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import re
import os
from src.constant import BASE_DIR
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
DATA_DIR = os.path.join(BASE_DIR, "data", "pmc")
class DataProcessor:
"""
Handles loading, cleaning, and chunking of text files
from the PubMed Central (PMC) dataset.
"""
def __init__(self, data_path: str = DATA_DIR):
self.data_path = data_path
def _load_files(self) -> list[dict]:
"""
Load raw text files from the dataset directory.
Returns a list of dictionaries with file name and raw content.
"""
data_list = []
for file_name in os.listdir(self.data_path):
if not file_name.endswith(".txt"):
continue
file_path = os.path.join(self.data_path, file_name)
with open(file_path, "r", encoding="utf-8", errors="replace") as file_ref:
data_list.append(
{
"file_name": file_name,
"page_content": file_ref.read()
}
)
return data_list
@staticmethod
def _decode_unicode(text: str) -> str:
"""
Convert escaped unicode sequences to proper text.
"""
if not isinstance(text, str):
return text
try:
return text.encode("utf-8", "ignore").decode("utf-8", "ignore")
except Exception:
return text
def _extract_body(self, text: str) -> str:
if not text:
return ""
text = text.replace("\r\n", "\n").replace("\r", "\n")
text = re.sub(r'-\n', '', text)
text = re.sub(r'\n{3,}', '\n\n', text)
start_patterns = [
r"====\s*Body", r"^Body\s*$", r"^BODY\s*$",
r"^Abstract\s*$", r"^ABSTRACT\s*$", r"^Introduction\s*$", r"^INTRODUCTION\s*$"
]
end_patterns = [
r"====\s*Back", r"^Back\s*$", r"^BACK\s*$",
r"^References\s*$", r"^REFERENCES\s*$", r"^Bibliography\s*$",
r"^Acknowledg", r"^Acknowledgments\s*$", r"^ACKNOWLEDGMENTS\s*$"
]
start_idx = None
for pat in start_patterns:
m = re.search(pat, text, flags=re.IGNORECASE | re.MULTILINE)
if m:
start_idx = m.end()
break
if start_idx is not None:
# find end after start_idx
end_idx = None
for pat in end_patterns:
m = re.search(pat, text[start_idx:], flags=re.IGNORECASE | re.MULTILINE)
if m:
end_idx = start_idx + m.start()
break
body = text[start_idx:end_idx] if end_idx else text[start_idx:]
else:
paragraphs = re.split(r'\n{2,}', text)
paragraphs = [p.strip() for p in paragraphs if p.strip()]
def is_metadata_para(p: str) -> bool:
# DOI / arXiv / ISSN / PMCID / PMID
if re.search(r'\b10\.\d{4,9}/\S+\b', p):
return True
if re.search(r'\bPMCID\b|\bPMID\b', p, re.I):
return True
if re.search(r'ISSN[:\s]', p, re.I):
return True
# common metadata keywords
if re.search(r'Correspondence:|Affiliat|Author|ORCID|E-mail:|Contact:', p, re.I):
return True
if re.search(r'©|license|all rights reserved|Published|Received|Accepted', p, re.I):
return True
words = p.split()
if len(p) < 200 and len(words) <= 12 and sum(1 for w in words if w.isupper())/max(1,len(words)) > 0.6:
return True
return False
wc = [len(p.split()) for p in paragraphs]
good = [ (wc_i >= 40 and not is_metadata_para(p)) for p, wc_i in zip(paragraphs, wc) ]
best_start = best_len = 0
cur_start = cur_len = 0
for i, g in enumerate(good):
if g:
if cur_len == 0:
cur_start = i
cur_len += 1
if cur_len > best_len:
best_len = cur_len
best_start = cur_start
else:
cur_len = 0
if best_len > 0:
body = "\n\n".join(paragraphs[best_start: best_start + best_len])
else:
# final fallback: pick the top N paragraphs by length (they likely contain body content)
top_idxs = sorted(range(len(paragraphs)), key=lambda i: wc[i], reverse=True)[:5]
top_idxs.sort()
body = "\n\n".join(paragraphs[i] for i in top_idxs)
body = re.sub(r'\n{2,}References[\s\S]*$', '', body, flags=re.IGNORECASE)
body = re.sub(r'\n{2,}Bibliography[\s\S]*$', '', body, flags=re.IGNORECASE)
body = re.sub(r'\n{2,}Acknowledg[\s\S]*$', '', body, flags=re.IGNORECASE)
# 4) Clean junk: remove URLs/emails, collapse whitespace
body = re.sub(r'https?://\S+', ' ', body)
body = re.sub(r'\S+@\S+', ' ', body)
body = re.sub(r'\s+', ' ', body).strip()
return body
def _preprocess(self, data: list[dict]) -> list[dict]:
"""
Apply preprocessing steps (e.g., unicode decoding) to raw data.
"""
cleaned_data = []
for record in data:
decoded_text = self._decode_unicode(record["page_content"])
main_body = self._extract_body(decoded_text)
cleaned_data.append(
{
"file_name": record["file_name"],
"page_content": main_body
}
)
return cleaned_data
def load_documents(self) -> list[Document]:
"""
Load and preprocess text files, converting them into
LangChain Document objects.
"""
raw_data = self._load_files()
cleaned_data = self._preprocess(raw_data)
return [
Document(
page_content=item["page_content"],
metadata={"source": item["file_name"]}
)
for item in cleaned_data
]
@staticmethod
def chunk_documents(documents: list[Document],
chunk_size: int = 1000,
chunk_overlap: int = 200) -> list[Document]:
"""
Split documents into smaller chunks for embedding and retrieval.
"""
splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=len
)
return splitter.split_documents(documents)
def build(self) -> tuple[list[Document], list[Document]]:
"""
End-to-end pipeline:
- Load documents
- Chunk them
Returns (chunks, original documents).
"""
documents = self.load_documents()
chunks = self.chunk_documents(documents)
return chunks, documents |