264 lines
13 KiB
Rust
264 lines
13 KiB
Rust
//! Anthropic 协议适配 — 协议数据结构与 SSE 事件解析（纯函数）。
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//!
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//! 本模块从 `anthropic_compat.rs` 抽离，承载与 HTTP 无关的纯协议逻辑：
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//! - 请求/响应结构体（`AnthropicRequest` 等）
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//! - 协议常量（`ANTHROPIC_VERSION` / `DEFAULT_MAX_TOKENS`）
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//! - SSE 事件 → `StreamChunk` 转换纯函数（`apply_anthropic_event`）
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//!
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//! Provider struct + impl（含 HTTP 调用）仍留在 `anthropic_compat.rs`，
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//! Rust impl 块不可跨文件，故仅搬迁 impl 块外部的类型/常量/纯函数。
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//! 零行为变更（纯搬迁）。
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use serde::{Deserialize, Serialize};
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use tracing::{error, warn};
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use crate::provider::{tool_call_id_or_fallback, StreamChunk, TokenUsage, ToolCallDelta};
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// ============================================================
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// Anthropic API 请求/响应结构体
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// ============================================================
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/// Anthropic `system` 字段形态(受 prompt caching 开关 `ANTHROPIC_CACHE_ENABLED` 控制):
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/// - `Plain`:纯字符串。开关关时保持原形态,兼容非官方网关(GLM 订阅端点 / 任意 Messages API
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/// 代理——部分网关会拒收 cache_control 字段)。
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/// - `Cached`:blocks 数组 `[{type:text, text, cache_control:{type:ephemeral}}]`。开关开时启用
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/// Anthropic prompt caching,让 system 稳定段在支持缓存的端点上命中。
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#[derive(Debug, Clone, Serialize)]
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#[serde(untagged)]
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pub(crate) enum SystemBlock {
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/// 纯字符串形态(兼容网关,开关关)
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Plain(String),
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/// blocks 数组形态(含 cache_control,开关开)
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Cached(Vec<serde_json::Value>),
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}
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/// Anthropic 请求体
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#[derive(Debug, Clone, Serialize)]
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pub(crate) struct AnthropicRequest {
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pub model: String,
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pub messages: Vec<serde_json::Value>,
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pub max_tokens: u32,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub system: Option<SystemBlock>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub temperature: Option<f32>,
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pub stream: bool,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tools: Option<Vec<AnthropicToolDef>>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_choice: Option<serde_json::Value>,
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}
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/// Anthropic 工具定义（input_schema 对应 OpenAI 的 parameters）
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#[derive(Debug, Clone, Serialize)]
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pub(crate) struct AnthropicToolDef {
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pub name: String,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub description: Option<String>,
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pub input_schema: serde_json::Value,
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}
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/// Anthropic 同步响应
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#[derive(Debug, Deserialize)]
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pub(crate) struct AnthropicResponse {
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// Anthropic 响应反序列化字段,保留以对齐响应结构(响应 id),标注意图消除 dead_code warning
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#[allow(dead_code)]
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id: String,
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pub model: String,
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pub content: Vec<AnthropicContentBlock>,
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// Anthropic 响应反序列化字段,保留以备调试/未来消费(如日志记录调用终止原因),标注意图消除 dead_code warning
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#[allow(dead_code)]
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stop_reason: Option<String>,
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pub usage: AnthropicUsage,
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}
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/// 响应 content 块（text 或 tool_use）
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#[derive(Debug, Deserialize)]
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pub(crate) struct AnthropicContentBlock {
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#[serde(rename = "type")]
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pub block_type: String,
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#[serde(default)]
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pub text: Option<String>,
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/// tool_use 块字段
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pub id: Option<String>,
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pub name: Option<String>,
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pub input: Option<serde_json::Value>,
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}
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#[derive(Debug, Deserialize)]
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pub(crate) struct AnthropicUsage {
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pub input_tokens: u32,
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pub output_tokens: u32,
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/// Anthropic prompt caching 扩展:cache 写入 token(本次写入缓存,计费如输入但稍便宜)。
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/// 映射到 TokenUsage.prompt_cache_miss_tokens(全价输入语义)。
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/// 非 cache 场景无此字段 → serde default 0。
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#[serde(default)]
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pub cache_creation_input_tokens: u32,
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/// Anthropic prompt caching 扩展:cache 命中读取 token(低价)。
