commit 5ede9e2e7efcce0e5d507e89dd8f75f15fd68c69 Author: Kade Heyborne Date: Wed Dec 3 16:54:29 2025 -0700 Initial commit: Phase 1 Dual Manifold Cognitive Architecture - Complete dual manifold memory system (episodic, semantic, persona layers) - Braiding engine with structural gates for optimal learning suggestions - FastAPI backend with comprehensive REST endpoints - Domain directory templates and configuration system - Comprehensive planning documentation for 5-phase development - Integrated UI design specifications for Phase 2 - Fixed linting issues and code quality standards diff --git a/README.md b/README.md new file mode 100644 index 0000000..46807b4 --- /dev/null +++ b/README.md @@ -0,0 +1,93 @@ +# Think Bigger - Advanced Second Brain PKM System + +A comprehensive Personal Knowledge Management system with AI-powered agents, knowledge graphs, and intelligent content processing. + +## Features + +- **Multi-Domain Knowledge Organization**: Structured knowledge domains with consistent templates +- **AI Agent Integration**: Dana language runtime for custom automation +- **Knowledge Graphs**: Neo4j-powered relationship mapping +- **Intelligent Search**: Semantic search with embeddings +- **File System Integration**: Real-time monitoring and processing +- **Extensible Architecture**: Plugin system for custom integrations + +## Quick Start + +1. Install dependencies: +```bash +uv sync +``` + +2. Set up configuration: +```bash +cp .config/think_bigger/config.json config/local.json +# Edit config/local.json with your settings +``` + +3. Run the development server: +```bash +uv run uvicorn src.api.main:app --reload +``` + +## Project Structure + +``` +think_bigger/ +├── src/ +│ ├── core/ # Core system components +│ ├── agents/ # AI agent management +│ ├── processing/ # Content processing pipelines +│ ├── api/ # FastAPI application +│ └── ui/ # Frontend components (future) +├── tests/ # Test suite +├── docs/ # Documentation +├── scripts/ # Utility scripts +└── config/ # Configuration files +``` + +## Development + +### Setup +```bash +# Install dependencies +uv sync + +# Install pre-commit hooks +uv run pre-commit install + +# Run tests +uv run pytest + +# Format code +uv run black src/ +uv run isort src/ +``` + +### Key Components + +- **File Monitor**: Cross-platform file system watching +- **Content Processor**: Document parsing and chunking +- **Embedding Service**: Vector generation for semantic search +- **Knowledge Graph**: Relationship storage and querying +- **Agent Runtime**: Dana language execution environment + +## Configuration + +The system uses a hierarchical configuration system: + +1. `.config/think_bigger/config.json` - Global defaults +2. `config/local.json` - Local overrides +3. Domain-specific configs in each domain's `_meta/` directory + +## Contributing + +This project follows subagent-driven development principles. Use the appropriate agent for your task: + +- `explore`: Codebase analysis and discovery +- `code-task-executor`: Isolated coding tasks +- `testing-agent`: Automated testing +- `documentation-updater`: Documentation maintenance + +## License + +MIT License - see LICENSE file for details. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..45b6395 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,43 @@ +[project] +name = "think-bigger" +version = "0.1.0" +description = "Advanced Second Brain PKM System" +readme = "README.md" +requires-python = ">=3.9" +dependencies = [ + "fastapi>=0.104.0", + "uvicorn>=0.24.0", + "pydantic>=2.5.0", + "watchdog>=3.0.0", + "sentence-transformers>=2.2.0", + "neo4j>=5.0.0", + "python-multipart>=0.0.6", + "aiofiles>=23.0.0", + "loguru>=0.7.0", + "pyyaml>=6.0.0", + "faiss-cpu>=1.7.0", + "rank-bm25>=0.2.0", + "networkx>=3.0.0", + "openai>=1.0.0", + "numpy>=1.24.0", + "scipy>=1.11.0", + "pandas>=2.0.0", + "tiktoken>=0.5.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src"] + +[tool.uv] +dev-dependencies = [ + "pytest>=7.4.0", + "pytest-asyncio>=0.21.0", + "black>=23.0.0", + "isort>=5.12.0", + "mypy>=1.7.0", + "ruff>=0.1.0", +] \ No newline at end of file diff --git a/src/think_bigger/api/__pycache__/main.cpython-312.pyc b/src/think_bigger/api/__pycache__/main.cpython-312.pyc new file mode 100644 index 0000000..9e581c7 Binary files /dev/null and b/src/think_bigger/api/__pycache__/main.cpython-312.pyc differ diff --git a/src/think_bigger/api/__pycache__/routes.cpython-312.pyc b/src/think_bigger/api/__pycache__/routes.cpython-312.pyc new file mode 100644 index 0000000..3441b18 Binary files /dev/null and b/src/think_bigger/api/__pycache__/routes.cpython-312.pyc differ diff --git a/src/think_bigger/api/endpoints/__pycache__/agents.cpython-312.pyc b/src/think_bigger/api/endpoints/__pycache__/agents.cpython-312.pyc new file mode 100644 index 0000000..da6e83e Binary files /dev/null and b/src/think_bigger/api/endpoints/__pycache__/agents.cpython-312.pyc differ diff --git a/src/think_bigger/api/endpoints/__pycache__/files.cpython-312.pyc b/src/think_bigger/api/endpoints/__pycache__/files.cpython-312.pyc new file mode 100644 index 0000000..1ae976c Binary files /dev/null and b/src/think_bigger/api/endpoints/__pycache__/files.cpython-312.pyc differ diff --git a/src/think_bigger/api/endpoints/__pycache__/knowledge.cpython-312.pyc b/src/think_bigger/api/endpoints/__pycache__/knowledge.cpython-312.pyc new file mode 100644 index 0000000..8626db7 Binary files /dev/null and b/src/think_bigger/api/endpoints/__pycache__/knowledge.cpython-312.pyc differ diff --git a/src/think_bigger/api/endpoints/agents.py b/src/think_bigger/api/endpoints/agents.py new file mode 100644 index 0000000..a847d93 --- /dev/null +++ b/src/think_bigger/api/endpoints/agents.py @@ -0,0 +1,26 @@ +"""AI agent endpoints.""" + +from fastapi import APIRouter + +router = APIRouter() + + +@router.get("/") +async def list_agents(): + """List all available agents.""" + # TODO: Implement agent listing + return {"agents": []} + + +@router.post("/{agent_id}/execute") +async def execute_agent(agent_id: str, input_data: dict): + """Execute an AI agent.""" + # TODO: Implement agent execution + return {"agent_id": agent_id, "status": "executing", "input": input_data} + + +@router.get("/dana/status") +async def dana_runtime_status(): + """Get Dana runtime status.""" + # TODO: Implement Dana status checking + return {"status": "ready", "version": "1.0.0"} diff --git a/src/think_bigger/api/endpoints/files.py b/src/think_bigger/api/endpoints/files.py new file mode 100644 index 0000000..aba327d --- /dev/null +++ b/src/think_bigger/api/endpoints/files.py @@ -0,0 +1,26 @@ +"""File system endpoints.""" + +from fastapi import APIRouter, UploadFile, File + +router = APIRouter() + + +@router.get("/domains") +async def list_domains(): + """List all knowledge domains.""" + # TODO: Implement domain listing + return {"domains": []} + + +@router.post("/upload") +async def upload_file(file: UploadFile = File(...)): + """Upload a file to the system.""" + # TODO: Implement file upload + return {"filename": file.filename, "status": "uploaded"} + + +@router.get("/status") +async def file_system_status(): + """Get file system monitoring status.""" + # TODO: Implement status checking + return {"status": "active", "watched_paths": []} diff --git a/src/think_bigger/api/endpoints/knowledge.py b/src/think_bigger/api/endpoints/knowledge.py new file mode 100644 index 0000000..414c191 --- /dev/null +++ b/src/think_bigger/api/endpoints/knowledge.py @@ -0,0 +1,317 @@ +"""Knowledge graph endpoints with dual manifold cognitive architecture.""" + +from typing import List, Optional, Dict, Any +from fastapi import APIRouter, HTTPException, BackgroundTasks +from pydantic import BaseModel + +from think_bigger.core.memory.episodic.episodic_memory import ( + EpisodicMemory, +) +from think_bigger.core.memory.semantic.semantic_distiller import ( + SemanticDistiller, +) +from think_bigger.core.memory.persona.persona_graph import PersonaGraph +from think_bigger.core.memory.manifolds.dual_manifold import DualManifoldEngine +from think_bigger.core.memory.braiding.braiding_engine import BraidingEngine, GateType +from think_bigger.core.config import config_manager + +router = APIRouter() + +# Global instances (in production, these would be dependency injected) +episodic_memory = None +semantic_distiller = None +persona_graph = None +dual_manifold_engine = None +braiding_engine = None + + +def get_memory_instances(): + """Get or create memory system instances.""" + global \ + episodic_memory, \ + semantic_distiller, \ + persona_graph, \ + dual_manifold_engine, \ + braiding_engine + + if episodic_memory is None: + config = config_manager.config + data_dir = config.system.data_directory + + # Initialize embedding model + from sentence_transformers import SentenceTransformer + + embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") + + # Initialize memory systems + episodic_memory = EpisodicMemory(f"{data_dir}/episodic") + semantic_distiller = SemanticDistiller( + f"{data_dir}/semantic", "your-openai-api-key-here" + ) # TODO: Get from config + persona_graph = PersonaGraph(f"{data_dir}/persona") + dual_manifold_engine = DualManifoldEngine( + f"{data_dir}/manifolds", + embedding_model, + "your-openai-api-key-here", # TODO: Get from config + ) + braiding_engine = BraidingEngine( + f"{data_dir}/braiding", + dual_manifold_engine, + "your-openai-api-key-here", # TODO: Get from config + ) + + return ( + episodic_memory, + semantic_distiller, + persona_graph, + dual_manifold_engine, + braiding_engine, + ) + + +# Pydantic models for API +class MemoryEntryModel(BaseModel): + content: str + source_file: str + metadata: Optional[Dict[str, Any]] = None + + +class SearchQuery(BaseModel): + query: str + manifold_type: Optional[str] = "individual" + top_k: Optional[int] = 10 + gate_types: Optional[List[str]] = None + + +class ConceptExtractionRequest(BaseModel): + entries: List[str] # Entry IDs to process + + +@router.get("/graph") +async def get_knowledge_graph(): + """Get the knowledge graph structure.""" + try: + _, _, persona_graph, _, _ = get_memory_instances() + + nodes = [] + for node in persona_graph.nodes.values(): + nodes.append( + { + "id": node.id, + "type": node.type, + "label": node.label, + "properties": node.properties, + } + ) + + edges = [] + for edge in persona_graph.edges: + edges.append( + { + "source": edge.source_id, + "target": edge.target_id, + "relationship": edge.relationship, + "weight": edge.weight, + } + ) + + return {"nodes": nodes, "edges": edges} + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error retrieving graph: {str(e)}") + + +@router.post("/search") +async def search_knowledge(search_query: SearchQuery): + """Search the knowledge base using braided search.""" + try: + _, _, _, _, braiding_engine = get_memory_instances() + + # Convert gate type strings to enum + gate_types = None + if search_query.gate_types: + gate_types = [ + GateType(gt.lower()) + for gt in search_query.gate_types + if gt.lower() in [e.value for e in GateType] + ] + + # Perform braided search + results = await braiding_engine.braid_search( + search_query.query, + search_query.manifold_type, + search_query.top_k, + gate_types, + ) + + # Convert results to dict format + formatted_results = [] + for result in results: + formatted_results.append( + { + "content_id": result.content_id, + "content": result.content, + "alpha_score": result.alpha_score, + "beta_score": result.beta_score, + "combined_score": result.combined_score, + "gate_type": result.gate_type.value, + "confidence": result.confidence, + "hallucination_risk": result.hallucination_risk, + "metadata": result.metadata, + } + ) + + return { + "query": search_query.query, + "results": formatted_results, + "total_results": len(formatted_results), + } + + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Error searching knowledge: {str(e)}" + ) + + +@router.post("/entries") +async def add_memory_entry(entry: MemoryEntryModel): + """Add a new memory entry to the system.""" + try: + episodic_memory, _, _, dual_manifold_engine, _ = get_memory_instances() + + # Add to episodic memory + entry_id = episodic_memory.add_entry( + entry.content, entry.source_file, entry.metadata + ) + + # Process through dual manifold + memory_entry = episodic_memory.get_entry_by_id(entry_id) + if memory_entry: + processing_results = dual_manifold_engine.process_memory_entries( + [memory_entry] + ) + + return { + "entry_id": entry_id, + "status": "added", + "processing_results": processing_results, + } + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error adding entry: {str(e)}") + + +@router.post("/concepts/extract") +async def extract_concepts( + request: ConceptExtractionRequest, background_tasks: BackgroundTasks +): + """Extract semantic concepts from memory entries.""" + try: + episodic_memory, semantic_distiller, _, _, _ = get_memory_instances() + + # Get the requested entries + entries = [] + for entry_id in request.entries: + entry = episodic_memory.get_entry_by_id(entry_id) + if entry: + entries.append(entry) + + if not entries: + raise HTTPException(status_code=404, detail="No valid entries found") + + # Extract concepts (run in background for heavy processing) + background_tasks.add_task(semantic_distiller.distill_entries, entries) + + return { + "status": "processing", + "entries_processed": len(entries), + "message": "Concept extraction started in background", + } + + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Error extracting concepts: {str(e)}" + ) + + +@router.get("/concepts") +async def get_concepts(): + """Get all extracted semantic concepts.""" + try: + _, semantic_distiller, _, _, _ = get_memory_instances() + + concepts = [] + for concept in semantic_distiller.concepts.values(): + concepts.append( + { + "id": concept.id, + "name": concept.name, + "description": concept.description, + "confidence": concept.confidence, + "related_entries": concept.related_entries, + } + ) + + return {"concepts": concepts, "total": len(concepts)} + + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Error retrieving concepts: {str(e)}" + ) + + +@router.get("/manifolds/stats") +async def get_manifold_stats(): + """Get statistics for the dual manifold system.""" + try: + _, _, _, dual_manifold_engine, braiding_engine = get_memory_instances() + + return { + "dual_manifold": dual_manifold_engine.get_manifold_stats(), + "braiding_engine": braiding_engine.get_gate_stats(), + } + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error retrieving stats: {str(e)}") + + +@router.get("/embeddings/status") +async def embedding_status(): + """Get embedding service status.""" + try: + episodic_memory, _, _, _, _ = get_memory_instances() + + stats = episodic_memory.get_stats() + return { + "status": "ready", + "model": "sentence-transformers/all-MiniLM-L6-v2", + "episodic_memory": stats, + } + + except Exception as e: + return { + "status": "error", + "error": str(e), + "model": "sentence-transformers/all-MiniLM-L6-v2", + } + + +@router.get("/health") +async def memory_system_health(): + """Check the health of the memory system.""" + try: + instances = get_memory_instances() + + return { + "status": "healthy", + "components": { + "episodic_memory": "initialized" if instances[0] else "failed", + "semantic_distiller": "initialized" if instances[1] else "failed", + "persona_graph": "initialized" if instances[2] else "failed", + "dual_manifold": "initialized" if instances[3] else "failed", + "braiding_engine": "initialized" if instances[4] else "failed", + }, + } + + except Exception as e: + return {"status": "unhealthy", "error": str(e)} diff --git a/src/think_bigger/api/main.py b/src/think_bigger/api/main.py new file mode 100644 index 0000000..1e3f86a --- /dev/null +++ b/src/think_bigger/api/main.py @@ -0,0 +1,52 @@ +"""Main FastAPI application for Think Bigger.""" + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from think_bigger.core.config import config_manager +from think_bigger.api.routes import api_router + +# Create FastAPI app +app = FastAPI( + title="Think Bigger API", + description="Advanced Second Brain PKM System API", + version="0.1.0", +) + +# Configure CORS +app.add_middleware( + CORSMiddleware, + allow_origins=[ + "http://localhost:3000", + "http://localhost:5173", + ], # React dev servers + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include API routes +app.include_router(api_router, prefix="/api/v1") + + +@app.get("/") +async def root(): + """Root endpoint.""" + return {"message": "Think Bigger API", "version": "0.1.0"} + + +@app.get("/health") +async def health_check(): + """Health check endpoint.""" + config = config_manager.config + return { + "status": "healthy", + "version": config.version, + "data_directory": config.system.data_directory, + } + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/src/think_bigger/api/routes.py b/src/think_bigger/api/routes.py new file mode 100644 index 0000000..ad6704e --- /dev/null +++ b/src/think_bigger/api/routes.py @@ -0,0 +1,14 @@ +"""API routes for Think Bigger.""" + +from fastapi import APIRouter + +from think_bigger.api.endpoints import files, knowledge, agents + +api_router = APIRouter() + +# Include endpoint routers +api_router.include_router(files.router, prefix="/files", tags=["files"]) + +api_router.include_router(knowledge.router, prefix="/knowledge", tags=["knowledge"]) + +api_router.include_router(agents.router, prefix="/agents", tags=["agents"]) diff --git a/src/think_bigger/core/__pycache__/config.cpython-312.pyc b/src/think_bigger/core/__pycache__/config.cpython-312.pyc new file mode 100644 index 0000000..f62ed16 Binary files /dev/null and b/src/think_bigger/core/__pycache__/config.cpython-312.pyc differ diff --git a/src/think_bigger/core/config.py b/src/think_bigger/core/config.py new file mode 100644 index 0000000..5128d14 --- /dev/null +++ b/src/think_bigger/core/config.py @@ -0,0 +1,131 @@ +"""Configuration management for Think Bigger.""" + +import json +import os +from pathlib import Path +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field + + +class SystemConfig(BaseModel): + """System-level configuration.""" + + data_directory: str = "~/think_bigger_data" + backup_directory: str = "~/think_bigger_backups" + log_level: str = "INFO" + auto_backup: bool = True + backup_frequency: str = "daily" + + +class ProcessingConfig(BaseModel): + """Content processing configuration.""" + + embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2" + chunk_size: int = 512 + overlap: int = 50 + max_file_size: str = "100MB" + supported_formats: list[str] = ["pdf", "md", "txt", "html", "docx"] + + +class UIConfig(BaseModel): + """UI configuration.""" + + theme: str = "dark" + font_size: str = "medium" + sidebar_width: int = 300 + graph_layout: str = "force" + default_view: str = "graph" + + +class AgentConfig(BaseModel): + """AI agent configuration.""" + + enabled: bool = True + max_concurrent: int = 3 + timeout: int = 300 + sandbox: bool = True + + +class IntegrationConfig(BaseModel): + """External integration configuration.""" + + notion: Dict[str, Any] = Field(default_factory=dict) + obsidian: Dict[str, Any] = Field(default_factory=dict) + + +class ThinkBiggerConfig(BaseModel): + """Main configuration model.""" + + version: str = "1.0.0" + system: SystemConfig = Field(default_factory=SystemConfig) + processing: ProcessingConfig = Field(default_factory=ProcessingConfig) + ui: UIConfig = Field(default_factory=UIConfig) + agents: AgentConfig = Field(default_factory=AgentConfig) + integrations: IntegrationConfig = Field(default_factory=IntegrationConfig) + + +class ConfigManager: + """Configuration manager with hierarchical loading.""" + + def __init__(self): + self.config_dir = Path.home() / ".config" / "think_bigger" + self.project_config_dir = Path("config") + self._config: Optional[ThinkBiggerConfig] = None + + def load_config(self) -> ThinkBiggerConfig: + """Load configuration from multiple sources.""" + if self._config is not None: + return self._config + + # Start with defaults + config = ThinkBiggerConfig() + + # Load global config + global_config_path = self.config_dir / "config.json" + if global_config_path.exists(): + global_data = json.loads(global_config_path.read_text()) + config = ThinkBiggerConfig(**global_data) + + # Load local overrides + local_config_path = self.project_config_dir / "local.json" + if local_config_path.exists(): + local_data = json.loads(local_config_path.read_text()) + # Deep merge local config over global + config = self._merge_configs(config, local_data) + + # Expand paths + config.system.data_directory = os.path.expanduser(config.system.data_directory) + config.system.backup_directory = os.path.expanduser( + config.system.backup_directory + ) + + self._config = config + return config + + def _merge_configs( + self, base: ThinkBiggerConfig, override: Dict[str, Any] + ) -> ThinkBiggerConfig: + """Deep merge override config into base config.""" + base_dict = base.model_dump() + self._deep_merge(base_dict, override) + return ThinkBiggerConfig(**base_dict) + + def _deep_merge(self, base: Dict[str, Any], override: Dict[str, Any]) -> None: + """Recursively merge override dict into base dict.""" + for key, value in override.items(): + if key in base and isinstance(base[key], dict) and isinstance(value, dict): + self._deep_merge(base[key], value) + else: + base[key] = value + + @property + def config(self) -> ThinkBiggerConfig: + """Get the loaded configuration.""" + if self._config is None: + return self.load_config() + return self._config + + +# Global config instance +config_manager = ConfigManager() diff --git a/src/think_bigger/core/memory/braiding/__pycache__/braiding_engine.cpython-312.pyc b/src/think_bigger/core/memory/braiding/__pycache__/braiding_engine.cpython-312.pyc new file mode 100644 index 0000000..ecfec30 Binary files /dev/null and b/src/think_bigger/core/memory/braiding/__pycache__/braiding_engine.cpython-312.pyc differ diff --git a/src/think_bigger/core/memory/braiding/braiding_engine.py b/src/think_bigger/core/memory/braiding/braiding_engine.py new file mode 100644 index 0000000..5571de2 --- /dev/null +++ b/src/think_bigger/core/memory/braiding/braiding_engine.py @@ -0,0 +1,501 @@ +"""Braiding engine with structural gates and hallucination filtering.""" + +import json +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Any, Tuple +from dataclasses import dataclass, asdict +from enum import Enum + +from loguru import logger + +from think_bigger.core.memory.manifolds.dual_manifold import DualManifoldEngine + + +class GateType(Enum): + """Types of structural gates in the braiding engine.""" + + SEMANTIC = "semantic" + TEMPORAL = "temporal" + STRUCTURAL = "structural" + CONTEXTUAL = "contextual" + + +@dataclass +class StructuralGate: + """Represents a structural gate in the braiding engine.""" + + id: str + gate_type: GateType + threshold: float + alpha_weight: float # Weight for vector similarity + beta_weight: float # Weight for graph centrality + filters: List[str] # Active filters + created_at: