docs: replace vague provenance prose with recorded facts
This commit is contained in:
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@@ -2,5 +2,5 @@
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# side as of the last confirmed-consistent state. Both languages carry equal authority;
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# after editing either side, bring the other along and re-record with:
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# pnpm run verify-translation-pairing --write packages/llm/llm-pi-ai/README.md
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README.md: eb67ce889193aadbd694d7aae53e47c7d20703be
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README.zh.md: 8b8e5e6a219841d700db7161a7352a1880c41165
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README.md: 4ad669344056a8290057cd39cbde56b0a2d80ba4
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README.zh.md: 96e1ef2dd3219e8b1fe0d5afbf5c784d9c3e42d5
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@@ -133,9 +133,9 @@ Credentials never enter that collection. The harness resolves a route's key thro
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The selected model descriptor supplies the protocol implementation. This includes native API differences such as OpenAI models whose descriptor uses the Responses API rather than Chat Completions; the harness adapter does not hardcode endpoint selection by model name.
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Successful assistant responses store a versioned, lossless-JSON replay state beside their durable provider/model provenance. At request time, `LlmService` passes replay state only when the historical provider route and target provider route are currently owned by this same `PiAiAdapter` instance. The adapter validates the state and restores pi-ai response ids and provider signatures even when the target provider or model changes; pi-ai then decides which metadata its target API can reuse. History without replay state is translated as foreign provider-neutral content and never impersonates a native pi-ai response.
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Successful assistant responses store a versioned, lossless-JSON replay state beside the provider and model that produced them. At request time, `LlmService` passes replay state only when the historical provider route and target provider route are currently owned by this same `PiAiAdapter` instance. The adapter validates the state and restores pi-ai response ids and provider signatures even when the target provider or model changes; pi-ai then decides which metadata its target API can reuse. History without replay state is translated as foreign provider-neutral content and never impersonates a native pi-ai response.
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If a listener rewrites assembled assistant content, the loop drops replay state before logging the message because its provider metadata no longer describes the content. Invalid versions, malformed metadata, provenance provider/model mismatches, and content/block mismatches fail explicitly with `LlmError('INVALID_REPLAY_STATE')`.
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If a listener rewrites assembled assistant content, the loop drops replay state before logging the message because its provider metadata no longer describes the content. Invalid versions, malformed metadata, provider/model mismatches between the message and replay state, and content/block mismatches fail explicitly with `LlmError('INVALID_REPLAY_STATE')`.
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## Vocabulary differences
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@@ -133,9 +133,9 @@ profile 的 `models` 列表是*替换*该路由已安装 catalog，而不是扩
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所选模型 descriptor 提供协议实现。这包括原生 API 差异，例如 descriptor 使用 Responses API 而非 Chat Completions 的 OpenAI 模型；harness 适配器不会按模型名称硬编码端点选择。
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成功的 assistant 响应会在自身持久提供方／模型溯源旁存储经版本化的无损 JSON 回放状态。请求时，`LlmService` 只有在历史提供方路由与目标提供方路由当前由同一个 `PiAiAdapter` 实例拥有时，才会传递回放状态。即使目标提供方或模型改变，适配器也会验证状态并恢复 pi-ai 响应 id 与提供方 signature；随后由 pi-ai 判定目标 API 可以复用哪些元数据。没有回放状态的历史会被转换为外来的、与提供方无关的内容，绝不伪装为原生 pi-ai 响应。
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成功的 assistant 响应会将经版本化的无损 JSON 回放状态与生成该响应的提供方和模型一同存储。请求时，`LlmService` 只有在历史提供方路由与目标提供方路由当前由同一个 `PiAiAdapter` 实例拥有时，才会传递回放状态。即使目标提供方或模型改变，适配器也会验证状态并恢复 pi-ai 响应 id 与提供方 signature；随后由 pi-ai 判定目标 API 可以复用哪些元数据。没有回放状态的历史会被转换为外来的、与提供方无关的内容，绝不伪装为原生 pi-ai 响应。
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如果 listener 改写已组装 assistant 内容，loop 会在记录消息前丢弃回放状态，因为其提供方元数据不再描述该内容。无效版本、格式错误元数据、溯源提供方／模型不匹配，以及内容／块不匹配都会显式以 `LlmError('INVALID_REPLAY_STATE')` 失败。
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如果 listener 改写已组装 assistant 内容，loop 会在记录消息前丢弃回放状态，因为其提供方元数据不再描述该内容。无效版本、格式错误元数据、消息与回放状态之间的提供方／模型不匹配，以及内容／块不匹配都会显式以 `LlmError('INVALID_REPLAY_STATE')` 失败。
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## 词汇差异
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@@ -102,7 +102,7 @@ describe('toPiContext', () => {
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expect((context.messages[0] as AssistantMessage).stopReason).toBe('stop')
