mirror of
https://github.com/farion1231/cc-switch.git
synced 2026-07-26 06:24:32 +08:00
Jason
a5903d8600
The provider panel health check sent a real streaming model request, which many third-party providers block (401/403/WAF), causing false negatives while only stable official endpoints passed. Replace it with a lightweight reachability probe: GET the provider base_url and treat any HTTP response (200/4xx/5xx) as reachable; only DNS/connect/TLS/timeout count as failure. Latency is the probe's TTFB. Backend (services/stream_check.rs): rewrite ~2200 -> ~350 lines, dropping real-request building, format conversion, auth and API-path resolution while keeping per-app base_url extraction. Defaults: 8s timeout, 1 retry, 1500ms degraded threshold. Failover invariant: the reachability check must never reset the circuit breaker (reachable != usable; a 403 host is reachable but broken for real traffic). Remove the resetCircuitBreaker call from useStreamCheck; failover failure detection stays driven solely by real proxy traffic (forwarder/circuit_breaker untouched). useResetCircuitBreaker is kept dormant for a future manual-recovery entry. Open the check to all providers: drop the official/copilot/codex-oauth/third-party gating and the 'sends a real request' confirm dialog. For official providers whose base_url is intentionally empty, fall back to the endpoint the client actually uses (Claude -> api.anthropic.com, Codex -> chatgpt.com/backend-api/codex, Gemini -> generativelanguage). Non-official providers with a missing base_url still error to avoid a false green light. Claude Desktop Official is native 1P mode (talks to claude.ai, cc-switch not in the request path, no reliable endpoint) so its button stays hidden. Slim StreamCheckConfig and per-provider testConfig to timeout/threshold/retries (drop test model + prompt); sync zh/en/ja/zh-TW. Retain the now-unused anthropic_to_openai/anthropic_to_gemini transform utilities and their test suites.
1730 lines
62 KiB
Rust
1730 lines
62 KiB
Rust
//! 格式转换模块
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//!
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//! 实现 Anthropic ↔ OpenAI 格式转换，用于 OpenRouter 支持
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//! 参考: anthropic-proxy-rs
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use crate::proxy::{error::ProxyError, json_canonical::canonical_json_string};
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use serde_json::{json, Value};
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const ANTHROPIC_BILLING_HEADER_PREFIX: &str = "x-anthropic-billing-header:";
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/// Strip only a leading Claude Code attribution line from system text.
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///
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/// Claude Code can send dynamic `x-anthropic-billing-header` metadata at the
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/// start of `system`. If forwarded into OpenAI Chat messages or Responses
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/// `instructions`, the rotating `cch=` value changes the prompt prefix on every
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/// request and prevents prefix cache reuse (#2350). Later occurrences are kept
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/// to avoid deleting user-authored prompt text.
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pub(crate) fn strip_leading_anthropic_billing_header(text: &str) -> &str {
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if !text.starts_with(ANTHROPIC_BILLING_HEADER_PREFIX) {
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return text;
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}
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let Some(line_end) = text
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.as_bytes()
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.iter()
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.position(|byte| *byte == b'\n' || *byte == b'\r')
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else {
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return "";
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};
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let bytes = text.as_bytes();
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let mut rest_start = line_end + 1;
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if bytes[line_end] == b'\r' && bytes.get(line_end + 1) == Some(&b'\n') {
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rest_start += 1;
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}
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let rest = &text[rest_start..];
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if let Some(stripped) = rest.strip_prefix("\r\n") {
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stripped
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} else if let Some(stripped) = rest.strip_prefix('\n') {
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stripped
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} else if let Some(stripped) = rest.strip_prefix('\r') {
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stripped
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} else {
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rest
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}
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}
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/// Detect OpenAI o-series reasoning models (o1, o3, o4-mini, etc.)
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/// These models require `max_completion_tokens` instead of `max_tokens`.
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pub fn is_openai_o_series(model: &str) -> bool {
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model.len() > 1
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&& model.starts_with('o')
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&& model.as_bytes().get(1).is_some_and(|b| b.is_ascii_digit())
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}
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/// Detect OpenAI models that support reasoning_effort.
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///
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/// Supported families:
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/// - o-series: o1, o3, o4-mini, etc.
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/// - GPT-5+: gpt-5, gpt-5.1, gpt-5.4, gpt-5-codex, etc.
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pub fn supports_reasoning_effort(model: &str) -> bool {
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is_openai_o_series(model)
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|| model
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.to_lowercase()
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.strip_prefix("gpt-")
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.and_then(|rest| rest.chars().next())
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.is_some_and(|c| c.is_ascii_digit() && c >= '5')
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}
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/// Resolve the appropriate OpenAI `reasoning_effort` from an Anthropic request body.
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///
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/// Priority:
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/// 1. Explicit `output_config.effort` — preserves the user's intent directly.
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/// `low`/`medium`/`high` map 1:1; `max` maps to `xhigh`
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/// (supported by mainstream GPT models). Unknown values are ignored.
