import logging logger = logging.getLogger(__name__) """ Configuration management for Graph RAG Service Extended with: temporal, multi-tenant, eval, semantic-cache, hybrid search, GoT settings """ from pydantic_settings import BaseSettings, SettingsConfigDict from typing import Optional, List from pathlib import Path class Settings(BaseSettings): """Application settings with environment variable support""" model_config = SettingsConfigDict( env_file=".env", env_file_encoding="utf-8", case_sensitive=False, extra="allow" ) # Application app_name: str = "Graph RAG Service" # Version is kept in sync with pyproject.toml (the single source of truth). # "2.0.0" was a documentation-only bump; the package version remains 0.1.0. app_version: str = "0.1.0" debug: bool = False environment: str = "development" # API Server api_host: str = "0.0.0.0" api_port: int = 8000 api_workers: int = 4 # Security secret_key: str = "change-this-in-production-to-a-secure-random-key" algorithm: str = "HS256" access_token_expire_minutes: int = 30 # Neo4j Configuration neo4j_uri: str = "bolt://localhost:7687" neo4j_user: str = "neo4j" neo4j_password: str = "password" neo4j_database: str = "neo4j" # Redis Configuration redis_host: str = "localhost" redis_port: int = 6379 redis_db: int = 0 redis_password: Optional[str] = None # Celery Configuration celery_broker_url: str = "redis://localhost:6379/0" celery_result_backend: str = "redis://localhost:6379/0" # LLM Configuration default_llm_provider: str = "ollama" # ollama, openai, anthropic, gemini # OpenAI openai_api_key: Optional[str] = None openai_model: str = "gpt-4" # Anthropic anthropic_api_key: Optional[str] = None anthropic_model: str = "claude-sonnet-4" # Google Gemini google_api_key: Optional[str] = None gemini_model: str = "gemini-2.5-flash" # LlamaCloud (for LlamaParse) llama_cloud_api_key: Optional[str] = None use_llama_parse: bool = True # Ollama ollama_base_url: str = "http://localhost:11434" ollama_model: str = "deepseek-v3.1:671b-cloud" ollama_embedding_model: str = "nomic-embed-text" # Embedding Configuration embedding_provider: str = "ollama" # ollama, openai embedding_dimension: int = 768 # nomic-embed-text dimension # Ingestion Configuration chunk_size: int = 1024 chunk_overlap: int = 200 max_concurrent_extractions: int = 2 # Ontology Configuration ontology_version: str = "v1.0" enable_ontology_evolution: bool = True entity_resolution_threshold: float = 0.85 # Agent Configuration max_agent_iterations: int = 5 agent_timeout_seconds: int = 30 enable_hallucination_guard: bool = True # ── Gap #1: Hybrid BM25 + Vector ────────────────────────────────────────── enable_hybrid_search: bool = True hybrid_bm25_weight: float = 0.3 # weight for BM25 in RRF fusion hybrid_vector_weight: float = 0.7 # weight for vector in RRF fusion rrf_k: int = 60 # RRF ranking constant # ── Gap #2: Community Summaries (LazyGraphRAG) ──────────────────────────── enable_community_search: bool = True community_summary_cache_ttl: int = 7200 # 2 hours in Redis max_community_entities: int = 50 # ── Gap #3: DRIFT-style Query Expansion ─────────────────────────────────── enable_drift_expansion: bool = True drift_expansion_threshold: float = 0.5 # confidence below this triggers drift max_drift_expansions: int = 2 # ── Gap #4: LLM-as-a-Judge ──────────────────────────────────────────────── enable_llm_judge: bool = True judge_temperature: float = 0.0 # ── Gap #5: Temporal Knowledge Graph ───────────────────────────────────── enable_temporal_store: bool = True temporal_default_validity_days: int = 3650 # ~10 years default # ── Gap #6: Graph-of-Thought Parallel Exploration ───────────────────────── enable_graph_of_thought: bool = True got_parallel_timeout: float = 20.0 # seconds before parallel search aborts # ── Gap #7: Multi-Tenant ────────────────────────────────────────────────── enable_multi_tenant: bool = True default_tenant_id: str = "default" # ── Gap #8: Semantic Query Cache ───────────────────────────────────────── enable_semantic_cache: bool = True cache_ttl_seconds: int = 3600 cache_similarity_threshold: float = 0.95 # cosine similarity for cache hit # ── Gap #9: Extended Ingestion Formats ─────────────────────────────────── allowed_file_types: List[str] = [ ".pdf", ".txt", ".md", ".docx", ".csv", ".xlsx", ".pptx", ".json" # NEW ] # Retrieval Configuration default_top_k: int = 5 vector_search_similarity_threshold: float = 0.7 graph_max_depth: int = 3 # File Upload Configuration max_upload_size_mb: int = 100 upload_dir: Path = Path("data/uploads").resolve() # Frontend Path frontend_dist_dir: Path = Path(__file__).parent.parent.parent / "frontend-react" / "dist" # Observability enable_tracing: bool = False enable_metrics: bool = False log_level: str = "INFO" otel_exporter_endpoint: Optional[str] = None # ── MiroFish Point 2: Entity Enricher ───────────────────────────────────── entity_enrichment_min_connections: int = 1 # min graph degree to qualify entity_enrichment_batch_size: int = 20 # entities per LLM batch # ── MiroFish Point 4: Ontology Drift Detection ──────────────────────────── enable_ontology_evolution: bool = True # flag was defined; now wired drift_sample_size: int = 10 # random chunks to re-sample drift_detection_schedule: str = "0 3 * * *" # cron: daily at 3 AM @property def redis_url(self) -> str: """Construct Redis URL""" if self.redis_password: return f"redis://:{self.redis_password}@{self.redis_host}:{self.redis_port}/{self.redis_db}" return f"redis://{self.redis_host}:{self.redis_port}/{self.redis_db}" def model_post_init(self, __context): """Fallback to local Ollama if cloud API keys are missing""" if self.default_llm_provider == "gemini" and not self.google_api_key: logger.info("WARNING: No GOOGLE_API_KEY found. Falling back to Ollama for LLM.") self.default_llm_provider = "ollama" if self.embedding_provider == "gemini" and not self.google_api_key: logger.info("WARNING: No GOOGLE_API_KEY found. Falling back to Ollama for embeddings.") self.embedding_provider = "ollama" def get_llm_config(self, provider: Optional[str] = None) -> dict: """Get LLM configuration for specified provider""" provider = provider or self.default_llm_provider configs = { "openai": { "api_key": self.openai_api_key, "model": self.openai_model, }, "anthropic": { "api_key": self.anthropic_api_key, "model": self.anthropic_model, }, "gemini": { "api_key": self.google_api_key, "model": self.gemini_model, }, "ollama": { "base_url": self.ollama_base_url, "model": self.ollama_model, }, } return configs.get(provider, configs["ollama"]) # Global settings instance settings = Settings()