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https://github.com/farion1231/cc-switch.git
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Hemilt0n
f38facd430
* fix(proxy): use max_completion_tokens for o1/o3 series models When converting Anthropic requests to OpenAI format for o1/o3 series models (like o1-mini, o3-mini), use max_completion_tokens instead of max_tokens to avoid unsupported_parameter errors. Fixes #1448 * fix: revert incorrect o-series max_completion_tokens in Responses API path Responses API uses max_output_tokens for all models including o-series. The o-series max_completion_tokens fix should only apply to Chat Completions API. --------- Co-authored-by: Hajen Teowideo <hajen.teowideo@example.com> Co-authored-by: Jason Young <44939412+farion1231@users.noreply.github.com> Co-authored-by: Jason <farion1231@gmail.com>
837 lines
29 KiB
Rust
837 lines
29 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;
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use serde_json::{json, Value};
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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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/// Anthropic 请求 → OpenAI 请求
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///
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/// `cache_key`: optional prompt_cache_key to inject for improved cache routing
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pub fn anthropic_to_openai(body: Value, cache_key: Option<&str>) -> 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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// 单个字符串
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messages.push(json!({"role": "system", "content": text}));
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} else if let Some(arr) = system.as_array() {
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// 多个 system message — preserve cache_control for compatible proxies
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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 mut sys_msg = json!({"role": "system", "content": text});
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if let Some(cc) = msg.get("cache_control") {
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sys_msg["cache_control"] = cc.clone();
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}
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messages.push(sys_msg);
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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)?;
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messages.extend(converted);
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}
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}
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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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// 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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let mut tool = 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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if let Some(cc) = t.get("cache_control") {
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tool["cache_control"] = cc.clone();
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}
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tool
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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"] = v.clone();
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}
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// Inject prompt_cache_key for improved cache routing on OpenAI-compatible endpoints
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if let Some(key) = cache_key {
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result["prompt_cache_key"] = json!(key);
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}
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Ok(result)
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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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) -> Result<Vec<Value>, ProxyError> {
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let mut result = Vec::new();
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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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// 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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// 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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for block in blocks {
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let block_type = block.get("type").and_then(|t| t.as_str()).unwrap_or("");
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match block_type {
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"text" => {
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if let Some(text) = block.get("text").and_then(|t| t.as_str()) {
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let mut part = json!({"type": "text", "text": text});
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if let Some(cc) = block.get("cache_control") {
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part["cache_control"] = cc.clone();
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}
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content_parts.push(part);
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}
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}
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"image" => {
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if let Some(source) = block.get("source") {
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let media_type = source
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.get("media_type")
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.and_then(|m| m.as_str())
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.unwrap_or("image/png");
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let data = source.get("data").and_then(|d| d.as_str()).unwrap_or("");
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content_parts.push(json!({
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"type": "image_url",
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"image_url": {"url": format!("data:{};base64,{}", media_type, data)}
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}));
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}
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}
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"tool_use" => {
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let id = block.get("id").and_then(|i| i.as_str()).unwrap_or("");
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let name = block.get("name").and_then(|n| n.as_str()).unwrap_or("");
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let input = block.get("input").cloned().unwrap_or(json!({}));
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tool_calls.push(json!({
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"id": id,
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"type": "function",
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"function": {
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"name": name,
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"arguments": serde_json::to_string(&input).unwrap_or_default()
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}
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}));
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}
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"tool_result" => {
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// tool_result 变成单独的 tool role 消息
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let tool_use_id = block
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.get("tool_use_id")
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.and_then(|i| i.as_str())
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.unwrap_or("");
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let content_val = block.get("content");
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let content_str = match content_val {
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Some(Value::String(s)) => s.clone(),
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Some(v) => serde_json::to_string(v).unwrap_or_default(),
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None => String::new(),
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};
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result.push(json!({
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"role": "tool",
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"tool_call_id": tool_use_id,
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"content": content_str
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}));
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}
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"thinking" => {
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// 跳过 thinking blocks
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}
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_ => {}
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}
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}
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// 添加带内容和/或工具调用的消息
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if !content_parts.is_empty() || !tool_calls.is_empty() {
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let mut msg = json!({"role": role});
