* feat(providers): explicit prompt cache for DashScope/Qwen
Extend OpenAI-compat path with Anthropic-style cache_control:ephemeral
inline blocks for Alibaba DashScope endpoints (provider_type=bailian or
URL contains "dashscope"). Verified live: qwen3.6-plus / qwen3.5-plus /
qwen3-coder-plus all return 99.7% cache hit rate on 6K-token prefix,
yielding ~89% token cost reduction on cached prefix (90% Alibaba discount).
- isDashScope(): 3-source detection (URL + providerType + name) handles
reverse-proxied endpoints; covers both "dashscope" and "bailian"
- buildRequestBody wraps system content with SplitSystemPromptForCache
(exported from anthropic_request.go) using <!-- GOCLAW_CACHE_BOUNDARY -->
- Tool prefix cache: cache_control on last tool definition, with 4-marker
budget guard
- Parse cache_creation_input_tokens from prompt_tokens_details into
Usage.CacheCreationTokens; propagates via existing span metadata writers
- Runtime escape hatch: GOCLAW_DISABLE_DASHSCOPE_CACHE=true
- Live integration smoke test (build tag integration, env-gated)
* chore(providers): polish per PR #1127 review
- Use strings.Repeat instead of custom repeat() helper in smoke test
- Clarify BuildRequestBodyForTest is test-only, not public API
* feat(providers): enable thinking for Qwen 3.7/3.6, observe cache in smoke test
Qwen3.7-plus and qwen3.6-plus support deep thinking but were missing from
dashscopeThinkingModels, so enable_thinking/thinking_budget was silently
skipped. Add both to the whitelist and test.
Add qwen3.7-plus to the cache smoke test as cache-optional: Alibaba's
context-cache doc does not yet list 3.7/3.6 for explicit cache, and the
cache wrap is a safe no-op when unsupported, so a no-cache result is logged
rather than failed to avoid a flaky live assertion.
Claude-Session: https://claude.ai/code/session_01X3jkrc7N3ar8ZGzUJWyNS5
* fix(providers): preserve DashScope detection for proxy routes
Remove ModelThinkingCapable interface and ChatRequest.ModelSupportsThinking
hint field — DashScope handles per-model checks internally via its own
whitelist. Fix double applyThinkingGuard on ChatStream→Chat tool fallback
by calling OpenAIProvider.Chat directly.
* feat(providers/dashscope): guard enable_thinking injection by per-model capability check
Introduces ModelThinkingCapable interface and ModelSupportsThinking field
on ChatRequest so DashScope can skip thinking-param injection for models
that do not support it (e.g. qwen3-plus, qwen3-turbo), preventing
\"model not supported\" API errors.
- types.go: add ModelThinkingCapable interface + ModelSupportsThinking *bool on ChatRequest
- dashscope.go: add dashscopeThinkingModels whitelist + ModelSupportsThinking(); honour pre-computed hint
- loop.go: detect ModelThinkingCapable and set hint on ChatRequest before LLM call
- provider_models.go: add qwen3.5-plus / qwen3.5-turbo to DashScope model list
- dashscope_test.go: full test suite for whitelist, injection, hint override, budget mapping
* Fix review code.
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Co-authored-by: Nguyen Gia Hoang Vinh <vinhngh@runsystem.net>
Plateau Nguyen