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Retrieval-Augmented Generation (RAG) pipeline for enhanced summarization
"""
import logging
from typing import List, Tuple, Optional, Dict, Any
import numpy as np
from sentence_transformers import SentenceTransformer
import faiss
logger = logging.getLogger(__name__)
class DocumentChunker:
"""Intelligently chunk documents while preserving context."""
def __init__(self, chunk_size: int = 512, overlap: int = 100, min_chunk_size: int = 100):
"""
Initialize chunker.
Args:
chunk_size: Target chunk size in characters
overlap: Overlap between chunks for context preservation
min_chunk_size: Minimum chunk size to keep
"""
self.chunk_size = chunk_size
self.overlap = overlap
self.min_chunk_size = min_chunk_size
def chunk_document(self, text: str, preserve_sentences: bool = True) -> List[str]:
"""
Intelligently chunk document.
Args:
text: Document text
preserve_sentences: Keep sentences intact when chunking
Returns:
List of text chunks
"""
if preserve_sentences:
return self._chunk_by_sentences(text)
else:
return self._chunk_by_characters(text)
def _chunk_by_sentences(self, text: str) -> List[str]:
"""Chunk by sentence boundaries."""
sentences = text.split('.')
chunks = []
current_chunk = ""
for sentence in sentences:
sentence = sentence.strip()
if not sentence:
continue
test_chunk = current_chunk + " " + sentence + "."
if len(test_chunk) <= self.chunk_size:
current_chunk = test_chunk.strip()
else:
if len(current_chunk) >= self.min_chunk_size:
chunks.append(current_chunk)
current_chunk = sentence + "."
if len(current_chunk) >= self.min_chunk_size:
chunks.append(current_chunk)
return chunks
def _chunk_by_characters(self, text: str) -> List[str]:
"""Chunk by character count with overlap."""
chunks = []
start = 0
while start < len(text):
end = min(start + self.chunk_size, len(text))
if end < len(text):
last_period = text.rfind('.', start, end)
if last_period > start + self.min_chunk_size:
end = last_period + 1
chunk = text[start:end].strip()
if len(chunk) >= self.min_chunk_size:
chunks.append(chunk)
start = end - self.overlap
return chunks
class EmbeddingGenerator:
"""Generate embeddings for text chunks."""
def __init__(self, model_name: str = 'sentence-transformers/all-MiniLM-L6-v2'):
"""
Initialize embedding generator.
Args:
model_name: HuggingFace model for embeddings (lightweight by default)
"""
self.model_name = model_name
self.model = None
self.embedding_dim = None
self._load_model()
def _load_model(self):
"""Load embedding model."""
try:
logger.info(f"Loading embedding model: {self.model_name}")
self.model = SentenceTransformer(self.model_name)
self.embedding_dim = self.model.get_sentence_embedding_dimension()
logger.info(f"Embedding dimension: {self.embedding_dim}")
except Exception as e:
logger.error(f"Error loading embedding model: {str(e)}")
raise
def generate_embeddings(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
"""
Generate embeddings for texts.
Args:
texts: List of text chunks
batch_size: Batch size for processing
Returns:
Matrix of embeddings (num_texts, embedding_dim)
"""
logger.info(f"Generating embeddings for {len(texts)} chunks")
embeddings = self.model.encode(texts, batch_size=batch_size, show_progress_bar=True)
return np.array(embeddings)
class VectorDatabase:
"""FAISS-based vector database for fast retrieval."""
def __init__(self, embedding_dim: int, index_type: str = 'flat'):
"""
Initialize vector database.
Args:
embedding_dim: Dimension of embeddings
index_type: Type of FAISS index ('flat' for exact, 'ivf' for approximate)
"""
self.embedding_dim = embedding_dim
self.index_type = index_type
self.index = None
self.chunks = []
self._create_index()
def _create_index(self):
"""Create FAISS index."""
if self.index_type == 'flat':
self.index = faiss.IndexFlatL2(self.embedding_dim)
elif self.index_type == 'ivf':
quantizer = faiss.IndexFlatL2(self.embedding_dim)
self.index = faiss.IndexIVFFlat(quantizer, self.embedding_dim, 100)
else:
self.index = faiss.IndexFlatL2(self.embedding_dim)
logger.info(f"Created FAISS index: {self.index_type}")
def add_chunks(self, chunks: List[str], embeddings: np.ndarray):
"""
Add chunks and their embeddings to database.
Args:
chunks: List of text chunks
embeddings: Corresponding embeddings
"""
if len(chunks) != len(embeddings):
raise ValueError("Number of chunks and embeddings must match")
self.chunks = chunks
self.index.add(embeddings.astype(np.float32))
logger.info(f"Added {len(chunks)} chunks to database")
def retrieve(self, query_embedding: np.ndarray, k: int = 5) -> Tuple[List[str], List[float]]:
"""
Retrieve top-K most similar chunks.
