按 DevDocs/design/01 §2.1/§2.2 与 design/04 §3.2 实现(规格含联网核过的官方依据:思考内容刻意不角色化、不写分步思考指令、思考控制只走参数级)。
- 人格 schema 新增 thinking:budgetTokens / effort / showToUser(*bool,省略=nil=保持现行为,显式 false=只落库) / mode(default|on|off→Enable 三态);非法 mode/负预算 → ErrInvalidThinking(不静默)
- 5 个死字段接上注入:personality→values→speechStyle→catchphrase→background 拼为独立段落(带【】小标题,空值不拼);**全空时输出与旧实现逐字节一致**(有专门测试)
- model.Request 新增 Thinking{ BudgetTokens, Enable } 与 ReasoningEffort;新增 model/reasoning.go:厂商识别(模型名→baseURL 兜底)、取值白名单(OpenAI 系 none…max / DeepSeek low|high|max)、非法值降级+WARN、**不支持一律 WARN 而非静默**;Anthropic 映射 thinking{budget_tokens} 并**保证 budget<max_tokens**(不足时抬高 max_tokens+WARN,正是历史上思考吃光预算→空回复那个坑)
- loop:新增 Options.ThinkingShowToUser(*bool) 仅门控 thinking_* 帧(正文/工具帧不受影响);场景级 mute 语义不变
- 补一处任务清单外但必需的接线:persona→model.Request 的 thinking 映射(否则 budget/effort/mode 会变成新的死字段);showToUser 刻意不产生 Thinking(纯呈现开关)
- 管理端:personaUpsertRequest 补 thinking DTO + 校验(400);PUT 省略时**保留已存块**(防旧客户端抹配置);写侧 yaml round-trip 已验证
- Docs/api-client v0.40→v0.41(可选请求字段)
- 测试:26 个新用例(loader 解析/拼接逐字节/映射、anthropic 非流式+流式预算约束、openai 透传/降级/丢弃、Qwen 诚实 WARN、loop 门控、DTO round-trip);3 次变异验证(省略与 false 混同 / 去掉预算约束 / 短路门控 各自 FAIL)
- 验收:go build/vet/test 全绿(含 57 包)+ linux/darwin 交叉编译 + 前端 build;唯一 FAIL 是既有 flake TestInterruptCancelsTurnAndClearsPending(3/20 新代码 vs 3/20 纯 HEAD,已归因非本次引入)
148 lines
8.4 KiB
Go
148 lines
8.4 KiB
Go
package loop
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import (
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"context"
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"log/slog"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/agent"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/capacity"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/guardrail"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/memory"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/model"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/tool"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/trace"
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)
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// Options configures a Loop. All fields are optional and nil is tolerated until
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// the step that actually needs the field: the agent is required for Run (Run
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// returns ErrNilAgent otherwise); a missing tool dispatcher causes a requested
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// call to be fed back as an error; a missing retriever/persona skips memory
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// injection; a missing extractor/store skips the memory hook; a missing
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// checkpoint skips persistence; a missing tracer skips tracing.
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type Options struct {
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// Agent is the loop's execution unit: an instruction string plus a backend
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// adapter, an optional tool dispatcher and optional guardrails.
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Agent *agent.Agent
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// Tools is the tool dispatcher used to execute model-requested calls. When
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// nil the loop falls back to the agent's own dispatcher (if any).
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Tools *tool.Dispatcher
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// Capacity, when non-nil and enabled, filters the tool schemas advertised to
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// the model each round to the allowed set for the current capacity state
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// (design/14 唤醒式能力渐进 §4): the loop only exposes the allowed set to the
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// model — a view, not a hard boundary (Invoke is left unblocked so an
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// emergency call still works). Nil (the default) keeps the pre-existing
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// behavior: every registered tool is advertised.
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Capacity *capacity.Decider
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// OnCapacity, when non-nil, receives capacity_awaken transition events the
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// loop observes while deciding a round (design/14 §4), so a caller can
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// surface the "能力点亮" progression to the frontend. Nil = no-op.
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OnCapacity func(capacity.Event)
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// OnAudit, when non-nil, receives an AuditEvent every time the loop denies a
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// tool call in a restricted (group/stranger) environment (design/11 §3 §8),
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// so the rejection lands in the audit trail and the frontend can render the
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// small "xx 工具在群聊被拒" note. Nil = the rejection is still fed back to the
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// model as a soft not_allowed result, just not recorded to an external sink.
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OnAudit func(AuditEvent)
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// OnTurnEnd, when non-nil, is called once per settled turn (a Run that
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// returned a Result) with the turn's user/assistant text and the persona the
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// loop is bound to, so a caller can advance state that follows the persona —
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// the emotion machine and affection curve of design/01 §2. Its return value
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// is recorded on Result.Mood and carried to the persisted assistant message.
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// Nil = the loop reports nothing and Result.Mood stays empty.
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OnTurnEnd TurnEndHook
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// Memory is the long-term memory store used to persist extracted items.
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Memory *memory.Store
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// Retriever injects relevant memories into the context, re-evaluated every
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// round (see assembleContext) so retrieval tracks the latest conversation.
