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e5e35a3 cc8beab e5e35a3 6710fbe e5e35a3 cc8beab e5e35a3 6710fbe e5e35a3 cc8beab e5e35a3 6710fbe e5e35a3 cc8beab e5e35a3 6710fbe e5e35a3 cc8beab e5e35a3 6710fbe e5e35a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | from __future__ import annotations
import os
from dataclasses import dataclass, field
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, List
import yaml
from dotenv import load_dotenv
def _split_csv(value: str) -> List[str]:
return [item.strip() for item in (value or "").split(",") if item.strip()]
@dataclass
class ModelSettings:
"""
Centralized model configuration.
Env overrides:
- FIN_ASSISTANT_ROUTER_MODEL
- FIN_ASSISTANT_AGENT_MODEL
- FIN_ASSISTANT_FORMATTER_MODEL
- FIN_ASSISTANT_EMBEDDING_MODEL
"""
router_model: str = "gpt-4o"
agent_model: str = "gpt-4o"
formatter_model: str = "gpt-4o"
embedding_model: str = "text-embedding-3-large"
@dataclass
class UISettings:
tabs: List[str] = field(default_factory=lambda: ["Chat"])
market_watchlist: List[str] = field(
default_factory=lambda: ["AAPL", "MSFT", "NVDA", "SPY", "QQQ"]
)
portfolio_default_input: str = "10 AAPL, 5 MSFT, 2 VTI"
portfolio_examples: List[str] = field(
default_factory=lambda: [
"10 AAPL, 5 MSFT, 2 VTI",
"100 NVDA, 40 SPY, 25 BND",
"12 SCHD, 8 VEA, 6 VWO",
]
)
@dataclass
class CacheSettings:
response_cache_ttl_days: int = 30
@dataclass
class KBSettings:
"""
Retrieval tuning for the local knowledge base.
Env overrides:
- FIN_ASSISTANT_KB_MIN_SCORE (float, 0-1)
"""
min_score: float = 0.70
@dataclass
class ProfileDefaults:
risk: str = "moderate"
experience: str = "beginner"
@dataclass
class AppSettings:
title: str = "Finance Assistant"
page_title: str = "Finance Assistant"
models: ModelSettings = field(default_factory=ModelSettings)
ui: UISettings = field(default_factory=UISettings)
cache: CacheSettings = field(default_factory=CacheSettings)
kb: KBSettings = field(default_factory=KBSettings)
default_user_profile: ProfileDefaults = field(default_factory=ProfileDefaults)
@classmethod
def load(cls, path: str = "config.yaml") -> "AppSettings":
load_dotenv()
settings = cls()
config_path = Path(path)
if config_path.exists():
with config_path.open("r", encoding="utf-8") as f:
raw = yaml.safe_load(f) or {}
settings = cls.from_dict(raw)
return settings.apply_env_overrides()
@classmethod
def from_dict(cls, raw: Dict[str, Any]) -> "AppSettings":
models_raw = raw.get("models", {}) or {}
ui_raw = raw.get("ui", {}) or {}
cache_raw = raw.get("cache", {}) or {}
kb_raw = raw.get("kb", {}) or {}
profile_raw = raw.get("default_user_profile", {}) or {}
return cls(
title=str(
raw.get("title", raw.get("app", {}).get("title", "Finance Assistant"))
),
page_title=str(
raw.get(
"page_title",
raw.get("app", {}).get("page_title", "Finance Assistant"),
)
),
models=ModelSettings(
router_model=str(models_raw.get("router_model", "gpt-4o")),
agent_model=str(models_raw.get("agent_model", "gpt-4o")),
formatter_model=str(models_raw.get("formatter_model", "gpt-4o")),
embedding_model=str(
models_raw.get("embedding_model", "text-embedding-3-large")
),
),
ui=UISettings(
tabs=list(ui_raw.get("tabs", ["Chat"])),
market_watchlist=list(
ui_raw.get(
"market_watchlist", ["AAPL", "MSFT", "NVDA", "SPY", "QQQ"]
)
),
portfolio_default_input=str(
ui_raw.get("portfolio_default_input", "10 AAPL, 5 MSFT, 2 VTI")
),
portfolio_examples=list(
ui_raw.get(
"portfolio_examples",
[
"10 AAPL, 5 MSFT, 2 VTI",
"100 NVDA, 40 SPY, 25 BND",
"12 SCHD, 8 VEA, 6 VWO",
],
)
),
),
cache=CacheSettings(
response_cache_ttl_days=int(
cache_raw.get("response_cache_ttl_days", 30)
)
),
kb=KBSettings(min_score=float(kb_raw.get("min_score", 0.75))),
default_user_profile=ProfileDefaults(
risk=str(profile_raw.get("risk", "moderate")),
experience=str(profile_raw.get("experience", "beginner")),
),
)
def apply_env_overrides(self) -> "AppSettings":
self.title = os.getenv("FIN_ASSISTANT_TITLE", self.title)
self.page_title = os.getenv("FIN_ASSISTANT_PAGE_TITLE", self.page_title)
self.models.router_model = os.getenv(
"FIN_ASSISTANT_ROUTER_MODEL", self.models.router_model
)
self.models.agent_model = os.getenv(
"FIN_ASSISTANT_AGENT_MODEL", self.models.agent_model
)
self.models.formatter_model = os.getenv(
"FIN_ASSISTANT_FORMATTER_MODEL", self.models.formatter_model
)
self.models.embedding_model = os.getenv(
"FIN_ASSISTANT_EMBEDDING_MODEL", self.models.embedding_model
)
tabs = os.getenv("FIN_ASSISTANT_TABS")
if tabs:
self.ui.tabs = _split_csv(tabs)
watchlist = os.getenv("FIN_ASSISTANT_MARKET_WATCHLIST")
if watchlist:
self.ui.market_watchlist = _split_csv(watchlist)
default_portfolio = os.getenv("FIN_ASSISTANT_PORTFOLIO_DEFAULT_INPUT")
if default_portfolio:
self.ui.portfolio_default_input = default_portfolio
examples = os.getenv("FIN_ASSISTANT_PORTFOLIO_EXAMPLES")
if examples:
self.ui.portfolio_examples = [
item.strip() for item in examples.split(";") if item.strip()
]
ttl_days = os.getenv("FIN_ASSISTANT_RESPONSE_CACHE_TTL_DAYS")
if ttl_days:
self.cache.response_cache_ttl_days = int(ttl_days)
kb_min_score = os.getenv("FIN_ASSISTANT_KB_MIN_SCORE")
if kb_min_score:
self.kb.min_score = float(kb_min_score)
self.default_user_profile.risk = os.getenv(
"FIN_ASSISTANT_DEFAULT_RISK", self.default_user_profile.risk
)
self.default_user_profile.experience = os.getenv(
"FIN_ASSISTANT_DEFAULT_EXPERIENCE", self.default_user_profile.experience
)
return self
@lru_cache(maxsize=1)
def get_settings() -> AppSettings:
return AppSettings.load()
|