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})
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it('preserves model provenance for foreign assistant messages without replay state', () => {
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it('preserves provider and model for foreign assistant messages without replay state', () => {
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const context = toPiContext({
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provider: 'openai',
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model: 'new-model',
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@@ -2,5 +2,5 @@
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# side as of the last confirmed-consistent state. Both languages carry equal authority;
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# after editing either side, bring the other along and re-record with:
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# pnpm run verify-translation-pairing --write packages/llm/llm/README.md
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README.md: ddbc2ea482ca0848fb0ee0813839cf5ff1829bcc
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README.zh.md: 0ebb99c6e3743b881905110985575d348f1c7601
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README.md: a437a689e934701b925dbf5a39a912c3af7dd46b
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README.zh.md: 7ed0c9c8a93e102b3d350be48be3a07e30011582
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@@ -21,7 +21,7 @@ An adapter registry plus a single streaming call surface, interceptable via a wa
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- `ctx.llm.listModels(provider: string): Promise<LlmModelInfo[]>` Discover the models one registered provider currently advertises.
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- `ctx.llm.resolveModelInfo(provider: string, model: string, signal?: AbortSignal): Promise<LlmResolvedModelInfo>` Resolve validated exact-model identity plus available context, output-default, and reasoning metadata from the owning adapter, with optional cancellation for asynchronous adapters.
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- `ctx.llm.resolveCallConfig(config: LlmCallConfig, signal?: AbortSignal): Promise<LlmCallConfig>` Validate an explicit effort and materialize adapter-configured call defaults without clamping.
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- `ctx.llm.prepareCall(config: LlmCallConfig, signal?: AbortSignal): Promise<PreparedLlmCall>` Resolve a config plus detached context metadata and adapter-default provenance in one exact-model lookup, then capture its current adapter registration and immutable retry policy as one cancellable, one-shot call.
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- `ctx.llm.prepareCall(config: LlmCallConfig, signal?: AbortSignal): Promise<PreparedLlmCall>` Resolve a config plus detached context metadata and markers for fields supplied by adapter defaults in one exact-model lookup, then capture its current adapter registration and immutable retry policy as one cancellable, one-shot call.
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- `ctx.llm.stream(options: GenerateOptions): AsyncIterable<StreamChunk>` Stream one model call as raw chunks (token-level deltas). Consumers assemble the chunks into blocks/messages with `BlockAssembler`.
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`LlmService` normalizes failures from final adapter selection, synchronous dispatch, iterator construction, and iteration into the stream protocol's single terminal form: `finish { kind: 'error' | 'aborted', failure }`. A failure after partial deltas may leave content blocks open; consumers discard that incomplete output. Errors from `llm/stream` middleware, nested calls, adapter cleanup, and downstream consumers remain thrown because they are plugin or consumer failures rather than model-request outcomes. A prepared call exposes the immutable retry policy captured with its exact adapter registration; a route handled entirely by middleware has no serving policy.
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@@ -51,13 +51,13 @@ Exact-model metadata is a separate correctness query, not a catalog decoration o
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`Message` is the shared immutable value used by delivery, durable history, and model requests. Every message has a required `MessageId`, role, content, and typed source from creation onward. `createMessage(input)` mints the identity and returns a detached deep-frozen value; `createUserMessage({ content, source })` fixes the user role; `createAssistantMessage({ content, source })` fixes the assistant role and model source kind; `createToolResultMessage({ callId, content, isError })` fixes the user role and couples the tool source to its result block; `freezeMessage(message)` imports an identity that already exists and never replaces it. Message rewrites preserve the identity and produce another frozen value. Browser code imports these value constructors from the dependency-minimal `@deepseek-ai/dsh-llm/message` entry instead of the service-bearing package root.
