Translations of the new items added in the latest versions.

…ng (#20547)

Refactored the file processing status streaming endpoint to avoid holding
a database connection for the entire stream duration (up to 2 hours).
Changes:
- Each status poll now creates its own short-lived database session instead
  of capturing the request's session in the generator closure
- Increased poll interval from 0.5s to 1s, halving database queries with
  negligible UX impact
This prevents a single file status stream from blocking a connection pool
slot for hours, which could contribute to pool exhaustion under load.

…#20534)

- Remove incorrect 403 check that blocked STT when ENGINE="" (local whisper)
- Change TTS empty ENGINE check from 403 to 404 for proper semantics

…ection pool exhaustion (#20542)

fix: use efficient COUNT queries in telemetry metrics to prevent connection pool exhaustion

This fixes database connection pool exhaustion issues reported after v0.7.0,
particularly affecting PostgreSQL deployments on high-latency networks (e.g., AWS Aurora).

## The Problem

The telemetry metrics callbacks (running every 10 seconds via OpenTelemetry's
PeriodicExportingMetricReader) were using inefficient queries that loaded entire
database tables into memory just to count records:

    len(Users.get_users()["users"])  # Loads ALL user records to count them

On high-latency network-attached databases like AWS Aurora, this would:
1. Hold database connections for hundreds of milliseconds while transferring data
2. Deserialize all records into Python objects
3. Only then count the list length

Under concurrent load, these long-held connections would stack up and drain the
connection pool, resulting in:

    sqlalchemy.exc.TimeoutError: QueuePool limit of size 5 overflow 10 reached,
    connection timed out, timeout 30.00

## The Fix

Replace inefficient full-table loads with efficient COUNT(*) queries using
methods that already exist in the codebase:

- `len(Users.get_users()["users"])` → `Users.get_num_users()`
- Similar changes for other telemetry callbacks as needed

COUNT(*) queries use database indexes and return a single integer, completing in
~5-10ms even on Aurora, versus potentially 500ms+ for loading all records.

## Why v0.7.1's Session Sharing Disable "Helped"

The v0.7.1 change to disable DATABASE_ENABLE_SESSION_SHARING by default appeared
to fix the issue, but it was masking the root cause. Disabling session sharing
causes connections to be returned to the pool faster (more connection churn),
which reduced the window for pool exhaustion but didn't address the underlying
inefficient queries.

With this fix, session sharing can be safely re-enabled for deployments that
benefit from it (especially PostgreSQL), as telemetry will no longer hold
connections for extended periods.

## Impact

- Telemetry connection usage drops from potentially seconds to ~30ms total per
  collection cycle
- Connection pool pressure from telemetry becomes negligible (~0.3% utilization)
- Enterprise PostgreSQL deployments (Aurora, RDS, etc.) should no longer
  experience pool exhaustion under normal load

…olding during LLM calls (#20545)

fix: release database connections immediately after auth instead of holding during LLM calls

Authentication was using Depends(get_session) which holds a database connection
for the entire request lifecycle. For chat completions, this meant connections
were held for 30-60 seconds while waiting for LLM responses, despite only needing
the connection for ~50ms of actual database work.

With a default pool of 15 connections, this limited concurrent chat users to ~15
before pool exhaustion and timeout errors:

    sqlalchemy.exc.TimeoutError: QueuePool limit of size 5 overflow 10 reached,
    connection timed out, timeout 30.00

The fix removes Depends(get_session) from get_current_user. Each database
operation now manages its own short-lived session internally:

    BEFORE: One session held for entire request
    ──────────────────────────────────────────────────
    │ auth │ queries │ LLM wait (30s) │ save │
    │         CONNECTION HELD ENTIRE TIME            │
    ──────────────────────────────────────────────────

    AFTER: Short-lived sessions, released immediately
    ┌──────┐ ┌───────┐                 ┌──────┐
    │ auth │ │ query │   LLM (30s)     │ save │
    │ 10ms │ │ 20ms  │  NO CONNECTION  │ 20ms │
    └──────┘ └───────┘                 └──────┘

This is safe because:
- User model has no lazy-loaded relationships (all simple columns)
- Pydantic conversion (UserModel.model_validate) happens while session is open
- Returned object is pure Pydantic with no SQLAlchemy ties

Combined with the telemetry efficiency fix, this resolves connection pool
exhaustion for high-concurrency deployments, particularly on network-attached
databases like AWS Aurora where connection hold time is more impactful.