Args:
query_embedding: Query embedding vector
k: Number of chunks to retrieve
Returns:
Tuple of (retrieved_chunks, similarity_scores)
"""
query_embedding = query_embedding.astype(np.float32).reshape(1, -1)
distances, indices = self.index.search(query_embedding, min(k, len(self.chunks)))
retrieved_chunks = [self.chunks[i] for i in indices[0]]
similarities = [1 / (1 + d) for d in distances[0]]
return retrieved_chunks, similarities
class RAGPipeline:
"""Complete RAG pipeline for context-aware summarization."""
def __init__(
self,
embedding_model: str = 'sentence-transformers/all-MiniLM-L6-v2',
chunk_size: int = 512,
overlap: int = 100
):
"""
Initialize RAG pipeline.
Args:
embedding_model: Model for generating embeddings
chunk_size: Size of document chunks
overlap: Overlap between chunks
"""
self.chunker = DocumentChunker(chunk_size=chunk_size, overlap=overlap)
self.embedding_generator = EmbeddingGenerator(embedding_model)
self.vector_db = VectorDatabase(self.embedding_generator.embedding_dim)
self.chunks = []
self.embeddings = None
def index_document(self, document: str) -> Dict[str, Any]:
"""
Index a document for retrieval.
Args:
document: Document text
Returns:
Indexing statistics
"""
logger.info("Starting document indexing")
chunks = self.chunker.chunk_document(document)
logger.info(f"Created {len(chunks)} chunks")
embeddings = self.embedding_generator.generate_embeddings(chunks)
self.vector_db.add_chunks(chunks, embeddings)
self.chunks = chunks
self.embeddings = embeddings
return {
'num_chunks': len(chunks),
'embedding_dimension': self.embedding_generator.embedding_dim,
'avg_chunk_length': np.mean([len(c) for c in chunks])
}
def retrieve_context(self, query: str, k: int = 5) -> List[Tuple[str, float]]:
"""
Retrieve relevant chunks for a query.
Args:
query: Query text
k: Number of chunks to retrieve
Returns:
List of (chunk, relevance_score) tuples
"""
query_embedding = self.embedding_generator.model.encode([query])[0]
chunks, scores = self.vector_db.retrieve(query_embedding, k)
return list(zip(chunks, scores))
def merge_context(self, chunks: List[str], weights: Optional[List[float]] = None) -> str:
"""
Merge retrieved chunks while preserving context.
Args:
chunks: List of retrieved chunks
weights: Optional importance weights for each chunk
Returns:
Merged context text
"""
if not chunks:
return ""
if weights is None:
weights = [1.0] * len(chunks)
weighted_chunks = []
for chunk, weight in zip(chunks, weights):
weight_factor = int(weight * 10)
weighted_chunks.extend([chunk] * max(1, weight_factor))
merged = " ".join(chunks)
return merged.strip()
class ContextPreserver:
"""Preserve important context from retrieved chunks."""
IMPORTANT_PATTERNS = {
'method': ['method', 'approach', 'technique', 'algorithm', 'framework'],
'metric': ['metric', 'accuracy', 'precision', 'recall', 'f1', 'score', 'performance'],
'dataset': ['dataset', 'corpus', 'benchmark', 'collection'],
'result': ['result', 'finding', 'conclusion', 'achieve', 'outperform'],
'baseline': ['baseline', 'sota', 'state-of-the-art', 'previous work'],
}
@classmethod
def extract_important_sentences(cls, text: str, category: Optional[str] = None) -> List[str]:
"""
Extract important sentences from text.
Args:
text: Text to analyze
category: Optional category to focus on
Returns:
List of important sentences
"""
sentences = text.split('.')
important = []
patterns = cls.IMPORTANT_PATTERNS.get(category, [])
if category:
patterns = cls.IMPORTANT_PATTERNS.get(category, [])
else:
patterns = []
for p_list in cls.IMPORTANT_PATTERNS.values():
patterns.extend(p_list)
for sentence in sentences:
sentence = sentence.strip()
if not sentence or len(sentence) < 10:
continue
if any(pattern in sentence.lower() for pattern in patterns):
important.append(sentence + ".")
return important
@classmethod
def assign_importance_scores(cls, sentences: List[str]) -> List[float]:
"""
Assign importance scores to sentences.
Args:
sentences: List of sentences
Returns:
List of importance scores (0-1)
"""
scores = []
for sentence in sentences:
score = 0.3
sentence_lower = sentence.lower()
for category, patterns in cls.IMPORTANT_PATTERNS.items():
if any(p in sentence_lower for p in patterns):
score += 0.2
score = min(score, 1.0)
scores.append(score)
return scores
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