mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-11 03:38:38 +00:00
50df072d95
* Feat: Add Weighted-Routing Failover
* test(router): cover weighted failover helper functions
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): align weighted failover deployment list type with mypy
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): address greptile review on weighted failover
- Narrow exception swallowing in `_maybe_run_weighted_failover` to
`openai.APIError` so model failures defer to the regular fallback
while programming bugs (AttributeError/KeyError/TypeError) surface.
- Note async-only limitation of `enable_weighted_failover` in the
Router constructor docstring.
- Make the weighted distribution test less flaky (1000 iterations,
looser bound) and make the non-simple-shuffle test deterministic by
failing both deployments instead of relying on the latency strategy's
first pick.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): ensure weighted failover metadata persists in kwargs
The previous `kwargs.setdefault(metadata_variable_name, {}) or {}` returned
a brand-new dict whenever the existing metadata was falsy (empty dict or
None), so writes to `_failover_excluded_ids` never made it back into
`kwargs`. Multi-hop weighted failover then re-selected previously failed
deployments and exhausted `max_fallbacks` prematurely.
Explicitly assign a fresh dict into kwargs when metadata is missing so
mutations are visible to subsequent failover hops.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(router): regression for weighted failover metadata persistence
Asserts kwargs["metadata"]["_failover_excluded_ids"] is populated after
_maybe_run_weighted_failover, proving the metadata dict written by the
helper is the same object that lives in kwargs (no disconnected copy).
Pairs with the prior fix that replaced `setdefault(..., {}) or {}` with
an explicit get/assign so writes survive across hops.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(router): harden weighted failover error/state handling
- Catch RouterRateLimitError (ValueError) alongside openai.APIError in
_maybe_run_weighted_failover so an exhausted intra-group retry falls
through to the regular cross-group fallback path instead of bubbling
out and bypassing configured fallbacks.
- Stop mutating the shared input_kwargs dict; build a local copy with
the weighted-failover keys so the entry (with _excluded_deployment_ids)
cannot leak into later fallback paths reading the same dict.
- _get_excluded_filtered_deployments now returns an empty list when the
exclusion filter removes every healthy deployment, instead of falling
back to the original list. The original-list behavior risked re-picking
the just-failed deployment; callers already handle the empty case by
raising their no-deployments error, which weighted failover now catches
and converts into a normal cross-group fallback.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(router): fall through to rpm/tpm when total weight is zero
When the weight metric's total is zero (e.g. after weighted-failover
exclusion leaves only zero-weight backups), continue to the next metric
(rpm/tpm) instead of returning a uniform random pick immediately. This
lets rpm/tpm still drive routing when present, and only falls back to
the uniform random pick at the end if no metric provides a positive
total weight.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(router): skip weighted failover when remaining deployments are all in cooldown
_maybe_run_weighted_failover was computing 'remaining' from all_deployments
(every deployment in the model group, including those in cooldown). This meant
that when all non-excluded deployments were in cooldown the method still invoked
run_async_fallback unnecessarily, which propagated into async_get_healthy_deployments,
found no eligible deployments, and raised RouterRateLimitError — only safely
caught thanks to the earlier exception-broadening fix.
The fix: before computing 'remaining', fetch the current cooldown set via
_async_get_cooldown_deployments and subtract it from all_ids. This allows
_maybe_run_weighted_failover to return None immediately (skipping the
run_async_fallback call entirely) when every non-failed deployment is in cooldown,
letting the caller fall through to the correct cross-group fallback path without
the wasteful extra round-trip.
Tests added:
- unit: _maybe_run_weighted_failover returns None without calling run_async_fallback
when all remaining deployments are in cooldown
- unit: _maybe_run_weighted_failover still calls run_async_fallback when at least
one healthy (non-cooldown) deployment is available
- integration: end-to-end fallthrough to cross-group fallback when remaining
deployments are in cooldown
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
|
||
|---|---|---|
| .. | ||
| adaptive_router | ||
| auto_router | ||
| complexity_router | ||
| quality_router | ||
| base_routing_strategy.py | ||
| budget_limiter.py | ||
| least_busy.py | ||
| lowest_cost.py | ||
| lowest_latency.py | ||
| lowest_tpm_rpm.py | ||
| lowest_tpm_rpm_v2.py | ||
| simple_shuffle.py | ||
| tag_based_routing.py | ||