ducoterra
022da8e2bc
Foundation (phase 01, verified): - FastAPI app: /api/health, /api/suggestions, /api/chat (placeholder), static frontend served locally (no CDN) - Postgres 17 + pgvector via db/Containerfile + compose.yaml (podman compose up -d db), Alembic initial migration (documents, chunks with vector(768), query_log) - LLM client targeting https://aipi.reeseapps.com/v1 (turbo/embed); scripts/llm_probe.py verified models + 768-dim embeddings live - Conditional debugpy: imported only when DEBUGPY=1 (attach on demand, :5678); logging config for clean single-line logs - Frontend shell: mobile-first chat + Sources pages, tokens, a11y baselines - Tests: 24 unit+integration (99% coverage on app/), ruff + pyright clean, Playwright smoke E2E (3 tests) against a deterministic mock LLM - Planning: .agent/PLAN.md (architecture + LOCKED decisions), AGENTS.md, 6 user stories, 7 phase files (one story / one phase / one Playwright suite each)
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""Application settings.
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Every setting can be overridden with an environment variable prefixed
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``BOR_`` (or a local gitignored ``.env`` file — see ``.env.example``).
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"""
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from __future__ import annotations
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import os
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from functools import lru_cache
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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env_prefix="BOR_",
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extra="ignore",
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)
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# --- App ---
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app_name: str = "Brain of Reese"
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app_version: str = "0.1.0"
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environment: str = "development"
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log_level: str = "INFO"
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static_dir: str = "frontend"
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# --- Database (PostgreSQL 17 + pgvector) ---
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database_url: str = "postgresql+psycopg://reese:reese@localhost:5432/brain_of_reese"
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# --- LLM (self-hosted, OpenAI-compatible "aipi" endpoint) ---
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llm_base_url: str = "https://aipi.reeseapps.com/v1"
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llm_api_key: str = ""
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llm_chat_model: str = "turbo"
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llm_embed_model: str = "embed"
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# --- RAG tuning ---
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embedding_dim: int = 768 # verified against aipi /v1 (embed model)
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top_k_chunks: int = 4
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top_n_docs: int = 2
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relevance_threshold: float = 0.30
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max_context_chars: int = 24_000
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chunk_target_chars: int = 2_000
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chunk_overlap_chars: int = 200
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embed_batch_size: int = 16
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# Suggested questions (onboarding + empty state).
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suggestions: list[str] = [
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"How is my Kubernetes cluster set up?",
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"What's my backup strategy?",
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"How do I deploy a new service?",
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"What's currently running in the homelab?",
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]
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@property
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def effective_api_key(self) -> str:
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"""API key for aipi: explicit setting, then $AIPI_KEY, then a placeholder."""
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return self.llm_api_key or os.environ.get("AIPI_KEY", "") or "not-needed"
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@lru_cache
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def get_settings() -> Settings:
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return Settings()
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