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/// 2. Fallback: `thinking.type` + `budget_tokens`:
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/// - `adaptive` → `xhigh` (adaptive = maximum reasoning effort)
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/// - `enabled` with budget → `low` (<4 000) / `medium` (4 000–15 999) / `high` (≥16 000)
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/// - `enabled` without budget → `high` (conservative default)
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/// - `disabled` / absent → `None`
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pub fn resolve_reasoning_effort(body: &Value) -> Option<&'static str> {
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// --- Priority 1: explicit output_config.effort ---
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if let Some(effort) = body
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.pointer("/output_config/effort")
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.and_then(|v| v.as_str())
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{
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return match effort {
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"low" => Some("low"),
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"medium" => Some("medium"),
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"high" => Some("high"),
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"max" => Some("xhigh"), // OpenAI xhigh = maximum reasoning effort
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_ => None, // unknown value — do not inject
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};
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}
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// --- Priority 2: thinking.type + budget_tokens fallback ---
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let thinking = body.get("thinking")?;
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match thinking.get("type").and_then(|t| t.as_str()) {
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Some("adaptive") => Some("xhigh"),
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Some("enabled") => {
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let budget = thinking.get("budget_tokens").and_then(|b| b.as_u64());
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match budget {
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Some(b) if b < 4_000 => Some("low"),
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Some(b) if b < 16_000 => Some("medium"),
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Some(_) => Some("high"),
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None => Some("high"), // enabled but no budget — assume strong reasoning
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}
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}
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_ => None, // disabled or missing
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}
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}
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/// Anthropic 请求 → OpenAI Chat Completions 请求
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///
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/// 转换工具库 API：当前无生产调用方（连通性检查不再发真实请求，曾是其唯一 crate 内
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/// 消费者），但保留其转换逻辑与下方测试套件，供代理转换路径复用 / 未来接线。
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#[allow(dead_code)]
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pub fn anthropic_to_openai(body: Value) -> Result<Value, ProxyError> {
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anthropic_to_openai_with_reasoning_content(body, false)
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}
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/// Anthropic 请求 → OpenAI Chat Completions 请求
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///
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/// `preserve_reasoning_content` 仅用于明确需要 Moonshot/Kimi/DeepSeek
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/// `reasoning_content` 兼容字段的 provider。默认转换保持通用 OpenAI-compatible
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/// 请求体，避免向严格后端发送未知字段。
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pub fn anthropic_to_openai_with_reasoning_content(
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body: Value,
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preserve_reasoning_content: bool,
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) -> Result<Value, ProxyError> {
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let mut result = json!({});
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// NOTE: 模型映射由上游统一处理（proxy::model_mapper），格式转换层只做结构转换。
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if let Some(model) = body.get("model").and_then(|m| m.as_str()) {
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result["model"] = json!(model);
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}
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let mut messages = Vec::new();
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// 处理 system prompt
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if let Some(system) = body.get("system") {
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if let Some(text) = system.as_str() {
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let text = strip_leading_anthropic_billing_header(text);
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if !text.is_empty() {
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messages.push(json!({"role": "system", "content": text}));
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}
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} else if let Some(arr) = system.as_array() {
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for msg in arr {
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if let Some(text) = msg.get("text").and_then(|t| t.as_str()) {
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let text = strip_leading_anthropic_billing_header(text);
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if text.is_empty() {
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continue;
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}
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messages.push(json!({"role": "system", "content": text}));
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}
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}
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}
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}
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// 转换 messages
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if let Some(msgs) = body.get("messages").and_then(|m| m.as_array()) {
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for msg in msgs {
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let role = msg.get("role").and_then(|r| r.as_str()).unwrap_or("user");
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let content = msg.get("content");
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let converted = convert_message_to_openai(role, content, preserve_reasoning_content)?;
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messages.extend(converted);
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}
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}
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normalize_openai_system_messages(&mut messages);
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result["messages"] = json!(messages);
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// 转换参数 — o-series 模型需要 max_completion_tokens
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let model = body.get("model").and_then(|m| m.as_str()).unwrap_or("");
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if let Some(v) = body.get("max_tokens") {
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if is_openai_o_series(model) {
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result["max_completion_tokens"] = v.clone();
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} else {
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result["max_tokens"] = v.clone();
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}
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}
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if let Some(v) = body.get("temperature") {
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result["temperature"] = v.clone();
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}
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if let Some(v) = body.get("top_p") {
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result["top_p"] = v.clone();
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}
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if let Some(v) = body.get("stop_sequences") {
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result["stop"] = v.clone();
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}
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if let Some(v) = body.get("stream") {
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result["stream"] = v.clone();
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}
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// Map Anthropic thinking → OpenAI reasoning_effort
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if supports_reasoning_effort(model) {
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if let Some(effort) = resolve_reasoning_effort(&body) {
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result["reasoning_effort"] = json!(effort);
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}
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}
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// 转换 tools (过滤 BatchTool)
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if let Some(tools) = body.get("tools").and_then(|t| t.as_array()) {
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let openai_tools: Vec<Value> = tools
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.iter()
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.filter(|t| t.get("type").and_then(|v| v.as_str()) != Some("BatchTool"))
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.map(|t| {
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json!({
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"type": "function",
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"function": {
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"name": t.get("name").and_then(|n| n.as_str()).unwrap_or(""),
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"description": t.get("description"),
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"parameters": clean_schema(t.get("input_schema").cloned().unwrap_or(json!({})))
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}
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})
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})
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.collect();
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if !openai_tools.is_empty() {
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result["tools"] = json!(openai_tools);
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}
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}
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if let Some(v) = body.get("tool_choice") {
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result["tool_choice"] = map_tool_choice_to_chat(v);
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}
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Ok(result)
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}
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/// 为 OpenAI Chat Completions 流式请求注入 `stream_options.include_usage`。
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///
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/// OpenAI 兼容上游在流式下默认不在 SSE 里返回 usage，必须显式声明 include_usage
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/// 才会在末尾吐 usage chunk。缺这一注入会导致流式请求的 token/成本/缓存全部漏记
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/// （input/output/cache 全为 0）。保留客户端可能透传的其它 stream_options 字段，
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/// 仅补 include_usage；非流式请求不动。
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///
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/// 由 Claude→openai_chat（claude.rs）与 Codex Responses→Chat（transform_codex_chat.rs）
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/// 两条转换路径共用，确保两个客户端方向行为一致。
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pub(crate) fn inject_openai_stream_include_usage(result: &mut Value) {
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let is_stream = result
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.get("stream")
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.and_then(|v| v.as_bool())
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.unwrap_or(false);
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if !is_stream {
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return;
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}
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match result.get_mut("stream_options") {
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Some(Value::Object(opts)) => {
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opts.insert("include_usage".to_string(), json!(true));
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}
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_ => {
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result["stream_options"] = json!({ "include_usage": true });
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}
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}
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}
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/// Translate an Anthropic `tool_choice` into the OpenAI Chat Completions form.
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///
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/// Anthropic forms:
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/// "auto" / "any" / "none" (string enum)
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/// {"type": "auto" | "any" | "none"}
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/// {"type": "tool", "name": "<X>"}
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///
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/// OpenAI Chat forms:
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/// "auto" / "none" / "required" (note: no "any" — use "required")
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/// {"type": "function", "function": {"name": "<X>"}}
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///
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/// The Responses API uses a flatter `{"type":"function","name":"X"}` selector,
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/// so it has a sibling `map_tool_choice_to_responses` in `transform_responses.rs`.
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/// Keep the two in sync.
