vLLM (and other OpenAI-compatible servers) report context overflow with
both the window and the prompt in tokens:
"This model's maximum context length is 131072 tokens. However, you
requested 65536 output tokens and your prompt contains at least 65537
input tokens, for a total of at least 131073 tokens."
parse_available_output_tokens_from_error() already classified this as an
output-cap error (the "requested N output tokens" gate), but none of the
extraction patterns matched the "prompt contains [at least] N input
tokens" phrasing, so it returned None. The recovery path then
misclassified the failure as prompt-too-long and looped through
compression — which frees little while each retry keeps requesting the
same oversized max_tokens — terminating in "cannot compress further"
even though simply lowering the output cap would have succeeded.
Add an extraction branch for the token-based phrasing: available output
= window - reported input. When the input alone is at or over the
window it still returns None, so the caller correctly falls through to
compression.
Relates to #43547.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
An over-cap model.max_tokens produces a provider 400 that mentions
max_tokens, which trips _CONTEXT_OVERFLOW_PATTERNS and is classified as
context_overflow. On providers whose wording isn't recognized by
parse_available_output_tokens_from_error() (e.g. DashScope/Qwen:
"Range of max_tokens should be [1, 65536]") the smart-retry is skipped
and the error falls into the compression fallback, which re-sends the
same oversized max_tokens, fails identically, and loops until
"cannot compress further" on a tiny conversation (#55546).
Root-cause fix for the whole class, not just DashScope:
- parse_available_output_tokens_from_error(): recognize the DashScope
"Range of max_tokens should be [1, N]" form and return N (smart-retry
then caps output and retries WITHOUT compressing).
- new is_output_cap_error(): broader yes/no gate for output-cap 400s.
In the loop, when the error is output-cap-shaped but unparseable, fail
fast with an actionable message (lower model.max_tokens) instead of
routing into compression. Mirrors the existing GPT-5 max_tokens guard.
Real input overflows and GPT-5 unsupported-param 400s are unchanged.
Add TestParseCharBasedOutputCap for the LM Studio / llama.cpp phrasing
(context in tokens, prompt in characters): the reported error resolves to
the available output budget, the retried cap plus the estimated input
stays inside the window, and a prompt larger than the window falls through
to None so the prompt-too-long/compression path still owns that case.
Two isolated reliability fixes:
- chat_completion_helpers: raise on a zero-chunk stream (no finish_reason,
no content/reasoning/tool_calls) so retry handles it instead of
fabricating a successful empty turn.
- model_metadata: parse the OpenRouter/Nous output-cap error phrasing
("maximum context length is N ... (A of text input, B of tool input,
C in the output)") so parse_available_output_tokens_from_error returns
a real cap and the caller stops looping on it.
Salvaged from #40405 (@ashishpatel26) — took the two stream/error-parsing
fixes. The PR also bundled compression-state changes (on_session_start
clearing _previous_summary; cron session-id prefix preservation, #38788);
those touch the compression hot path and are split out for separate review.
Co-authored-by: ashishpatel26 <ashishpatel26@users.noreply.github.com>
