重构: audit第三批cache + anthropic_compat拆分(strategy并行)
- audit 第三批: 新建 audit/cache.rs(151行 find_cached_high_risk_result/canonical_args_key/sort_object_keys/PENDING_APPROVAL_PLACEHOLDER), audit/mod.rs 586→451(-135); pub(crate) re-export, F-09 per_conv保留 - anthropic_compat 拆分: 新建 df-ai/anthropic_helpers.rs(223行 apply_anthropic_event + 5 struct + 2常量), anthropic_compat.rs 1036→842; Provider impl保留(impl块约束), 字段pub(crate) 主代兜底: cargo check --workspace 0 + test df-ai 119 + test devflow 98 strategy: 核心库自底向上, 单批1-2文件原子, impl块约束(Provider方法保留只抽纯函数/类型) git add指定(audit/* + df-ai/*, 不含其他会话)
This commit is contained in:
@@ -5,18 +5,20 @@
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//!
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//! 与 OpenAI 协议的关键差异由本模块内部完成转换，对外仍暴露统一的 LlmProvider trait，
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//! 上层 (Agentic Loop / AiNode) 无需感知协议。
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//!
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//! 协议数据结构（请求/响应 struct）与 SSE 事件解析纯函数已抽至 `anthropic_helpers`，
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//! 本模块仅保留 Provider struct + impl（HTTP 调用），Rust impl 块不可跨文件故作此切分。
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use async_trait::async_trait;
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use eventsource_stream::Eventsource;
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use futures::StreamExt;
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use reqwest::Client;
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use serde::{Deserialize, Serialize};
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use std::time::Duration;
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use tracing::{debug, error, warn};
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use crate::provider::{
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CompletionRequest, CompletionResponse, LlmProvider, MessageRole,
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StreamChunk, StreamResult, TokenUsage, ToolCall, ToolCallDelta,
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StreamResult, TokenUsage, ToolCall,
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};
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// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
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#[cfg(test)]
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@@ -25,204 +27,12 @@ use crate::retry::{
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retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
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};
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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 请求体
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#[derive(Debug, Clone, Serialize)]
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struct AnthropicRequest {
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model: String,
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messages: Vec<serde_json::Value>,
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max_tokens: u32,
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#[serde(skip_serializing_if = "Option::is_none")]
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system: Option<String>,
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#[serde(skip_serializing_if = "Option::is_none")]
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temperature: Option<f32>,
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stream: bool,
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#[serde(skip_serializing_if = "Option::is_none")]
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tools: Option<Vec<AnthropicToolDef>>,
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#[serde(skip_serializing_if = "Option::is_none")]
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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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struct AnthropicToolDef {
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name: String,
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#[serde(skip_serializing_if = "Option::is_none")]
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description: Option<String>,
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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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struct AnthropicResponse {
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// SW-260618-25: 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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model: String,
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content: Vec<AnthropicContentBlock>,