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fn map_tool_choice_to_chat(tool_choice: &Value) -> Value {
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match tool_choice {
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Value::String(s) => match s.as_str() {
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"any" => json!("required"),
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_ => json!(s),
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},
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Value::Object(obj) => match obj.get("type").and_then(|t| t.as_str()) {
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Some("any") => json!("required"),
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Some("auto") => json!("auto"),
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Some("none") => json!("none"),
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Some("tool") => {
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let name = obj.get("name").and_then(|n| n.as_str()).unwrap_or("");
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json!({
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"type": "function",
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"function": { "name": name }
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})
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}
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_ => tool_choice.clone(),
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},
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_ => tool_choice.clone(),
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}
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}
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fn normalize_openai_system_messages(messages: &mut Vec<Value>) {
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let system_count = messages
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.iter()
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.filter(|message| message.get("role").and_then(|value| value.as_str()) == Some("system"))
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.count();
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if system_count == 0 {
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return;
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}
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if system_count == 1 {
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if let Some(index) = messages.iter().position(|message| {
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message.get("role").and_then(|value| value.as_str()) == Some("system")
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}) {
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if index > 0 {
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let message = messages.remove(index);
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messages.insert(0, message);
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}
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}
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return;
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}
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let mut parts = Vec::new();
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messages.retain(|message| {
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if message.get("role").and_then(|value| value.as_str()) != Some("system") {
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return true;
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}
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match message.get("content") {
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Some(Value::String(text)) if !text.is_empty() => parts.push(text.clone()),
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Some(Value::Array(content_parts)) => {
|
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let text = content_parts
|
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.iter()
|
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.filter_map(|part| part.get("text").and_then(|value| value.as_str()))
|
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.collect::<Vec<_>>()
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.join("\n");
|
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if !text.is_empty() {
|
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parts.push(text);
|
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}
|
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}
|
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_ => {}
|
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}
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false
|
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});
|
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|
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if !parts.is_empty() {
|
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messages.insert(0, json!({"role": "system", "content": parts.join("\n")}));
|
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}
|
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}
|
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/// 转换单条消息到 OpenAI 格式（可能产生多条消息）
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fn convert_message_to_openai(
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role: &str,
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content: Option<&Value>,
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preserve_reasoning_content: bool,
|
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) -> Result<Vec<Value>, ProxyError> {
|
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let mut result = Vec::new();
|
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|
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let content = match content {
|
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Some(c) => c,
|
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None => {
|
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result.push(json!({"role": role, "content": null}));
|
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return Ok(result);
|
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}
|
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};
|
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|
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// 字符串内容
|
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if let Some(text) = content.as_str() {
|
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result.push(json!({"role": role, "content": text}));
|
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return Ok(result);
|
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}
|
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|
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// 数组内容（多模态/工具调用）
|
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if let Some(blocks) = content.as_array() {
|
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let mut content_parts = Vec::new();
|
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let mut tool_calls = Vec::new();
|
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// reasoning_parts: 仅在兼容 Moonshot/Kimi/DeepSeek thinking tool-call 路径时
|
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// 生成 reasoning_content，通用 OpenAI-compatible 路径不发送该非标准字段。
|
||
let mut reasoning_parts = Vec::new();
|
||
|
||
for block in blocks {
|
||
let block_type = block.get("type").and_then(|t| t.as_str()).unwrap_or("");
|
||
|
||
match block_type {
|
||
"text" => {
|
||
if let Some(text) = block.get("text").and_then(|t| t.as_str()) {
|
||
content_parts.push(json!({"type": "text", "text": text}));
|
||
}
|
||
}
|
||
"image" => {
|
||
if let Some(source) = block.get("source") {
|
||
let media_type = source
|
||
.get("media_type")
|
||
.and_then(|m| m.as_str())
|
||
.unwrap_or("image/png");
|
||
let data = source.get("data").and_then(|d| d.as_str()).unwrap_or("");
|
||
content_parts.push(json!({
|
||
"type": "image_url",
|
||
"image_url": {"url": format!("data:{};base64,{}", media_type, data)}
|
||
}));
|
||
}
|
||
}
|
||
"tool_use" => {
|
||
let id = block.get("id").and_then(|i| i.as_str()).unwrap_or("");
|
||
let name = block.get("name").and_then(|n| n.as_str()).unwrap_or("");
|
||
let input = block.get("input").cloned().unwrap_or(json!({}));
|
||
tool_calls.push(json!({
|
||
"id": id,
|
||
"type": "function",
|
||
"function": {
|
||
"name": name,
|
||
"arguments": canonical_json_string(&input)
|
||
}
|
||
}));
|
||
}
|
||
"tool_result" => {
|
||
// tool_result 变成单独的 tool role 消息
|
||
let tool_use_id = block
|
||
.get("tool_use_id")
|
||
.and_then(|i| i.as_str())
|
||
.unwrap_or("");
|
||
let content_val = block.get("content");
|
||
let content_str = match content_val {
|
||
Some(Value::String(s)) => s.clone(),
|
||
Some(v) => canonical_json_string(v),
|
||
None => String::new(),
|
||
};
|
||
result.push(json!({
|
||
"role": "tool",
|
||
"tool_call_id": tool_use_id,
|
||
"content": content_str
|
