until the user accepts model download/cache/runtime costs.
  • Do not use OpenAI mode for private documents unless the user explicitly accepts the privacy and cost tradeoff.
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