fix
This commit is contained in:
@@ -83,6 +83,134 @@ async def test_embeddings_endpoint(
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return await probe_embeddings(settings_map)
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@router.post("/llm/test")
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async def test_llm_endpoint(
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payload: Dict[str, Any] = Body(default={}),
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db: AsyncSession = Depends(get_db_dep),
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_: User = Depends(require_admin),
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):
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"""Probe the currently configured LLM endpoint from inside the backend container.
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Accepts an optional `overrides` dict with llm.* keys (e.g. to test a new
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endpoint before saving). Returns: ok, base_url, model, http_status,
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latency_ms, response_preview (or error + error_type).
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This is the diagnostic tool to use when the LLM call fails with
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`ConnectError: All connection attempts failed` — it tells you whether
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the backend container can actually reach the LLM URL.
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"""
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import time
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import httpx
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import socket
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settings_map = await get_all_settings(db)
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overrides = (payload or {}).get("overrides") or {}
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for k, v in overrides.items():
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if k in EDITABLE_SETTING_KEYS:
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settings_map[k] = v
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base_url = str(settings_map.get("llm.base_url", "")).rstrip("/")
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model = str(settings_map.get("llm.model", "local-model"))
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api_key = str(settings_map.get("llm.api_key", "dummy"))
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timeout_s = float(settings_map.get("llm.request_timeout", 30) or 30)
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result: Dict[str, Any] = {
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"base_url": base_url,
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"model": model,
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"ok": False,
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}
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# === Stage 1: DNS / TCP connect (without TLS) ===
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try:
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from urllib.parse import urlparse
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parsed = urlparse(base_url)
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host = parsed.hostname or ""
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port = parsed.port or (443 if parsed.scheme == "https" else 80)
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if not host:
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result["error"] = "invalid_base_url: no host"
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result["error_type"] = "ConfigError"
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return result
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# Try to resolve + connect TCP
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addrs = socket.getaddrinfo(host, port, type=socket.SOCK_STREAM)
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result["dns_resolved"] = True
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result["resolved_addrs"] = [a[4][0] for a in addrs[:3]]
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# Try to actually open a TCP connection
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.settimeout(5.0)
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try:
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sock.connect((host, port))
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result["tcp_connect_ok"] = True
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finally:
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sock.close()
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except socket.gaierror as e:
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result["dns_resolved"] = False
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result["error"] = f"DNS resolution failed for {host}: {e}"
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result["error_type"] = "DNSError"
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return result
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except (socket.timeout, ConnectionRefusedError, OSError) as e:
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result["tcp_connect_ok"] = False
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result["error"] = f"TCP connect to {host}:{port} failed: {type(e).__name__}: {e}"
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result["error_type"] = type(e).__name__
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return result
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# === Stage 2: HTTP request to /v1/models (lightweight probe) ===
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headers = {"Content-Type": "application/json"}
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if api_key and api_key != "dummy":
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headers["Authorization"] = f"Bearer {api_key}"
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started = time.monotonic()
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try:
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async with httpx.AsyncClient(timeout=httpx.Timeout(connect=10.0, read=timeout_s, write=10.0, pool=5.0)) as client:
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# First try /models (lightweight, exists on every OpenAI-compatible server)
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models_url = f"{base_url}/models"
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try:
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resp = await client.get(models_url, headers=headers)
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result["models_endpoint_status"] = resp.status_code
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if resp.status_code == 200:
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data = resp.json()
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model_ids = []
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if isinstance(data, dict) and isinstance(data.get("data"), list):
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model_ids = [m.get("id", "?") for m in data["data"][:10]]
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result["available_models"] = model_ids
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except Exception as e:
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result["models_endpoint_error"] = f"{type(e).__name__}: {e}"
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# Now try the actual chat completions endpoint with a minimal payload
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chat_url = f"{base_url}/chat/completions"
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chat_payload = {
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"model": model,
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"messages": [{"role": "user", "content": "Reply with the single word: ok"}],
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"max_tokens": 10,
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"temperature": 0.1,
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"stream": False,
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}
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resp = await client.post(chat_url, json=chat_payload, headers=headers)
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result["chat_endpoint_status"] = resp.status_code
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result["latency_ms"] = int((time.monotonic() - started) * 1000)
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if resp.status_code >= 400:
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result["error"] = f"HTTP {resp.status_code}: {resp.text[:500]}"
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result["error_type"] = "HTTPError"
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return result
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data = resp.json()
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choice = (data.get("choices") or [{}])[0]
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msg = choice.get("message", {})
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result["ok"] = True
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result["response_preview"] = (msg.get("content") or "")[:200]
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result["usage"] = data.get("usage", {})
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return result
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except httpx.ConnectError as e:
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cause = getattr(e, "__cause__", None) or getattr(e, "__context__", None)
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result["error"] = f"ConnectError: {e}"
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if cause:
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result["error"] += f" (cause: {cause})"
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result["error_type"] = "ConnectError"
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return result
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except Exception as e:
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result["error"] = f"{type(e).__name__}: {e}"
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result["error_type"] = type(e).__name__
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return result
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@router.get("/llm-logs", response_model=List[LlmLogOut])
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async def list_llm_logs(
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limit: int = 50,
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@@ -79,7 +79,15 @@ class LlmClient:
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tool_calls: List[Dict[str, Any]] = []
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usage: Dict[str, int] = {}
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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# Use explicit timeout config so connect/read/write/pool timeouts
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# are all visible — a bare `timeout=N` hides WHICH stage failed.