datetime + metadata: Dict[str, Any] + + +@dataclass +class BraidedResult: + """Result from the braiding engine with scoring.""" + + content_id: str + content: str + alpha_score: float # Vector similarity score + beta_score: float # Graph centrality score + combined_score: float + gate_type: GateType + confidence: float + hallucination_risk: float + metadata: Dict[str, Any] + + +class BraidingEngine: + """Braiding engine with structural gates for optimal knowledge suggestions.""" + + def __init__( + self, + data_dir: str, + dual_manifold_engine: DualManifoldEngine, + openai_api_key: str, + ): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + + self.dual_manifold_engine = dual_manifold_engine + self.openai_api_key = openai_api_key + + # Structural gates + self.gates: Dict[str, StructuralGate] = {} + + # Braiding results cache + self.results_cache: Dict[str, List[Dict[str, Any]]] = {} + + # Persistence paths + self.gates_path = self.data_dir / "structural_gates.json" + self.cache_path = self.data_dir / "braiding_cache.json" + + # Load existing data + self._load_persistent_data() + + # Initialize default gates + self._initialize_default_gates() + + def _load_persistent_data(self): + """Load persisted braiding data.""" + try: + # Load gates + if self.gates_path.exists(): + with open(self.gates_path, "r") as f: + gates_data = json.load(f) + for gate_dict in gates_data: + gate = StructuralGate(**gate_dict) + gate.gate_type = GateType(gate.gate_type) + gate.created_at = datetime.fromisoformat( + gate_dict["created_at"] + ) + self.gates[gate.id] = gate + + # Load cache + if self.cache_path.exists(): + with open(self.cache_path, "r") as f: + self.results_cache = json.load(f) + + logger.info(f"Loaded braiding engine with {len(self.gates)} gates") + + except Exception as e: + logger.error(f"Error loading braiding data: {e}") + + def _save_persistent_data(self): + """Save braiding data to disk.""" + try: + # Save gates + gates_data = [] + for gate in self.gates.values(): + gate_dict = asdict(gate) + gate_dict["gate_type"] = gate.gate_type.value + gate_dict["created_at"] = gate.created_at.isoformat() + gates_data.append(gate_dict) + + with open(self.gates_path, "w") as f: + json.dump(gates_data, f, indent=2) + + # Save cache + with open(self.cache_path, "w") as f: + json.dump(self.results_cache, f, indent=2) + + except Exception as e: + logger.error(f"Error saving braiding data: {e}") + + def _initialize_default_gates(self): + """Initialize default structural gates.""" + default_gates = [ + { + "id": "semantic_gate", + "gate_type": GateType.SEMANTIC, + "threshold": 0.7, + "alpha_weight": 0.6, + "beta_weight": 0.4, + "filters": ["hallucination_check", "relevance_filter"], + "metadata": { + "description": "Semantic similarity with concept validation" + }, + }, + { + "id": "temporal_gate", + "gate_type": GateType.TEMPORAL, + "threshold": 0.5, + "alpha_weight": 0.3, + "beta_weight": 0.7, + "filters": ["recency_boost", "temporal_coherence"], + "metadata": { + "description": "Temporal relevance with recency weighting" + }, + }, + { + "id": "structural_gate", + "gate_type": GateType.STRUCTURAL, + "threshold": 0.8, + "alpha_weight": 0.4, + "beta_weight": 0.6, + "filters": ["graph_centrality", "connectivity_filter"], + "metadata": {"description": "Graph structure with centrality measures"}, + }, + { + "id": "contextual_gate", + "gate_type": GateType.CONTEXTUAL, + "threshold": 0.6, + "alpha_weight": 0.5, + "beta_weight": 0.5, + "filters": ["context_awareness", "domain_relevance"], + "metadata": { + "description": "Context-aware filtering with domain knowledge" + }, + }, + ] + + for gate_data in default_gates: + if gate_data["id"] not in self.gates: + gate = StructuralGate( + id=gate_data["id"], + gate_type=gate_data["gate_type"], + threshold=gate_data["threshold"], + alpha_weight=gate_data["alpha_weight"], + beta_weight=gate_data["beta_weight"], + filters=gate_data["filters"], + created_at=datetime.now(), + metadata=gate_data["metadata"], + ) + self.gates[gate.id] = gate + + self._save_persistent_data() + + async def braid_search( + self, + query: str, + manifold_type: str = "individual", + top_k: int = 10, + gate_types: Optional[List[GateType]] = None, + ) -> List[BraidedResult]: + """Perform braided search using structural gates.""" + + # Use cache if available + cache_key = f"{query}_{manifold_type}_{top_k}" + if cache_key in self.results_cache: + return [BraidedResult(**r) for r in self.results_cache[cache_key]] + + # Get base search results from dual manifold + search_results = self.dual_manifold_engine.search_dual_manifold( + query, + manifold_type, + top_k * 2, # Get more results for filtering + ) + + # Apply structural gates + if gate_types is None: + gate_types = list(GateType) + + braided_results = [] + active_gates = [g for g in self.gates.values() if g.gate_type in gate_types] + + for gate in active_gates: + gate_results = await self._apply_gate(query, search_results, gate) + braided_results.extend(gate_results) + + # Remove duplicates and sort by combined score + seen_ids = set() + unique_results = [] + for result in braided_results: + if result.content_id not in seen_ids: + seen_ids.add(result.content_id) + unique_results.append(result) + + unique_results.sort(key=lambda x: x.combined_score, reverse=True) + final_results = unique_results[:top_k] + + # Cache results + self.results_cache[cache_key] = [asdict(r) for r in final_results] + self._save_persistent_data() + + return final_results + + async def _apply_gate( + self, query: str, search_results: Dict[str, Any], gate: StructuralGate + ) -> List[BraidedResult]: + """Apply a structural gate to search results.""" + + results = [] + + for manifold_type, manifold_results in search_results.items(): + for result in manifold_results: + # Calculate alpha score (vector similarity) + alpha_score = self._calculate_alpha_score(result, gate) + + # Calculate beta score (graph/structural measures) + beta_score = self._calculate_beta_score(result, gate, manifold_type) + + # Apply filters + filtered_scores = self._apply_filters( + result, gate, alpha_score, beta_score + ) + + if filtered_scores is None: + continue # Filtered out + + alpha_score, beta_score = filtered_scores + + # Calculate combined score + combined_score = ( + gate.alpha_weight * alpha_score + gate.beta_weight * beta_score + ) + + # Check threshold + if combined_score < gate.threshold: + continue + + # Calculate hallucination risk + hallucination_risk = await self._assess_hallucination_risk( + query, result + ) + + # Calculate confidence + confidence = min(1.0, combined_score * (1 - hallucination_risk)) + + braided_result = BraidedResult( + content_id=result.get( + "point_id", result.get("entry_id", "unknown") + ), + content=result.get("content", ""), + alpha_score=alpha_score, + beta_score=beta_score, + combined_score=combined_score, + gate_type=gate.gate_type, + confidence=confidence, + hallucination_risk=hallucination_risk, + metadata={ + "gate_id": gate.id, + "manifold_type": manifold_type, + "original_result": result, + }, + ) + + results.append(braided_result) + + return results + + def _calculate_alpha_score( + self, result: Dict[str, Any], gate: StructuralGate + ) -> float: + """Calculate alpha score (vector similarity).""" + if gate.gate_type == GateType.SEMANTIC: + return result.get("similarity", 0.5) + elif gate.gate_type == GateType.TEMPORAL: + # Consider recency + timestamp_str = result.get("timestamp", "") + if timestamp_str: + try: + timestamp = datetime.fromisoformat( + timestamp_str.replace("Z", "+00:00") + ) + days_old = (datetime.now() - timestamp).days + recency_score = max( + 0.1, 1.0 / (1.0 + days_old / 30) + ) # Decay over months + return recency_score + except (ValueError, TypeError, AttributeError): + pass + return 0.5 + else: + return result.get("similarity", 0.5) + + def _calculate_beta_score( + self, result: Dict[str, Any], gate: StructuralGate, manifold_type: str + ) -> float: + """Calculate beta score (graph/structural measures).""" + if gate.gate_type == GateType.STRUCTURAL: + # Use centrality measures if available + centrality = result.get("centrality", 0.5) + connectivity = result.get("connectivity", 0.5) + return (centrality + connectivity) / 2 + elif gate.gate_type == GateType.CONTEXTUAL: + # Consider metadata richness and relevance + metadata_score = min(1.0, len(result.get("metadata", {})) / 10) + return metadata_score + else: + return 0.5 + + def _apply_filters( + self, + result: Dict[str, Any], + gate: StructuralGate, + alpha_score: float, + beta_score: float, + ) -> Optional[Tuple[float, float]]: + """Apply active filters to scores.""" + + for filter_name in gate.filters: + if filter_name == "hallucination_check": + # Basic hallucination check based on content coherence + content = result.get("content", "") + if len(content.split()) < 3: # Too short + return None + if content.count("?") > content.count("."): # Too many questions + alpha_score *= 0.5 + + elif filter_name == "relevance_filter": + # Check if content actually relates to the query structure + if not self._check_relevance(result): + return None + + elif filter_name == "recency_boost": + # Already handled in alpha score calculation + pass + + elif filter_name == "temporal_coherence": + # Check temporal consistency + if not self._check_temporal_coherence(result): + beta_score *= 0.7 + + elif filter_name == "graph_centrality": + # Boost based on graph position + centrality = result.get("centrality", 0.5) + beta_score = (beta_score + centrality) / 2 + + elif filter_name == "connectivity_filter": + # Filter based on connectivity + connections = result.get("connections", 0) + if connections < 1: + beta_score *= 0.8 + + return alpha_score, beta_score + + def _check_relevance(self, result: Dict[str, Any]) -> bool: + """Check if result is relevant.""" + content = result.get("content", "").lower() + + # Basic relevance checks + if len(content) < 10: # Too short + return False + + if content.count("lorem ipsum") > 0: # Placeholder content + return False + + return True + + def _check_temporal_coherence(self, result: Dict[str, Any]) -> bool: + """Check temporal coherence of the result.""" + timestamp_str = result.get("timestamp", "") + if not timestamp_str: + return True # No timestamp to check + + try: + timestamp = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00")) + now = datetime.now() + # Check if timestamp is not in the future and not too far in the past + return ( + timestamp <= now and (now - timestamp).days < 365 * 10 + ) # Within 10 years + except (ValueError, TypeError, AttributeError): + return False + + async def _assess_hallucination_risk( + self, query: str, result: Dict[str, Any] + ) -> float: + """Assess hallucination risk using LLM analysis.""" + + content = result.get("content", "") + if len(content) < 50: # Too short for meaningful analysis + return 0.5 + + # Simple heuristic-based assessment (could be enhanced with LLM) + risk_factors = 0 + + # Check for generic/placeholder content + generic_phrases = ["lorem ipsum", "example text", "placeholder", "todo"] + for phrase in generic_phrases: + if phrase in content.lower(): + risk_factors += 0.3 + + # Check for repetitive patterns + words = content.split() + if len(words) > 10: + unique_ratio = len(set(words)) / len(words) + if unique_ratio < 0.3: # Very repetitive + risk_factors += 0.4 + + # Check for nonsensical content + if content.count("!") > 5 or content.count("?") > 5: # Excessive punctuation + risk_factors += 0.2 + + return min(1.0, risk_factors) + + def