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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/streaming.rs
+58 -9
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@@ -60,6 +60,20 @@ struct Usage {
prompt_tokens: u32,
#[serde(default)]
completion_tokens: u32,
#[serde(default)]
prompt_tokens_details: Option<PromptTokensDetails>,
/// Some compatible servers return Anthropic-style cache fields directly
#[serde(default)]
cache_read_input_tokens: Option<u32>,
#[serde(default)]
cache_creation_input_tokens: Option<u32>,
}
/// Nested token details from OpenAI format
#[derive(Debug, Deserialize)]
struct PromptTokensDetails {
#[serde(default)]
cached_tokens: u32,
}
/// 创建 Anthropic SSE 流
@@ -115,6 +129,21 @@ pub fn create_anthropic_sse_stream(
if let Some(choice) = chunk.choices.first() {
if !has_sent_message_start {
// Build usage with cache tokens if available from first chunk
let mut start_usage = json!({
"input_tokens": 0,
"output_tokens": 0
});
if let Some(u) = &chunk.usage {
start_usage["input_tokens"] = json!(u.prompt_tokens);
if let Some(cached) = extract_cache_read_tokens(u) {
start_usage["cache_read_input_tokens"] = json!(cached);
}
if let Some(created) = u.cache_creation_input_tokens {
start_usage["cache_creation_input_tokens"] = json!(created);
}
}
let event = json!({
"type": "message_start",
"message": {
@@ -122,10 +151,7 @@ pub fn create_anthropic_sse_stream(
"type": "message",
"role": "assistant",
"model": current_model.clone().unwrap_or_default(),
"usage": {
"input_tokens": 0,
"output_tokens": 0
}
"usage": start_usage
}
});
let sse_data = format!("event: message_start\ndata: {}\n\n",
@@ -272,11 +298,20 @@ pub fn create_anthropic_sse_stream(
}
let stop_reason = map_stop_reason(Some(finish_reason));
// 构建 usage 信息，包含 input_tokens 和 output_tokens
let usage_json = chunk.usage.as_ref().map(|u| json!({
"input_tokens": u.prompt_tokens,
"output_tokens": u.completion_tokens
}));
// Build usage with cache token fields
let usage_json = chunk.usage.as_ref().map(|u| {
let mut uj = json!({
"input_tokens": u.prompt_tokens,
"output_tokens": u.completion_tokens
});
if let Some(cached) = extract_cache_read_tokens(u) {
uj["cache_read_input_tokens"] = json!(cached);
}
if let Some(created) = u.cache_creation_input_tokens {
uj["cache_creation_input_tokens"] = json!(created);
}
uj
});
let event = json!({
"type": "message_delta",
"delta": {
@@ -314,6 +349,20 @@ pub fn create_anthropic_sse_stream(
}
}
/// Extract cache_read tokens from Usage, checking both direct field and nested details
fn extract_cache_read_tokens(usage: &Usage) -> Option<u32> {
// Direct field takes priority (compatible servers)
if let Some(v) = usage.cache_read_input_tokens {
return Some(v);
}
// OpenAI standard: prompt_tokens_details.cached_tokens
usage
.prompt_tokens_details
.as_ref()
.map(|d| d.cached_tokens)
.filter(|&v| v > 0)
}
/// 映射停止原因
fn map_stop_reason(finish_reason: Option<&str>) -> Option<String> {
finish_reason.map(|r| {