Local Paper Index

Local PDF Index Builder

Build a searchable Asta document index from a collection of local PDF files. Each PDF is converted to markdown, split into ~2000-character chunks, and written as documents to an asta-documents YAML index — enabling semantic search across the full text of the collection.

Assets

This skill includes standalone scripts in the assets/ directory:

Locate the assets directory relative to this skill file. The scripts are self-contained and can be copied to the working directory or run in place.

Procedure

Step 0: Interview the user for paths and collection name

Before starting, ask the user for four things:

1. PDF directory — Where are the PDFs?
2. Markdown output directory — Where should extracted markdown go?
3. Collection name — A short label for the collection (e.g., my-papers, cs-reading-list).
4. Include images? — Whether to extract and save images embedded in the PDFs alongside the markdown. Images are useful for papers with figures/diagrams but increase storage. Default: no.

Suggest a directory layout where the PDFs and markdown live as siblings under a common parent, with the index file alongside them. For example, if the PDFs are at /data/papers/pdfs/:

/data/papers/                          # DATASET_ROOT — parent of everything
├── pdfs/                              # PDF_DIR (user already has this)
├── markdown/                          # MARKDOWN_DIR (suggested: sibling of pdfs/)
│   ├── paper1.md                      # without --images: flat .md files
│   ├── paper2.md
│   ├── paper3/                        # with --images: per-PDF subdirectories
│   │   ├── paper3.md
│   │   └── img-0.jpeg
│   └── ...
└── index.yaml                         # INDEX_PATH (auto-created here)

This layout matters because the index stores relative paths to the markdown files. Keeping everything under one root makes the index portable and git-friendly.

Key rules for the suggestion:

• The markdown directory should be adjacent to the PDF directory, not inside .asta.
• The DATASET_ROOT is the parent directory that contains both PDF_DIR and MARKDOWN_DIR.
• The index lives at DATASET_ROOT/index.yaml.
• If the user's PDFs are at /home/user/research/pdfs, suggest /home/user/research/markdown and /home/user/research/index.yaml.

Once the user confirms (or provides their own paths), set these variables:

PDF_DIR="/data/papers/pdfs"                               # user-provided
MARKDOWN_DIR="/data/papers/markdown"                       # user-confirmed
DATASET_ROOT="/data/papers"                                # parent of both
INDEX_PATH="$DATASET_ROOT/index.yaml"                      # derived
COLLECTION="my-papers"                                     # user-chosen
IMAGES=false                                                # true if user wants images

Step 1: Discover PDFs and show estimates

PDF_COUNT=$(find "$PDF_DIR" -name "*.pdf" -type f | wc -l)
TOTAL_SIZE=$(find "$PDF_DIR" -name "*.pdf" -type f -exec du -ch {} + | tail -1 | awk '{print $1}')
echo "Found $PDF_COUNT PDFs ($TOTAL_SIZE total)"

Present this estimate to the user before proceeding:

Ask the user to confirm before starting, especially for large collections (>20 PDFs).

Step 2: Extract PDFs to markdown

# Without images (flat layout: markdown/paper.md)
bash /path/to/assets/extract-pdfs.sh "$PDF_DIR" "$MARKDOWN_DIR"

# With images (per-PDF subdirectories: markdown/paper/paper.md + images)
bash /path/to/assets/extract-pdfs.sh --images "$PDF_DIR" "$MARKDOWN_DIR"

Pass --images only if the user opted in during Step 0. When --images is used, each PDF gets its own subdirectory under MARKDOWN_DIR to avoid image filename collisions across PDFs. The chunking script in Step 3 handles both layouts automatically.

The script:

• Skips PDFs whose markdown already exists (resumable)
• Handles large PDFs (>50 pages) by extracting in 50-page increments
• Reports progress and counts

For large batches (>20 PDFs), asta pdf-extraction olmocr with --workers is significantly faster. See the pdf-extraction skill for details.