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Retriever *memory.Retriever
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// WorkingMemory is the per-session sliding window, provided for the
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// orchestrator to inspect; the loop does not mutate it.
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WorkingMemory *memory.WorkingMemory
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// Extractor derives memory items to persist after every step.
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Extractor memory.Extractor
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// Checkpoint persists a durable snapshot of the loop state at every step and,
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// on a later Run with the same sessionID, resumes an interrupted run from it
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// (design §1 durable execution). *persist.Store satisfies the narrow
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// checkpointStore interface; nil disables both persistence and resume.
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Checkpoint checkpointStore
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// Tracer emits a structured span for every step, LLM call and tool call.
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Tracer *trace.Tracer
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// Flow, when non-nil, accumulates the run's trace spans into a per-session
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// flow graph and publishes ws.graph_update on each span transition (design/19
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// §1). Nil disables the realtime flow visualization.
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Flow *FlowPublisher
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// Guards validates request/response payloads on the streaming path, where
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// RunStep is bypassed. On the non-streaming path the agent runs its own
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// guardrails inside RunStep.
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Guards *guardrail.Pipeline
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// PersonaID scopes memory retrieval and extraction. Blank skips both.
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PersonaID string
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// Timezone is the IANA zone used to render the per-round "current time"
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// system message (e.g. "Asia/Shanghai"). Empty disables time injection
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// entirely (backward compatible); a name that fails to load also disables it.
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Timezone string
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// MaxRounds is the tool-loop upper bound. Non-positive selects
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// DefaultMaxRounds (8).
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MaxRounds int
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// OnStream, when non-nil, switches the loop to the streaming path: the model
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// adapter's ChatStream is driven directly and each structured stream event is
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// forwarded to this callback (model.StreamEvent), so the frontend can render
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// body text, tool calls and (eventually) reasoning segments independently.
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OnStream func(model.StreamEvent)
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// Thinking carries the reasoning knobs (budget / explicit switch) injected
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// into every model request the loop builds. nil means "do not intervene",
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// which is the pre-existing behavior. It is a loop option rather than a
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// persona read inside the loop so the per-turn, per-role resolution a later
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// multi-character batch adds only changes the wiring-time resolver.
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Thinking *model.Thinking
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// ReasoningEffort is the OpenAI-compatible strength knob injected into every
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// model request. Empty means "do not intervene".
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ReasoningEffort string
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// ThinkingShowToUser gates the thinking frames the loop broadcasts: nil (the
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// default) keeps the pre-existing behavior and pushes them; an explicit false
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// keeps thinking_start/delta/end out of the client stream while text and tool
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// frames are unaffected. The frames are still produced and still reach the
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// model/transcript paths — only the UI broadcast is gated, so display and
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// persistence stay decoupled (design/04 §3.2).
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ThinkingShowToUser *bool
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// ToolExecution selects how a batch of tool calls is run: "parallel" (the
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// default) or "sequential". Empty means parallel.
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ToolExecution string
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// ToolParallelism caps how many tool calls run concurrently when ToolExecution
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// is "parallel". Non-positive selects DefaultToolParallelism (4).
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ToolParallelism int
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// Hooks installs optional per-batch/per-call tool-loop hooks. Every field is
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// nil-tolerant; these are reserved for "敏感操作代审" (design/11) and the
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// future asynchronous-tool path. A zero Hooks value is a no-op.
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Hooks ToolLoopHooks
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// MaxContextTokens is the context budget that triggers overflow compaction.
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// Non-positive (the zero value) disables compaction, preserving the loop's
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// previous behavior. Setting it to a positive value (e.g.
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// DefaultMaxContextTokens) enables compaction: when the loop's estimated
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// context exceeds the budget it summarizes older turns into a leading summary
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// message while keeping the recent tail (including trailing tool results)
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// intact (design §1.3). Nil-tolerant: leaving it unset changes nothing.
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MaxContextTokens int
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// Compactor compacts an over-budget conversation. Nil selects the built-in
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// structural compactor (no model call); a non-nil value lets a caller inject
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// an LLM-backed or custom summarizer. It is only invoked when
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// MaxContextTokens is positive.
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Compactor Compactor
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// Vision, when non-nil, rewrites an image-bearing user message at the start
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// of Run so the main model can reason over the image (design/06 §0). The
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// concrete resolver (vision.MessageResolver) decides via the vision Router
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// whether to keep the image for a vision-capable main model or fold it into a
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// text description from the vision/OCR tool ("先看图再对话"). Nil keeps the
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// image content as-is, passing it through to a vision-capable main model.
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Vision ImageResolver
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// TransformContext, when non-nil, is called immediately before every model
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// step with the assembled message list (after memory injection and any
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// compaction) and the current round number (1-based). It may project, filter
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// or re-order the messages the model sees. It is the loop's equivalent of
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// OpenClaw's transformContext (design §1.3). Nil = no projection.
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TransformContext func(ctx context.Context, messages []model.Message, round int) []model.Message
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// Log is the base logger. Nil is replaced with a discard logger tagged with
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// source "runtime/loop".
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Log *slog.Logger
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}
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