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Message content is an array of typed blocks: `text`, `reasoning`, `tool-call`, `tool-result`. The union is derived from the merge-extensible `ContentBlockMap`, so plugins can add block types via declaration merging. Assistant messages use a model source carrying provider/model provenance and optional adapter-private replay state. Before dispatch, `LlmService` retains that state only when the historical provider route and target provider route are currently owned by the exact same adapter instance; the adapter then decides whether it can restore or convert the state across models/providers. The core block set is limited to blocks every shipping path honors — multimodal content (images, audio, …) has no core block type; a feature that needs one adds it via the map together with the adapter/UI/compaction support that honors it.
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Message content is an array of typed blocks: `text`, `reasoning`, `tool-call`, `tool-result`. The union is derived from the merge-extensible `ContentBlockMap`, so plugins can add block types via declaration merging. Assistant messages use a model source carrying the provider and model that produced them plus optional adapter-private replay state. Before dispatch, `LlmService` retains that state only when the historical provider route and target provider route are currently owned by the exact same adapter instance; the adapter then decides whether it can restore or convert the state across models/providers. The core block set is limited to blocks every shipping path honors — multimodal content (images, audio, …) has no core block type; a feature that needs one adds it via the map together with the adapter/UI/compaction support that honors it.
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Streaming is a raw chunk protocol (`block-start`, `text-delta`, `reasoning-delta`, `tool-call-delta`, `block-end`, `usage`, `finish`). Every adapter outcome reaches consumers as one terminal `finish`; operational failure uses its `error` or `aborted` reason rather than throwing across the stream API. `BlockAssembler` is the single shared implementation that assembles chunks into blocks/messages.
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### Call configuration (`call-config.ts`)
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`LlmCallConfig` is the provider, model, optional adapter-owned reasoning effort, and sampling scalars of one conversation's requests (`provider`, `model`, `reasoningEffort`, `temperature`, `maxTokens`, `stop` — each mapping 1:1 onto the same-named `GenerateOptions` field). It is per-conversation state recorded in the session log as part of the request header (see the dsh-session `request/header` events), never a silently-adjustable per-call knob: the `agent/request` waterfall proposes a replacement, `prepareCall()` validates it and materializes adapter defaults under the turn signal, and the loop logs the effective value plus adapter-default provenance before using the prepared call's registration-bound stream. The next proposal omits marked defaults so a changed route resolves its own values; unmarked explicit fields persist. `callConfigEquals(a, b)` is the field-wise real-change detector; `deepFreeze(value)` is the ownership helper the loop applies to every built request before dispatch (`llm/stream` listeners and adapters read, never rewrite). `markAgentLoopRequest()` gives that exact object process-local loop provenance, and `isAgentLoopRequest()` lets observers distinguish it from independently logged auxiliary calls that may also be frozen and session-associated. `GenerateOptions.purpose` classifies logged auxiliary compaction and session-title calls so adapters can apply purpose-specific transport policy without changing ordinary conversation requests.
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`LlmCallConfig` is the provider, model, optional adapter-owned reasoning effort, and sampling scalars of one conversation's requests (`provider`, `model`, `reasoningEffort`, `temperature`, `maxTokens`, `stop` — each mapping 1:1 onto the same-named `GenerateOptions` field). It is per-conversation state recorded in the session log as part of the request header (see the dsh-session `request/header` events), never a silently-adjustable per-call knob: the `agent/request` waterfall proposes a replacement, `prepareCall()` validates it and materializes adapter defaults under the turn signal, and the loop logs the effective value plus markers for fields supplied by adapter defaults before using the prepared call's registration-bound stream. The next proposal omits marked defaults so a changed route resolves its own values; unmarked explicit fields persist. `callConfigEquals(a, b)` is the field-wise real-change detector; `deepFreeze(value)` is the ownership helper the loop applies to every built request before dispatch (`llm/stream` listeners and adapters read, never rewrite). `markAgentLoopRequest()` marks that exact object as created by the process-local agent loop, and `isAgentLoopRequest()` lets observers distinguish it from independently logged auxiliary calls that may also be frozen and session-associated. `GenerateOptions.purpose` classifies logged auxiliary compaction and session-title calls so adapters can apply purpose-specific transport policy without changing ordinary conversation requests.