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/// 映射到 TokenUsage.prompt_cache_hit_tokens。
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/// 非 cache 场景无此字段 → serde default 0。
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#[serde(default)]
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pub cache_read_input_tokens: u32,
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}
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// ============================================================
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// 协议常量
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// ============================================================
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/// Anthropic 流式协议版本头
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pub(crate) const ANTHROPIC_VERSION: &str = "2023-06-01";
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/// Anthropic max_tokens 必填，缺省时的兜底值
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pub(crate) const DEFAULT_MAX_TOKENS: u32 = 4096;
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// ============================================================
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// SSE 解析纯函数（与 HTTP 解耦，便于单测）
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// ============================================================
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/// 将一条 Anthropic Messages SSE 事件 data 解析为 StreamChunk，并按需更新 usage 累加器。
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///
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/// 按 `type` 字段分发：
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/// - `message_start` → 用 `message.usage.input_tokens` 初始化累加器（output 置 0）。
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/// - `message_delta` → **output_tokens 是累计值（非增量）**，直接覆盖 `completion_tokens` 并重算 `total`。
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/// - `content_block_delta` (text_delta/input_json_delta) → 文本/工具入参增量。
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/// - `content_block_start` (tool_use) → 工具块开始，带 id+name。
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/// - `message_stop` → 返回 `finished=true` 终态 chunk，`usage` 取自累加器（`take()`）。
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/// - `error` → 返回 `finished=true` 终态空 chunk。
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/// - 其它（content_block_stop / ping 等）→ 空 chunk。
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///
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/// 等价性：content_block / message_stop / error 等事件分支与原 stream() 闭包逐字一致；
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/// usage 透传（message_stop 终态 take() 带出、message_delta 的 output_tokens 按累计值覆盖）
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/// 为本次新增能力，对应 StreamChunk 新增的 usage 字段。
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pub(crate) fn apply_anthropic_event(data: &str, usage_accum: &mut Option<TokenUsage>) -> StreamChunk {
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// 解析 data 中的 JSON，按 type 字段决定如何转 StreamChunk
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let v: serde_json::Value = match serde_json::from_str(data) {
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Ok(v) => v,
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Err(_) => {
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return StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
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}
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};
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let ty = v.get("type").and_then(|t| t.as_str()).unwrap_or("");
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match ty {
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// 消息开始：取 input_tokens 初始化累积器（output 此时未知，置 0）。
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// anthropic prompt caching:cache_creation/read 在 message_start.usage 携带。
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"message_start" => {
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if let Some(inp) = v
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.get("message")
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.and_then(|m| m.get("usage"))
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.and_then(|u| u.get("input_tokens"))
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.and_then(|t| t.as_u64())
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{
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let u_obj = v.get("message").and_then(|m| m.get("usage"));
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let cache_read = u_obj
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.and_then(|u| u.get("cache_read_input_tokens"))
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.and_then(|t| t.as_u64())
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.unwrap_or(0) as u32;
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let cache_creation = u_obj
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.and_then(|u| u.get("cache_creation_input_tokens"))
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.and_then(|t| t.as_u64())
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.unwrap_or(0) as u32;
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*usage_accum = Some(TokenUsage {
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prompt_tokens: inp as u32,
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completion_tokens: 0,
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total_tokens: inp as u32,
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prompt_cache_hit_tokens: cache_read,
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prompt_cache_miss_tokens: cache_creation,
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reasoning_tokens: 0,
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});
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}
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StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: usage_accum.clone(), error: None, reasoning_content: None }
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}
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// 消息增量：output_tokens 是累计值（非增量），直接覆盖 completion + 重算 total
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"message_delta" => {
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if let Some(out) = v.get("usage").and_then(|u| u.get("output_tokens")).and_then(|t| t.as_u64()) {
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let acc = usage_accum
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.get_or_insert(TokenUsage::default());
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acc.completion_tokens = out as u32;
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acc.total_tokens = acc.prompt_tokens.saturating_add(acc.completion_tokens);
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}
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// 加固:usage 挂到本帧(而非仅 message_stop 带出),中途断连/端点不发 message_stop
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// 时仍能拿到真实 usage,对称 openai_helpers 的修复。
