curo1305
52a2967f61
config_reader.py now merges environment variables (AI_PROVIDER, LMSTUDIO_BASE_URL, LMSTUDIO_API_KEY, LMSTUDIO_MODEL, OLLAMA_*, ANTHROPIC_*) on top of the JSON config file, so the dev .env file can pin the AI connection without writing to the shared config volume. docker-compose.dev.yml loads features/doc-service/.env (gitignored) into the doc-service container so the token is never committed. .env.example updated with all supported override variables and comments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
"""
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Reads doc_service_config.json from the shared config volume.
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Caches the result for 30 seconds to avoid hitting the filesystem on every request.
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Uses asyncio.to_thread so the synchronous file read doesn't block the event loop.
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Env var overrides (take precedence over the JSON config file, never committed):
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AI_PROVIDER — "lmstudio" | "ollama" | "anthropic"
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LMSTUDIO_BASE_URL — e.g. http://host.docker.internal:1234/v1
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LMSTUDIO_API_KEY
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LMSTUDIO_MODEL
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OLLAMA_BASE_URL
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OLLAMA_MODEL
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OLLAMA_API_KEY
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ANTHROPIC_API_KEY
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ANTHROPIC_MODEL
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"""
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import asyncio
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import json
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import os
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import time
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from copy import deepcopy
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from pathlib import Path
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from app.core.config import settings
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_DEFAULT_CONFIG: dict = {
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"ai": {
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# Default: LM Studio running on the host machine at port 1234.
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# Inside Docker, host.docker.internal resolves to the host; for local
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# dev outside Docker use http://localhost:1234/v1 instead.
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"provider": "lmstudio",
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"anthropic": {"api_key": "", "model": "claude-haiku-4-5-20251001"},
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"ollama": {"base_url": "http://host.docker.internal:11434/v1", "model": "llama3.2", "api_key": "ollama"},
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"lmstudio": {"base_url": "http://host.docker.internal:1234/v1", "model": "local-model", "api_key": "lm-studio"},
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},
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"documents": {"max_pdf_bytes": 20 * 1024 * 1024},
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}
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_cache: dict | None = None
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_cache_at: float = 0.0
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_CACHE_TTL = 30.0
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def _read_config_sync() -> dict:
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path = Path(settings.CONFIG_PATH)
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if not path.exists():
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base = deepcopy(_DEFAULT_CONFIG)
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else:
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with open(path) as f:
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base = json.load(f)
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return _apply_env_overrides(base)
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def _apply_env_overrides(config: dict) -> dict:
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"""
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Merge environment variable overrides into the config dict.
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Env vars win over whatever is stored in the JSON file.
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This lets the dev .env file pin the AI connection without writing to the
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shared volume (which would affect all users).
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"""
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cfg = deepcopy(config)
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ai = cfg.setdefault("ai", {})
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if provider := os.environ.get("AI_PROVIDER"):
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ai["provider"] = provider
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# LM Studio
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lms = ai.setdefault("lmstudio", {})
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if v := os.environ.get("LMSTUDIO_BASE_URL"):
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lms["base_url"] = v
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if v := os.environ.get("LMSTUDIO_API_KEY"):
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lms["api_key"] = v
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if v := os.environ.get("LMSTUDIO_MODEL"):
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lms["model"] = v
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# Ollama
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oll = ai.setdefault("ollama", {})
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if v := os.environ.get("OLLAMA_BASE_URL"):
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oll["base_url"] = v
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if v := os.environ.get("OLLAMA_MODEL"):
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oll["model"] = v
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if v := os.environ.get("OLLAMA_API_KEY"):
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oll["api_key"] = v
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# Anthropic
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ant = ai.setdefault("anthropic", {})
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if v := os.environ.get("ANTHROPIC_API_KEY"):
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ant["api_key"] = v
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if v := os.environ.get("ANTHROPIC_MODEL"):
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ant["model"] = v
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return cfg
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async def load_doc_config() -> dict:
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global _cache, _cache_at
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now = time.monotonic()
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if _cache is not None and (now - _cache_at) < _CACHE_TTL:
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return _cache
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data = await asyncio.to_thread(_read_config_sync)
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_cache = data
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_cache_at = now
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return data
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