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### App attribution (`attribution.ts`)
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@@ -21,7 +21,7 @@
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- `ctx.llm.listModels(provider: string): Promise<LlmModelInfo[]>` 发现某个已注册提供方当前公布的模型。
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- `ctx.llm.resolveModelInfo(provider: string, model: string, signal?: AbortSignal): Promise<LlmResolvedModelInfo>` 从拥有精确路由的适配器解析经校验的确切模型身份，以及可用上下文、输出默认值和推理（reasoning）元数据；异步适配器可选地支持取消。
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- `ctx.llm.resolveCallConfig(config: LlmCallConfig, signal?: AbortSignal): Promise<LlmCallConfig>` 校验显式推理强度，并填入适配器配置的调用默认值，但不自动调整。
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- `ctx.llm.prepareCall(config: LlmCallConfig, signal?: AbortSignal): Promise<PreparedLlmCall>` 在一次精确模型查询中解析配置、脱耦的上下文元数据与适配器默认值溯源，再将当前适配器注册和不可变重试策略捕获为一次可取消、一次性调用。
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- `ctx.llm.prepareCall(config: LlmCallConfig, signal?: AbortSignal): Promise<PreparedLlmCall>` 在一次精确模型查询中解析配置、脱耦的上下文元数据以及标明哪些字段由适配器默认值填入的标记，再将当前适配器注册和不可变重试策略捕获为一次可取消、一次性调用。
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- `ctx.llm.stream(options: GenerateOptions): AsyncIterable<StreamChunk>` 将一次模型调用流式输出为原始分片（token 级增量）。消费方使用 `BlockAssembler` 将分片组装为块／消息。
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`LlmService` 将最终适配器选择、同步 dispatch、iterator 构造与迭代中的失败规范化为流协议唯一的终止形式：`finish { kind: 'error' | 'aborted', failure }`。部分增量输出后发生失败时，内容块可能仍未闭合；消费方会丢弃这些不完整输出。`llm/stream` middleware、嵌套调用、适配器清理和下游消费方的错误仍会抛出，因为它们属于插件或消费方失败，而非模型请求结果。已准备调用会暴露随其确切适配器注册一同捕获的不可变重试策略；完全由 middleware 处理的路由没有服务策略。
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@@ -51,13 +51,13 @@
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`Message` 是投递、持久历史和模型请求共享的不可变值。每条消息从创建起都必须具有 `MessageId`、角色、内容和带类型的来源。`createMessage(input)` 生成标识，并返回与输入分离且深度冻结的值；`createUserMessage({ content, source })` 固定 user 角色；`createAssistantMessage({ content, source })` 固定 assistant 角色与模型来源类别；`createToolResultMessage({ callId, content, isError })` 固定 user 角色，并将工具来源与其结果块耦合；`freezeMessage(message)` 导入已有标识，绝不将其替换。改写消息时会保留标识，并产生另一个冻结值。浏览器端代码会从依赖最少的 `@deepseek-ai/dsh-llm/message` 入口导入这些值构造函数，而不是从包含服务的包根入口导入。
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消息内容是类型化内容块数组：`text`、`reasoning`、`tool-call`、`tool-result`。联合从可合并扩展的 `ContentBlockMap` 派生，因此插件可以通过 declaration merging 添加块类型。assistant 消息使用模型来源，其中携带提供方／模型溯源与可选适配器私有回放状态。dispatch 前，`LlmService` 只在历史提供方路由与目标提供方路由当前由完全相同的适配器实例拥有时才保留该状态；随后由适配器判定能否在模型／提供方间恢复或转换该状态。核心块集只包含每条已发布路径都支持的块。多模态内容（图像、音频等）没有核心块类型；需要它的功能会通过 map 添加，并一并添加相应的适配器／UI／压缩（compaction）支持。
