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refactor: deduplicate and improve OpenAI Responses API conversion

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- Extract shared map_responses_stop_reason and build_anthropic_usage_from_responses into transform_responses.rs as pub(crate)
- Align cache token extraction priority: OpenAI nested details as fallback, direct Anthropic fields as override
- Extract resolve_content_index helper to eliminate 3x copy-paste in streaming_responses.rs
- Add streaming reasoning/thinking event handlers (response.reasoning.delta/done)
- Add explanatory comment to transform_response heuristic detection
- Add openai_responses to api_format doc comment and needs_transform test
- Add explicit no-op match arms for lifecycle events
- Add promptCacheKey to TS ProviderMeta type
- Update toast i18n key to be generic for both OpenAI formats (zh/en/ja)
This commit is contained in:
Jason
2026-03-05 21:52:41 +08:00
parent a30e2096bb
commit 11f70f676e
11 changed files with 1201 additions and 110 deletions
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src-tauri/src/proxy/providers/transform.rs
+171 -19
View File
@@ -7,7 +7,9 @@ use crate::proxy::error::ProxyError;
use serde_json::{json, Value};
/// Anthropic 请求 → OpenAI 请求
pub fn anthropic_to_openai(body: Value) -> Result<Value, ProxyError> {
///
/// `cache_key`: optional prompt_cache_key to inject for improved cache routing
pub fn anthropic_to_openai(body: Value, cache_key: Option<&str>) -> Result<Value, ProxyError> {
let mut result = json!({});
// 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。
@@ -23,10 +25,14 @@ pub fn anthropic_to_openai(body: Value) -> Result<Value, ProxyError> {
// 单个字符串
messages.push(json!({"role": "system", "content": text}));
} else if let Some(arr) = system.as_array() {
// 多个 system message
// 多个 system message — preserve cache_control for compatible proxies
for msg in arr {
if let Some(text) = msg.get("text").and_then(|t| t.as_str()) {
messages.push(json!({"role": "system", "content": text}));
let mut sys_msg = json!({"role": "system", "content": text});
if let Some(cc) = msg.get("cache_control") {
sys_msg["cache_control"] = cc.clone();
}
messages.push(sys_msg);
}
}
}
@@ -67,14 +73,18 @@ pub fn anthropic_to_openai(body: Value) -> Result<Value, ProxyError> {
.iter()
.filter(|t| t.get("type").and_then(|v| v.as_str()) != Some("BatchTool"))
.map(|t| {
json!({
let mut tool = json!({
"type": "function",
"function": {
"name": t.get("name").and_then(|n| n.as_str()).unwrap_or(""),
"description": t.get("description"),
"parameters": clean_schema(t.get("input_schema").cloned().unwrap_or(json!({})))
}
})
});
if let Some(cc) = t.get("cache_control") {
tool["cache_control"] = cc.clone();
}
tool
})
.collect();
@@ -87,6 +97,11 @@ pub fn anthropic_to_openai(body: Value) -> Result<Value, ProxyError> {
result["tool_choice"] = v.clone();
}
// Inject prompt_cache_key for improved cache routing on OpenAI-compatible endpoints
if let Some(key) = cache_key {
result["prompt_cache_key"] = json!(key);
}
Ok(result)
}
@@ -122,7 +137,11 @@ fn convert_message_to_openai(
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}));
let mut part = json!({"type": "text", "text": text});
if let Some(cc) = block.get("cache_control") {
part["cache_control"] = cc.clone();
}
content_parts.push(part);
}
}
"image" => {
@@ -184,8 +203,14 @@ fn convert_message_to_openai(
if content_parts.is_empty() {
msg["content"] = Value::Null;
} else if content_parts.len() == 1 {
if let Some(text) = content_parts[0].get("text") {
msg["content"] = text.clone();
// When cache_control is present, keep array format to preserve it
let has_cache_control = content_parts[0].get("cache_control").is_some();
if !has_cache_control {
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);
}
@@ -288,7 +313,7 @@ pub fn openai_to_anthropic(body: Value) -> Result<Value, ProxyError> {
other => other,
});
// usage
// usage — map cache tokens from OpenAI format to Anthropic format
let usage = body.get("usage").cloned().unwrap_or(json!({}));
let input_tokens = usage
.get("prompt_tokens")
@@ -299,6 +324,26 @@ pub fn openai_to_anthropic(body: Value) -> Result<Value, ProxyError> {
.and_then(|v| v.as_u64())
.unwrap_or(0) as u32;
let mut usage_json = json!({
"input_tokens": input_tokens,
"output_tokens": output_tokens
});
// OpenAI standard: prompt_tokens_details.cached_tokens
if let Some(cached) = usage
.pointer("/prompt_tokens_details/cached_tokens")
.and_then(|v| v.as_u64())
{
usage_json["cache_read_input_tokens"] = json!(cached);
}
// Some compatible servers return these fields directly
if let Some(v) = usage.get("cache_read_input_tokens") {
usage_json["cache_read_input_tokens"] = v.clone();
}
if let Some(v) = usage.get("cache_creation_input_tokens") {
usage_json["cache_creation_input_tokens"] = v.clone();
}
let result = json!({
"id": body.get("id").and_then(|i| i.as_str()).unwrap_or(""),
"type": "message",
@@ -307,10 +352,7 @@ pub fn openai_to_anthropic(body: Value) -> Result<Value, ProxyError> {
"model": body.get("model").and_then(|m| m.as_str()).unwrap_or(""),
"stop_reason": stop_reason,
"stop_sequence": null,
"usage": {
"input_tokens": input_tokens,
"output_tokens": output_tokens
}
"usage": usage_json
});
Ok(result)
@@ -328,7 +370,7 @@ mod tests {
"messages": [{"role": "user", "content": "Hello"}]
});
let result = anthropic_to_openai(input).unwrap();
let result = anthropic_to_openai(input, None).unwrap();
assert_eq!(result["model"], "claude-3-opus");
assert_eq!(result["max_tokens"], 1024);
assert_eq!(result["messages"][0]["role"], "user");
@@ -344,7 +386,7 @@ mod tests {
"messages": [{"role": "user", "content": "Hello"}]
});
let result = anthropic_to_openai(input).unwrap();
let result = anthropic_to_openai(input, None).unwrap();
assert_eq!(result["messages"][0]["role"], "system");
assert_eq!(
result["messages"][0]["content"],
@@ -366,7 +408,7 @@ mod tests {
}]
});
let result = anthropic_to_openai(input).unwrap();
let result = anthropic_to_openai(input, None).unwrap();
assert_eq!(result["tools"][0]["type"], "function");
assert_eq!(result["tools"][0]["function"]["name"], "get_weather");
}
@@ -385,7 +427,7 @@ mod tests {
}]
});
let result = anthropic_to_openai(input).unwrap();
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());
@@ -405,7 +447,7 @@ mod tests {
}]
});
let result = anthropic_to_openai(input).unwrap();
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");
@@ -477,7 +519,117 @@ mod tests {
"messages": [{"role": "user", "content": "Hello"}]
});
let result = anthropic_to_openai(input).unwrap();
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);
}
}