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import os
import re
from typing import List, Dict, Tuple
import chromadb
from chromadb.utils import embedding_functions
from config import CHUNK_SIZE, CHUNK_OVERLAP, DATABASE_DIR, EMBEDDING_MODEL
class KodeksProcessor:
def __init__(self):
self.client = chromadb.PersistentClient(path=DATABASE_DIR)
try:
self.collection = self.client.get_collection("kodeksy")
except:
self.collection = self.client.create_collection(
name="kodeksy",
embedding_function=embedding_functions.SentenceTransformerEmbeddingFunction(
model_name=EMBEDDING_MODEL
)
)
def extract_metadata(self, text: str) -> Dict:
metadata = {}
dz_u_match = re.search(r'Dz\.U\.(\d{4})\.(\d+)\.(\d+)', text)
if dz_u_match:
metadata['dz_u'] = f"Dz.U.{dz_u_match.group(1)}.{dz_u_match.group(2)}.{dz_u_match.group(3)}"
metadata['rok'] = dz_u_match.group(1)
nazwa_match = re.search(r'USTAWA\s+z dnia(.*?)\n(.*?)\n', text)
if nazwa_match:
metadata['data_ustawy'] = nazwa_match.group(1).strip()
metadata['nazwa'] = nazwa_match.group(2).strip()
return metadata
def split_header_and_content(self, text: str) -> Tuple[str, str]:
parts = text.split("USTAWA", 1)
if len(parts) > 1:
return parts[0], "USTAWA" + parts[1]
return "", text
def process_article(self, article_text: str) -> Dict:
art_num_match = re.match(r'Art\.\s*(\d+)', article_text)
article_num = art_num_match.group(1) if art_num_match else ""
paragraphs = re.findall(r'§\s*(\d+)[.\s]+(.*?)(?=§\s*\d+|$)', article_text, re.DOTALL)
if not paragraphs:
return {
"article_num": article_num,
"content": article_text.strip(),
"has_paragraphs": False
}
return {
"article_num": article_num,
"paragraphs": paragraphs,
"has_paragraphs": True
}
def split_into_chunks(self, text: str, metadata: Dict) -> List[Dict]:
chunks = []
chapters = re.split(r'(Rozdział \d+\n\n[^\\n]+)\n', text)
current_chapter = ""
for i, section in enumerate(chapters):
if section.startswith('Rozdział'):
current_chapter = section.strip()
continue
articles = re.split(r'(Art\.\s*\d+.*?)(?=Art\.\s*\d+|$)', section)
for article in articles:
if not article.strip():
continue
if article.startswith('Art.'):
processed_article = self.process_article(article)
chunk_metadata = {
**metadata,
"chapter": current_chapter,
"article": processed_article["article_num"]
}
if processed_article["has_paragraphs"]:
for par_num, par_content in processed_article["paragraphs"]:
chunks.append({
"text": f"Art. {processed_article['article_num']} § {par_num}. {par_content}",
"metadata": {**chunk_metadata, "paragraph": par_num}
})
else:
chunks.append({
"text": processed_article["content"],
"metadata": chunk_metadata
})
return chunks
def process_file(self, filepath: str) -> None:
print(f"Przetwarzanie pliku: {filepath}")
with open(filepath, 'r', encoding='utf-8') as file:
content = file.read()
header, main_content = self.split_header_and_content(content)
metadata = self.extract_metadata(main_content)
metadata['filename'] = os.path.basename(filepath)
chunks = self.split_into_chunks(main_content, metadata)
for i, chunk in enumerate(chunks):
self.collection.add(
documents=[chunk["text"]],
metadatas=[chunk["metadata"]],
ids=[f"{metadata['filename']}_{chunk['metadata']['article']}_{i}"]
)
print(f"Dodano {len(chunks)} chunków z pliku {metadata['filename']}")
def process_all_files(self, directory: str) -> None:
for filename in os.listdir(directory):
if filename.endswith('.txt'):
filepath = os.path.join(directory, filename)
self.process_file(filepath)
def search(self, query: str, n_results: int = 3) -> Dict:
results = self.collection.query(
query_texts=[query],
n_results=n_results
)
return results |