Files
project-lyra/lyra/config.py
T
serversdown d7c258eba0 feat: tiered, compacting memory (phase 1.5)
Older sessions fade to a general idea; details stay retrievable.

- memory: summaries table (one compacted gist per session, embedded), plus
  store_summary/get_summary/recall_summaries and unsummarized_count (tracks
  exchanges newer than the current summary)
- lyra/summary.py: summarize_session compacts a session's raw turns into a
  third-person gist (default SUMMARY_BACKEND=local, so compaction is free);
  maybe_summarize re-summarizes once SUMMARIZE_AFTER new turns accumulate
- chat.build_messages now layers context in tiers: persona -> gists of other
  sessions -> a few sharp raw cross-session details -> current session raw
  turns -> new message; respond() compacts the session after each turn
- web: POST /sessions/{id}/summarize to compact on demand
- summarization activity surfaces in the live log

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-15 18:52:58 +00:00

38 lines
1.2 KiB
Python

"""Environment-driven configuration."""
from __future__ import annotations
import os
from dataclasses import dataclass
from pathlib import Path
from dotenv import load_dotenv
load_dotenv()
@dataclass(frozen=True)
class Config:
local_base_url: str
local_model: str
openai_api_key: str
cloud_model: str
embed_backend: str # "cloud" (OpenAI) or "local" (Ollama)
embed_model: str # OpenAI embedding model
local_embed_model: str # Ollama embedding model
summary_backend: str # "local" or "cloud" — backend used to compact memory
db_path: Path
def load() -> Config:
return Config(
local_base_url=os.getenv("LOCAL_BASE_URL", "http://localhost:11434"),
local_model=os.getenv("LOCAL_MODEL", "qwen2.5:7b-instruct"),
openai_api_key=os.getenv("OPENAI_API_KEY", ""),
cloud_model=os.getenv("CLOUD_MODEL", "gpt-4o-mini"),
embed_backend=os.getenv("EMBED_BACKEND", "cloud").lower(),
embed_model=os.getenv("EMBED_MODEL", "text-embedding-3-small"),
local_embed_model=os.getenv("LOCAL_EMBED_MODEL", "nomic-embed-text"),
summary_backend=os.getenv("SUMMARY_BACKEND", "local").lower(),
db_path=Path(os.getenv("LYRA_DB_PATH", "data/lyra.db")),
)