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from rank_bm25 import BM25Okapi
from typing import List, Dict, Any, Tuple
import pickle
import os
from utils.text_processor import VietnameseTextProcessor
from config import Config
from tqdm.auto import tqdm
class BM25Retriever:
"""BM25 retriever for initial document retrieval"""
def __init__(self):
self.text_processor = VietnameseTextProcessor()
self.bm25 = None
self.documents = []
self.tokenized_corpus = []
self.index_file = "bm25_index.pkl"
def build_index(self, documents: List[Dict[str, Any]]):
"""Build BM25 index from documents"""
print("Building BM25 index...")
self.documents = documents
self.tokenized_corpus = []
# Tokenize all documents
for doc in tqdm(documents):
content = doc.get("content", "")
title = doc.get("title", "")
# Combine title and content for better search
full_text = f"{title} {content}"
# Preprocess and tokenize
processed_text = self.text_processor.preprocess_for_search(full_text)
tokens = processed_text.split()
self.tokenized_corpus.append(tokens)
# Build BM25 index
self.bm25 = BM25Okapi(self.tokenized_corpus, b=Config.BM25_B, k1=Config.BM25_K1)
print(f"BM25 index built with {len(self.documents)} documents")
def save_index(self, filepath: str = None):
"""Save BM25 index to file"""
if filepath is None:
filepath = self.index_file
try:
index_data = {
"bm25": self.bm25,
"documents": self.documents,
"tokenized_corpus": self.tokenized_corpus,
}
with open(filepath, "wb") as f:
pickle.dump(index_data, f)
print(f"BM25 index saved to {filepath}")
except Exception as e:
print(f"Error saving BM25 index: {e}")
def load_index(self, filepath: str = None):
"""Load BM25 index from file"""
if filepath is None:
filepath = self.index_file
try:
if not os.path.exists(filepath):
print(f"Index file {filepath} not found")
return False
with open(filepath, "rb") as f:
index_data = pickle.load(f)
self.bm25 = index_data["bm25"]
self.documents = index_data["documents"]
self.tokenized_corpus = index_data["tokenized_corpus"]
print(f"BM25 index loaded from {filepath}")
return True
except UnicodeDecodeError as e:
print(f"Encoding error loading BM25 index: {e}")
print(f"Removing corrupted index file: {filepath}")
try:
os.remove(filepath)
except:
pass
return False
except (pickle.UnpicklingError, EOFError) as e:
print(f"Corrupted BM25 index file: {e}")
print(f"Removing corrupted index file: {filepath}")
try:
os.remove(filepath)
except:
pass
return False
except Exception as e:
print(f"Error loading BM25 index: {e}")
return False
def search(
self, query: str, top_k: int = None
) -> List[Tuple[Dict[str, Any], float]]:
"""Search documents using BM25"""
if top_k is None:
top_k = Config.BM25_TOP_K
if self.bm25 is None:
print("BM25 index not built. Please build index first.")
return []
# Preprocess query
processed_query = self.text_processor.preprocess_for_search(query)
query_tokens = processed_query.split()
if not query_tokens:
return []
# Get BM25 scores
scores = self.bm25.get_scores(query_tokens)
# Get top documents with scores
doc_score_pairs = [
(self.documents[i], scores[i]) for i in range(len(self.documents))
]
# Sort by score (descending)
doc_score_pairs.sort(key=lambda x: x[1], reverse=True)
# Return top k
results = doc_score_pairs[:top_k]
print(f"BM25 search returned {len(results)} results for query: {query}")
return results
def get_relevant_documents(
self, query: str, top_k: int = None
) -> List[Dict[str, Any]]:
"""Get relevant documents using BM25"""
results = self.search(query, top_k)
# Filter by minimum score and enhance with score normalization
filtered_results = []
max_score = max([score for _, score in results]) if results else 0
min_score = min([score for _, score in results]) if results else 0
for doc, score in results:
if score >= min_score:
# Add normalized score for better comparison
doc_with_score = doc.copy()
# doc_with_score['score'] = score
doc_with_score['score'] = float((score - min_score) / (max_score - min_score)) if max_score > 0 and min_score > 0 and max_score != min_score else 0
doc_with_score['retrieval_method'] = 'bm25'
filtered_results.append(doc_with_score)
print(f"BM25 found {len(filtered_results)} relevant documents")
return filtered_results
def search_with_keywords(
self, keywords: List[str], top_k: int = None
) -> List[Tuple[Dict[str, Any], float]]:
"""Search using multiple keywords"""
# Combine keywords into a single query
query = " ".join(keywords)
return self.search(query, top_k)
def get_index_stats(self) -> Dict[str, Any]:
"""Get statistics about the BM25 index"""
if self.bm25 is None:
return {}
return {
"total_documents": len(self.documents),
"total_tokens": sum(len(tokens) for tokens in self.tokenized_corpus),
"average_document_length": sum(
len(tokens) for tokens in self.tokenized_corpus
)
/ len(self.tokenized_corpus)
if self.tokenized_corpus
else 0,
"vocabulary_size": len(
set(token for tokens in self.tokenized_corpus for token in tokens)
),
}
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