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examples: programs that run, in four languages
Eight of them, each doing something a pipeline actually has to do, and each starting from something anyone can fetch — a public domain book from Project Gutenberg, a NASA broadcast, or a sample carried in the file. Shell, because the tools are filters first: a book to passages and what its notes cite; a recording to chapter marks, citable passages and the one ffmpeg call that makes it play on a phone; passages into pgvector and a search that asks twice and reconciles. Rust, for the case where the caller already holds the text and a subprocess would be serialising for nothing. Python, for the shape a pipeline stage takes once the reading is a library call. TypeScript, for the app side — chapter marks and deep links, and fusing two rankings in process. Three of them end with what re-ranking means, because it is two different things under one name: fusing ranked lists, which is what `rrf` does and needs no model, and a cross-encoder scoring query and passage together, which is accurate, slow, and runs over what retrieval already found.
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"""A book to rows a database can take, without a subprocess in sight.
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This is the shape a pipeline stage ends up as once the reading is a library
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call: convert, then loop, and what is left is the part that is actually about
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your schema.
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python examples/python/book_to_citations.py path/to/book.epub
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With no argument it fetches Gibbon's first volume from Project Gutenberg,
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which is public domain and has a few hundred pages of footnotes.
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"""
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import pathlib
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import subprocess
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import sys
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import urllib.request
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import catena
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GUTENBERG = "https://www.gutenberg.org/cache/epub/731/pg731.epub"
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def fetch(into: pathlib.Path) -> pathlib.Path:
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"""The sample book, downloaded once."""
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book = into / "gibbon.epub"
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if not book.exists():
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into.mkdir(parents=True, exist_ok=True)
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print(f"fetching {GUTENBERG}")
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with urllib.request.urlopen(GUTENBERG, timeout=120) as reply: # noqa: S310
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book.write_bytes(reply.read())
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return book
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def to_markdown(book: pathlib.Path) -> str:
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"""Pandoc is the one part of this that is somebody else's program.
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`commonmark` rather than `commonmark_x`: the extended flavour writes
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anchors as bracketed spans, and they end up inside the passages.
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"""
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done = subprocess.run(
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["pandoc", "-f", "epub", "-t", "commonmark", "--wrap=none", str(book)],
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capture_output=True,
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text=True,
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check=True,
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)
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return done.stdout
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def rows(markdown: str) -> list[dict]:
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"""Passages in, citation rows out.
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Two library calls do the work. `chunk_markdown` cuts the book into
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passages and flags the ones carrying citation material — which is the
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whole reason to ask it rather than looking for a heading, since a page of
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notes at the foot of a chapter has no heading over it. `find_citations`
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then reads each of those.
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"""
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out = []
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for chunk in catena.chunk_markdown(catena.scrub(markdown)):
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if "footnote_citations" not in chunk["flags"] and chunk["role"] != "bibliography":
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continue
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for found in catena.find_citations(chunk["text"]):
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# An `ibid.` or a short cite points at an entry above rather than
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# naming a work of its own, so there is nothing to store.
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if found["continuation"]:
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continue
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if not (found["title"] or found["authors"] or found["ids"]):
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continue
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out.append(
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{
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"page": chunk["page"],
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"chapter": chunk["chapter"],
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"citation_text": found["text"],
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"referenced_work": found["title"],
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"referenced_author": "; ".join(found["authors"]) or None,
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"referenced_year": found["year"],
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"referenced_container": found["container"],
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"kind": found["kind"],
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"unsettled": found["unsettled"],
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}
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)
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return out
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def main() -> int:
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where = pathlib.Path(sys.argv[1]) if len(sys.argv) > 1 else fetch(pathlib.Path("work"))
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print(f"== {where.name}")
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identified = catena.identify(where.name, where.read_bytes())
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print(f" a {identified['kind']}, {identified['bytes']} bytes")
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markdown = to_markdown(where)
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passages = catena.chunk_markdown(catena.scrub(markdown))
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carrying = [c for c in passages if "footnote_citations" in c["flags"]]
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print(f" {len(passages)} passages, {len(carrying)} carrying citation material")
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found = rows(markdown)
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named = [r for r in found if r["referenced_work"]]
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print(f"\n== {len(found)} rows, {len(named)} naming a work")
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# What a stage like this would do next is its own business: one executemany
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# against your own table. The point is that everything above it is two
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# function calls rather than a parser you have to keep.
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print("\n INSERT INTO citations (citation_text, referenced_work, referenced_author, …)")
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whole = [r for r in named if r["referenced_author"]] or named
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for row in whole[:5]:
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work = (row["referenced_work"] or "")[:40]
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who = (row["referenced_author"] or "—")[:22]
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print(f" (…, {work!r}, {who!r}, {row['referenced_year']})")
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# Nothing is invented. Where a field could not be read the record says so
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# rather than guessing, and a caller that wants a model to fill the gaps
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# has `citation_prompt` and `merge_citation_reply` for exactly that.
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# Gibbon cites in the classical short form — `Tacit. Annal. xiii. 5.` —
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# so most of his notes name a passage rather than a book. Nothing is
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# invented to cover that: the record says which fields it could not read,
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# and `citation_prompt` and `merge_citation_reply` are there for a caller
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# that wants a model to finish them.
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unsettled = [r for r in found if r["unsettled"]]
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print(f"\n {len(unsettled)} rows name something they could not finish reading:")
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for row in unsettled[:3]:
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print(f" {row['citation_text'][:56]} → {', '.join(row['unsettled'])}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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