||
}));
|
||
}
|
||
"thinking" => {
|
||
// 提取 thinking 内容，后续可作为 reasoning_content 传给需要它的上游。
|
||
if let Some(thinking) = block.get("thinking").and_then(|t| t.as_str()) {
|
||
if !thinking.is_empty() {
|
||
reasoning_parts.push(thinking.to_string());
|
||
}
|
||
}
|
||
}
|
||
"redacted_thinking" if preserve_reasoning_content => {
|
||
// Claude Code encrypts historical thinking into redacted_thinking blocks.
|
||
// MiMo/DeepSeek require non-empty reasoning_content on assistant tool-call
|
||
// messages, so inject a minimal placeholder when the real content is
|
||
// unavailable. Skip when preserve_reasoning_content is off (generic
|
||
// OpenAI-compatible path).
|
||
reasoning_parts.push("[redacted thinking]".to_string());
|
||
}
|
||
_ => {}
|
||
}
|
||
}
|
||
|
||
// 添加带内容和/或工具调用的消息
|
||
if !content_parts.is_empty() || !tool_calls.is_empty() {
|
||
let mut msg = json!({"role": role});
|
||
|
||
// 内容处理
|
||
if content_parts.is_empty() {
|
||
msg["content"] = Value::Null;
|
||
} else if content_parts.len() == 1 {
|
||
// 单 text block 简化为纯字符串
|
||
if let Some(text) = content_parts[0].get("text") {
|
||
msg["content"] = text.clone();
|
||
} else {
|
||
msg["content"] = json!(content_parts);
|
||
}
|
||
} else {
|
||
msg["content"] = json!(content_parts);
|
||
}
|
||
|
||
// 工具调用
|
||
if !tool_calls.is_empty() {
|
||
msg["tool_calls"] = json!(tool_calls);
|
||
}
|
||
|
||
if preserve_reasoning_content && role == "assistant" && !tool_calls.is_empty() {
|
||
let reasoning_content = if reasoning_parts.is_empty() {
|
||
"tool call".to_string()
|
||
} else {
|
||
reasoning_parts.join("\n")
|
||
};
|
||
msg["reasoning_content"] = json!(reasoning_content);
|
||
}
|
||
|
||
result.push(msg);
|
||
}
|
||
|
||
return Ok(result);
|
||
}
|
||
|
||
// 其他情况直接透传
|
||
result.push(json!({"role": role, "content": content}));
|
||
Ok(result)
|
||
}
|
||
|
||
/// 清理 JSON schema（移除不支持的 format）
|
||
pub fn clean_schema(mut schema: Value) -> Value {
|
||
if let Some(obj) = schema.as_object_mut() {
|
||
// 移除 "format": "uri"
|
||
if obj.get("format").and_then(|v| v.as_str()) == Some("uri") {
|
||
obj.remove("format");
|
||
}
|
||
|
||
// 递归清理嵌套 schema
|
||
if let Some(properties) = obj.get_mut("properties").and_then(|v| v.as_object_mut()) {
|
||
for (_, value) in properties.iter_mut() {
|
||
*value = clean_schema(value.clone());
|
||
}
|
||
}
|
||
|
||
if let Some(items) = obj.get_mut("items") {
|
||
*items = clean_schema(items.clone());
|
||
}
|
||
}
|
||
schema
|
||
}
|
||
|
||
/// OpenAI 响应 → Anthropic 响应
|
||
pub fn openai_to_anthropic(body: Value) -> Result<Value, ProxyError> {
|
||
let choices = body
|
||
.get("choices")
|
||
.and_then(|c| c.as_array())
|
||
.ok_or_else(|| ProxyError::TransformError("No choices in response".to_string()))?;
|
||
|
||
let choice = choices
|
||
.first()
|
||
.ok_or_else(|| ProxyError::TransformError("Empty choices array".to_string()))?;
|
||
|
||
let message = choice
|
||
.get("message")
|
||
.ok_or_else(|| ProxyError::TransformError("No message in choice".to_string()))?;
|
||
|
||
let mut content = Vec::new();
|
||
let mut has_tool_use = false;
|
||
|
||
// DeepSeek provider 会把思考内容放在 message.reasoning_content。
|
||
if let Some(reasoning_content) = message.get("reasoning_content").and_then(|r| r.as_str()) {
|
||
if !reasoning_content.is_empty() {
|
||
content.push(json!({"type": "thinking", "thinking": reasoning_content}));
|
||
}
|
||
}
|
||
|
||
// 文本/拒绝内容
|
||
if let Some(msg_content) = message.get("content") {
|
||
if let Some(text) = msg_content.as_str() {
|
||
if !text.is_empty() {
|
||
content.push(json!({"type": "text", "text": text}));
|
||
}
|
||
} else if let Some(parts) = msg_content.as_array() {
|
||
for part in parts {
|
||
let part_type = part.get("type").and_then(|t| t.as_str()).unwrap_or("");
|
||
match part_type {
|
||
"text" | "output_text" => {
|
||
if let Some(text) = part.get("text").and_then(|t| t.as_str()) {
|
||
if !text.is_empty() {
|
||
content.push(json!({"type": "text", "text": text}));
|
||
}
|
||
}
|
||
}
|
||
"refusal" => {
|
||
if let Some(refusal) = part.get("refusal").and_then(|r| r.as_str()) {
|
||
if !refusal.is_empty() {
|
||
content.push(json!({"type": "text", "text": refusal}));
|
||
}
|
||
}
|
||
}
|
||
_ => {}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
// Some providers put refusal at message-level.
|
||
if let Some(refusal) = message.get("refusal").and_then(|r| r.as_str()) {
|
||
if !refusal.is_empty() {
|
||
content.push(json!({"type": "text", "text": refusal}));
|
||
}
|
||
}
|
||
|
||
// 工具调用（tool_calls）
|
||
if let Some(tool_calls) = message.get("tool_calls").and_then(|t| t.as_array()) {
|
||
if !tool_calls.is_empty() {
|
||
has_tool_use = true;
|
||
}
|
||
for tc in tool_calls {
|
||
let id = tc.get("id").and_then(|i| i.as_str()).unwrap_or("");
|
||
let empty_obj = json!({});
|
||
let func = tc.get("function").unwrap_or(&empty_obj);
|
||
let name = func.get("name").and_then(|n| n.as_str()).unwrap_or("");
|
||
let args_str = func
|
||
.get("arguments")
|
||
.and_then(|a| a.as_str())
|
||
.unwrap_or("{}");
|
||
let input: Value = serde_json::from_str(args_str).unwrap_or(json!({}));
|
||
|
||
content.push(json!({
|
||
"type": "tool_use",
|
||
"id": id,
|
||
"name": name,
|
||
"input": input
|
||
}));
|
||
}
|
||
}
|
||
// 兼容旧格式（function_call）
|
||
if !has_tool_use {
|
||
if let Some(function_call) = message.get("function_call") {
|
||
let id = function_call
|
||
.get("id")
|
||
.and_then(|i| i.as_str())
|
||
.unwrap_or("");
|
||
let name = function_call
|
||
.get("name")
|
||
.and_then(|n| n.as_str())
|
||
.unwrap_or("");
|
||
let has_arguments = function_call.get("arguments").is_some();
|
||
|
||
let input = match function_call.get("arguments") {
|
||
Some(Value::String(s)) => serde_json::from_str(s).unwrap_or(json!({})),
|
||
Some(v @ Value::Object(_)) | Some(v @ Value::Array(_)) => v.clone(),
|
||
_ => json!({}),
|
||
};
|
||
|
||
if !name.is_empty() || has_arguments {
|
||
content.push(json!({
|
||
"type": "tool_use",
|
||
"id": id,
|
||
"name": name,
|
||
"input": input
|
||
}));
|
||
has_tool_use = true;
|
||
}
|
||
}
|
||
}
|
||
|
||
// 映射 finish_reason → stop_reason
|
||
let stop_reason = choice
|
||
.get("finish_reason")
|
||
.and_then(|r| r.as_str())
|
||
.map(|r| match r {
|
||
"stop" => "end_turn",
|
||
"length" => "max_tokens",
|
||
"tool_calls" | "function_call" => "tool_use",
|
||
"content_filter" => "end_turn",
|
||
other => {
|
||
log::warn!(
|
||
"[Claude/OpenAI] Unknown finish_reason in non-streaming response: {other}"
|
||
);
|
||
"end_turn"
|
||
}
|
||
})
|
||
.or(if has_tool_use { Some("tool_use") } else { None });
|
||
|
||
// usage — map cache tokens from OpenAI format to Anthropic format
|
||
let usage = body.get("usage").cloned().unwrap_or(json!({}));
|
||
// OpenAI prompt_tokens 含缓存命中，Anthropic input_tokens 不含 → 减去 cache_read 与
|
||
// cache_creation，使 input 成为 fresh input。本路径以 app_type="claude" 记账（calculator
|
||
// 不再扣减），若不减则缓存会被计入 input 与各 cache 桶两次。三桶互斥，恒等：
|
||
// input + cache_read + cache_creation == prompt_tokens（inclusive 上游）。
|
||
// 与流式 build_anthropic_usage_json (#2774) 及 transform_gemini 的 saturating_sub 对称。
|
||
// 最终 cache_read：直传字段优先于 nested；cache_creation 仅来自直传字段（OpenAI 无此概念）。
|
||
let cached = usage
|
||
.get("cache_read_input_tokens")
|
||
.and_then(|v| v.as_u64())
|
||
.or_else(|| {
|
||
usage
|
||
.pointer("/prompt_tokens_details/cached_tokens")
|
||
.and_then(|v| v.as_u64())
|
||
})
|
||
.unwrap_or(0);
|
||
let cache_creation = usage
|
||
.get("cache_creation_input_tokens")
|
||
.and_then(|v| v.as_u64())
|
||
.unwrap_or(0);
|
||
let input_tokens = usage
|
||
.get("prompt_tokens")
|
||
.and_then(|v| v.as_u64())
|
||
.unwrap_or(0)
|
||
.saturating_sub(cached)
|
||
.saturating_sub(cache_creation) as u32;
|
||
let output_tokens = usage
|
||
.get("completion_tokens")
|
||
.and_then(|v| v.as_u64())
|
||
.unwrap_or(0) as u32;
|
||
|
||
let mut usage_json = json!({
|
||
"input_tokens": input_tokens,
|
||
"output_tokens": output_tokens
|
||
});
|
||
|
||
if cached > 0 {
|
||
usage_json["cache_read_input_tokens"] = json!(cached);
|
||
}
|
||
if cache_creation > 0 {
|
||
usage_json["cache_creation_input_tokens"] = json!(cache_creation);
|
||
}
|
||
|
||
let result = json!({
|
||
"id": body.get("id").and_then(|i| i.as_str()).unwrap_or(""),
|
||
"type": "message",
|
||
"role": "assistant",
|
||
"content": content,
|
||
"model": body.get("model").and_then(|m| m.as_str()).unwrap_or(""),
|
||
"stop_reason": stop_reason,
|
||
"stop_sequence": null,
|
||
"usage": usage_json
|
||
});
|
||
|
||
Ok(result)
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_simple() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["model"], "claude-3-opus");
|
||
assert_eq!(result["max_tokens"], 1024);
|
||
assert_eq!(result["messages"][0]["role"], "user");
|
||
assert_eq!(result["messages"][0]["content"], "Hello");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_with_system() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": "You are a helpful assistant.",
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"You are a helpful assistant."
