mirror of
https://github.com/farion1231/cc-switch.git
synced 2026-07-29 09:37:37 +08:00
feat(proxy): implement provider adapter pattern with OpenRouter support
This major refactoring introduces a modular provider adapter architecture to support format transformation between different AI API formats. New features: - Add ProviderAdapter trait for unified provider abstraction - Implement Claude, Codex, and Gemini adapters with specific logic - Add Anthropic ↔ OpenAI format transformation for OpenRouter compatibility - Support model mapping from provider configuration (ANTHROPIC_MODEL, etc.) - Add OpenRouter preset to Claude provider presets Refactoring: - Extract authentication logic into auth.rs with AuthInfo and AuthStrategy - Move URL building and request transformation to individual adapters - Simplify ProviderRouter to only use proxy target providers - Refactor RequestForwarder to use adapter-based request/response handling - Use whitelist mode for header forwarding (only pass necessary headers) Architecture: - providers/adapter.rs: ProviderAdapter trait definition - providers/auth.rs: AuthInfo, AuthStrategy types - providers/claude.rs: Claude adapter with OpenRouter detection - providers/codex.rs: Codex (OpenAI) adapter - providers/gemini.rs: Gemini (Google) adapter - providers/models/: Anthropic and OpenAI API data models - providers/transform.rs: Bidirectional format transformation
This commit is contained in:
YoVinchen
@@ -0,0 +1,586 @@
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//! 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::provider::Provider;
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use crate::proxy::error::ProxyError;
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use serde_json::{json, Value};
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/// 从 Provider 配置中获取模型映射
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fn get_model_from_provider(model: &str, provider: &Provider) -> String {
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let env = provider.settings_config.get("env");
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// 根据请求的模型类型选择对应的配置模型
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let model_lower = model.to_lowercase();
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if let Some(env) = env {
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// 检查是否是 haiku 模型
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if model_lower.contains("haiku") {
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if let Some(m) = env
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.get("ANTHROPIC_DEFAULT_HAIKU_MODEL")
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.and_then(|v| v.as_str())
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{
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return m.to_string();
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}
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}
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// 检查是否是 opus 模型
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if model_lower.contains("opus") {
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if let Some(m) = env
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.get("ANTHROPIC_DEFAULT_OPUS_MODEL")
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.and_then(|v| v.as_str())
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{
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return m.to_string();
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}
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}
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// 检查是否是 sonnet 模型
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if model_lower.contains("sonnet") {
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if let Some(m) = env
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.get("ANTHROPIC_DEFAULT_SONNET_MODEL")
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.and_then(|v| v.as_str())
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{
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return m.to_string();
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}
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}
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// 默认使用 ANTHROPIC_MODEL
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if let Some(m) = env.get("ANTHROPIC_MODEL").and_then(|v| v.as_str()) {
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return m.to_string();
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}
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}
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// 如果没有配置，返回原始模型名
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model.to_string()
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}
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/// Anthropic 请求 → OpenAI 请求
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pub fn anthropic_to_openai(body: Value, provider: &Provider) -> Result<Value, ProxyError> {
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let mut result = json!({});
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// 模型映射：使用 Provider 配置中的模型
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if let Some(model) = body.get("model").and_then(|m| m.as_str()) {
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let mapped_model = get_model_from_provider(model, provider);
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result["model"] = json!(mapped_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
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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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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)?;
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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
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if let Some(v) = body.get("max_tokens") {
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result["max_tokens"] = v.clone();
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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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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"] = v.clone();
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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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content_parts.push(json!({"type": "text", "text": text}));
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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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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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// 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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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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// 文本内容
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if let Some(text) = message.get("content").and_then(|c| c.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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// 工具调用
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if let Some(tool_calls) = message.get("tool_calls").and_then(|t| t.as_array()) {
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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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// 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" => "tool_use",
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other => other,
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});
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// usage
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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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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",
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"role": "assistant",
|
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"content": content,
|
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"model": body.get("model").and_then(|m| m.as_str()).unwrap_or(""),
|
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"stop_reason": stop_reason,
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"stop_sequence": null,
|
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"usage": {
|
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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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|
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Ok(result)
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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|
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fn create_provider(env_config: Value) -> Provider {
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Provider {
|
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id: "test".to_string(),
|
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name: "Test Provider".to_string(),
|
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settings_config: json!({"env": env_config}),
|
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website_url: None,
|
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category: None,
|
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created_at: None,
|
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sort_index: None,
|
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notes: None,
|
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meta: None,
|
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icon: None,
|
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icon_color: None,
|
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is_proxy_target: None,
|
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}
|
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}
|
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|
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fn create_openrouter_provider() -> Provider {
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create_provider(json!({
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"ANTHROPIC_BASE_URL": "https://openrouter.ai/api",
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"ANTHROPIC_MODEL": "anthropic/claude-sonnet-4.5",
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"ANTHROPIC_DEFAULT_HAIKU_MODEL": "anthropic/claude-haiku-4.5",
|
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"ANTHROPIC_DEFAULT_SONNET_MODEL": "anthropic/claude-sonnet-4.5",
|
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"ANTHROPIC_DEFAULT_OPUS_MODEL": "anthropic/claude-opus-4.5"
|
||||
}))
|
||||
}
|
||||
|
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#[test]
|
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fn test_anthropic_to_openai_simple() {
|
||||
let provider = create_openrouter_provider();
|
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let input = json!({
|
||||
"model": "claude-3-opus",
|
||||
"max_tokens": 1024,
|
||||
"messages": [{"role": "user", "content": "Hello"}]
|
||||
});
|
||||
|
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let result = anthropic_to_openai(input, &provider).unwrap();
|
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// opus 模型映射到配置的 ANTHROPIC_DEFAULT_OPUS_MODEL
|
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assert_eq!(result["model"], "anthropic/claude-opus-4.5");
|
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assert_eq!(result["max_tokens"], 1024);
|
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assert_eq!(result["messages"][0]["role"], "user");
|
||||
assert_eq!(result["messages"][0]["content"], "Hello");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_anthropic_to_openai_with_system() {
|
||||
let provider = create_openrouter_provider();
|
||||
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, &provider).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 provider = create_openrouter_provider();
|
||||
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, &provider).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 provider = create_openrouter_provider();
|
||||
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, &provider).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 provider = create_openrouter_provider();
|
||||
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, &provider).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_mapping_from_provider() {
|
||||
let provider = create_openrouter_provider();
|
||||
|
||||
// sonnet 模型
|
||||
assert_eq!(
|
||||
get_model_from_provider("claude-sonnet-4-5-20250929", &provider),
|
||||
"anthropic/claude-sonnet-4.5"
|
||||
);
|
||||
|
||||
// haiku 模型
|
||||
assert_eq!(
|
||||
get_model_from_provider("claude-haiku-4-5-20250929", &provider),
|
||||
"anthropic/claude-haiku-4.5"
|
||||
);
|
||||
|
||||
// opus 模型
|
||||
assert_eq!(
|
||||
get_model_from_provider("claude-opus-4-5", &provider),
|
||||
"anthropic/claude-opus-4.5"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_anthropic_to_openai_model_mapping() {
|
||||
let provider = create_openrouter_provider();
|
||||
let input = json!({
|
||||
"model": "claude-sonnet-4-5-20250929",
|
||||
"max_tokens": 1024,
|
||||
"messages": [{"role": "user", "content": "Hello"}]
|
||||
});
|
||||
|
||||
let result = anthropic_to_openai(input, &provider).unwrap();
|
||||
assert_eq!(result["model"], "anthropic/claude-sonnet-4.5");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user