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timeout = httpx.Timeout(
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connect=10.0, # 10s to establish TCP connection
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read=float(self.timeout), # full request timeout
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write=10.0,
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pool=5.0,
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)
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async with httpx.AsyncClient(timeout=timeout) as client:
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resp = await client.post(url, json=payload, headers=self._headers())
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resp.raise_for_status()
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data = resp.json()
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@@ -88,9 +96,30 @@ class LlmClient:
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text = msg.get("content") or ""
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tool_calls = msg.get("tool_calls") or []
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usage = data.get("usage") or {}
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except httpx.ConnectError as e:
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err = f"ConnectError: {e}"
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# Surface the URL + cause so the operator can see WHY (DNS, refused, etc.)
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cause = getattr(e, "__cause__", None) or getattr(e, "__context__", None)
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log.error(
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"llm_connect_failed",
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purpose=purpose,
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url=url,
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base_url=self.base_url,
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model=self.model,
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error=err,
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cause=str(cause) if cause else None,
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)
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raise
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except Exception as e:
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err = f"{type(e).__name__}: {e}"
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log.error("llm_call_failed", purpose=purpose, error=err)
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log.error(
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"llm_call_failed",
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purpose=purpose,
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url=url,
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base_url=self.base_url,
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model=self.model,
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error=err,
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)
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raise
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finally:
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latency_ms = int((time.monotonic() - started) * 1000)
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@@ -55,21 +55,11 @@ DEFAULT_SETTINGS = [
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]
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# Keys whose values come from environment variables (via Settings fields).
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# These are re-applied on EVERY startup so .env is the source of truth.
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# Admin-panel changes to these keys are runtime overrides that get reset on
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# restart unless the operator also updates .env.
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ENV_DERIVED_SETTING_KEYS = {
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"llm.base_url",
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"llm.api_key",
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"llm.model",
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"embedding.provider",
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"embedding.base_url",
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"embedding.api_key",
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"embedding.model",
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"embedding.dim",
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"embedding.request_timeout",
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}
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# NOTE: env-derived defaults (llm.base_url, llm.api_key, llm.model,
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# embedding.* etc.) are ONLY applied on the very first run via _seed_settings.
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# After that, the admin panel is the source of truth — restarting the
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# container will NOT overwrite admin-configured values with .env values.
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# To force a re-seed, drop the `settings` table or delete the relevant rows.
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async def init_db() -> None:
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@@ -91,7 +81,6 @@ async def init_db() -> None:
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)
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)
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await _seed_settings(session)
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await _sync_env_derived_settings(session)
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await _seed_builtin_presets(session)
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await session.commit()
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except Exception as e:
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@@ -99,7 +88,6 @@ async def init_db() -> None:
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log.warning("advisory_lock_unavailable_proceeding", error=f"{type(e).__name__}: {e}")
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async with AsyncSessionLocal() as session:
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await _seed_settings(session)
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await _sync_env_derived_settings(session)
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await _seed_builtin_presets(session)
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await session.commit()
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@@ -167,46 +155,6 @@ async def _seed_settings(session) -> None:
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log.info("settings_already_exist")
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async def _sync_env_derived_settings(session) -> None:
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"""Re-apply env-derived setting values from .env on every startup.
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This makes .env the source of truth for these keys: changing .env and
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restarting the container takes effect immediately. Admin-panel edits to
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these keys are runtime overrides that are reset on the next restart
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(unless the operator also updates .env).
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Only the env-derived keys (see ENV_DERIVED_SETTING_KEYS) are touched;
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other settings (temperature, context params, etc.) are preserved as
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configured via the admin panel.
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"""
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env_values = {key: value for key, value, _desc in DEFAULT_SETTINGS if key in ENV_DERIVED_SETTING_KEYS}
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updated = 0
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for key, new_value in env_values.items():
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result = await session.execute(select(Setting).where(Setting.key == key))
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row = result.scalars().first()
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if row is None:
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# Shouldn't happen (seeded above) but handle defensively.
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session.add(Setting(key=key, value=new_value, description="Env-derived"))
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updated += 1
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else:
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if row.value != new_value:
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log.info(
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"env_setting_resynced",
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key=key,
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old_value=str(row.value)[:80],
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new_value=str(new_value)[:80],
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)
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row.value = new_value
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updated += 1
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if updated:
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try:
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await session.commit()
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log.info("env_settings_synced", count=updated)
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except IntegrityError:
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await session.rollback()
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log.warning("env_settings_sync_failed_concurrent")
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async def _seed_builtin_presets(session) -> None:
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"""Insert built-in presets if none exist yet."""
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result = await session.execute(select(Preset).where(Preset.is_builtin.is_(True)))
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@@ -72,6 +72,13 @@ services:
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DEFAULT_EMBEDDING_MODEL: ${DEFAULT_EMBEDDING_MODEL:-text-embedding-3-small}
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DEFAULT_EMBEDDING_DIM: ${DEFAULT_EMBEDDING_DIM:-0}
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DEFAULT_EMBEDDING_REQUEST_TIMEOUT: ${DEFAULT_EMBEDDING_REQUEST_TIMEOUT:-60}
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# Make `host.docker.internal` resolvable inside the container (Linux).
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# On Docker Desktop (Mac/Win) this is added automatically; on Linux it's not,
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# so we add it explicitly. Allows pointing DEFAULT_LLM_BASE_URL at
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# http://host.docker.internal:1234/v1 to reach an LM Studio / llama.cpp
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# running on the host.
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extra_hosts:
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- "host.docker.internal:host-gateway"
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ports:
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- "8000:8000"
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volumes:
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