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// SW-260618-25: 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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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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struct AnthropicContentBlock {
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#[serde(rename = "type")]
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block_type: String,
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#[serde(default)]
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text: Option<String>,
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/// tool_use 块字段
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id: Option<String>,
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name: Option<String>,
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input: Option<serde_json::Value>,
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}
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#[derive(Debug, Deserialize)]
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struct AnthropicUsage {
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input_tokens: u32,
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output_tokens: u32,
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}
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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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"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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*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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});
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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 { prompt_tokens: 0, completion_tokens: 0, total_tokens: 0 });
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acc.completion_tokens = out as u32;
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acc.total_tokens = acc.prompt_tokens + acc.completion_tokens;
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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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// 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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// id 缺失时用占位 id 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；占位 id 保证回传的 tool_use_id 非空，避免 GLM 500。
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let id = match cb.get("id").and_then(|t| t.as_str()).map(|s| s.to_string()) {
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Some(id) if !id.is_empty() => Some(id),
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_ => {
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let placeholder = format!("tool_missing_{}", idx);
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warn!(%placeholder, name = ?name, "Anthropic 流式 tool_use 块缺少 id，已填占位 id（原样回传会触发 GLM 500）");
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Some(placeholder)
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}
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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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// 协议数据结构 + SSE 纯解析（apply_anthropic_event / AnthropicRequest 等）抽至 anthropic_helpers，
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// 此处 use 引入以保持本模块内引用路径不变（零行为变更搬迁）。
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use crate::anthropic_helpers::{
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apply_anthropic_event, AnthropicRequest, AnthropicResponse, AnthropicToolDef,
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ANTHROPIC_VERSION, DEFAULT_MAX_TOKENS,
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||||
};
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||||
|
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// ============================================================
|
||||
// Provider 实现
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@@ -236,11 +46,6 @@ pub struct AnthropicCompatProvider {
|
||||
default_model: String,
|
||||
}
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||||
|
||||
/// Anthropic 流式协议版本头
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||||
const ANTHROPIC_VERSION: &str = "2023-06-01";
|
||||
/// Anthropic max_tokens 必填，缺省时的兜底值
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||||
const DEFAULT_MAX_TOKENS: u32 = 4096;
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|
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impl AnthropicCompatProvider {
|
||||
/// 创建 Provider
|
||||
///
|
||||
