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vectorize.py
------------
Builds (or loads, if already built) a persisted vectorstore for a single research
paper PDF. Designed to be imported by qa.py, but can also be run standalone:
python vectorize.py /path/to/paper.pdf
Chunking strategy:
1. Split the paper by detected section headers ("1 Introduction", "3.2 Inference
with V-RAG", etc.) so each chunk is a real semantic unit, not an arbitrary
page cut.
2. Within each section, protect table-like blocks (lines dense with numbers) so
they stay intact as their own chunk instead of getting sliced by the
recursive splitter.
3. Any remaining oversized prose is split with RecursiveCharacterTextSplitter
(paragraph -> sentence -> word fallback separators).
4. If no section headers are detected at all (unusual paper formatting), falls
back to page-wise chunks with a small overlap so nothing is silently lost.
Install once:
pip install --break-system-packages pymupdf langchain langchain-core \
langchain-text-splitters langchain-chroma langchain-community \
sentence-transformers chromadb
Embedding model: BAAI/bge-base-en-v1.5 (local, ~440MB, runs fine on CPU or a
sliver of GPU -- no API key, no rate limits, no VRAM worries).
"""
import os
import re
import sys
import hashlib
import fitz # PyMuPDF
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
# Matches headers like "1 Introduction", "3.2 Inference with V-RAG", "6 Conclusion"
HEADER_PATTERN = re.compile(r'\n(\d{1,2}(?:\.\d{1,2})?\s+[A-Z][A-Za-z][^\n]{2,60})(?=\n)')
# A line "looks like a table row" if enough of its tokens are numeric.
NUMERIC_TOKEN = re.compile(r'^-?\d+\.\d+$|^\d+$')
PAGE_OVERLAP_CHARS = 200
MAX_CHUNK_CHARS = 900
def get_pdf_hash(pdf_path: str) -> str:
"""Short content hash -> stable, unique persist directory per PDF."""
with open(pdf_path, "rb") as f:
return hashlib.md5(f.read()).hexdigest()[:12]
def extract_sections(pdf_path: str):
"""Return a list of {title, text, page} dicts, one per detected section.
Falls back to page-wise chunks (with overlap) if no section headers are
detected, so unusual paper formats still produce usable chunks.
"""
doc = fitz.open(pdf_path)
full_text = ""
page_map = [] # (char_start, char_end, page_number) per page
for i, page in enumerate(doc):
t = page.get_text()
page_map.append((len(full_text), len(full_text) + len(t), i + 1))
full_text += t
matches = list(HEADER_PATTERN.finditer(full_text))
sections = []
if matches:
# Preamble before the first header = title/authors/abstract
if matches[0].start() > 0:
preamble = full_text[:matches[0].start()].strip()
if preamble:
page_no = next(p for s, e, p in page_map if s <= 0 < e)
sections.append({"title": "Abstract / Preamble", "text": preamble, "page": page_no})
for idx, m in enumerate(matches):
start = m.start()
end = matches[idx + 1].start() if idx + 1 < len(matches) else len(full_text)
title = m.group(1).strip()
body = full_text[m.end():end].strip()
if body:
page_no = next(p for s, e, p in page_map if s <= start < e)
sections.append({"title": title, "text": body, "page": page_no})
else:
print("[vectorize] No section headers detected -- falling back to page-wise chunking.")
page_texts = [page.get_text() for page in doc]
for i, t in enumerate(page_texts):
prefix = page_texts[i - 1][-PAGE_OVERLAP_CHARS:] if i > 0 else ""
sections.append({"title": f"Page {i + 1}", "text": prefix + t, "page": i + 1})
return sections
def _looks_tabular(line: str) -> bool:
tokens = line.split()
if len(tokens) < 3:
return False
numeric = sum(1 for t in tokens if NUMERIC_TOKEN.match(t))
return (numeric / len(tokens)) >= 0.4
def _split_protecting_tables(text: str, splitter: RecursiveCharacterTextSplitter):
"""Group consecutive table-like lines into their own block; split the rest
normally. Returns a list of (is_table: bool, chunk_text: str)."""
lines = text.split("\n")
blocks = []
buf, buf_is_table = [], None
for line in lines:
is_table = _looks_tabular(line)
if buf_is_table is None:
buf_is_table = is_table
if is_table == buf_is_table:
buf.append(line)
else:
blocks.append((buf_is_table, "\n".join(buf)))
buf, buf_is_table = [line], is_table
if buf:
blocks.append((buf_is_table, "\n".join(buf)))
chunks = []
for is_table, block_text in blocks:
block_text = block_text.strip()
if not block_text:
continue
if is_table or len(block_text) <= MAX_CHUNK_CHARS:
chunks.append((is_table, block_text))
else:
for piece in splitter.split_text(block_text):
chunks.append((False, piece))
return chunks
def get_embeddings(device: str = "cpu"):
"""Local, lightweight embedding model -- no API key, no rate limits.
device stays "cpu" by default (standalone runs behave exactly as before).
It's a parameter rather than a hardcoded string because on HF ZeroGPU only
code inside an @spaces.GPU window may touch "cuda" -- so the caller has to
say which side of that window it's on. Letting the model auto-detect would
put it on "cuda" at import and blow up on first use outside the window.
"""
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
return HuggingFaceBgeEmbeddings(
model_name="BAAI/bge-base-en-v1.5",
model_kwargs={"device": device},
encode_kwargs={"normalize_embeddings": True},
)
def build_vectorstore(pdf_path: str, persist_root: str = "./vectorstores", force_rebuild: bool = False,
device: str = "cpu"):
"""Build a new vectorstore for pdf_path, or load the existing one if it was
already built for this exact file.
device is forwarded to get_embeddings. Building on "cuda" and re-opening on
"cpu" is a supported pattern: the persisted vectors are identical either way,
so the expensive bulk encode can run on a GPU and the cheap query-time encode
on the CPU.
"""
pdf_hash = get_pdf_hash(pdf_path)
persist_dir = os.path.join(persist_root, pdf_hash)
embeddings = get_embeddings(device)
if os.path.isdir(persist_dir) and os.listdir(persist_dir) and not force_rebuild:
print(f"[vectorize] Existing vectorstore found at {persist_dir} -- loading it.")
return Chroma(persist_directory=persist_dir, embedding_function=embeddings)
print(f"[vectorize] Building vectorstore for: {pdf_path}")
sections = extract_sections(pdf_path)
splitter = RecursiveCharacterTextSplitter(
chunk_size=700, chunk_overlap=100,
separators=["\n\n", "\n", ". ", " "],
)
docs = []
for sec in sections:
for is_table, piece_text in _split_protecting_tables(sec["text"], splitter):
docs.append(Document(
page_content=piece_text,
metadata={
"section": sec["title"],
"page": sec["page"],
"type": "table" if is_table else "text",
"source": os.path.basename(pdf_path),
},
))
if not docs:
raise ValueError(
f"No extractable text found in {pdf_path}. This PDF may be scanned "
"(image-only) rather than a native text PDF -- it needs OCR first."
)
print(f"[vectorize] {len(docs)} chunks created across {len(sections)} sections.")
os.makedirs(persist_dir, exist_ok=True)
vectorstore = Chroma.from_documents(docs, embeddings, persist_directory=persist_dir)
print(f"[vectorize] Saved to {persist_dir}")
return vectorstore
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: python vectorize.py /path/to/paper.pdf")
sys.exit(1)
path = sys.argv[1]
if not os.path.isfile(path):
print(f"File not found: {path}")
sys.exit(1)
build_vectorstore(path) |