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// 内容处理
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if content_parts.is_empty() {
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msg["content"] = Value::Null;
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} else if content_parts.len() == 1 {
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// When cache_control is present, keep array format to preserve it
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let has_cache_control = content_parts[0].get("cache_control").is_some();
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if !has_cache_control {
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if let Some(text) = content_parts[0].get("text") {
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msg["content"] = text.clone();
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} else {
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msg["content"] = json!(content_parts);
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}
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} else {
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msg["content"] = json!(content_parts);
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}
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} else {
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msg["content"] = json!(content_parts);
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}
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// 工具调用
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if !tool_calls.is_empty() {
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msg["tool_calls"] = json!(tool_calls);
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}
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result.push(msg);
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}
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return Ok(result);
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}
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// 其他情况直接透传
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result.push(json!({"role": role, "content": content}));
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Ok(result)
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}
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/// 清理 JSON schema（移除不支持的 format）
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pub fn clean_schema(mut schema: Value) -> Value {
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if let Some(obj) = schema.as_object_mut() {
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// 移除 "format": "uri"
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if obj.get("format").and_then(|v| v.as_str()) == Some("uri") {
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obj.remove("format");
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}
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// 递归清理嵌套 schema
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if let Some(properties) = obj.get_mut("properties").and_then(|v| v.as_object_mut()) {
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for (_, value) in properties.iter_mut() {
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*value = clean_schema(value.clone());
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}
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}
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if let Some(items) = obj.get_mut("items") {
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*items = clean_schema(items.clone());
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}
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}
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schema
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}
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/// OpenAI 响应 → Anthropic 响应
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pub fn openai_to_anthropic(body: Value) -> Result<Value, ProxyError> {
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let choices = body
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.get("choices")
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.and_then(|c| c.as_array())
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.ok_or_else(|| ProxyError::TransformError("No choices in response".to_string()))?;
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let choice = choices
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.first()
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.ok_or_else(|| ProxyError::TransformError("Empty choices array".to_string()))?;
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let message = choice
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.get("message")
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.ok_or_else(|| ProxyError::TransformError("No message in choice".to_string()))?;
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let mut content = Vec::new();
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let mut has_tool_use = false;
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// 文本/拒绝内容
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if let Some(msg_content) = message.get("content") {
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if let Some(text) = msg_content.as_str() {
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if !text.is_empty() {
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content.push(json!({"type": "text", "text": text}));
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}
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} else if let Some(parts) = msg_content.as_array() {
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for part in parts {
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let part_type = part.get("type").and_then(|t| t.as_str()).unwrap_or("");
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match part_type {
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"text" | "output_text" => {
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if let Some(text) = part.get("text").and_then(|t| t.as_str()) {
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if !text.is_empty() {
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content.push(json!({"type": "text", "text": text}));
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}
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}
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}
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"refusal" => {
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if let Some(refusal) = part.get("refusal").and_then(|r| r.as_str()) {
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if !refusal.is_empty() {
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content.push(json!({"type": "text", "text": refusal}));
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}
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}
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}
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_ => {}
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}
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}
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}
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}
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// Some providers put refusal at message-level.
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if let Some(refusal) = message.get("refusal").and_then(|r| r.as_str()) {
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if !refusal.is_empty() {
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content.push(json!({"type": "text", "text": refusal}));
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}
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}
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// 工具调用（tool_calls）
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if let Some(tool_calls) = message.get("tool_calls").and_then(|t| t.as_array()) {
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if !tool_calls.is_empty() {
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has_tool_use = true;
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}
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for tc in tool_calls {
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let id = tc.get("id").and_then(|i| i.as_str()).unwrap_or("");
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let empty_obj = json!({});
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let func = tc.get("function").unwrap_or(&empty_obj);
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let name = func.get("name").and_then(|n| n.as_str()).unwrap_or("");
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let args_str = func
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.get("arguments")
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.and_then(|a| a.as_str())
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.unwrap_or("{}");
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let input: Value = serde_json::from_str(args_str).unwrap_or(json!({}));
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content.push(json!({
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"type": "tool_use",
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"id": id,
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"name": name,
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"input": input
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}));
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}
