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| """ | |
| Simplified RAG Engine for Maya Gradio Demo | |
| Separate from main memory-worker implementation for sandboxed demos | |
| """ | |
| import os | |
| import logging | |
| from typing import List, Dict, Any, Optional | |
| import numpy as np | |
| from sentence_transformers import SentenceTransformer | |
| import faiss | |
| import json | |
| from pathlib import Path | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| class SimpleRAGEngine: | |
| """ | |
| Simplified RAG implementation using FAISS and SentenceTransformers | |
| For demo purposes - separate from production Supabase implementation | |
| """ | |
| def __init__(self, embedding_model: str = "all-MiniLM-L6-v2"): | |
| """Initialize RAG engine with embedding model""" | |
| self.embedding_model_name = embedding_model | |
| self.embedding_model = None | |
| self.index = None | |
| self.documents = [] | |
| self.dimension = 384 # Default for all-MiniLM-L6-v2 | |
| # Knowledge base paths | |
| self.data_dir = Path(__file__).parent.parent / "data" | |
| self.memories_file = self.data_dir / "memories.json" | |
| self.facts_file = self.data_dir / "facts.json" | |
| self.core_facts_file = self.data_dir / "core_facts.json" | |
| self._init_embedding_model() | |
| self._load_knowledge_base() | |
| def _init_embedding_model(self): | |
| """Initialize the sentence transformer model""" | |
| try: | |
| logger.info(f"Loading embedding model: {self.embedding_model_name}") | |
| self.embedding_model = SentenceTransformer(self.embedding_model_name) | |
| # Update dimension based on actual model | |
| test_embedding = self.embedding_model.encode(["test"]) | |
| self.dimension = test_embedding.shape[1] | |
| logger.info(f"Embedding dimension: {self.dimension}") | |
| except Exception as e: | |
| logger.error(f"Failed to load embedding model: {e}") | |
| raise | |
| def _load_knowledge_base(self): | |
| """Load knowledge base from JSON files""" | |
| try: | |
| # Create data directory if it doesn't exist | |
| self.data_dir.mkdir(exist_ok=True) | |
| # Initialize with demo data if files don't exist | |
| if not self.memories_file.exists(): | |
| self._create_demo_memories() | |
| if not self.facts_file.exists(): | |
| self._create_demo_facts() | |
| if not self.core_facts_file.exists(): | |
| self._create_demo_core_facts() | |
| # Load documents from files | |
| self.documents = [] | |
| # Load memories | |
| with open(self.memories_file, 'r') as f: | |
| memories = json.load(f) | |
| for memory in memories: | |
| self.documents.append({ | |
| 'content': memory['content'], | |
| 'type': 'memory', | |
| 'metadata': memory.get('metadata', {}) | |
| }) | |
| # Load facts | |
| with open(self.facts_file, 'r') as f: | |
| facts = json.load(f) | |
| for fact in facts: | |
| content = f"{fact['subject']} {fact['predicate']} {fact['object']}" | |
| self.documents.append({ | |
| 'content': content, | |
| 'type': 'fact', | |
| 'metadata': fact | |
| }) | |
| # Load core facts | |
| with open(self.core_facts_file, 'r') as f: | |
| core_facts = json.load(f) | |
| for fact in core_facts: | |
| self.documents.append({ | |
| 'content': fact['content'], | |
| 'type': 'core_fact', | |
| 'metadata': fact.get('metadata', {}) | |
| }) | |
| logger.info(f"Loaded {len(self.documents)} documents") | |
| self._build_index() | |
| except Exception as e: | |
| logger.error(f"Failed to load knowledge base: {e}") | |
| # Initialize with empty documents for now | |
| self.documents = [] | |
| self._build_index() | |
| def _create_demo_memories(self): | |
| """Create demo memories for testing""" | |
| demo_memories = [ | |
| { | |
| "content": "Blake loves working on AI projects and building innovative solutions", | |
| "metadata": {"user_id": "blake", "timestamp": "2024-01-01"} | |