|
||
);
|
||
assert_eq!(result["messages"][1]["role"], "user");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_leading_billing_header_from_system_string() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": "x-anthropic-billing-header: cc_version=2.1.119.47e; cc_entrypoint=sdk-cli; cch=a7754;\n\nYou are a helpful assistant.",
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"You are a helpful assistant."
|
||
);
|
||
assert_eq!(result["messages"][1]["role"], "user");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_billing_header_from_system_array_parts() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "x-anthropic-billing-header: cc_version=2.1.119.47e; cc_entrypoint=sdk-cli; cch=a7754;\n"},
|
||
{"type": "text", "text": "Stable prompt"}
|
||
],
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(result["messages"][0]["content"], "Stable prompt");
|
||
assert_eq!(result["messages"][1]["role"], "user");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_preserves_prompt_after_billing_header_in_same_part() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "x-anthropic-billing-header: cc_version=2.1.119.47e; cc_entrypoint=sdk-cli; cch=a7754;\n\nStable prompt part 1"},
|
||
{"type": "text", "text": "Stable prompt part 2"}
|
||
],
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"Stable prompt part 1\nStable prompt part 2"
|
||
);
|
||
assert_eq!(result["messages"][1]["role"], "user");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_keeps_non_leading_billing_header_text() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": "Keep this literal:\nx-anthropic-billing-header: example",
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"Keep this literal:\nx-anthropic-billing-header: example"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_with_tools() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "What's the weather?"}],
|
||
"tools": [{
|
||
"name": "get_weather",
|
||
"description": "Get weather info",
|
||
"input_schema": {"type": "object", "properties": {"location": {"type": "string"}}}
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["tools"][0]["type"], "function");
|
||
assert_eq!(result["tools"][0]["function"]["name"], "get_weather");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_cache_control_from_merged_system() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "You are Claude Code.", "cache_control": {"type": "ephemeral"}},
|
||
{"type": "text", "text": "Be concise.", "cache_control": {"type": "ephemeral"}}
|
||
],
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"].as_array().unwrap().len(), 2);
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"You are Claude Code.\nBe concise."
|
||
);
|
||
assert!(result["messages"][0].get("cache_control").is_none());
|
||
assert_eq!(result["messages"][1]["role"], "user");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_cache_control_from_mixed_system() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "You are Claude Code.", "cache_control": {"type": "ephemeral"}},
|
||
{"type": "text", "text": "Be concise."}
|
||
],
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"You are Claude Code.\nBe concise."
|
||
);
|
||
assert!(result["messages"][0].get("cache_control").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_cache_control_from_conflicting_system() {
|
||
let input = json!({
|
||
"model": "claude-3-sonnet",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "You are Claude Code.", "cache_control": {"type": "ephemeral"}},
|
||
{"type": "text", "text": "Be concise.", "cache_control": {"type": "ephemeral", "ttl": "5m"}}
|
||
],
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"][0]["role"], "system");
|
||
assert_eq!(
|
||
result["messages"][0]["content"],
|
||
"You are Claude Code.\nBe concise."
|
||
);
|
||
assert!(result["messages"][0].get("cache_control").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_tool_use() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "text", "text": "Let me check"},
|
||
{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Tokyo"}}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["role"], "assistant");
|
||
assert!(msg.get("tool_calls").is_some());
|
||
assert_eq!(msg["tool_calls"][0]["id"], "call_123");
|
||
assert!(msg.get("reasoning_content").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_tool_use_preserves_reasoning_content() {
|
||
let input = json!({
|
||
"model": "kimi-k2.6",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "thinking", "thinking": "I should call the tool."},
|
||
{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Tokyo"}}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai_with_reasoning_content(input, true).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["role"], "assistant");
|
||
assert_eq!(msg["reasoning_content"], "I should call the tool.");
|
||
assert!(msg.get("tool_calls").is_some());
|
||
assert_eq!(msg["tool_calls"][0]["id"], "call_123");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_tool_use_injects_placeholder_reasoning_content_when_missing() {
|
||
let input = json!({
|
||
"model": "kimi-k2.6",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Tokyo"}}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai_with_reasoning_content(input, true).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["role"], "assistant");
|
||
assert_eq!(msg["reasoning_content"], "tool call");
|
||
assert!(msg.get("tool_calls").is_some());
|
||
assert_eq!(msg["tool_calls"][0]["id"], "call_123");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_tool_use_uses_redacted_thinking_placeholder() {
|
||
let input = json!({
|
||
"model": "mimo-v2.5-pro",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "redacted_thinking", "data": "opaque"},
|
||
{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Tokyo"}}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai_with_reasoning_content(input, true).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["reasoning_content"], "[redacted thinking]");
|
||
assert_eq!(msg["tool_calls"][0]["id"], "call_123");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_does_not_emit_reasoning_content_by_default() {
|
||
let input = json!({
|
||
"model": "gpt-5.4",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "thinking", "thinking": "I should call the tool."},
|
||
{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Tokyo"}}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["role"], "assistant");
|
||
assert!(msg.get("tool_calls").is_some());
|
||