add_custom_gate( + self, + gate_id: str, + gate_type: GateType, + threshold: float, + alpha_weight: float, + beta_weight: float, + filters: List[str], + metadata: Optional[Dict[str, Any]] = None, + ) -> str: + """Add a custom structural gate.""" + + gate = StructuralGate( + id=gate_id, + gate_type=gate_type, + threshold=threshold, + alpha_weight=alpha_weight, + beta_weight=beta_weight, + filters=filters, + created_at=datetime.now(), + metadata=metadata or {}, + ) + + self.gates[gate_id] = gate + self._save_persistent_data() + + logger.info(f"Added custom gate: {gate_id}") + return gate_id + + def get_gate_stats(self) -> Dict[str, Any]: + """Get statistics about structural gates.""" + gate_types = {} + for gate in self.gates.values(): + gate_type = gate.gate_type.value + if gate_type not in gate_types: + gate_types[gate_type] = 0 + gate_types[gate_type] += 1 + + return { + "total_gates": len(self.gates), + "gate_types": gate_types, + "cache_size": len(self.results_cache), + "data_directory": str(self.data_dir), + } + + def clear_cache(self): + """Clear the braiding results cache.""" + self.results_cache = {} + self._save_persistent_data() + logger.info("Cleared braiding cache") diff --git a/src/think_bigger/core/memory/episodic/__pycache__/episodic_memory.cpython-312.pyc b/src/think_bigger/core/memory/episodic/__pycache__/episodic_memory.cpython-312.pyc new file mode 100644 index 0000000..19ed61b Binary files /dev/null and b/src/think_bigger/core/memory/episodic/__pycache__/episodic_memory.cpython-312.pyc differ diff --git a/src/think_bigger/core/memory/episodic/episodic_memory.py b/src/think_bigger/core/memory/episodic/episodic_memory.py new file mode 100644 index 0000000..199de67 --- /dev/null +++ b/src/think_bigger/core/memory/episodic/episodic_memory.py @@ -0,0 +1,269 @@ +"""Episodic memory layer with hybrid indexing (dense vectors + BM25).""" + +import json +import pickle +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Any +from dataclasses import dataclass, asdict + +import faiss +import numpy as np +from rank_bm25 import BM25Okapi +from sentence_transformers import SentenceTransformer +from loguru import logger + + +@dataclass +class MemoryEntry: + """Represents a single episodic memory entry.""" + + id: str + content: str + timestamp: datetime + source_file: str + metadata: Dict[str, Any] + embedding: Optional[np.ndarray] = None + tokenized_content: Optional[List[str]] = None + + +@dataclass +class SearchResult: + """Search result with hybrid scoring.""" + + entry_id: str + content: str + source_file: str + timestamp: datetime + vector_score: float + bm25_score: float + combined_score: float + metadata: Dict[str, Any] + + +class EpisodicMemory: + """Episodic memory layer with hybrid FAISS + BM25 indexing.""" + + def __init__( + self, + data_dir: str, + embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2", + ): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + + # Initialize embedding model + self.embedding_model = SentenceTransformer(embedding_model) + self.embedding_dim = self.embedding_model.get_sentence_embedding_dimension() + + # Initialize FAISS index + self.vector_index = faiss.IndexFlatIP( + self.embedding_dim + ) # Inner product for cosine similarity + + # BM25 components + self.bm25_index: Optional[BM25Okapi] = None + self.corpus: List[List[str]] = [] + + # Memory storage + self.entries: Dict[str, MemoryEntry] = {} + self.id_to_idx: Dict[str, int] = {} # Maps entry ID to FAISS index + + # Persistence paths + self.entries_path = self.data_dir / "episodic_entries.json" + self.vector_index_path = self.data_dir / "vector_index.faiss" + self.bm25_corpus_path = self.data_dir / "bm25_corpus.pkl" + + # Load existing data + self._load_persistent_data() + + def _load_persistent_data(self): + """Load persisted memory data.""" + try: + # Load entries + if self.entries_path.exists(): + with open(self.entries_path, "r") as f: + entries_data = json.load(f) + for entry_dict in entries_data: + entry = MemoryEntry(**entry_dict) + entry.timestamp = datetime.fromisoformat( + entry_dict["timestamp"] + ) + if entry.embedding is not None: + entry.embedding = np.array(entry.embedding) + self.entries[entry.id] = entry + + # Load FAISS index + if self.vector_index_path.exists(): + self.vector_index = faiss.read_index(str(self.vector_index_path)) + + # Load BM25 corpus + if self.bm25_corpus_path.exists(): + with open(self.bm25_corpus_path, "rb") as f: + self.corpus = pickle.load(f) + self.bm25_index = BM25Okapi(self.corpus) + + # Rebuild ID to index mapping + self.id_to_idx = { + entry_id: idx for idx, entry_id in enumerate(self.entries.keys()) + } + + logger.info(f"Loaded {len(self.entries)} episodic memory entries") + + except Exception as e: + logger.error(f"Error loading persistent data: {e}") + # Reset to empty state + self.entries = {} + self.id_to_idx = {} + self.vector_index = faiss.IndexFlatIP(self.embedding_dim) + + def _save_persistent_data(self): + """Save memory data to disk.""" + try: + # Save entries + entries_data = [] + for entry in self.entries.values(): + entry_dict = asdict(entry) + entry_dict["timestamp"] = entry.timestamp.isoformat() + if entry.embedding is not None: + entry_dict["embedding"] = entry.embedding.tolist() + entries_data.append(entry_dict) + + with open(self.entries_path, "w") as f: + json.dump(entries_data, f, indent=2) + + # Save FAISS index + faiss.write_index(self.vector_index, str(self.vector_index_path)) + + # Save BM25 corpus + with open(self.bm25_corpus_path, "wb") as f: + pickle.dump(self.corpus, f) + + except Exception as e: + logger.error(f"Error saving persistent data: {e}") + + def add_entry( + self, content: str, source_file: str, metadata: Optional[Dict[str, Any]] = None + ) -> str: + """Add a new memory entry.""" + entry_id = f"{source_file}_{datetime.now().isoformat()}" + + # Create embedding + embedding = self.embedding_model.encode(content, convert_to_numpy=True) + embedding = embedding / np.linalg.norm( + embedding + ) # Normalize for cosine similarity + + # Tokenize for BM25 + tokenized = content.lower().split() # Simple tokenization + + # Create entry + entry = MemoryEntry( + id=entry_id, + content=content, + timestamp=datetime.now(), + source_file=source_file, + metadata=metadata or {}, + embedding=embedding, + tokenized_content=tokenized, + ) + + # Add to storage + self.entries[entry_id] = entry + self.id_to_idx[entry_id] = len(self.entries) - 1 + + # Update vector index + self.vector_index.add(embedding.reshape(1, -1)) + + # Update BM25 index + self.corpus.append(tokenized) + self.bm25_index = BM25Okapi(self.corpus) + + # Persist changes + self._save_persistent_data() + + logger.info(f"Added episodic memory entry: {entry_id}") + return entry_id + + def hybrid_search( + self, query: str, top_k: int = 10, alpha: float = 0.5 + ) -> List[SearchResult]: + """Perform hybrid search combining vector and BM25 scores.""" + if not self.entries: + return [] + + # Vector search + query_embedding = self.embedding_model.encode(query, convert_to_numpy=True) + query_embedding = query_embedding / np.linalg.norm(query_embedding) + + vector_scores, vector_indices = self.vector_index.search( + query_embedding.reshape(1, -1), min(top_k * 2, len(self.entries)) + ) + + # BM25 search + query_tokens = query.lower().split() + bm25_scores = ( + self.bm25_index.get_scores(query_tokens) + if self.bm25_index + else np.zeros(len(self.entries)) + ) + + # Combine results + results = [] + entry_ids = list(self.entries.keys()) + + for idx, (vector_score, bm25_score) in enumerate( + zip(vector_scores[0], bm25_scores) + ): + if idx >= len(entry_ids): + break + + entry_id = entry_ids[idx] + entry = self.entries[entry_id] + + # Normalize scores (vector scores are already in [0,1] for cosine, BM25 can be > 1) + normalized_bm25 = ( + bm25_score / max(bm25_scores) if max(bm25_scores) > 0 else 0 + ) + + # Combined score with alpha weighting + combined_score = alpha * vector_score + (1 - alpha) * normalized_bm25 + + results.append( + SearchResult( + entry_id=entry_id, + content=entry.content, + source_file=entry.source_file, + timestamp=entry.timestamp, + vector_score=float(vector_score), + bm25_score=float(normalized_bm25), + combined_score=float(combined_score), + metadata=entry.metadata, + ) + ) + + # Sort by combined score and return top_k + results.sort(key=lambda x: x.combined_score, reverse=True) + return results[:top_k] + + def get_recent_entries(self, limit: int = 50) -> List[MemoryEntry]: + """Get most recent memory entries.""" + sorted_entries = sorted( + self.entries.values(), key=lambda x: x.timestamp, reverse=True + ) + return sorted_entries[:limit] + + def get_entry_by_id(self, entry_id: str) -> Optional[MemoryEntry]: + """Get a specific memory entry by ID.""" + return self.entries.get(entry_id) + + def get_stats(self) -> Dict[str, Any]: + """Get memory statistics.""" + return { + "total_entries": len(self.entries), + "vector_dimension": self.embedding_dim, + "data_directory": str(self.data_dir), + "last_updated": max( + (e.timestamp for e in self.entries.values()), default=None + ), + } diff --git a/src/think_bigger/core/memory/manifolds/__pycache__/dual_manifold.cpython-312.pyc b/src/think_bigger/core/memory/manifolds/__pycache__/dual_manifold.cpython-312.pyc new file mode 100644 index 0000000..966ec87 Binary files /dev/null and b/src/think_bigger/core/memory/manifolds/__pycache__/dual_manifold.cpython-312.pyc differ diff --git a/src/think_bigger/core/memory/manifolds/dual_manifold.py b/src/think_bigger/core/memory/manifolds/dual_manifold.py new file mode 100644 index 0000000..314169c --- /dev/null +++ b/src/think_bigger/core/memory/manifolds/dual_manifold.py @@ -0,0 +1,607 @@ +"""Dual manifold construction for individual and collective knowledge representation.""" + +import json +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Any, Tuple +from dataclasses import dataclass, asdict +from collections import defaultdict + +import numpy as np +import networkx as nx +from loguru import logger + +from think_bigger.core.memory.episodic.episodic_memory import MemoryEntry +from think_bigger.core.memory.semantic.semantic_distiller import ( + SemanticConcept, +) + + +@dataclass +class ManifoldPoint: + """Represents a point in the knowledge manifold.""" + + id: str + coordinates: np.ndarray # High-dimensional coordinates + content: str + metadata: Dict[str, Any] + created_at: datetime + + +@dataclass +class ManifoldConnection: + """Represents a connection between manifold points.""" + + source_id: str + target_id: str + strength: float + connection_type: str # 'semantic', 'temporal', 'structural' + metadata: Dict[str, Any] + + +@dataclass +class GravityWell: + """Represents a gravity well in the manifold.""" + + center_point_id: str + radius: float + mass: float + influenced_points: List[str] + well_type: str # 'individual', 'collective' + created_at: datetime + + +class IndividualManifold: + """Individual knowledge manifold for personal knowledge representation.""" + + def __init__(self, data_dir: str, embedding_model): + self.data_dir = Path(data_dir) / "individual" + self.data_dir.mkdir(parents=True, exist_ok=True) + + self.embedding_model = embedding_model + + # Manifold components + self.points: Dict[str, ManifoldPoint] = {} + self.connections: List[ManifoldConnection] = [] + self.gravity_wells: Dict[str, GravityWell] = {} + + # NetworkX graph for manifold structure + self.manifold_graph: nx.Graph = nx.Graph() + + # Persistence paths + self.points_path = self.data_dir / "manifold_points.json" + self.connections_path = self.data_dir / "manifold_connections.json" + self.wells_path = self.data_dir / "gravity_wells.json" + + # Load existing data + self._load_persistent_data() + + def _load_persistent_data(self): + """Load persisted manifold data.""" + try: + # Load points + if self.points_path.exists(): + with open(self.points_path, "r") as f: + points_data = json.load(f) + for point_dict in points_data: + point = ManifoldPoint(**point_dict) + point.coordinates = np.array(point.coordinates) + point.created_at = datetime.fromisoformat( + point_dict["created_at"] + ) + self.points[point.id] = point + self.manifold_graph.add_node(point.id, **asdict(point)) + + # Load connections + if self.connections_path.exists(): + with open(self.connections_path, "r") as f: + connections_data = json.load(f) + for conn_dict in connections_data: + connection = ManifoldConnection(**conn_dict) + self.connections.append(connection) + self.manifold_graph.add_edge( + connection.source_id, + connection.target_id, + weight=connection.strength, + type=connection.connection_type, + **connection.metadata, + ) + + # Load gravity wells + if self.wells_path.exists(): + with open(self.wells_path, "r") as f: + wells_data = json.load(f) + for well_dict in wells_data: + well = GravityWell(**well_dict) + well.created_at = datetime.fromisoformat( + well_dict["created_at"] + ) + self.gravity_wells[well.center_point_id] = well + + logger.info( + f"Loaded individual manifold with {len(self.points)} points, {len(self.connections)} connections" + ) + + except Exception as e: + logger.error(f"Error loading manifold data: {e}") + + def _save_persistent_data(self): + """Save manifold data to disk.""" + try: + # Save points + points_data = [] + for point in self.points.values(): + point_dict = asdict(point) + point_dict["coordinates"] = point.coordinates.tolist() + point_dict["created_at"] = point.created_at.isoformat() + points_data.append(point_dict) + + with open(self.points_path, "w") as f: + json.dump(points_data, f, indent=2) + + # Save connections + connections_data = [asdict(conn) for conn in self.connections] + with open(self.connections_path, "w") as f: + json.dump(connections_data, f, indent=2) + + # Save gravity wells + wells_data = [] + for well in self.gravity_wells.values(): + well_dict = asdict(well) + well_dict["created_at"] = well.created_at.isoformat() + wells_data.append(well_dict) + + with open(self.wells_path, "w") as f: + json.dump(wells_data, f, indent=2) + + except Exception as e: + logger.error(f"Error saving manifold data: {e}") + + def add_memory_entry(self, entry: MemoryEntry) -> str: + """Add a memory entry as a point in the manifold.""" + # Create embedding for the entry + coordinates = self.embedding_model.encode(entry.content, convert_to_numpy=True) + + point_id = f"point_{entry.id}" + + point = ManifoldPoint( + id=point_id, + coordinates=coordinates, + content=entry.content, + metadata={ + "entry_id": entry.id, + "source_file": entry.source_file, + "timestamp": entry.timestamp.isoformat(), + "type": "memory_entry", + }, + created_at=datetime.now(), + ) + + self.points[point_id] = point + self.manifold_graph.add_node(point_id, **asdict(point)) + + self._save_persistent_data() + return point_id + + def add_semantic_concept(self, concept: SemanticConcept) -> str: + """Add a semantic concept as a point in the manifold.""" + # Create embedding for the concept + coordinates = self.embedding_model.encode( + f"{concept.name}: {concept.description}", convert_to_numpy=True + ) + + point_id = f"concept_{concept.id}" + + point = ManifoldPoint( + id=point_id, + coordinates=coordinates, + content=f"{concept.name}: {concept.description}", + metadata={ + "concept_id": concept.id, + "confidence": concept.confidence, + "related_entries": concept.related_entries, + "type": "semantic_concept", + }, + created_at=datetime.now(), + ) + + self.points[point_id] = point + self.manifold_graph.add_node(point_id, **asdict(point)) + + self._save_persistent_data() + return point_id + + def create_connections( + self, entries: List[MemoryEntry], concepts: List[SemanticConcept] + ) -> None: + """Create connections between points in the manifold.""" + # Connect entries to their related concepts + for concept in concepts: + concept_point_id = f"concept_{concept.id}" + if concept_point_id not in self.points: + continue + + for entry_id in concept.related_entries: + entry_point_id = f"point_{entry_id}" + if entry_point_id in self.points: + # Calculate semantic similarity + concept_coords = self.points[concept_point_id].coordinates + entry_coords = self.points[entry_point_id].coordinates + + similarity = np.dot(concept_coords, entry_coords) / ( + np.linalg.norm(concept_coords) * np.linalg.norm(entry_coords) + ) + + connection = ManifoldConnection( + source_id=concept_point_id, + target_id=entry_point_id, + strength=float(similarity), + connection_type="semantic", + metadata={"confidence": concept.confidence}, + ) + + self.connections.append(connection) + self.manifold_graph.add_edge( + concept_point_id, + entry_point_id, + weight=connection.strength, + type=connection.connection_type, + **connection.metadata, + ) + + # Create temporal connections between entries + sorted_entries = sorted(entries, key=lambda x: x.timestamp) + for i in range(len(sorted_entries) - 1): + current_entry = sorted_entries[i] + next_entry = sorted_entries[i + 1] + + current_point_id = f"point_{current_entry.id}" + next_point_id = f"point_{next_entry.id}" + + if current_point_id in self.points and next_point_id in self.points: + # Temporal proximity weight (inverse of time difference) + time_diff = ( + next_entry.timestamp - current_entry.timestamp + ).total_seconds() + temporal_weight = max( + 0.1, 1.0 / (1.0 + time_diff / 86400) + ) # Decay over days + + connection = ManifoldConnection( + source_id=current_point_id, + target_id=next_point_id, + strength=temporal_weight, + connection_type="temporal", + metadata={"time_diff_seconds": time_diff}, + ) + + self.connections.append(connection) + self.manifold_graph.add_edge( + current_point_id, + next_point_id, + weight=connection.strength, + type=connection.connection_type, + **connection.metadata, + ) + + self._save_persistent_data() + + def compute_gravity_wells(self) -> List[GravityWell]: + """Compute gravity wells based on manifold structure.""" + if not self.points: + return [] + + # Use graph centrality to identify potential centers + try: + centrality = nx.eigenvector_centrality_numpy( + self.manifold_graph, weight="weight" + ) + except (nx.PowerIterationFailedConvergence, ValueError): + centrality = nx.degree_centrality(self.manifold_graph) + + # Sort points by centrality + sorted_points = sorted(centrality.items(), key=lambda x: x[1], reverse=True) + + gravity_wells = [] + for point_id, centrality_score in sorted_points[:5]: # Top 5 centers + if centrality_score > 0.1: # Minimum threshold + # Find influenced points within radius + influenced_points = self._find_nearby_points(point_id, radius=0.8) + + # Calculate mass based on centrality and connections + mass = centrality_score * (1 + len(influenced_points) * 0.1) + + well = GravityWell( + center_point_id=point_id, + radius=0.8, + mass=mass, + influenced_points=influenced_points, + well_type="individual", + created_at=datetime.now(), + ) + + gravity_wells.append(well) + self.gravity_wells[point_id] = well + + self._save_persistent_data() + return gravity_wells + + def _find_nearby_points(self, center_point_id: str, radius: float) -> List[str]: + """Find points within a certain distance in the manifold.""" + if center_point_id not in self.points: + return [] + + center_coords = self.points[center_point_id].coordinates + nearby_points = [] + + for point_id, point in self.points.items(): + if point_id == center_point_id: + continue + + distance = np.linalg.norm(point.coordinates - center_coords) + if distance <= radius: + nearby_points.append(point_id) + + return nearby_points + + def manifold_search(self, query: str, top_k: int = 10) -> List[Tuple[str, float]]: + """Search the manifold for similar content.""" + if not self.points: + return [] + + query_coords = self.embedding_model.encode(query, convert_to_numpy=True) + similarities = [] + + for point_id, point in self.points.items(): + similarity = np.dot(query_coords, point.coordinates) / ( + np.linalg.norm(query_coords) * np.linalg.norm(point.coordinates) + ) + similarities.append((point_id, float(similarity))) + + # Sort by similarity + similarities.sort(key=lambda x: x[1], reverse=True) + return similarities[:top_k] + + def get_stats(self) -> Dict[str, Any]: + """Get manifold statistics.""" + return { + "total_points": len(self.points), + "total_connections": len(self.connections), + "total_gravity_wells": len(self.gravity_wells), + "connection_types": self._count_connection_types(), + "point_types": self._count_point_types(), + "is_connected": nx.is_connected(self.manifold_graph) + if self.points + else False, + "average_degree": 0.0, # Placeholder - would need proper calculation + } + + def _count_connection_types(self) -> Dict[str, int]: + """Count connections by type.""" + type_counts = defaultdict(int) + for conn in self.connections: + type_counts[conn.connection_type] += 1 + return dict(type_counts) + + def _count_point_types(self) -> Dict[str, int]: + """Count points by type.""" + type_counts = defaultdict(int) + for point in self.points.values(): + point_type = point.metadata.get("type", "unknown") + type_counts[point_type] += 1 + return dict(type_counts) + + +class CollectiveManifold: + """Collective knowledge manifold for shared knowledge representation.""" + + def __init__(self, data_dir: str, openalex_api_key: Optional[str] = None): + self.data_dir = Path(data_dir) / "collective" + self.data_dir.mkdir(parents=True, exist_ok=True) + + self.openalex_api_key = openalex_api_key + + # Collective manifold components + self.points: Dict[str, ManifoldPoint] = {} + self.connections: List[ManifoldConnection] = [] + self.gravity_wells: Dict[str, GravityWell] = {} + + # NetworkX graph for collective structure + self.manifold_graph: nx.Graph = nx.Graph() + + # External knowledge sources + self.external_sources = { + "openalex": self._integrate_openalex, + "wikipedia": self._integrate_wikipedia, + "arxiv": self._integrate_arxiv, + } + + def integrate_external_knowledge( + self, topic: str, source: str = "openalex" + ) -> List[str]: + """Integrate knowledge from external sources into the collective manifold.""" + if source not in self.external_sources: + logger.warning(f"Unknown external source: {source}") + return [] + + integrator = self.external_sources[source] + return integrator(topic) + + def _integrate_openalex(self, topic: str) -> List[str]: + """Integrate knowledge from OpenAlex API.""" + # Placeholder for OpenAlex integration + # This would make API calls to OpenAlex to get research papers, concepts, etc. + logger.info(f"Integrating OpenAlex knowledge for topic: {topic}") + + # For now, return empty list - would need actual API implementation + return [] + + def _integrate_wikipedia(self, topic: str) -> List[str]: + """Integrate knowledge from Wikipedia.""" + # Placeholder for Wikipedia integration + logger.info(f"Integrating Wikipedia knowledge for topic: {topic}") + return [] + + def _integrate_arxiv(self, topic: str) -> List[str]: + """Integrate knowledge from arXiv.""" + # Placeholder for arXiv integration + logger.info(f"Integrating arXiv knowledge for topic: {topic}") + return [] + + def merge_individual_manifold( + self, individual_manifold: IndividualManifold, merge_threshold: float = 0.8 + ) -> None: + """Merge an individual manifold into the collective manifold.""" + # Add points that don't exist or are significantly different + for point_id, point in individual_manifold.points.items(): + if point_id not in self.points: + self.points[point_id] = point + self.manifold_graph.add_node(point_id, **asdict(point)) + else: + # Check if points are similar enough to merge + existing_coords = self.points[point_id].coordinates + similarity = np.dot(point.coordinates, existing_coords) / ( + np.linalg.norm(point.coordinates) * np.linalg.norm(existing_coords) + ) + + if similarity < merge_threshold: + # Create new point with combined information + combined_coords = (point.coordinates + existing_coords) / 2 + combined_point = ManifoldPoint( + id=f"{point_id}_merged", + coordinates=combined_coords, + content=f"{point.content} | {self.points[point_id].content}", + metadata={**point.metadata, **self.points[point_id].metadata}, + created_at=datetime.now(), + ) + self.points[f"{point_id}_merged"] = combined_point + self.manifold_graph.add_node( + f"{point_id}_merged", **asdict(combined_point) + ) + + # Add connections + for connection in individual_manifold.connections: + if ( + connection.source_id in self.points + and connection.target_id in self.points + ): + self.connections.append(connection) + self.manifold_graph.add_edge( + connection.source_id, + connection.target_id, + weight=connection.strength, + type=connection.connection_type, + **connection.metadata, + ) + + def get_stats(self) -> Dict[str, Any]: + """Get collective manifold statistics.""" + return { + "total_points": len(self.points), + "total_connections": len(self.connections), + "total_gravity_wells": len(self.gravity_wells), + "external_sources": list(self.external_sources.keys()), + "is_connected": nx.is_connected(self.manifold_graph) + if self.points + else False, + } + + +class DualManifoldEngine: + """Engine for managing dual manifold construction and operations.""" + + def __init__( + self, + data_dir: str, + embedding_model, + openai_api_key: str, + openalex_api_key: Optional[str] = None, + ): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + + self.individual_manifold = IndividualManifold( + str(self.data_dir), embedding_model + ) + self.collective_manifold = CollectiveManifold( + str(self.data_dir), openalex_api_key + ) + + # Semantic distiller for concept extraction + from think_bigger.core.memory.semantic.semantic_distiller import ( + SemanticDistiller, + ) + + self.semantic_distiller = SemanticDistiller( + str(self.data_dir / "semantic"), openai_api_key + ) + + async def process_memory_entries( + self, entries: List[MemoryEntry] + ) -> Dict[str, Any]: + """Process memory entries through the dual manifold pipeline.""" + results = { + "individual_points_added": 0, + "concepts_extracted": 0, + "connections_created": 0, + "gravity_wells_computed": 0, + } + + # Add entries to individual manifold + for entry in entries: + self.individual_manifold.add_memory_entry(entry) + results["individual_points_added"] += 1 + + # Extract semantic concepts + concepts = await self.semantic_distiller.distill_entries(entries) + results["concepts_extracted"] = len(concepts) + + # Add concepts to individual manifold + for concept in concepts: + self.individual_manifold.add_semantic_concept(concept) + + # Create connections in individual manifold + self.individual_manifold.create_connections(entries, concepts) + results["connections_created"] = len(self.individual_manifold.connections) + + # Compute gravity wells + gravity_wells = self.individual_manifold.compute_gravity_wells() + results["gravity_wells_computed"] = len(gravity_wells) + + # Optionally merge into collective manifold + self.collective_manifold.merge_individual_manifold(self.individual_manifold) + + return results + + def search_dual_manifold( + self, query: str, manifold_type: str = "individual", top_k: int = 10 + ) -> Dict[str, Any]: + """Search across the dual manifold system.""" + results = {} + + if manifold_type in ["individual", "both"]: + individual_results = self.individual_manifold.manifold_search(query, top_k) + results["individual"] = [ + { + "point_id": point_id, + "similarity": similarity, + "content": self.individual_manifold.points[point_id].content[:200] + + "...", + "metadata": self.individual_manifold.points[point_id].metadata, + } + for point_id, similarity in individual_results + ] + + if manifold_type in ["collective", "both"]: + # Collective search would be more complex, involving external sources + results["collective"] = [] + + return results + + def get_manifold_stats(self) -> Dict[str, Any]: + """Get statistics for both manifolds.""" + return { + "individual": self.individual_manifold.get_stats(), + "collective": self.collective_manifold.get_stats(), + "semantic": self.semantic_distiller.get_stats(), + } diff --git a/src/think_bigger/core/memory/persona/__pycache__/persona_graph.cpython-312.pyc b/src/think_bigger/core/memory/persona/__pycache__/persona_graph.cpython-312.pyc new file mode 100644 index 0000000..65dca3f Binary files /dev/null and b/src/think_bigger/core/memory/persona/__pycache__/persona_graph.cpython-312.pyc differ diff --git a/src/think_bigger/core/memory/persona/persona_graph.py b/src/think_bigger/core/memory/persona/persona_graph.py new file mode 100644 index 0000000..e72e229 --- /dev/null +++ b/src/think_bigger/core/memory/persona/persona_graph.py @@ -0,0 +1,454 @@ +"""Persona layer with knowledge graphs and centrality measures.""" + +import json +import networkx as nx +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Any, Tuple +from dataclasses import dataclass, asdict +from collections import defaultdict + +from loguru import logger + +from think_bigger.core.memory.episodic.episodic_memory import MemoryEntry +from think_bigger.core.memory.semantic.semantic_distiller import SemanticConcept + + +@dataclass +class KnowledgeNode: + """Represents a node in the knowledge graph.""" + + id: str + type: str # 'concept', 'entry', 'entity', 'topic' + label: str + properties: Dict[str, Any] + created_at: datetime + updated_at: datetime + + +@dataclass +class KnowledgeEdge: + """Represents an edge in the knowledge graph.""" + + source_id: str + target_id: str + relationship: str + weight: float + properties: Dict[str, Any] + created_at: datetime + + +@dataclass +class GravityWell: + """Represents a gravity well in the knowledge graph.""" + + center_node_id: str + radius: float + mass: float # Based on centrality and connectivity + influenced_nodes: List[str] + created_at: datetime + + +class PersonaGraph: + """Knowledge graph for persona layer with centrality measures and gravity wells.""" + + def __init__(self, data_dir: str): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + + # NetworkX graph + self.graph: nx.DiGraph = nx.DiGraph() + + # Storage + self.nodes: Dict[str, KnowledgeNode] = {} + self.edges: List[KnowledgeEdge] = [] + self.gravity_wells: Dict[str, GravityWell] = {} + + # Persistence paths + self.graph_path = self.data_dir / "persona_graph.graphml" + self.nodes_path = self.data_dir / "persona_nodes.json" + self.edges_path = self.data_dir / "persona_edges.json" + self.wells_path = self.data_dir / "gravity_wells.json" + + # Load existing data + self._load_persistent_data() + + def _load_persistent_data(self): + """Load persisted graph data.""" + try: + # Load nodes + if self.nodes_path.exists(): + with open(self.nodes_path, "r") as f: + nodes_data = json.load(f) + for node_dict in nodes_data: + node = KnowledgeNode(**node_dict) + node.created_at = datetime.fromisoformat( + node_dict["created_at"] + ) + node.updated_at = datetime.fromisoformat( + node_dict["updated_at"] + ) + self.nodes[node.id] = node + self.graph.add_node(node.id, **asdict(node)) + + # Load edges + if self.edges_path.exists(): + with open(self.edges_path, "r") as f: + edges_data = json.load(f) + for edge_dict in edges_data: + edge = KnowledgeEdge(**edge_dict) + edge.created_at = datetime.fromisoformat( + edge_dict["created_at"] + ) + self.edges.append(edge) + self.graph.add_edge( + edge.source_id, + edge.target_id, + relationship=edge.relationship, + weight=edge.weight, + **edge.properties, + ) + + # Load gravity wells + if self.wells_path.exists(): + with open(self.wells_path, "r") as f: + wells_data = json.load(f) + for well_dict in wells_data: + well = GravityWell(**well_dict) + well.created_at = datetime.fromisoformat( + well_dict["created_at"] + ) + self.gravity_wells[well.center_node_id] = well + + logger.info( + f"Loaded persona graph with {len(self.nodes)} nodes, {len(self.edges)} edges, {len(self.gravity_wells)} gravity wells" + ) + + except Exception as e: + logger.error(f"Error loading graph data: {e}") + # Reset to empty graph + self.graph = nx.DiGraph() + self.nodes = {} + self.edges = [] + self.gravity_wells = {} + + def _save_persistent_data(self): + """Save graph data to disk.""" + try: + # Save nodes + nodes_data = [] + for node in self.nodes.values(): + node_dict = asdict(node) + node_dict["created_at"] = node.created_at.isoformat() + node_dict["updated_at"] = node.updated_at.isoformat() + nodes_data.append(node_dict) + + with open(self.nodes_path, "w") as f: + json.dump(nodes_data, f, indent=2) + + # Save edges + edges_data = [] + for edge in self.edges: + edge_dict = asdict(edge) + edge_dict["created_at"] = edge.created_at.isoformat() + edges_data.append(edge_dict) + + with open(self.edges_path, "w") as f: + json.dump(edges_data, f, indent=2) + + # Save gravity wells + wells_data = [] + for well in self.gravity_wells.values(): + well_dict = asdict(well) + well_dict["created_at"] = well.created_at.isoformat() + wells_data.append(well_dict) + + with open(self.wells_path, "w") as f: + json.dump(wells_data, f, indent=2) + + # Save NetworkX graph + nx.write_graphml(self.graph, self.graph_path) + + except Exception as e: + logger.error(f"Error saving graph data: {e}") + + def add_concept_node(self, concept: SemanticConcept) -> str: + """Add a concept as a node in the knowledge graph.""" + node_id = f"concept_{concept.id}" + + node = KnowledgeNode( + id=node_id, + type="concept", + label=concept.name, + properties={ + "description": concept.description, + "confidence": concept.confidence, + "related_entries": concept.related_entries, + "semantic_id": concept.id, + }, + created_at=datetime.now(), + updated_at=datetime.now(), + ) + + self.nodes[node_id] = node + self.graph.add_node(node_id, **asdict(node)) + + self._save_persistent_data() + return node_id + + def add_entry_node(self, entry: MemoryEntry) -> str: + """Add a memory entry as a node in the knowledge graph.""" + node_id = f"entry_{entry.id}" + + node = KnowledgeNode( + id=node_id, + type="entry", + label=f"Entry from {entry.source_file}", + properties={ + "content": entry.content[:200] + "..." + if len(entry.content) > 200 + else entry.content, + "timestamp": entry.timestamp.isoformat(), + "source_file": entry.source_file, + "entry_id": entry.id, + "metadata": entry.metadata, + }, + created_at=datetime.now(), + updated_at=datetime.now(), + ) + + self.nodes[node_id] = node + self.graph.add_node(node_id, **asdict(node)) + + self._save_persistent_data() + return node_id + + def add_relationship( + self, + source_id: str, + target_id: str, + relationship: str, + weight: float = 1.0, + properties: Optional[Dict[str, Any]] = None, + ) -> None: + """Add a relationship between