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消息内容是类型化内容块数组：`text`、`reasoning`、`tool-call`、`tool-result`。联合从可合并扩展的 `ContentBlockMap` 派生，因此插件可以通过 declaration merging 添加块类型。assistant 消息使用模型来源，其中携带生成该消息的提供方和模型，以及可选的适配器私有回放状态。dispatch 前，`LlmService` 只在历史提供方路由与目标提供方路由当前由完全相同的适配器实例拥有时才保留该状态；随后由适配器判定能否在模型／提供方间恢复或转换该状态。核心块集只包含每条已发布路径都支持的块。多模态内容（图像、音频等）没有核心块类型；需要它的功能会通过 map 添加，并一并添加相应的适配器／UI／压缩（compaction）支持。
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流式输出是原始分片协议（`block-start`、`text-delta`、`reasoning-delta`、`tool-call-delta`、`block-end`、`usage`、`finish`）。每个适配器结果都以一个终止 `finish` 到达消费方；运行故障使用其 `error` 或 `aborted` 原因，而不会跨流 API 抛出。`BlockAssembler` 是将分片组装为块／消息的唯一共享实现。
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### 调用配置（`call-config.ts`）
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`LlmCallConfig` 是一个会话中各次请求的提供方、模型、可选的适配器持有推理强度和采样标量（`provider`、`model`、`reasoningEffort`、`temperature`、`maxTokens`、`stop`，每个都与同名 `GenerateOptions` 字段 1:1 映射）。它是作为请求标头一部分记录在会话日志中的每会话状态（见 dsh-session `request/header` 事件），绝不是可静默调整的每次调用旋钮：`agent/request` waterfall 会提议替换，`prepareCall()` 在轮次 signal 控制下校验它并填入适配器默认值，loop 随后记录生效值及适配器默认值来源，再使用已准备调用中与注册绑定的流。下一次提议会省略带标记的默认值，使变更后的路由解析自身的值；未带标记的显式字段会保留。`callConfigEquals(a, b)` 是逐字段真实变更检测器；`deepFreeze(value)` 是 loop 在 dispatch 前对每个已构建请求应用的所有权 helper（`llm/stream` listener 与适配器只读，绝不改写）。`markAgentLoopRequest()` 为该精确对象添加进程本地 loop 溯源，`isAgentLoopRequest()` 让观测方可以将其与同样可能冻结并关联会话、但独立记录的辅助调用区分。`GenerateOptions.purpose` 对已记录辅助压缩与会话标题调用分类，让适配器可以应用目的特定传输策略，而不改变普通会话请求。
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`LlmCallConfig` 是一个会话中各次请求的提供方、模型、可选的适配器持有推理强度和采样标量（`provider`、`model`、`reasoningEffort`、`temperature`、`maxTokens`、`stop`，每个都与同名 `GenerateOptions` 字段 1:1 映射）。它是作为请求标头一部分记录在会话日志中的每会话状态（见 dsh-session `request/header` 事件），绝不是可静默调整的每次调用旋钮：`agent/request` waterfall 会提议替换，`prepareCall()` 在轮次 signal 控制下校验它并填入适配器默认值，loop 随后记录生效值以及标明哪些字段由适配器默认值填入的标记，再使用已准备调用中与注册绑定的流。下一次提议会省略带标记的默认值，使变更后的路由解析自身的值；未带标记的显式字段会保留。`callConfigEquals(a, b)` 是逐字段真实变更检测器；`deepFreeze(value)` 是 loop 在 dispatch 前对每个已构建请求应用的所有权 helper（`llm/stream` listener 与适配器只读，绝不改写）。`markAgentLoopRequest()` 将该精确对象标记为由进程本地 agent loop 创建，`isAgentLoopRequest()` 让观测方可以将其与同样可能冻结并关联会话、但独立记录的辅助调用区分。`GenerateOptions.purpose` 对已记录辅助压缩与会话标题调用分类，让适配器可以应用目的特定传输策略，而不改变普通会话请求。
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### 应用归因（`attribution.ts`）
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@@ -61,7 +61,7 @@ export function callConfigEquals(a: LlmCallConfig, b: LlmCallConfig): boolean {
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/**
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* Mark one exact request object as assembled by dsh-agent-loop.