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StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: usage_accum.clone(), error: None, reasoning_content: None }
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}
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// 文本增量
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"content_block_delta" => {
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if let Some(delta) = v.get("delta") {
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if delta.get("type").and_then(|t| t.as_str()) == Some("text_delta") {
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let text = delta.get("text").and_then(|t| t.as_str()).unwrap_or("").to_string();
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return StreamChunk { delta: text, finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None };
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}
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// 工具入参增量
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if delta.get("type").and_then(|t| t.as_str()) == Some("input_json_delta") {
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let partial = delta.get("partial_json").and_then(|t| t.as_str()).unwrap_or("").to_string();
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let idx = v.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as u32;
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return StreamChunk {
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delta: String::new(),
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finished: false,
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tool_calls: Some(vec![ToolCallDelta {
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index: idx,
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id: None,
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function_name: None,
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function_arguments: Some(partial),
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}]),
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usage: None,
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error: None,
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reasoning_content: None,
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};
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}
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}
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StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
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}
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// 工具块开始：带 id + name
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"content_block_start" => {
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if let Some(cb) = v.get("content_block") {
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if cb.get("type").and_then(|t| t.as_str()) == Some("tool_use") {
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let idx = v.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as u32;
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let name = cb.get("name").and_then(|t| t.as_str()).map(|s| s.to_string());
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// CR-空 id：id 缺失/空时用 fallback 兜底（流式后续 input_json_delta 按 index 累加，
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// 中途无法整体跳过）。与同步路径 + OpenAI 路径共用 tool_call_id_or_fallback（DRY），
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// prefix=`gen_anthropic_stream` 区分来源。非空原样。
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let raw_id = cb.get("id").and_then(|t| t.as_str()).unwrap_or("");
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let id = if raw_id.is_empty() {
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let fallback = tool_call_id_or_fallback(raw_id, idx as usize, "gen_anthropic_stream");
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warn!(%fallback, name = ?name, "Anthropic 流式 tool_use 块 id 为空，已生成 fallback id（原样回传会触发 GLM 500）");
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Some(fallback)
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} else {
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Some(raw_id.to_string())
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};
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return StreamChunk {
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delta: String::new(),
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finished: false,
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tool_calls: Some(vec![ToolCallDelta {
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index: idx,
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id,
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function_name: name,
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function_arguments: None,
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}]),
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usage: None,
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error: None,
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reasoning_content: None,
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};
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}
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}
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StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
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}
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// 消息结束：带出累积 usage
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"message_stop" => StreamChunk {
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delta: String::new(),
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finished: true,
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tool_calls: None,
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usage: usage_accum.take(),
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error: None,
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reasoning_content: None,
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},
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// 错误事件：流中途出错。不走 finished 完成路径（避免残缺响应被当正常完成入库），
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// 改由 stream_llm 识别 error 非空 → 发 AiError + 丢弃残缺（与 OpenAI 路径 Err 一致）。
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"error" => {
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let msg = v.get("error").and_then(|e| e.get("message")).and_then(|m| m.as_str()).unwrap_or("stream error").to_string();
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error!(%msg, "Anthropic 流式错误事件");
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StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: Some(msg), reasoning_content: None }
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}
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// content_block_stop / ping 等不产出 chunk
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_ => StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None },
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}
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}
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