assert!(msg.get("reasoning_content").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_skips_thinking_only_message() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "thinking", "thinking": "No visible content yet."}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["messages"].as_array().unwrap().len(), 0);
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_tool_result() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "user",
|
||
"content": [
|
||
{"type": "tool_result", "tool_use_id": "call_123", "content": "Sunny, 25°C"}
|
||
]
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
let msg = &result["messages"][0];
|
||
assert_eq!(msg["role"], "tool");
|
||
assert_eq!(msg["tool_call_id"], "call_123");
|
||
assert_eq!(msg["content"], "Sunny, 25°C");
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_simple() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"object": "chat.completion",
|
||
"created": 1234567890,
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "Hello!"},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["id"], "chatcmpl-123");
|
||
assert_eq!(result["type"], "message");
|
||
assert_eq!(result["content"][0]["type"], "text");
|
||
assert_eq!(result["content"][0]["text"], "Hello!");
|
||
assert_eq!(result["stop_reason"], "end_turn");
|
||
assert_eq!(result["usage"]["input_tokens"], 10);
|
||
assert_eq!(result["usage"]["output_tokens"], 5);
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_preserves_id_for_usage_dedup() {
|
||
let input = json!({
|
||
"id": "chatcmpl-claude-compatible",
|
||
"object": "chat.completion",
|
||
"model": "claude-sonnet-4-5",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "Hello!"},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
let usage = crate::proxy::usage::parser::TokenUsage::from_claude_response(&result)
|
||
.expect("converted Anthropic response should parse usage");
|
||
|
||
assert_eq!(
|
||
usage.message_id.as_deref(),
|
||
Some("chatcmpl-claude-compatible")
|
||
);
|
||
assert_eq!(
|
||
usage.dedup_request_id(),
|
||
"session:chatcmpl-claude-compatible"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_with_tool_calls() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"object": "chat.completion",
|
||
"created": 1234567890,
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {
|
||
"role": "assistant",
|
||
"content": null,
|
||
"tool_calls": [{
|
||
"id": "call_123",
|
||
"type": "function",
|
||
"function": {"name": "get_weather", "arguments": "{\"location\": \"Tokyo\"}"}
|
||
}]
|
||
},
|
||
"finish_reason": "tool_calls"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["content"][0]["type"], "tool_use");
|
||
assert_eq!(result["content"][0]["id"], "call_123");
|
||
assert_eq!(result["content"][0]["name"], "get_weather");
|
||
assert_eq!(result["content"][0]["input"]["location"], "Tokyo");
|
||
assert_eq!(result["stop_reason"], "tool_use");
|
||
}
|
||
|
||
#[test]
|
||
fn test_deepseek_reasoning_content_round_trips_for_tool_calls() {
|
||
let upstream_response = json!({
|
||
"id": "chatcmpl-deepseek",
|
||
"object": "chat.completion",
|
||
"created": 1234567890,
|
||
"model": "deepseek-v4-flash",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {
|
||
"role": "assistant",
|
||
"reasoning_content": "Need the current date before calling weather.",
|
||
"content": "Let me check the date first.",
|
||
"tool_calls": [{
|
||
"id": "call_date",
|
||
"type": "function",
|
||
"function": {"name": "get_date", "arguments": "{}"}
|
||
}]
|
||
},
|
||
"finish_reason": "tool_calls"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
|
||
});
|
||
|
||
let anthropic_response = openai_to_anthropic(upstream_response).unwrap();
|
||
assert_eq!(anthropic_response["content"][0]["type"], "thinking");
|
||
assert_eq!(
|
||
anthropic_response["content"][0]["thinking"],
|
||
"Need the current date before calling weather."
|
||
);
|
||
assert_eq!(anthropic_response["content"][1]["type"], "text");
|
||
assert_eq!(anthropic_response["content"][2]["type"], "tool_use");
|
||
assert_eq!(anthropic_response["content"][2]["id"], "call_date");
|
||
|
||
let follow_up_request = json!({
|
||
"model": "deepseek-v4-flash",
|
||
"max_tokens": 1024,
|
||
"messages": [{
|
||
"role": "assistant",
|
||
"content": anthropic_response["content"].clone()
|
||
}]
|
||
});
|
||
let replayed = anthropic_to_openai_with_reasoning_content(follow_up_request, true).unwrap();
|
||
let msg = &replayed["messages"][0];
|
||
|
||
assert_eq!(
|
||
msg["reasoning_content"],
|
||
"Need the current date before calling weather."
|
||
);
|
||
assert_eq!(msg["tool_calls"][0]["id"], "call_date");
|
||
assert_eq!(msg["tool_calls"][0]["function"]["name"], "get_date");
|
||
}
|
||
|
||
#[test]
|
||
fn test_model_passthrough() {
|
||
// 格式转换层只做结构转换，模型映射由上游 proxy::model_mapper 处理
|
||
let input = json!({
|
||
"model": "gpt-4o",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["model"], "gpt-4o");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_does_not_inject_prompt_cache_key() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert!(result.get("prompt_cache_key").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_strips_all_cache_control() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "System prompt", "cache_control": {"type": "ephemeral"}}
|
||
],
|
||
"messages": [{
|
||
"role": "user",
|
||
"content": [
|
||
{"type": "text", "text": "Hello", "cache_control": {"type": "ephemeral", "ttl": "5m"}}
|
||
]
|
||
}],
|
||
"tools": [{
|
||
"name": "get_weather",
|
||
"description": "Get weather",
|
||
"input_schema": {"type": "object"},
|
||
"cache_control": {"type": "ephemeral"}
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
// System message: no cache_control
|
||
assert!(result["messages"][0].get("cache_control").is_none());
|
||
// User message: content simplified to string (no cache_control → flat string)
|
||
assert_eq!(result["messages"][1]["content"], "Hello");
|
||
// Tool: no cache_control
|
||
assert!(result["tools"][0].get("cache_control").is_none());
|
||
}
|
||
|
||
/// 精确复现 Issue #3805 报告的 400 错误场景:
|
||
/// GLM/Qwen 等严格校验模型拒绝 cache_control 和 content 数组格式
|
||
#[test]
|
||
fn test_regression_gh3805_no_cache_control_leak_to_openai() {
|
||
let input = json!({
|
||
"model": "glm-5.1",
|
||
"max_tokens": 1024,
|
||
"system": [
|
||
{"type": "text", "text": "You are helpful.", "cache_control": {"type": "ephemeral"}}
|
||
],
|
||
"messages": [
|
||
{"role": "user", "content": [
|
||
{"type": "text", "text": "Hello", "cache_control": {"type": "ephemeral"}}
|
||
]}
|
||
],
|
||
"tools": [{
|
||
"name": "search",
|
||
"description": "Search the web",
|
||
"input_schema": {"type": "object"},
|
||
"cache_control": {"type": "ephemeral"}
|
||
}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
|
||
// 验证: messages 中不存在 cache_control
|
||