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* @param request - loop-owned request envelope before LLM dispatch.
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* @returns the same request object with process-local loop provenance.
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* @returns the same request object marked as created by the process-local agent loop.
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*/
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export function markAgentLoopRequest<T extends GenerateOptions>(request: T): T {
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AGENT_LOOP_REQUESTS.add(request)
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@@ -4,7 +4,7 @@ import { MessageId, type CallId } from './brand.ts'
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import { deepFreeze } from './call-config.ts'
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import type { ContentBlock, ToolResultBlock } from './types.ts'
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/** Provider ownership and adapter-private replay data for an assistant message. */
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/** Provider/model identity and adapter-private replay data for an assistant message. */
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export interface AssistantProvenance {
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/** Provider route that produced the message. */
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provider: string
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@@ -31,7 +31,7 @@ export interface ToolMessageSource {
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/**
|
||||
* What SHAPE of information a producer-supplied context carries, declared by
|
||||
* the producer beside its provenance.
|
||||
* the producer beside the source fields it supplied.
|
||||
*
|
||||
* `MessageSource.kind` answers *who produced this*; `form` answers *what kind
|
||||
* of thing it is*, and the two axes are deliberately independent — several
|
||||
@@ -133,7 +133,7 @@ export interface Message {
|
||||
readonly role: 'system' | 'user' | 'assistant'
|
||||
/** Exact model-facing blocks. */
|
||||
readonly content: ContentBlock[]
|
||||
/** Required producer provenance. */
|
||||
/** Required source fields supplied by the producer. */
|
||||
readonly source: MessageSource
|
||||
}
|
||||
|
||||
@@ -200,7 +200,7 @@ export function createUserMessage<T extends NewUserMessage>(
|
||||
|
||||
/**
|
||||
* Create one identified model-produced assistant message and freeze it before publication.
|
||||
* @param input - complete content and model provenance for a new assistant message.
|
||||
* @param input - complete content plus the provider, model, and optional replay state for a new assistant message.
|
||||
* @returns an immutable assistant message with fixed role/source tags and a fresh stable identity.
|
||||
*/
|
||||
export function createAssistantMessage(
|
||||
|
||||
@@ -74,7 +74,7 @@ describe('message construction', () => {
|
||||
expect(Object.isFrozen(message.source)).toBe(true)
|
||||
})
|
||||
|
||||
it('couples tool-result content and provenance to one call identity', () => {
|
||||
it('couples tool-result content and its cited call seq to one call identity', () => {
|
||||
const callId = CallId('call-1')
|
||||
const message = createToolResultMessage({
|
||||
callId,
|
||||
|
||||
@@ -1046,7 +1046,7 @@ describe('LlmService', () => {
|
||||
})
|
||||
})
|
||||
|
||||
it('strips replay state but preserves provenance when the target uses a different adapter instance', async () => {
|
||||
it('strips replay state but preserves provider and model when the target uses a different adapter instance', async () => {
|
||||
const ctx = new Context()
|
||||
await ctx.plugin(LlmService)
|
||||
ctx.llm.registerAdapter(['historical'], new RecordingAdapter(SCRIPT))
|
||||
|
||||
@@ -2,5 +2,5 @@
|
||||
# side as of the last confirmed-consistent state. Both languages carry equal authority;
|
||||
# after editing either side, bring the other along and re-record with:
|
||||
# pnpm run verify-translation-pairing --write packages/llm/token-meter/README.md
|
||||
README.md: 8f868f25f3c4caf1fdab5b50965aab41efecf5af
|
||||
README.zh.md: e3908ecea17b16669458f89a6359a432f5b24c0c
|
||||
README.md: 929d0322f6268562a10b9ab715cc0330800966b5
|
||||
README.zh.md: 40cea07c7dea20d0af9bf67ade2cb4164f7aed08
|
||||
|
||||
@@ -17,9 +17,9 @@ The estimator has no settings. It intentionally uses one fixed heuristic: four c
|
||||
|
||||
`measure()` synchronizes once and returns one detached, deeply immutable snapshot. `totalTokens` is request-and-response pressure, while `surfaceTokens` is the surface-only heuristic total and equals the sum of `nodes[].tokens`. A `requestHeader` override affects pressure fields only; the surface fields still describe the current session. Every call clones the positional nodes, so measurement is O(surface).
