gofmt -l reported 13 files with misaligned struct tags / stale formatting.
This commit contains ONLY formatting: no behaviour change, no logic touched.
Files that also carry real changes in this series are formatted by their own
commits.
handleImage recorded the raw request model id, so AUTO image
generations showed up as model=AUTO in the request records and
by-model aggregates instead of the image model actually served
(e.g. Kwai-Kolors/Kolors).
UnifiedResponse gains an optional Model field; Provider.Image fills
it with the resolved id (AUTO resolves to the source's best image
model), and handleImage prefers it when writing the audit record.
Live testing across the zen pool showed models report
prompt_tokens_details.cached_tokens even when the hit count is 0 (e.g.
nemotron-3-ultra-free returns cached_tokens:0, audio_tokens:0,
cache_write_tokens:0). The previous >0 guard dropped those objects, so a
cache-enabled upstream looked identical to one without cache support.
- types: PromptTokensDetails.CachedTokens always emitted (drop inner
omitempty) so clients see cached_tokens:0 explicitly; dsh reads it as
a 0% hit instead of 'no data'
- adapters (9): forward prompt_tokens_details whenever the upstream
provides it (presence check instead of >0)
- Req: add cache_reported flag set when usage carried cache accounting;
WebUI shows an amber 0% tag for reported-but-missed rows and keeps
the em-dash only for sources that never report cache data
When only the legacy DeepSeek fields (prompt_cache_hit_tokens) are set but
the OpenAI-standard prompt_tokens_details is nil, MarshalJSON now auto-
generates the nested object. This ensures dsh (which reads the standard
format first) sees cache hit data regardless of which adapter format the
upstream uses.
dsh displays cache-hit %, but llmsproxy dropped every upstream's cache
fields — deepseek prompt_cache_hit_tokens, OpenAI prompt_tokens_details.
cached_tokens, anthropic cache_read_input_tokens, gemini cachedContentTokenCount.
Changes:
- TokenUsage: add PromptTokensDetails (with CachedTokens) + PromptCacheHit/Miss
- MarshalJSON: emit prompt_tokens_details.cached_tokens (OpenAI v2 standard)
and prompt_cache_hit/miss_tokens (DeepSeek legacy) — dsh reads the former
first, falls back to the latter
- mergeUsage: preserve cache fields across stream chunks
- standardSSEChunk: parse the upstream raw prompt_tokens_details too
- deepseek.lua: forward prompt_cache_hit/miss_tokens + create
prompt_tokens_details from them
- openai.lua: forward prompt_tokens_details.cached_tokens and legacy
prompt_cache_hit/miss_tokens; normalize legacy hits into the standard
object so dsh sees them regardless of upstream format
- anthropic.lua: map cache_read_input_tokens → prompt_tokens_details
- gemini.lua: map cachedContentTokenCount → prompt_tokens_details
provider.oneLineStr and gateway.oneLine had byte-identical bodies (flatten
whitespace + cap length). Move the single implementation into the types
package, which both layers already depend on, and delete both locals.
The gateway hardcoded "stop" on every terminating stream chunk, so
tool-call rounds reported finish_reason=stop and length caps were
invisible to clients. UnifiedChunk now carries finish_reason; adapters
emit it (with empty-string finish reasons like sensenova treated as
non-terminal), standardSSEChunk passes it through for un-adapted
upstreams, [DONE] no longer emits a duplicate reason-less done chunk,
and both streaming paths emit the real reason with "stop" as fallback.
Also vendor sensenova/agentrouter adapters into the repo: they were
WebUI-only uploads and a deploy sync silently removed them while live
AUTO-chain slots still referenced them.
Streaming responses now carry the upstream's real token usage instead of
gateway estimates:
- UnifiedChunk gains an optional Usage field; adapters (opencode, openai)
extract usage from upstream stream chunks (including the final chunk with
empty choices) and pass it through.
- standardSSEChunk preserves usage for passthrough adapters.
- Gateway emits the exact usage in the final stream chunk when available,
falling back to estimates only when the upstream provided none.
Non-streaming usage was already fixed to emit OpenAI-standard keys.
- TokenUsage.MarshalJSON now emits both standard (prompt_tokens,
completion_tokens, total_tokens) and legacy (prompt, completion, total)
keys, so OpenAI-compatible clients (DSH, DevEco Code, etc.) can read
token usage.
- ChatChunk gains an optional Usage field; stream responses now send a
final usage chunk (empty choices) before [DONE].
Refs: usage not visible in clients because the gateway serialized only the
internal short keys and never emitted a streaming usage chunk.
