from typing import List, Dict, Optional, Union, Any from dataclasses import dataclass from pathlib import Path import numpy as np from scipy.sparse import csr_matrix from sklearn.cluster import AgglomerativeClustering from sentence_transformers import SentenceTransformer import faiss import pickle import time from loguru import logger from concurrent.futures import ThreadPoolExecutor import threading import uuid @dataclass class Document: """Represents a document in the HQD-RAG system. Attributes: id (str): Unique identifier for the document content (str): Raw text content of the document embedding (Optional[np.ndarray]): Quantum-inspired embedding vector cluster_id (Optional[int]): ID of the cluster this document belongs to """ id: str content: str embedding: Optional[np.ndarray] = None cluster_id: Optional[int] = None class HQDRAG: """ Hierarchical Quantum-Inspired Distributed RAG (HQD-RAG) System A production-grade implementation of the HQD-RAG algorithm for ultra-fast and reliable document retrieval. Uses quantum-inspired embeddings and hierarchical clustering for efficient search. Attributes: embedding_dim (int): Dimension of the quantum-inspired embeddings num_clusters (int): Number of hierarchical clusters similarity_threshold (float): Threshold for quantum similarity matching reliability_threshold (float): Threshold for reliability verification """ def __init__( self, embedding_dim: int = 768, num_clusters: int = 128, similarity_threshold: float = 0.75, reliability_threshold: float = 0.85, model_name: str = "all-MiniLM-L6-v2" ): """Initialize the HQD-RAG system. Args: embedding_dim: Dimension of document embeddings num_clusters: Number of clusters for hierarchical organization similarity_threshold: Minimum similarity score for retrieval reliability_threshold: Minimum reliability score for verification model_name: Name of the sentence transformer model to use """ logger.info(f"Initializing HQD-RAG with {embedding_dim} dimensions") self.embedding_dim = embedding_dim self.num_clusters = num_clusters self.similarity_threshold = similarity_threshold self.reliability_threshold = reliability_threshold # Initialize components self.documents: Dict[str, Document] = {} self.encoder = SentenceTransformer(model_name) self.index = faiss.IndexFlatIP(embedding_dim) # Inner product index self.clustering = AgglomerativeClustering( n_clusters=num_clusters, metric='euclidean', linkage='ward' ) # Thread safety self._lock = threading.Lock() self._executor = ThreadPoolExecutor(max_workers=4) logger.info("HQD-RAG system initialized successfully") def _compute_quantum_embedding(self, text: str) -> np.ndarray: """Compute quantum-inspired embedding for text. Args: text: Input text to embed Returns: Quantum-inspired embedding vector """ # Get base embedding base_embedding = self.encoder.encode([text])[0] # Apply quantum-inspired transformation # Simulate superposition by adding phase components phase = np.exp(2j * np.pi * np.random.random(self.embedding_dim)) quantum_embedding = base_embedding * phase # Normalize to unit length return quantum_embedding / np.linalg.norm(quantum_embedding) def _verify_reliability(self, doc: Document, query_embedding: np.ndarray) -> float: """Verify the reliability of a document match. Args: doc: Document to verify query_embedding: Query embedding vector Returns: Reliability score between 0 and 1 """ if doc.embedding is None: return 0.0 # Compute consistency score consistency = np.abs(np.dot(doc.embedding, query_embedding)) # Add quantum noise resistance check noise = np.random.normal(0, 0.1, self.embedding_dim) noisy_query = query_embedding + noise noisy_query = noisy_query / np.linalg.norm(noisy_query) noise_resistance = np.abs(np.dot(doc.embedding, noisy_query)) return (consistency + noise_resistance) / 2 def add(self, content: str, doc_id: Optional[str] = None) -> str: """Add a document to the system. Args: content: Document text content doc_id: Optional custom document ID Returns: Document ID """ doc_id = doc_id or str(uuid.uuid4()) with self._lock: try: # Compute embedding embedding = self._compute_quantum_embedding(content) # Create document doc = Document( id=doc_id, content=content, embedding=embedding ) # Add to storage self.documents[doc_id] = doc self.index.add(embedding.reshape(1, -1)) # Update clustering self._update_clusters() logger.info(f"Successfully added document {doc_id}") return doc_id except Exception as e: logger.error(f"Error adding document: {str(e)}") raise def query( self, query: str, k: int = 5, return_scores: bool = False ) -> Union[List[str], List[tuple[str, float]]]: """Query the system for relevant documents. Args: query: Query text k: Number of results to return return_scores: Whether to return similarity scores Returns: List of document IDs or (document ID, score) tuples """ try: # Compute query embedding query_embedding = self._compute_quantum_embedding(query) # Search index scores, indices = self.index.search( query_embedding.reshape(1, -1), k * 2 # Get extra results for reliability filtering ) results = [] for score, idx in zip(scores[0], indices[0]): # Get document doc_id = list(self.documents.keys())[idx] doc = self.documents[doc_id] # Verify reliability reliability = self._verify_reliability(doc, query_embedding) if reliability >= self.reliability_threshold: results.append((doc_id, float(score))) if len(results) >= k: break logger.info(f"Query returned {len(results)} results") if return_scores: return results return [doc_id for doc_id, _ in results] except Exception as e: logger.error(f"Error processing query: {str(e)}") raise def update(self, doc_id: str, new_content: str) -> None: """Update an existing document. Args: doc_id: ID of document to update new_content: New document content """ with self._lock: try: if doc_id not in self.documents: raise KeyError(f"Document {doc_id} not found") # Remove old embedding old_doc = self.documents[doc_id] if old_doc.embedding is not None: self.index.remove_ids(np.array([list(self.documents.keys()).index(doc_id)])) # Compute new embedding new_embedding = self._compute_quantum_embedding(new_content) # Update document self.documents[doc_id] = Document( id=doc_id, content=new_content, embedding=new_embedding ) # Add new embedding self.index.add(new_embedding.reshape(1, -1)) # Update clustering self._update_clusters() logger.info(f"Successfully updated document {doc_id}") except Exception as e: logger.error(f"Error updating document: {str(e)}") raise def delete(self, doc_id: str) -> None: """Delete a document from the system. Args: doc_id: ID of document to delete """ with self._lock: try: if doc_id not in self.documents: raise KeyError(f"Document {doc_id} not found") # Remove from index idx = list(self.documents.keys()).index(doc_id) self.index.remove_ids(np.array([idx])) # Remove from storage del self.documents[doc_id] # Update clustering self._update_clusters() logger.info(f"Successfully deleted document {doc_id}") except Exception as e: logger.error(f"Error deleting document: {str(e)}") raise def _update_clusters(self) -> None: """Update hierarchical document clusters.""" if len(self.documents) < 2: return # Get all embeddings embeddings = np.vstack([ doc.embedding for doc in self.documents.values() if doc.embedding is not None ]) # Update clustering clusters = self.clustering.fit_predict(embeddings) # Assign cluster IDs for doc, cluster_id in zip(self.documents.values(), clusters): doc.cluster_id = int(cluster_id) def save(self, path: Union[str, Path]) -> None: """Save the system state to disk. Args: path: Path to save directory """ path = Path(path) path.mkdir(parents=True, exist_ok=True) try: # Save documents with open(path / "documents.pkl", "wb") as f: pickle.dump(self.documents, f) # Save index faiss.write_index(self.index, str(path / "index.faiss")) logger.info(f"Successfully saved system state to {path}") except Exception as e: logger.error(f"Error saving system state: {str(e)}") raise def load(self, path: Union[str, Path]) -> None: """Load the system state from disk. Args: path: Path to save directory """ path = Path(path) try: # Load documents with open(path / "documents.pkl", "rb") as f: self.documents = pickle.load(f) # Load index self.index = faiss.read_index(str(path / "index.faiss")) logger.info(f"Successfully loaded system state from {path}") except Exception as e: logger.error(f"Error loading system state: {str(e)}") raise # Example usage: if __name__ == "__main__": # Configure logging logger.add( "hqd_rag.log", rotation="1 day", retention="1 week", level="INFO" ) # Initialize system rag = HQDRAG() # Add some documents doc_ids = [] docs = [ "The quick brown fox jumps over the lazy dog", "Machine learning is a subset of artificial intelligence", "Python is a popular programming language" ] for doc in docs: doc_id = rag.add(doc) doc_ids.append(doc_id) # Query results = rag.query("What is machine learning?", return_scores=True) print("Query results:", results) # # Update a document # rag.update(doc_ids[0], "The fast brown fox jumps over the sleepy dog") # # Delete a document # rag.delete(doc_ids[-1]) # # Save state # rag.save("hqd_rag_state")