for (i, msg) in result["messages"].as_array().unwrap().iter().enumerate() {
|
||
assert!(
|
||
msg.get("cache_control").is_none(),
|
||
"messages[{i}] must not have cache_control"
|
||
);
|
||
}
|
||
|
||
// 验证: content 中没有 cache_control
|
||
for (i, msg) in result["messages"].as_array().unwrap().iter().enumerate() {
|
||
if let Some(content) = msg.get("content") {
|
||
assert!(
|
||
!content.is_array()
|
||
|| content
|
||
.as_array()
|
||
.unwrap()
|
||
.iter()
|
||
.all(|part| part.get("cache_control").is_none()),
|
||
"messages[{i}] content parts must not have cache_control"
|
||
);
|
||
}
|
||
}
|
||
|
||
// 验证: system content 为纯字符串格式（不是数组）
|
||
let sys_msg = &result["messages"][0];
|
||
assert_eq!(sys_msg["role"], "system");
|
||
assert!(
|
||
sys_msg["content"].is_string(),
|
||
"system content must be string, got: {}",
|
||
sys_msg["content"]
|
||
);
|
||
|
||
// 验证: user content 为纯字符串格式（不是数组）
|
||
let user_msg = &result["messages"][1];
|
||
assert_eq!(user_msg["role"], "user");
|
||
assert!(
|
||
user_msg["content"].is_string(),
|
||
"user content must be string, got: {}",
|
||
user_msg["content"]
|
||
);
|
||
|
||
// 验证: tools 中不存在 cache_control
|
||
if let Some(tools) = result["tools"].as_array() {
|
||
for (i, tool) in tools.iter().enumerate() {
|
||
assert!(
|
||
tool.get("cache_control").is_none(),
|
||
"tools[{i}] must not have cache_control"
|
||
);
|
||
}
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_with_cache_tokens() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "Hello!"},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {
|
||
"prompt_tokens": 100,
|
||
"completion_tokens": 50,
|
||
"prompt_tokens_details": {
|
||
"cached_tokens": 80
|
||
}
|
||
}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
// prompt_tokens(100) 含 cached(80)，转换后 input 应为 fresh = 100 - 80 = 20
|
||
assert_eq!(result["usage"]["input_tokens"], 20);
|
||
assert_eq!(result["usage"]["output_tokens"], 50);
|
||
assert_eq!(result["usage"]["cache_read_input_tokens"], 80);
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_with_direct_cache_fields() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "Hello!"},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {
|
||
"prompt_tokens": 100,
|
||
"completion_tokens": 50,
|
||
"cache_read_input_tokens": 60,
|
||
"cache_creation_input_tokens": 20
|
||
}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
// cache_read(60)+cache_creation(20) 均从 prompt(100) 扣除，fresh = 100 - 60 - 20 = 20
|
||
// 守恒：input(20) + cache_read(60) + cache_creation(20) == prompt(100)
|
||
assert_eq!(result["usage"]["input_tokens"], 20);
|
||
assert_eq!(result["usage"]["cache_read_input_tokens"], 60);
|
||
assert_eq!(result["usage"]["cache_creation_input_tokens"], 20);
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_clamps_input_when_cache_exceeds_prompt() {
|
||
// prompt(100) < cache_read(60)+cache_creation(50)=110：saturating 钳到 0，防下溢。
|
||
// 钉桩：阻止未来把 saturating_sub 误改成普通减法(debug panic / release wrap)。
|
||
let input = json!({
|
||
"id": "chatcmpl-uf",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "x"},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {
|
||
"prompt_tokens": 100,
|
||
"completion_tokens": 10,
|
||
"cache_read_input_tokens": 60,
|
||
"cache_creation_input_tokens": 50
|
||
}
|
||
});
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["usage"]["input_tokens"], 0);
|
||
assert_eq!(result["usage"]["cache_read_input_tokens"], 60);
|
||
assert_eq!(result["usage"]["cache_creation_input_tokens"], 50);
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_finish_reason_content_filter_maps_end_turn() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {"role": "assistant", "content": "Blocked"},
|
||
"finish_reason": "content_filter"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 1}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["stop_reason"], "end_turn");
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_with_legacy_function_call() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {
|
||
"role": "assistant",
|
||
"content": null,
|
||
"function_call": {
|
||
"name": "get_weather",
|
||
"arguments": "{\"location\":\"Tokyo\"}"
|
||
}
|
||
},
|
||
"finish_reason": "function_call"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["content"][0]["type"], "tool_use");
|
||
assert_eq!(result["content"][0]["name"], "get_weather");
|
||
assert_eq!(result["content"][0]["input"]["location"], "Tokyo");
|
||
assert_eq!(result["stop_reason"], "tool_use");
|
||
}
|
||
|
||
#[test]
|
||
fn test_openai_to_anthropic_with_content_parts_and_refusal() {
|
||
let input = json!({
|
||
"id": "chatcmpl-123",
|
||
"model": "gpt-4",
|
||
"choices": [{
|
||
"index": 0,
|
||
"message": {
|
||
"role": "assistant",
|
||
"content": [
|
||
{"type": "text", "text": "Hello"},
|
||
{"type": "refusal", "refusal": "I can't do that"}
|
||
]
|
||
},
|
||
"finish_reason": "stop"
|
||
}],
|
||
"usage": {"prompt_tokens": 10, "completion_tokens": 5}
|
||
});
|
||
|
||
let result = openai_to_anthropic(input).unwrap();
|
||
assert_eq!(result["content"][0]["type"], "text");
|
||
assert_eq!(result["content"][0]["text"], "Hello");
|
||
assert_eq!(result["content"][1]["type"], "text");
|
||
assert_eq!(result["content"][1]["text"], "I can't do that");
|
||
}
|
||
|
||
#[test]
|
||
fn test_is_openai_o_series() {
|
||
assert!(is_openai_o_series("o1"));
|
||
assert!(is_openai_o_series("o1-preview"));
|
||
assert!(is_openai_o_series("o1-mini"));
|
||
assert!(is_openai_o_series("o3"));
|
||
assert!(is_openai_o_series("o3-mini"));
|
||
assert!(is_openai_o_series("o4-mini"));
|
||
assert!(!is_openai_o_series("gpt-4o"));
|
||
assert!(!is_openai_o_series("openai-gpt"));
|
||
assert!(!is_openai_o_series("o"));
|
||
assert!(!is_openai_o_series(""));
|
||
}
|
||
|
||
#[test]
|
||
fn test_supports_reasoning_effort() {
|
||
assert!(supports_reasoning_effort("o1"));
|
||
assert!(supports_reasoning_effort("o3-mini"));
|
||
assert!(supports_reasoning_effort("gpt-5"));
|
||
assert!(supports_reasoning_effort("gpt-5.4"));
|
||
assert!(supports_reasoning_effort("gpt-5-codex"));
|
||
assert!(!supports_reasoning_effort("gpt-4o"));
|
||
assert!(!supports_reasoning_effort("claude-sonnet-4-6"));
|
||
}
|
||
|
||
// ── resolve_reasoning_effort unit tests ──
|
||
|
||
#[test]
|
||
fn test_output_config_low_maps_to_reasoning_effort_low() {
|
||
let body = json!({"output_config": {"effort": "low"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("low"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_output_config_medium_maps_to_reasoning_effort_medium() {
|
||