Step 3: Chunk and build index

uv run --with pyyaml python3 /path/to/assets/chunk-and-index.py "$COLLECTION" "$MARKDOWN_DIR" --index-path "$INDEX_PATH" --pdf-dir "$PDF_DIR"

The --index-path argument is required. The script:

• Computes paths relative to the index file's directory, storing relative paths in the url field — making the index portable across machines. The indexed document is the markdown: its url points at the .md file.
• Reads each markdown file, splits into ~2000-char chunks at paragraph/sentence boundaries
• Writes all documents to the index YAML in a single pass
• Preserves any existing documents in the index (appends, does not overwrite)
• Skips markdown files already indexed for this collection (resumability keys on url; safe to re-run)
• Resolves the upstream PDF for each .md by iterating --pdf-dir and matching on basename (the per-PDF subdirectory name, or the flat file stem) — it finds the PDF actually on disk rather than assuming a filename. A .md with no matching PDF is indexed without a source_pdf and warned about.
• Each document gets:
  • Shared metadata (in extra): collection, plus source_pdf (a relative/file:// pointer to the upstream PDF) only when --pdf-dir is given and a matching PDF is found
  • Per-chunk metadata: chunk_index, total_chunks, chunk_chars, chunk_offset, file_chars (in extra)
  • Tags: <collection-name>, plus pdf-index for PDF-derived markdown or md-index for raw markdown

Options:

• --chunk-size 2000 — adjust chunk size (default 2000 chars)
• --pdf-dir "$PDF_DIR" — directory of upstream source PDFs. Omit it when indexing authored markdown (see Indexing raw markdown below).

Step 4: Warm the search cache

bash /path/to/assets/warm-cache.sh "$DATASET_ROOT"

The argument is required:

• $DATASET_ROOT — the root directory containing index.yaml

This step is required. The first search after indexing builds the internal BM25 + embedding indexes. Without warming, the user's first real search will be unexpectedly slow.

Step 5: Report results

asta documents --root "$DATASET_ROOT" list --tags="$COLLECTION"
asta documents --root "$DATASET_ROOT" show

Tell the user:

• Number of PDFs processed and chunks created
• Dataset root: the DATASET_ROOT path
• Index location: the INDEX_PATH
• Collection tag for filtering: the chosen collection name
• How to search: asta documents --root "$DATASET_ROOT" search --summary="query" --tags="COLLECTION"

Searching the Index

After building, search across all indexed PDFs:

# Semantic search within the collection
asta documents --root "$DATASET_ROOT" search --summary="neural network architecture" --tags="my-papers"

# With relevance scores
asta documents --root "$DATASET_ROOT" search --summary="attention mechanism" --tags="my-papers" --show-scores

# Filter by source PDF
asta documents --root "$DATASET_ROOT" search --extra=".source_pdf contains some-paper"

# List all documents in the collection
asta documents --root "$DATASET_ROOT" list --tags="my-papers"

Indexing raw markdown (no PDFs)

If your corpus is already markdown (authored .md docs, exported notes, an investigation record, a wiki), there is nothing to extract — skip Steps 1–2 and point the chunker straight at the markdown directory. Just omit --pdf-dir:

COLLECTION="my-notes"
MARKDOWN_DIR="/data/notes"            # a tree of .md files (rglob, nested OK)
INDEX_PATH="/data/notes/index.yaml"

uv run --with pyyaml python3 /path/to/assets/chunk-and-index.py \
  "$COLLECTION" "$MARKDOWN_DIR" --index-path "$INDEX_PATH"

bash /path/to/assets/warm-cache.sh "$(dirname "$INDEX_PATH")"
asta documents --root "$(dirname "$INDEX_PATH")" search \
  --summary="your query" --tags="$COLLECTION" --show-scores

The markdown is the source: each document's url points at the .md, the secondary tag is md-index, and extra.source_pdf is absent (there is no upstream PDF). Chunking, relative-path URLs, and resumability are identical to the PDF path — the only difference between the two is whether extra.source_pdf is present.

Storage Estimates

Total storage is roughly 2-3x the extracted text (markdown files + index YAML with chunk text in the summary field).

Time Estimates

Important Notes

• Warm the cache. The first asta documents search --summary=... builds the search index. Always run the warm-cache script after indexing.
• Chunk size tradeoff. 2000 chars balances search precision with context. Smaller chunks = more precise hits, less context. Larger chunks = more context, diluted relevance.
• Resumable. Both extraction and indexing skip already-processed files. Safe to re-run after interruption.
• Index is append-only. The chunking script preserves existing documents in the index. To rebuild from scratch, delete index.yaml first.
• PyYAML required. The chunking script needs pyyaml. Install with pip install pyyaml or uv pip install pyyaml if not available.
• Relative paths. The index stores relative paths (e.g., markdown/paper.md) so the dataset is portable. This requires the markdown directory to be under the same directory as the index file.