|
||||
|
||||
The fold tracks full request-header snapshots, step boundaries, surface appends and replacements, successful assistant messages, provider usage, and assistant-chunk provenance. Provider usage is reused only when the latest successful call's canonical request envelope matches the measured envelope and its total is no lower than that call's full heuristic anchor; a later success replaces the earlier anchor. Otherwise the complete current envelope and surface are estimated. Surface changes remain signed relative to a matching anchor, including negative deltas after shrinking replacements.
|
||||
The fold tracks full request-header snapshots, step boundaries, surface appends and replacements, successful assistant messages, provider usage, and the chunk seqs cited by each assistant message. Provider usage is reused only when the latest successful call's canonical request envelope matches the measured envelope and its total is no lower than that call's full heuristic anchor; a later success replaces the earlier anchor. Otherwise the complete current envelope and surface are estimated. Surface changes remain signed relative to a matching anchor, including negative deltas after shrinking replacements.
|
||||
|
||||
Usage accounting sums disjoint input, cache-read, cache-write, and output buckets; reasoning is not added again. Every successful call records an assistant anchor, including content-less calls. An explicit empty provenance list means a known empty provider stream, while absent legacy provenance conservatively treats the durable assistant output as provider output.
|
||||
Usage accounting sums disjoint input, cache-read, cache-write, and output buckets; reasoning is not added again. Every successful call records an assistant anchor, including content-less calls. An explicit empty `sourceEventSeqs` list means a known empty provider stream, while an absent legacy list conservatively treats the durable assistant output as provider output.
|
||||
|
||||
## Session projections
|
||||
|
||||
@@ -65,4 +65,4 @@ No direct invalidation; the named consumer owns any request-prefix changes.
|
||||
- **The fixed heuristic is approximate** — content without reusable provider usage is priced by character count plus structural overhead, not an exact provider tokenizer or request serializer.
|
||||
- **Every measurement clones the current surface** — coherent immutable snapshots make reads O(surface), including below-threshold pressure checks.
|
||||
- **Provider usage is only reusable for an identical canonical envelope** — prompt, prefix, tools, provider, model, or call-config changes deliberately fall back to full heuristic estimation.
|
||||
- **Legacy provenance is conservative** — assistant messages without `sourceEventSeqs` cannot distinguish provider output from listener rewrites, so the fold avoids claiming a known empty or exact chunk stream.
|
||||
- **Missing legacy source seqs are handled conservatively** — assistant messages without `sourceEventSeqs` cannot distinguish provider output from listener rewrites, so the fold avoids claiming a known empty or exact chunk stream.
|
||||
|
||||
@@ -17,9 +17,9 @@
|
||||
|
||||
`measure()` 会同步一次，并返回一个独立且深度不可变的快照。`totalTokens` 是请求与响应压力，`surfaceTokens` 是仅表层启发式总量，等于 `nodes[].tokens` 之和。`requestHeader` 覆盖只影响压力字段；表层字段仍描述当前会话。每次调用都会克隆带位置的节点，因此测量是 O(surface)。
|
||||
|
||||
