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RAG engine module for embeddings and FAISS-based retrieval.
"""
import os
import warnings
from typing import List, Dict, Tuple
import numpy as np
import faiss
import pickle
# Suppress PyTorch internal warnings
warnings.filterwarnings('ignore', category=UserWarning, module='torch')
from sentence_transformers import SentenceTransformer
class RAGEngine:
"""In-memory RAG engine using FAISS for similarity search with persistence."""
def __init__(self, index_path: str = "faiss_index"):
"""Initialize the RAG engine with embedding model."""
self.model = None
self.index = None
self.chunks = [] # Store chunk texts and metadata
self.dimension = 384 # MiniLM-L6-v2 embedding dimension
self.index_path = index_path
# Try to load existing index
self._load_index()
def _load_model(self):
"""Lazy load the embedding model."""
if self.model is None:
print("🔄 Loading embedding model...")
self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
print("✅ Embedding model loaded")
def _initialize_index(self):
"""Initialize FAISS index if not already created."""
if self.index is None:
self._load_model()
self.index = faiss.IndexFlatL2(self.dimension)
def _save_index(self):
"""Save FAISS index and chunks to disk."""
if self.index is not None and self.index.ntotal > 0:
try:
# Create directory if it doesn't exist
os.makedirs(self.index_path, exist_ok=True)
# Save FAISS index
index_file = os.path.join(self.index_path, "index.faiss")
faiss.write_index(self.index, index_file)
# Save chunks metadata
chunks_file = os.path.join(self.index_path, "chunks.pkl")
with open(chunks_file, 'wb') as f:
pickle.dump(self.chunks, f)
print(f"💾 Index saved to {self.index_path}")
except Exception as e:
print(f"❌ Failed to save index: {str(e)}")
def _load_index(self):
"""Load FAISS index and chunks from disk."""
if os.path.exists(self.index_path):
try:
index_file = os.path.join(self.index_path, "index.faiss")
chunks_file = os.path.join(self.index_path, "chunks.pkl")
if os.path.exists(index_file) and os.path.exists(chunks_file):
# Load FAISS index
self.index = faiss.read_index(index_file)
# Load chunks metadata
with open(chunks_file, 'rb') as f:
self.chunks = pickle.load(f)
print(f"✅ Loaded existing index with {len(self.chunks)} chunks")
return True
except Exception as e:
print(f"⚠️ Failed to load existing index: {str(e)}")
print("🔄 Will create new index...")
return False
def add_documents(self, chunks: List[Dict], save_index: bool = True):
"""
Add document chunks to the index.
Args:
chunks: List of dictionaries with 'text', 'source', 'chunk_id' keys
save_index: Whether to save index to disk after adding
"""
if not chunks:
return
self._initialize_index()
# Extract texts for embedding
texts = [chunk['text'] for chunk in chunks]
print(f"🧠 Generating embeddings for {len(texts)} chunks...")
# Generate embeddings
embeddings = self.model.encode(texts, show_progress_bar=True, convert_to_numpy=True)
embeddings = embeddings.astype('float32')
# Add to FAISS index
self.index.add(embeddings)
# Store chunk metadata
self.chunks.extend(chunks)
print(f"✅ Added {len(chunks)} chunks to index")
# Save index to disk
if save_index:
self._save_index()
def search(self, query: str, top_k: int = 5) -> List[Dict]:
"""
Search for similar chunks.
Args:
query: Search query text
top_k: Number of results to return
Returns:
List of dictionaries with 'text', 'source', 'chunk_id', 'score' keys
"""
if self.index is None or self.index.ntotal == 0:
return []
self._load_model()
# Generate query embedding
query_embedding = self.model.encode([query], convert_to_numpy=True).astype('float32')
# Search in FAISS
k = min(top_k, self.index.ntotal)
distances, indices = self.index.search(query_embedding, k)
# Format results
results = []
for i, idx in enumerate(indices[0]):
if idx < len(self.chunks):
chunk_data = self.chunks[idx].copy()
# Convert L2 distance to similarity score (lower distance = higher similarity)
distance = float(distances[0][i])
# Simple similarity: 1 / (1 + distance)
similarity = 1.0 / (1.0 + distance)
chunk_data['score'] = similarity
chunk_data['distance'] = distance
results.append(chunk_data)
return results
def get_chunk_count(self) -> int:
"""Get total number of indexed chunks."""
if self.index is None:
return 0
return self.index.ntotal
def reset(self):
"""Reset the index and clear all chunks."""
self.index = None
self.chunks = []
# Remove saved index files
if os.path.exists(self.index_path):
try:
import shutil
shutil.rmtree(self.index_path)
print("🗑️ Removed saved index files")
except Exception as e:
print(f"⚠️ Failed to remove index files: {str(e)}")
def rebuild_from_data(self, data_dir: str = "data"):
"""
Rebuild the entire index from documents in data directory.
Args:
data_dir: Directory containing documents to index
"""
from .processing import process_documents_from_directory
# Reset current index
self.reset()
# Process documents and build index
try:
chunks = process_documents_from_directory(data_dir)
if chunks:
self.add_documents(chunks)
return len(chunks)
else:
print("⚠️ No documents found to process")
return 0
except Exception as e:
print(f"❌ Failed to rebuild index: {str(e)}")
return 0 |