@@ -788,6 +593,7 @@ impl LlmProvider for AnthropicCompatProvider {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
// apply_anthropic_event / TokenUsage 经 super::*（含 anthropic_helpers::apply_anthropic_event 的 use）可见。
|
||||
|
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/// 辅助：构造 message_start 事件
|
||||
fn message_start(input_tokens: u32) -> String {
|
||||
|
||||
223
crates/df-ai/src/anthropic_helpers.rs
Normal file
223
crates/df-ai/src/anthropic_helpers.rs
Normal file
@@ -0,0 +1,223 @@
|
||||
//! Anthropic 协议适配 — 协议数据结构与 SSE 事件解析（纯函数）。
|
||||
//!
|
||||
//! 本模块从 `anthropic_compat.rs` 抽离，承载与 HTTP 无关的纯协议逻辑：
|
||||
//! - 请求/响应结构体（`AnthropicRequest` 等）
|
||||
//! - 协议常量（`ANTHROPIC_VERSION` / `DEFAULT_MAX_TOKENS`）
|
||||
//! - SSE 事件 → `StreamChunk` 转换纯函数（`apply_anthropic_event`）
|
||||
//!
|
||||
//! Provider struct + impl（含 HTTP 调用）仍留在 `anthropic_compat.rs`，
|
||||
//! Rust impl 块不可跨文件，故仅搬迁 impl 块外部的类型/常量/纯函数。
|
||||
//! 零行为变更（纯搬迁）。
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
use tracing::{error, warn};
|
||||
|
||||
use crate::provider::{StreamChunk, TokenUsage, ToolCallDelta};
|
||||
|
||||
// ============================================================
|
||||
// Anthropic API 请求/响应结构体
|
||||
// ============================================================
|
||||
|
||||
/// Anthropic 请求体
|
||||
#[derive(Debug, Clone, Serialize)]
|
||||
pub(crate) struct AnthropicRequest {
|
||||
pub model: String,
|
||||
pub messages: Vec<serde_json::Value>,
|
||||
pub max_tokens: u32,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub system: Option<String>,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub temperature: Option<f32>,
|
||||
pub stream: bool,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub tools: Option<Vec<AnthropicToolDef>>,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub tool_choice: Option<serde_json::Value>,
|
||||
}
|
||||
|
||||
/// Anthropic 工具定义（input_schema 对应 OpenAI 的 parameters）
|
||||
#[derive(Debug, Clone, Serialize)]
|
||||
pub(crate) struct AnthropicToolDef {
|
||||
pub name: String,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub description: Option<String>,
|
||||
pub input_schema: serde_json::Value,
|
||||
}
|
||||
|
||||
/// Anthropic 同步响应
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub(crate) struct AnthropicResponse {
|
||||
// SW-260618-25: 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
|
||||
#[allow(dead_code)]
|
||||
id: String,
|
||||
pub model: String,
|
||||
pub content: Vec<AnthropicContentBlock>,
|
||||
// SW-260618-25: 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
|
||||
#[allow(dead_code)]
|
||||
stop_reason: Option<String>,
|
||||
pub usage: AnthropicUsage,
|
||||
}
|
||||
|
||||
/// 响应 content 块（text 或 tool_use）
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub(crate) struct AnthropicContentBlock {
|
||||
#[serde(rename = "type")]
|
||||
pub block_type: String,
|
||||
#[serde(default)]
|
||||
pub text: Option<String>,
|
||||
/// tool_use 块字段
|
||||
pub id: Option<String>,
|
||||
pub name: Option<String>,
|
||||
pub input: Option<serde_json::Value>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub(crate) struct AnthropicUsage {
|
||||
pub input_tokens: u32,
|
||||
pub output_tokens: u32,
|
||||
}
|
||||
|
||||
// ============================================================
|
||||
// 协议常量
|
||||
// ============================================================
|
||||
|
||||
/// Anthropic 流式协议版本头
|
||||
pub(crate) const ANTHROPIC_VERSION: &str = "2023-06-01";
|
||||
/// Anthropic max_tokens 必填，缺省时的兜底值
|
||||
pub(crate) const DEFAULT_MAX_TOKENS: u32 = 4096;
|
||||
|
||||
// ============================================================
|
||||
// SSE 解析纯函数（与 HTTP 解耦，便于单测）
|
||||
// ============================================================
|
||||
|
||||
/// 将一条 Anthropic Messages SSE 事件 data 解析为 StreamChunk，并按需更新 usage 累加器。
|
||||
///
|
||||
/// 按 `type` 字段分发：
|
||||
/// - `message_start` → 用 `message.usage.input_tokens` 初始化累加器（output 置 0）。
|
||||
/// - `message_delta` → **output_tokens 是累计值（非增量）**，直接覆盖 `completion_tokens` 并重算 `total`。
|
||||
/// - `content_block_delta` (text_delta/input_json_delta) → 文本/工具入参增量。
|
||||
/// - `content_block_start` (tool_use) → 工具块开始，带 id+name。
|
||||
/// - `message_stop` → 返回 `finished=true` 终态 chunk，`usage` 取自累加器（`take()`）。
|
||||
/// - `error` → 返回 `finished=true` 终态空 chunk。
|
||||
/// - 其它（content_block_stop / ping 等）→ 空 chunk。
|
||||
///
|
||||
/// 等价性：content_block / message_stop / error 等事件分支与原 stream() 闭包逐字一致；
|
||||
/// usage 透传（message_stop 终态 take() 带出、message_delta 的 output_tokens 按累计值覆盖）
|
||||
/// 为本次新增能力，对应 StreamChunk 新增的 usage 字段。
|
||||
pub(crate) fn apply_anthropic_event(data: &str, usage_accum: &mut Option<TokenUsage>) -> StreamChunk {
|
||||