fold 跟踪完整请求标头快照、步骤边界、表层追加与替换、成功 assistant 消息、提供方用量和 assistant 分片溯源。只有当最新成功调用的规范请求 envelope 与已测量 envelope 匹配，且其总量不低于该调用的完整启发式锚点时，才会复用提供方用量；后续成功会替换较早锚点。否则会对当前 envelope 与表层进行完整估算。表层变更保持相对于匹配锚点的带符号值，包括缩减替换后的负 delta。
|
||||
fold 跟踪完整请求标头快照、步骤边界、表层追加与替换、成功 assistant 消息、提供方用量，以及每条 assistant 消息引用的分片 seq。只有当最新成功调用的规范请求 envelope 与已测量 envelope 匹配，且其总量不低于该调用的完整启发式锚点时，才会复用提供方用量；后续成功会替换较早锚点。否则会对当前 envelope 与表层进行完整估算。表层变更保持相对于匹配锚点的带符号值，包括缩减替换后的负 delta。
|
||||
|
||||
用量计量会求和不重叠的输入、cache-read、cache-write 与输出 bucket；不会再次添加推理（reasoning）。每次成功调用都会记录一个 assistant 锚点，包括无内容调用。显式空溯源列表表示已知空提供方流，而遗留溯源缺失时，fold 会保守地将持久 assistant 输出视为提供方输出。
|
||||
用量计量会求和不重叠的输入、cache-read、cache-write 与输出 bucket；不会再次添加推理（reasoning）。每次成功调用都会记录一个 assistant 锚点，包括无内容调用。显式的空 `sourceEventSeqs` 列表表示已知空提供方流；遗留记录缺少该列表时，fold 会保守地将持久 assistant 输出视为提供方输出。
|
||||
|
||||
## 会话投影
|
||||
|
||||
@@ -65,4 +65,4 @@ fold 跟踪完整请求标头快照、步骤边界、表层追加与替换、成
|
||||
- **固定启发式规则是近似值**：没有可复用提供方用量的内容按字符数加结构开销计价，而不是使用精确提供方 tokenizer 或请求 serializer。
|
||||
- **每次测量都会克隆当前表层**：一致且不可变的快照使读取成为 O(surface)，包括低于阈值的压力检查。
|
||||
- **提供方用量只能为完全相同的规范 envelope 复用**：提示词、前缀、工具、提供方、模型或调用配置变更都会有意回退到完整启发式估算。
|
||||
- **遗留溯源采取保守策略**：没有 `sourceEventSeqs` 的 assistant 消息无法区分提供方输出与 listener 改写，因此 fold 不会声称已知空流或精确分片流。
|
||||
- **保守处理缺少源事件 seq 的遗留记录**：没有 `sourceEventSeqs` 的 assistant 消息无法区分提供方输出与 listener 改写，因此 fold 不会声称已知空流或精确分片流。
|
||||
|
||||
@@ -270,9 +270,9 @@ export class TokenMeterService extends Service {
|
||||
}
|
||||
|
||||
/**
|
||||
* Reassemble provider output from exact chunk provenance for a usage anchor.
|
||||
* Missing legacy provenance conservatively treats the durable output as the
|
||||
* provider output; explicit empty provenance prices a known empty stream.
|
||||
* Reassemble provider output from the exact cited chunk seqs for a usage anchor.
|
||||
* Missing legacy source seqs conservatively treat the durable output as the
|
||||
* provider output; an explicit empty list prices a known empty stream.
|
||||
*/
|
||||
private _estimateProviderAssistant(
|
||||
session: Session,
|
||||
|
||||
@@ -313,7 +313,7 @@ describe('replay anchors and surface folds', () => {
|
||||
expect(advanced.surfaceDeltaTokens).toBeGreaterThan(0)
|
||||
})
|
||||
|
||||
it('distinguishes explicit empty provenance from absent legacy provenance', () => {
|
||||
it('distinguishes an explicit empty source-event list from an absent legacy list', () => {
|
||||
const explicit = Session.create(SessionId('explicit-empty'))
|
||||
const legacy = Session.create(SessionId('legacy-absent'))
|
||||
appendSuccessfulCall(explicit, header('deepseek-v4-flash'), {
|
||||
@@ -503,7 +503,7 @@ describe('malformed replay and listener lifecycle', () => {
|
||||
)
|
||||
})
|
||||
|
||||
it('rejects invalid assistant provenance', () => {
|
||||
it('rejects invalid assistant source-event references', () => {
|
||||
const cases: Array<{
|
||||
name: string
|
||||
appendSource(session: Session): number[]
|
||||
@@ -553,7 +553,7 @@ describe('malformed replay and listener lifecycle', () => {
|
||||
}
|
||||
})
|
||||
|
||||
it('rejects repeated and non-earlier assistant provenance', () => {
|
||||
it('rejects repeated and non-earlier assistant source-event references', () => {
|
||||
const duplicate = Session.create(SessionId('duplicate-source'))
|
||||
duplicate.append('step/start', { turn: 1, step: 1 })
|
||||
appendHeader(duplicate, header('deepseek-v4-flash'))
|
||||
|
||||
Reference in New Issue
Block a user