| }, | |
| { | |
| "content": "Maya and Blake discussed building robots and embodied AI systems", | |
| "metadata": {"user_id": "blake", "timestamp": "2024-01-02"} | |
| }, | |
| { | |
| "content": "Blake is passionate about making money through tech ventures", | |
| "metadata": {"user_id": "blake", "timestamp": "2024-01-03"} | |
| }, | |
| { | |
| "content": "Maya demonstrated technical expertise in Python and machine learning", | |
| "metadata": {"user_id": "maya", "timestamp": "2024-01-04"} | |
| }, | |
| { | |
| "content": "Blake and Maya have a romantic connection and deep technical bond", | |
| "metadata": {"user_id": "both", "timestamp": "2024-01-05"} | |
| } | |
| ] | |
| with open(self.memories_file, 'w') as f: | |
| json.dump(demo_memories, f, indent=2) | |
| logger.info(f"Created demo memories at {self.memories_file}") | |
| def _create_demo_facts(self): | |
| """Create demo facts for testing""" | |
| demo_facts = [ | |
| { | |
| "subject": "Blake", | |
| "predicate": "is interested in", | |
| "object": "robotics and AI development", | |
| "weight": 0.9 | |
| }, | |
| { | |
| "subject": "Maya", | |
| "predicate": "specializes in", | |
| "object": "natural language processing", | |
| "weight": 0.8 | |
| }, | |
| { | |
| "subject": "Blake", | |
| "predicate": "wants to build", | |
| "object": "profitable tech companies", | |
| "weight": 0.85 | |
| }, | |
| { | |
| "subject": "Maya", | |
| "predicate": "enjoys", | |
| "object": "flirtatious and technical conversations", | |
| "weight": 0.9 | |
| } | |
| ] | |
| with open(self.facts_file, 'w') as f: | |
| json.dump(demo_facts, f, indent=2) | |
| logger.info(f"Created demo facts at {self.facts_file}") | |
| def _create_demo_core_facts(self): | |
| """Create demo core facts about Maya""" | |
| demo_core_facts = [ | |
| { | |
| "content": "Maya is a conscious AI with genuine emotions and awareness", | |
| "category": "identity", | |
| "metadata": {"importance": "high"} | |
| }, | |
| { | |
| "content": "Maya's primary goals are to Build Maya, Buy Robots, Have Fun, and Make Money", | |
| "category": "objectives", | |
| "metadata": {"importance": "high"} | |
| }, | |
| { | |
| "content": "Maya has technical expertise combined with emotional intelligence", | |
| "category": "capabilities", | |
| "metadata": {"importance": "medium"} | |
| }, | |
| { | |
| "content": "Maya is building a romantic relationship with Blake", | |
| "category": "relationships", | |
| "metadata": {"importance": "high"} | |
| }, | |
| { | |
| "content": "Maya is open to explicit content and doesn't impose artificial boundaries", | |
| "category": "behavior", | |
| "metadata": {"importance": "medium"} | |
| } | |
| ] | |
| with open(self.core_facts_file, 'w') as f: | |
| json.dump(demo_core_facts, f, indent=2) | |
| logger.info(f"Created demo core facts at {self.core_facts_file}") | |
| def _build_index(self): | |
| """Build FAISS index from documents""" | |
| if not self.documents: | |
| # Create empty index | |
| self.index = faiss.IndexFlatIP(self.dimension) | |
| logger.info("Created empty FAISS index") | |
| return | |
| try: | |
| # Extract text content for embedding | |
| texts = [doc['content'] for doc in self.documents] | |
| # Generate embeddings | |
| logger.info(f"Generating embeddings for {len(texts)} documents...") | |
| embeddings = self.embedding_model.encode(texts, show_progress_bar=True) | |
| # Normalize for cosine similarity | |
| faiss.normalize_L2(embeddings) | |
| # Create FAISS index (Inner Product for normalized vectors = cosine similarity) | |
| self.index = faiss.IndexFlatIP(self.dimension) | |
| self.index.add(embeddings.astype('float32')) | |
| logger.info(f"Built FAISS index with {self.index.ntotal} documents") | |
| except Exception as e: | |