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}
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// 兼容旧格式（function_call）
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if !has_tool_use {
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if let Some(function_call) = message.get("function_call") {
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let id = function_call
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.get("id")
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.and_then(|i| i.as_str())
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.unwrap_or("");
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let name = function_call
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.get("name")
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.and_then(|n| n.as_str())
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.unwrap_or("");
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let has_arguments = function_call.get("arguments").is_some();
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let input = match function_call.get("arguments") {
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Some(Value::String(s)) => serde_json::from_str(s).unwrap_or(json!({})),
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Some(v @ Value::Object(_)) | Some(v @ Value::Array(_)) => v.clone(),
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_ => json!({}),
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};
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if !name.is_empty() || has_arguments {
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content.push(json!({
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"type": "tool_use",
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"id": id,
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"name": name,
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"input": input
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}));
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has_tool_use = true;
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}
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}
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}
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// 映射 finish_reason → stop_reason
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let stop_reason = choice
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.get("finish_reason")
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.and_then(|r| r.as_str())
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.map(|r| match r {
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"stop" => "end_turn",
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"length" => "max_tokens",
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"tool_calls" | "function_call" => "tool_use",
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"content_filter" => "end_turn",
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other => {
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log::warn!(
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"[Claude/OpenAI] Unknown finish_reason in non-streaming response: {other}"
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);
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"end_turn"
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}
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})
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.or(if has_tool_use { Some("tool_use") } else { None });
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|
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// usage — map cache tokens from OpenAI format to Anthropic format
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let usage = body.get("usage").cloned().unwrap_or(json!({}));
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let input_tokens = usage
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.get("prompt_tokens")
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.and_then(|v| v.as_u64())
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.unwrap_or(0) as u32;
|
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let output_tokens = usage
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.get("completion_tokens")
|
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.and_then(|v| v.as_u64())
|
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.unwrap_or(0) as u32;
|
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|
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let mut usage_json = json!({
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"input_tokens": input_tokens,
|
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"output_tokens": output_tokens
|
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});
|
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|
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// OpenAI standard: prompt_tokens_details.cached_tokens
|
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if let Some(cached) = usage
|
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.pointer("/prompt_tokens_details/cached_tokens")
|
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.and_then(|v| v.as_u64())
|
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{
|
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usage_json["cache_read_input_tokens"] = json!(cached);
|
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}
|
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// Some compatible servers return these fields directly
|
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if let Some(v) = usage.get("cache_read_input_tokens") {
|
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usage_json["cache_read_input_tokens"] = v.clone();
|
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}
|
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if let Some(v) = usage.get("cache_creation_input_tokens") {
|
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usage_json["cache_creation_input_tokens"] = v.clone();
|
||
}
|
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|
||
let result = json!({
|
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"id": body.get("id").and_then(|i| i.as_str()).unwrap_or(""),
|
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"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"}]
|
||
});
|
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|
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let result = anthropic_to_openai(input, None).unwrap();
|
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assert_eq!(result["model"], "claude-3-opus");
|
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assert_eq!(result["max_tokens"], 1024);
|
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assert_eq!(result["messages"][0]["role"], "user");
|
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assert_eq!(result["messages"][0]["content"], "Hello");
|
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}
|
||
|
||
#[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, None).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_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, None).unwrap();
|
||
assert_eq!(result["tools"][0]["type"], "function");
|
||
assert_eq!(result["tools"][0]["function"]["name"], "get_weather");
|
||
}
|
||
|
||
#[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, None).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");
|
||
}
|
||
|
||
#[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, None).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_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_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, None).unwrap();
|
||
assert_eq!(result["model"], "gpt-4o");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_with_cache_key() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input, Some("provider-123")).unwrap();
|
||
assert_eq!(result["prompt_cache_key"], "provider-123");
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_no_cache_key() {
|
||
let input = json!({
|
||
"model": "claude-3-opus",
|
||
"max_tokens": 1024,
|
||
"messages": [{"role": "user", "content": "Hello"}]
|
||
});
|
||
|
||
let result = anthropic_to_openai(input, None).unwrap();
|
||
assert!(result.get("prompt_cache_key").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn test_anthropic_to_openai_cache_control_preserved() {
|
||
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, None).unwrap();
|
||
// System message cache_control preserved
|
||
assert_eq!(result["messages"][0]["cache_control"]["type"], "ephemeral");
|
||
// Text block cache_control preserved
|
||
assert_eq!(
|
||
result["messages"][1]["content"][0]["cache_control"]["type"],
|
||
"ephemeral"
|
||
);
|
||
assert_eq!(
|
||
result["messages"][1]["content"][0]["cache_control"]["ttl"],
|
||
"5m"
|
||
);
|
||
// Tool cache_control preserved
|
||
assert_eq!(result["tools"][0]["cache_control"]["type"], "ephemeral");
|
||
}
|
||
|
||
#[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();
|
||
assert_eq!(result["usage"]["input_tokens"], 100);
|
||
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();
|
||
assert_eq!(result["usage"]["cache_read_input_tokens"], 60);
|
||
assert_eq!(result["usage"]["cache_creation_input_tokens"], 20);
|
||
}
|
||
|
||
#[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_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, None).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, None).unwrap();
|
||
assert_eq!(result["max_tokens"], 1024);
|
||
assert!(result.get("max_completion_tokens").is_none());
|
||
}
|
||
}
|