let body = json!({"output_config": {"effort": "medium"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("medium"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_output_config_high_maps_to_reasoning_effort_high() {
|
||
let body = json!({"output_config": {"effort": "high"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("high"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_output_config_max_maps_to_reasoning_effort_xhigh() {
|
||
let body = json!({"output_config": {"effort": "max"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("xhigh"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_output_config_takes_priority_over_thinking() {
|
||
// Even with thinking.adaptive present, explicit effort wins
|
||
let body = json!({
|
||
"output_config": {"effort": "low"},
|
||
"thinking": {"type": "adaptive"}
|
||
});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("low"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_output_config_unknown_value_no_reasoning_effort() {
|
||
let body = json!({"output_config": {"effort": "turbo"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), None);
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_enabled_small_budget_maps_low() {
|
||
let body = json!({"thinking": {"type": "enabled", "budget_tokens": 1024}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("low"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_enabled_medium_budget_maps_medium() {
|
||
let body = json!({"thinking": {"type": "enabled", "budget_tokens": 8000}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("medium"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_enabled_large_budget_maps_high() {
|
||
let body = json!({"thinking": {"type": "enabled", "budget_tokens": 32000}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("high"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_enabled_without_budget_maps_high() {
|
||
let body = json!({"thinking": {"type": "enabled"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("high"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_adaptive_maps_xhigh() {
|
||
let body = json!({"thinking": {"type": "adaptive"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), Some("xhigh"));
|
||
}
|
||
|
||
#[test]
|
||
fn test_thinking_disabled_no_reasoning_effort() {
|
||
let body = json!({"thinking": {"type": "disabled"}});
|
||
assert_eq!(resolve_reasoning_effort(&body), None);
|
||
}
|
||
|
||
#[test]
|
||
fn test_no_thinking_field_no_reasoning_effort() {
|
||
let body = json!({"messages": [{"role": "user", "content": "Hello"}]});
|
||
assert_eq!(resolve_reasoning_effort(&body), None);
|
||
}
|
||
|
||
// ── Integration: anthropic_to_openai with resolve_reasoning_effort ──
|
||
|
||
#[test]
|
||
fn test_non_reasoning_model_no_reasoning_effort() {
|
||
let input = json!({
|
||
"model": "gpt-4o",
|
||
"max_tokens": 1024,
|
||
"thinking": {"type": "enabled", "budget_tokens": 2048},
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert!(result.get("reasoning_effort").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_reasoning_model_with_output_config_effort() {
|
||
let input = json!({
|
||
"model": "gpt-5.4",
|
||
"max_tokens": 1024,
|
||
"output_config": {"effort": "medium"},
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["reasoning_effort"], "medium");
|
||
}
|
||
|
||
#[test]
|
||
fn test_reasoning_model_with_output_config_max() {
|
||
let input = json!({
|
||
"model": "gpt-5.4",
|
||
"max_tokens": 1024,
|
||
"output_config": {"effort": "max"},
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["reasoning_effort"], "xhigh");
|
||
}
|
||
|
||
#[test]
|
||
fn test_reasoning_model_thinking_enabled_small_budget() {
|
||
let input = json!({
|
||
"model": "o3",
|
||
"max_tokens": 1024,
|
||
"thinking": {"type": "enabled", "budget_tokens": 2048},
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["reasoning_effort"], "low");
|
||
}
|
||
|
||
#[test]
|
||
fn test_reasoning_model_thinking_adaptive() {
|
||
let input = json!({
|
||
"model": "gpt-5.4",
|
||
"max_tokens": 1024,
|
||
"thinking": {"type": "adaptive"},
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["reasoning_effort"], "xhigh");
|
||
}
|
||
|
||
#[test]
|
||
fn test_reasoning_model_no_thinking_no_effort() {
|
||
let input = json!({
|
||
"model": "gpt-5.4",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert!(result.get("reasoning_effort").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_o_series_max_completion_tokens() {
|
||
for model in &["o1", "o3-mini", "o4-mini"] {
|
||
let input = json!({
|
||
"model": model,
|
||
"max_tokens": 4096,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert!(
|
||
result.get("max_tokens").is_none(),
|
||
"{model} should not have max_tokens"
|
||
);
|
||
assert_eq!(
|
||
result["max_completion_tokens"], 4096,
|
||
"{model} should use max_completion_tokens"
|
||
);
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_non_o_series_keeps_max_tokens() {
|
||
let input = json!({
|
||
"model": "gpt-4o",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input).unwrap();
|
||
assert_eq!(result["max_tokens"], 1024);
|
||
assert!(result.get("max_completion_tokens").is_none());
|
||
}
|
||
|
||
fn run_tool_choice(value: Value) -> Value {
|
||
let input = json!({
|
||
"model": "gpt-4o",
|
||
"messages": [{"role": "user", "content": "Hello"}],
|
||
"tools": [{
|
||
"name": "search",
|
||
"description": "search the web",
|
||
"input_schema": {"type": "object", "properties": {}}
|
||
}],
|
||
"tool_choice": value,
|
||
});
|
||
anthropic_to_openai(input).unwrap()["tool_choice"].clone()
|
||
}
|
||
|
||
#[test]
|
||
fn tool_choice_string_any_maps_to_required() {
|
||
assert_eq!(run_tool_choice(json!("any")), json!("required"));
|
||
}
|
||
|
||
#[test]
|
||
fn tool_choice_string_auto_and_none_pass_through() {
|
||
assert_eq!(run_tool_choice(json!("auto")), json!("auto"));
|
||
assert_eq!(run_tool_choice(json!("none")), json!("none"));
|
||
}
|
||
|
||
#[test]
|
||
fn tool_choice_object_any_maps_to_required() {
|
||
assert_eq!(run_tool_choice(json!({"type": "any"})), json!("required"));
|
||
}
|
||
|
||
#[test]
|
||
fn tool_choice_object_auto_and_none_collapse_to_string() {
|
||
assert_eq!(run_tool_choice(json!({"type": "auto"})), json!("auto"));
|
||
assert_eq!(run_tool_choice(json!({"type": "none"})), json!("none"));
|
||
}
|
||
|
||
#[test]
|
||
fn tool_choice_forced_tool_maps_to_nested_function_selector() {
|
||
// Anthropic {"type":"tool","name":"X"} must become OpenAI Chat
|
||
// {"type":"function","function":{"name":"X"}} — the *nested* form, not
|
||
// the flat Responses-API form.
|
||
assert_eq!(
|
||
run_tool_choice(json!({"type": "tool", "name": "search"})),
|
||
json!({"type": "function", "function": {"name": "search"}}),
|
||
);
|
||
}
|
||
}
|