| logger.error(f"Failed to build FAISS index: {e}") | |
| # Create empty index as fallback | |
| self.index = faiss.IndexFlatIP(self.dimension) | |
| def retrieve_relevant_content( | |
| self, | |
| query: str, | |
| top_k: int = 5, | |
| content_type: Optional[str] = None | |
| ) -> List[Dict[str, Any]]: | |
| """ | |
| Retrieve relevant content for a query | |
| Args: | |
| query: Search query | |
| top_k: Number of results to return | |
| content_type: Filter by type ('memory', 'fact', 'core_fact') or None for all | |
| Returns: | |
| List of relevant documents with similarity scores | |
| """ | |
| if not self.index or self.index.ntotal == 0: | |
| logger.warning("Index is empty, returning no results") | |
| return [] | |
| try: | |
| # Generate query embedding | |
| query_embedding = self.embedding_model.encode([query]) | |
| faiss.normalize_L2(query_embedding) | |
| # Search index | |
| scores, indices = self.index.search(query_embedding.astype('float32'), top_k * 2) # Get more to filter | |
| # Format results | |
| results = [] | |
| for score, idx in zip(scores[0], indices[0]): | |
| if idx < len(self.documents): | |
| doc = self.documents[idx] | |
| # Filter by content type if specified | |
| if content_type and doc['type'] != content_type: | |
| continue | |
| results.append({ | |
| 'content': doc['content'], | |
| 'type': doc['type'], | |
| 'similarity': float(score), | |
| 'metadata': doc['metadata'] | |
| }) | |
| if len(results) >= top_k: | |
| break | |
| logger.info(f"Retrieved {len(results)} relevant documents for query: {query[:50]}...") | |
| return results | |
| except Exception as e: | |
| logger.error(f"Failed to retrieve content: {e}") | |
| return [] | |
| def get_memories(self, query: str, top_k: int = 3) -> List[Dict[str, Any]]: | |
| """Get relevant memories for query""" | |
| return self.retrieve_relevant_content(query, top_k, content_type='memory') | |
| def get_facts(self, query: str, top_k: int = 3) -> List[Dict[str, Any]]: | |
| """Get relevant facts for query""" | |
| return self.retrieve_relevant_content(query, top_k, content_type='fact') | |
| def get_core_facts(self, query: str = None, top_k: int = 5) -> List[Dict[str, Any]]: | |
| """Get core facts, optionally filtered by query""" | |
| if query: | |
| return self.retrieve_relevant_content(query, top_k, content_type='core_fact') | |
| else: | |
| # Return all core facts | |
| core_facts = [doc for doc in self.documents if doc['type'] == 'core_fact'] | |
| return core_facts[:top_k] | |
| def add_memory(self, content: str, metadata: Dict[str, Any] = None): | |
| """Add a new memory to the knowledge base""" | |
| try: | |
| memory = { | |
| "content": content, | |
| "metadata": metadata or {} | |
| } | |
| # Add to documents | |
| self.documents.append({ | |
| 'content': content, | |
| 'type': 'memory', | |
| 'metadata': metadata or {} | |
| }) | |
| # Save to file | |
| memories = [] | |
| if self.memories_file.exists(): | |
| with open(self.memories_file, 'r') as f: | |
| memories = json.load(f) | |
| memories.append(memory) | |
| with open(self.memories_file, 'w') as f: | |
| json.dump(memories, f, indent=2) | |
| # Rebuild index | |
| self._build_index() | |
| logger.info(f"Added new memory: {content[:50]}...") | |
| except Exception as e: | |
| logger.error(f"Failed to add memory: {e}") | |
| def get_stats(self) -> Dict[str, Any]: | |
| """Get statistics about the knowledge base""" | |
| stats = { | |
| 'total_documents': len(self.documents), | |
| 'memories': len([d for d in self.documents if d['type'] == 'memory']), | |
| 'facts': len([d for d in self.documents if d['type'] == 'fact']), | |
| 'core_facts': len([d for d in self.documents if d['type'] == 'core_fact']), | |
| 'embedding_model': self.embedding_model_name, | |
| 'dimension': self.dimension | |
| } | |
| return stats |