nodes.""" + if source_id not in self.nodes or target_id not in self.nodes: + logger.warning( + f"Cannot add relationship: nodes {source_id} or {target_id} not found" + ) + return + + edge = KnowledgeEdge( + source_id=source_id, + target_id=target_id, + relationship=relationship, + weight=weight, + properties=properties or {}, + created_at=datetime.now(), + ) + + self.edges.append(edge) + self.graph.add_edge( + source_id, + target_id, + relationship=relationship, + weight=weight, + **(properties or {}), + ) + + self._save_persistent_data() + + def connect_concept_to_entries( + self, concept_node_id: str, entry_node_ids: List[str] + ) -> None: + """Connect a concept node to its related entry nodes.""" + for entry_node_id in entry_node_ids: + if entry_node_id in self.nodes: + self.add_relationship( + concept_node_id, + entry_node_id, + "relates_to", + weight=0.8, + properties={"connection_type": "semantic"}, + ) + + def compute_centrality_measures(self) -> Dict[str, Dict[str, float]]: + """Compute various centrality measures for the graph.""" + centrality_measures = {} + + if len(self.graph.nodes) == 0: + return centrality_measures + + try: + # Degree centrality + degree_centrality = nx.degree_centrality(self.graph) + centrality_measures["degree"] = degree_centrality + + # Betweenness centrality + betweenness_centrality = nx.betweenness_centrality(self.graph) + centrality_measures["betweenness"] = betweenness_centrality + + # Eigenvector centrality + eigenvector_centrality = nx.eigenvector_centrality_numpy(self.graph) + centrality_measures["eigenvector"] = eigenvector_centrality + + # PageRank + pagerank = nx.pagerank(self.graph) + centrality_measures["pagerank"] = pagerank + + except Exception as e: + logger.error(f"Error computing centrality measures: {e}") + + return centrality_measures + + def identify_gravity_wells( + self, centrality_measures: Optional[Dict[str, Dict[str, float]]] = None + ) -> List[GravityWell]: + """Identify gravity wells based on centrality and connectivity.""" + if centrality_measures is None: + centrality_measures = self.compute_centrality_measures() + + gravity_wells = [] + + # Use combined centrality score to identify potential centers + combined_scores = {} + for node_id in self.graph.nodes(): + degree_score = centrality_measures.get("degree", {}).get(node_id, 0) + betweenness_score = centrality_measures.get("betweenness", {}).get( + node_id, 0 + ) + eigenvector_score = centrality_measures.get("eigenvector", {}).get( + node_id, 0 + ) + pagerank_score = centrality_measures.get("pagerank", {}).get(node_id, 0) + + # Weighted combination + combined_score = ( + 0.3 * degree_score + + 0.3 * betweenness_score + + 0.2 * eigenvector_score + + 0.2 * pagerank_score + ) + combined_scores[node_id] = combined_score + + # Sort nodes by combined centrality + sorted_nodes = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True) + + # Create gravity wells for top nodes + for node_id, score in sorted_nodes[:10]: # Top 10 potential centers + if score > 0.1: # Minimum threshold + # Find nodes within "gravitational influence" + influenced_nodes = self._find_influenced_nodes(node_id, max_distance=3) + + # Calculate mass based on centrality and connections + mass = score * (1 + len(influenced_nodes) * 0.1) + + well = GravityWell( + center_node_id=node_id, + radius=2.0, # Fixed radius for now + mass=mass, + influenced_nodes=influenced_nodes, + created_at=datetime.now(), + ) + + gravity_wells.append(well) + self.gravity_wells[node_id] = well + + self._save_persistent_data() + return gravity_wells + + def _find_influenced_nodes( + self, center_node: str, max_distance: int = 3 + ) -> List[str]: + """Find nodes within gravitational influence of a center node.""" + try: + # Get all nodes within max_distance + distances = nx.single_source_shortest_path_length( + self.graph, center_node, cutoff=max_distance + ) + return list(distances.keys())[1:] # Exclude the center node itself + except Exception: + return [] + + def get_node_by_id(self, node_id: str) -> Optional[KnowledgeNode]: + """Get a node by its ID.""" + return self.nodes.get(node_id) + + def get_neighbors( + self, node_id: str, relationship_type: Optional[str] = None + ) -> List[Tuple[str, Dict[str, Any]]]: + """Get neighbors of a node, optionally filtered by relationship type.""" + if node_id not in self.graph: + return [] + + neighbors = [] + for neighbor_id in self.graph.neighbors(node_id): + edge_data = self.graph.get_edge_data(node_id, neighbor_id) + if ( + relationship_type is None + or edge_data.get("relationship") == relationship_type + ): + neighbors.append((neighbor_id, edge_data)) + + return neighbors + + def find_paths_between_concepts( + self, start_concept: str, end_concept: str, max_length: int = 5 + ) -> List[List[str]]: + """Find paths between two concepts in the graph.""" + try: + paths = list( + nx.all_simple_paths( + self.graph, start_concept, end_concept, cutoff=max_length + ) + ) + return paths + except Exception: + return [] + + def get_subgraph_around_node(self, node_id: str, radius: int = 2) -> nx.DiGraph: + """Get a subgraph centered around a node.""" + try: + subgraph_nodes = set() + # Get nodes within radius + distances = nx.single_source_shortest_path_length( + self.graph, node_id, cutoff=radius + ) + subgraph_nodes.update(distances.keys()) + + # Also include nodes that point to this node (incoming edges) + for predecessor in self.graph.predecessors(node_id): + subgraph_nodes.add(predecessor) + + return self.graph.subgraph(subgraph_nodes).copy() + except Exception: + return nx.DiGraph() + + def get_stats(self) -> Dict[str, Any]: + """Get graph statistics.""" + return { + "total_nodes": len(self.nodes), + "total_edges": len(self.edges), + "total_gravity_wells": len(self.gravity_wells), + "node_types": self._count_node_types(), + "edge_types": self._count_edge_types(), + "is_connected": nx.is_weakly_connected(self.graph) if self.graph else False, + "average_degree": sum(dict(self.graph.degree()).values()) / len(self.graph) + if self.graph + else 0, + "data_directory": str(self.data_dir), + } + + def _count_node_types(self) -> Dict[str, int]: + """Count nodes by type.""" + type_counts = defaultdict(int) + for node in self.nodes.values(): + type_counts[node.type] += 1 + return dict(type_counts) + + def _count_edge_types(self) -> Dict[str, int]: + """Count edges by relationship type.""" + type_counts = defaultdict(int) + for edge in self.edges: + type_counts[edge.relationship] += 1 + return dict(type_counts) diff --git a/src/think_bigger/core/memory/semantic/__pycache__/semantic_distiller.cpython-312.pyc b/src/think_bigger/core/memory/semantic/__pycache__/semantic_distiller.cpython-312.pyc new file mode 100644 index 0000000..44e72bb Binary files /dev/null and b/src/think_bigger/core/memory/semantic/__pycache__/semantic_distiller.cpython-312.pyc differ diff --git a/src/think_bigger/core/memory/semantic/semantic_distiller.py b/src/think_bigger/core/memory/semantic/semantic_distiller.py new file mode 100644 index 0000000..de7bb6d --- /dev/null +++ b/src/think_bigger/core/memory/semantic/semantic_distiller.py @@ -0,0 +1,348 @@ +"""Semantic distillation layer for cognitive trajectory analysis.""" + +import json +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Any +from dataclasses import dataclass, asdict + +import openai +from loguru import logger + +from think_bigger.core.memory.episodic.episodic_memory import MemoryEntry + + +@dataclass +class SemanticConcept: + """Represents a distilled semantic concept.""" + + id: str + name: str + description: str + confidence: float + related_entries: List[str] + created_at: datetime + updated_at: datetime + metadata: Dict[str, Any] + + +@dataclass +class CognitiveTrajectory: + """Represents a cognitive trajectory through concepts.""" + + id: str + concepts: List[str] # Concept IDs in chronological order + transitions: List[Dict[str, Any]] # Transition metadata + strength: float + entry_points: List[str] # Entry IDs that form this trajectory + created_at: datetime + + +class SemanticDistiller: + """Semantic distillation using LLM analysis for cognitive trajectories.""" + + def __init__(self, data_dir: str, openai_api_key: str, model: str = "gpt-4"): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + + # OpenAI setup + self.client = openai.OpenAI(api_key=openai_api_key) + self.model = model + + # Storage + self.concepts: Dict[str, SemanticConcept] = {} + self.trajectories: Dict[str, CognitiveTrajectory] = {} + + # Persistence paths + self.concepts_path = self.data_dir / "semantic_concepts.json" + self.trajectories_path = self.data_dir / "cognitive_trajectories.json" + + # Load existing data + self._load_persistent_data() + + def _load_persistent_data(self): + """Load persisted semantic data.""" + try: + # Load concepts + if self.concepts_path.exists(): + with open(self.concepts_path, "r") as f: + concepts_data = json.load(f) + for concept_dict in concepts_data: + concept = SemanticConcept(**concept_dict) + concept.created_at = datetime.fromisoformat( + concept_dict["created_at"] + ) + concept.updated_at = datetime.fromisoformat( + concept_dict["updated_at"] + ) + self.concepts[concept.id] = concept + + # Load trajectories + if self.trajectories_path.exists(): + with open(self.trajectories_path, "r") as f: + trajectories_data = json.load(f) + for traj_dict in trajectories_data: + trajectory = CognitiveTrajectory(**traj_dict) + trajectory.created_at = datetime.fromisoformat( + traj_dict["created_at"] + ) + self.trajectories[trajectory.id] = trajectory + + logger.info( + f"Loaded {len(self.concepts)} concepts and {len(self.trajectories)} trajectories" + ) + + except Exception as e: + logger.error(f"Error loading semantic data: {e}") + + def _save_persistent_data(self): + """Save semantic data to disk.""" + try: + # Save concepts + concepts_data = [] + for concept in self.concepts.values(): + concept_dict = asdict(concept) + concept_dict["created_at"] = concept.created_at.isoformat() + concept_dict["updated_at"] = concept.updated_at.isoformat() + concepts_data.append(concept_dict) + + with open(self.concepts_path, "w") as f: + json.dump(concepts_data, f, indent=2) + + # Save trajectories + trajectories_data = [] + for trajectory in self.trajectories.values(): + traj_dict = asdict(trajectory) + traj_dict["created_at"] = trajectory.created_at.isoformat() + trajectories_data.append(traj_dict) + + with open(self.trajectories_path, "w") as f: + json.dump(trajectories_data, f, indent=2) + + except Exception as e: + logger.error(f"Error saving semantic data: {e}") + + async def distill_entries( + self, entries: List[MemoryEntry] + ) -> List[SemanticConcept]: + """Distill semantic concepts from memory entries using LLM analysis.""" + + if not entries: + return [] + + # Prepare content for analysis + content_blocks = [] + for entry in entries: + content_blocks.append( + f"Entry {entry.id} ({entry.timestamp.date()}): {entry.content[:500]}..." + ) + + combined_content = "\n\n".join(content_blocks) + + # Create distillation prompt + prompt = f""" +Analyze the following collection of memory entries and extract the key semantic concepts and themes. +For each concept, provide: +1. A concise name +2. A brief description +3. Confidence score (0-1) of how well it represents the content +4. Which entries are most related to this concept + +Content to analyze: +{combined_content} + +Return your analysis as a JSON array of concept objects with the following structure: +[ + {{ + "name": "Concept Name", + "description": "Brief description of the concept", + "confidence": 0.85, + "related_entries": ["entry_id_1", "entry_id_2"] + }} +] +""" + + try: + response = self.client.chat.completions.create( + model=self.model, + messages=[ + { + "role": "system", + "content": "You are a semantic analysis expert. Extract meaningful concepts from text collections.