// 解析 data 中的 JSON，按 type 字段决定如何转 StreamChunk
|
||||
let v: serde_json::Value = match serde_json::from_str(data) {
|
||||
Ok(v) => v,
|
||||
Err(_) => {
|
||||
return StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
|
||||
}
|
||||
};
|
||||
let ty = v.get("type").and_then(|t| t.as_str()).unwrap_or("");
|
||||
match ty {
|
||||
// 消息开始：取 input_tokens 初始化累积器（output 此时未知，置 0）
|
||||
"message_start" => {
|
||||
if let Some(inp) = v
|
||||
.get("message")
|
||||
.and_then(|m| m.get("usage"))
|
||||
.and_then(|u| u.get("input_tokens"))
|
||||
.and_then(|t| t.as_u64())
|
||||
{
|
||||
*usage_accum = Some(TokenUsage {
|
||||
prompt_tokens: inp as u32,
|
||||
completion_tokens: 0,
|
||||
total_tokens: inp as u32,
|
||||
});
|
||||
}
|
||||
StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
|
||||
}
|
||||
// 消息增量：output_tokens 是累计值（非增量），直接覆盖 completion + 重算 total
|
||||
"message_delta" => {
|
||||
if let Some(out) = v.get("usage").and_then(|u| u.get("output_tokens")).and_then(|t| t.as_u64()) {
|
||||
let acc = usage_accum
|
||||
.get_or_insert(TokenUsage { prompt_tokens: 0, completion_tokens: 0, total_tokens: 0 });
|
||||
acc.completion_tokens = out as u32;
|
||||
acc.total_tokens = acc.prompt_tokens + acc.completion_tokens;
|
||||
}
|
||||
StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
|
||||
}
|
||||
// 文本增量
|
||||
"content_block_delta" => {
|
||||
if let Some(delta) = v.get("delta") {
|
||||
if delta.get("type").and_then(|t| t.as_str()) == Some("text_delta") {
|
||||
let text = delta.get("text").and_then(|t| t.as_str()).unwrap_or("").to_string();
|
||||
return StreamChunk { delta: text, finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None };
|
||||
}
|
||||
// 工具入参增量
|
||||
if delta.get("type").and_then(|t| t.as_str()) == Some("input_json_delta") {
|
||||
let partial = delta.get("partial_json").and_then(|t| t.as_str()).unwrap_or("").to_string();
|
||||
let idx = v.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as u32;
|
||||
return StreamChunk {
|
||||
delta: String::new(),
|
||||
finished: false,
|
||||
tool_calls: Some(vec![ToolCallDelta {
|
||||
index: idx,
|
||||
id: None,
|
||||
function_name: None,
|
||||
function_arguments: Some(partial),
|
||||
}]),
|
||||
usage: None,
|
||||
error: None,
|
||||
reasoning_content: None,
|
||||
};
|
||||
}
|
||||
}
|
||||
StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
|
||||
}
|
||||
// 工具块开始：带 id + name
|
||||
"content_block_start" => {
|
||||
if let Some(cb) = v.get("content_block") {
|
||||
if cb.get("type").and_then(|t| t.as_str()) == Some("tool_use") {
|
||||
let idx = v.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as u32;
|
||||
let name = cb.get("name").and_then(|t| t.as_str()).map(|s| s.to_string());
|
||||
// id 缺失时用占位 id 兜底：流式后续 input_json_delta 按 index 累加，
|
||||
// 中途无法整体跳过；占位 id 保证回传的 tool_use_id 非空，避免 GLM 500。
|
||||
let id = match cb.get("id").and_then(|t| t.as_str()).map(|s| s.to_string()) {
|
||||
Some(id) if !id.is_empty() => Some(id),
|
||||
_ => {
|
||||
let placeholder = format!("tool_missing_{}", idx);
|
||||
warn!(%placeholder, name = ?name, "Anthropic 流式 tool_use 块缺少 id，已填占位 id（原样回传会触发 GLM 500）");
|
||||
Some(placeholder)
|
||||
}
|
||||
};
|
||||
return StreamChunk {
|
||||
delta: String::new(),
|
||||
finished: false,
|
||||
tool_calls: Some(vec![ToolCallDelta {
|
||||
index: idx,
|
||||
id,
|
||||
function_name: name,
|
||||
function_arguments: None,
|
||||
}]),
|
||||
usage: None,
|
||||
error: None,
|
||||
reasoning_content: None,
|
||||
};
|
||||
}
|
||||
}
|
||||
StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None }
|
||||
}
|
||||
// 消息结束：带出累积 usage
|
||||
"message_stop" => StreamChunk {
|
||||
delta: String::new(),
|
||||
finished: true,
|
||||
tool_calls: None,
|
||||
usage: usage_accum.take(),
|
||||
error: None,
|
||||
reasoning_content: None,
|
||||
},
|
||||
// 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），
|
||||
// 改由 stream_llm 识别 error 非空 → 发 AiError + 丢弃残缺（与 OpenAI 路径 Err 一致）。
|
||||
"error" => {
|
||||
let msg = v.get("error").and_then(|e| e.get("message")).and_then(|m| m.as_str()).unwrap_or("stream error").to_string();
|
||||
error!(%msg, "Anthropic 流式错误事件");
|
||||
StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: Some(msg), reasoning_content: None }
|
||||
}
|
||||
// content_block_stop / ping 等不产出 chunk
|
||||
_ => StreamChunk { delta: String::new(), finished: false, tool_calls: None, usage: None, error: None, reasoning_content: None },
|
||||
}
|
||||
}
|
||||
@@ -2,6 +2,7 @@
|
||||
|
||||
pub mod ai_tools;
|
||||
pub mod anthropic_compat;
|
||||
pub mod anthropic_helpers;
|
||||
pub mod context;
|
||||
pub mod context_helpers;
|
||||
pub mod coordinator;
|
||||
|
||||
Reference in New Issue
Block a user