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.3, + max_tokens=2000, + ) + + result_text = response.choices[0].message.content.strip() + + # Parse JSON response + if result_text.startswith("```json"): + result_text = result_text[7:-3].strip() + elif result_text.startswith("```"): + result_text = result_text[3:-3].strip() + + concepts_data = json.loads(result_text) + + # Create concept objects + new_concepts = [] + for concept_data in concepts_data: + concept_id = ( + f"concept_{datetime.now().isoformat()}_{len(self.concepts)}" + ) + + concept = SemanticConcept( + id=concept_id, + name=concept_data["name"], + description=concept_data["description"], + confidence=concept_data["confidence"], + related_entries=concept_data["related_entries"], + created_at=datetime.now(), + updated_at=datetime.now(), + metadata={"source": "llm_distillation"}, + ) + + self.concepts[concept_id] = concept + new_concepts.append(concept) + + # Save changes + self._save_persistent_data() + + logger.info(f"Distilled {len(new_concepts)} semantic concepts") + return new_concepts + + except Exception as e: + logger.error(f"Error in semantic distillation: {e}") + return [] + + async def build_trajectory( + self, concept_ids: List[str], entries: List[MemoryEntry] + ) -> Optional[CognitiveTrajectory]: + """Build a cognitive trajectory from related concepts.""" + + if len(concept_ids) < 2: + return None + + # Get concept details + concepts = [self.concepts[cid] for cid in concept_ids if cid in self.concepts] + + # Prepare trajectory analysis prompt + concept_summaries = [] + for concept in concepts: + concept_summaries.append(f"- {concept.name}: {concept.description}") + + prompt = f""" +Analyze the following concepts and determine if they form a coherent cognitive trajectory or learning path. +A cognitive trajectory shows how ideas evolve, build upon each other, or represent progressive understanding. + +Concepts: +{chr(10).join(concept_summaries)} + +Determine: +1. Whether these concepts form a meaningful trajectory +2. The logical order/sequence of the concepts +3. The strength of the trajectory connection (0-1) +4. Key transition points between concepts + +Return as JSON: +{{ + "is_trajectory": true/false, + "sequence": ["concept_name_1", "concept_name_2", ...], + "strength": 0.75, + "transitions": [ + {{"from": "concept_1", "to": "concept_2", "description": "transition explanation"}} + ] +}} +""" + + try: + response = self.client.chat.completions.create( + model=self.model, + messages=[ + { + "role": "system", + "content": "You are a cognitive science expert analyzing learning trajectories.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.2, + max_tokens=1500, + ) + + result_text = response.choices[0].message.content.strip() + + # Parse JSON response + if result_text.startswith("```json"): + result_text = result_text[7:-3].strip() + elif result_text.startswith("```"): + result_text = result_text[3:-3].strip() + + trajectory_data = json.loads(result_text) + + if not trajectory_data.get("is_trajectory", False): + return None + + # Map concept names back to IDs + name_to_id = {self.concepts[cid].name: cid for cid in concept_ids} + sequence_ids = [ + name_to_id[name] + for name in trajectory_data["sequence"] + if name in name_to_id + ] + + trajectory = CognitiveTrajectory( + id=f"trajectory_{datetime.now().isoformat()}_{len(self.trajectories)}", + concepts=sequence_ids, + transitions=trajectory_data["transitions"], + strength=trajectory_data["strength"], + entry_points=[e.id for e in entries], + created_at=datetime.now(), + ) + + self.trajectories[trajectory.id] = trajectory + self._save_persistent_data() + + logger.info(f"Built cognitive trajectory: {trajectory.id}") + return trajectory + + except Exception as e: + logger.error(f"Error building trajectory: {e}") + return None + + def get_related_concepts(self, entry_ids: List[str]) -> List[SemanticConcept]: + """Get concepts related to specific entries.""" + related_concepts = [] + for concept in self.concepts.values(): + if any(entry_id in concept.related_entries for entry_id in entry_ids): + related_concepts.append(concept) + return related_concepts + + def get_trajectory_by_concepts( + self, concept_ids: List[str] + ) -> List[CognitiveTrajectory]: + """Find trajectories that contain the given concepts.""" + matching_trajectories = [] + for trajectory in self.trajectories.values(): + if any(cid in trajectory.concepts for cid in concept_ids): + matching_trajectories.append(trajectory) + return matching_trajectories + + def get_stats(self) -> Dict[str, Any]: + """Get semantic layer statistics.""" + return { + "total_concepts": len(self.concepts), + "total_trajectories": len(self.trajectories), + "avg_concept_confidence": sum(c.confidence for c in self.concepts.values()) + / len(self.concepts) + if self.concepts + else 0, + "avg_trajectory_strength": sum( + t.strength for t in self.trajectories.values() + ) + / len(self.trajectories) + if self.trajectories + else 0, + "data_directory": str(self.data_dir), + } diff --git a/test_phase_1.py b/test_phase_1.py new file mode 100644 index 0000000..7376447 --- /dev/null +++ b/test_phase_1.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +"""Test script for Phase 1 Dual Manifold Cognitive Architecture implementation.""" + +import asyncio +import os +from pathlib import Path + +# Set up data directory +data_dir = Path.home() / "think_bigger_test_data" +data_dir.mkdir(exist_ok=True) + + +async def test_phase_1_implementation(): + """Test the complete Phase 1 implementation.""" + + print("🧠 Testing Phase 1: Dual Manifold Cognitive Architecture") + print("=" * 60) + + try: + # Import required modules + from sentence_transformers import SentenceTransformer + from think_bigger.core.memory.episodic.episodic_memory import EpisodicMemory + from think_bigger.core.memory.semantic.semantic_distiller import ( + SemanticDistiller, + ) + from think_bigger.core.memory.persona.persona_graph import PersonaGraph + from think_bigger.core.memory.manifolds.dual_manifold import DualManifoldEngine + from think_bigger.core.memory.braiding.braiding_engine import BraidingEngine + + print("✅ Imports successful") + + # Initialize embedding model + print("🔄 Initializing embedding model...") + embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") + print("✅ Embedding model ready") + + # Initialize memory systems + print("🔄 Initializing memory systems...") + + # Episodic Memory + episodic_memory = EpisodicMemory(str(data_dir / "episodic")) + print("✅ Episodic memory initialized") + + # Semantic Distiller (using mock API key for testing) + semantic_distiller = SemanticDistiller( + str(data_dir / "semantic"), "mock-api-key" + ) + print("✅ Semantic distiller initialized") + + # Persona Graph + persona_graph = PersonaGraph(str(data_dir / "persona")) + print("✅ Persona graph initialized") + + # Dual Manifold Engine + dual_manifold_engine = DualManifoldEngine( + str(data_dir / "manifolds"), embedding_model, "mock-api-key" + ) + print("✅ Dual manifold engine initialized") + + # Braiding Engine + braiding_engine = BraidingEngine( + str(data_dir / "braiding"), dual_manifold_engine, "mock-api-key" + ) + print("✅ Braiding engine initialized") + + print("\n📝 Testing memory entry addition...") + + # Add some test memory entries + test_entries = [ + { + "content": "The dual manifold cognitive architecture combines individual and collective knowledge representations through manifold learning techniques.", + "source_file": "architecture_notes.md", + "metadata": {"topic": "architecture", "importance": "high"}, + }, + { + "content": "Episodic memory stores specific experiences with temporal preservation, using hybrid indexing with FAISS and BM25.", + "source_file": "memory_system.md", + "metadata": {"topic": "memory", "component": "episodic"}, + }, + { + "content": "Semantic distillation extracts cognitive trajectories from memory entries using LLM analysis to identify meaningful concepts and their relationships.", + "source_file": "semantic_layer.md", + "metadata": {"topic": "memory", "component": "semantic"}, + }, + { + "content": "Knowledge graphs in the persona layer use centrality measures and gravity wells to represent structured knowledge with NetworkX.", + "source_file": "persona_layer.md", + "metadata": {"topic": "memory", "component": "persona"}, + }, + ] + + added_entries = [] + for entry_data in test_entries: + entry_id = episodic_memory.add_entry( + entry_data["content"], entry_data["source_file"], entry_data["metadata"] + ) + added_entries.append(episodic_memory.get_entry_by_id(entry_id)) + print(f"✅ Added entry: {entry_id}") + + print(f"\n📊 Episodic memory stats: {episodic_memory.get_stats()}") + + print("\n🔄 Processing entries through dual manifold...") + + # Process entries through dual manifold + processing_results = await dual_manifold_engine.process_memory_entries( + added_entries + ) + print(f"✅ Processing results: {processing_results}") + + print("\n🔍 Testing hybrid search...") + + # Test hybrid search + search_results = episodic_memory.hybrid_search( + "cognitive architecture", top_k=3 + ) + print(f"✅ Found {len(search_results)} results for 'cognitive architecture'") + for result in search_results[:2]: # Show top 2 + print(f" - {result.content[:100]}... (score: {result.combined_score:.3f})") + + print("\n🧵 Testing braiding engine...") + + # Test braiding engine + braided_results = await braiding_engine.braid_search("memory systems", top_k=2) + print(f"✅ Braided search found {len(braided_results)} results") + for result in braided_results: + print(f" - {result.content[:80]}... (confidence: {result.confidence:.3f})") + + print("\n📈 System Statistics:") + + # Get system statistics + manifold_stats = dual_manifold_engine.get_manifold_stats() + braiding_stats = braiding_engine.get_gate_stats() + + print( + f"Individual Manifold: {manifold_stats['individual']['total_points']} points" + ) + print( + f"Collective Manifold: {manifold_stats['collective']['total_points']} points" + ) + print(f"Braiding Gates: {braiding_stats['total_gates']} gates") + print( + f"Semantic Concepts: {manifold_stats['semantic']['total_concepts']} concepts" + ) + + print("\n🎉 Phase 1 implementation test completed successfully!") + print("\nKey Deliverables Implemented:") + print("✅ Episodic memory with hybrid FAISS + BM25 indexing") + print("✅ Semantic distillation pipeline") + print("✅ Knowledge graph construction with NetworkX") + print("✅ Dual manifold representation") + print("✅ Braiding engine with structural gates") + print("✅ FastAPI endpoints for all components") + + return True + + except Exception as e: + print(f"❌ Test failed with error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + success = asyncio.run(test_phase_1_implementation()) + exit(0 if success else 1) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..dac5dcc --- /dev/null +++ b/uv.lock @@ -0,0 +1,3172 @@ +version = 1 +revision = 3 +requires-python = ">=3.9" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", + "python_full_version < '3.10'", +] + +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" } +wheels = [ + { 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