mirror of
https://github.com/fawney19/Aether.git
synced 2026-09-03 01:40:21 +08:00
refactor: 大规模模块拆分与代码精简，新增 ai-pipeline/data-contracts 独立 crate
- 新增 aether-ai-pipeline 和 aether-data-contracts crate，将 pipeline 逻辑与数据契约从 gateway 中解耦 - 重构 admin handlers：拆分单体模块为 auth/billing/endpoint/features/model/observability/provider/system 等独立子模块 - 合并 chat/cli 重复代码路径：精简 conversion、finalize、planner 中的 sync/chat/cli 分支 - 重构 scheduler/executor/data 层，引入 facade 模式降低模块间耦合 - 移除冗余的 intent 模块，将 plan_fallback/policy/stream_path/sync_path 迁移至 executor - 前端适配：调整 admin API 调用和 provider 模型测试对话框
This commit is contained in:
fawney19
376
crates/aether-ai-pipeline/src/planner/standard/matrix.rs
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376
crates/aether-ai-pipeline/src/planner/standard/matrix.rs
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@@ -0,0 +1,376 @@
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use serde_json::Value;
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use crate::conversion::request::{
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convert_openai_chat_request_to_claude_request, convert_openai_chat_request_to_gemini_request,
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convert_openai_chat_request_to_openai_cli_request,
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normalize_claude_request_to_openai_chat_request,
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normalize_gemini_request_to_openai_chat_request,
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normalize_openai_cli_request_to_openai_chat_request,
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};
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pub fn build_standard_request_body(
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body_json: &Value,
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client_api_format: &str,
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mapped_model: &str,
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provider_api_format: &str,
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request_path: &str,
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upstream_is_stream: bool,
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) -> Option<Value> {
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let canonical_request = normalize_standard_request_to_openai_chat_request(
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body_json,
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client_api_format,
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request_path,
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)?;
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build_standard_request_body_from_canonical(
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&canonical_request,
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mapped_model,
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provider_api_format,
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upstream_is_stream,
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)
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}
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pub fn build_standard_request_body_from_canonical(
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canonical_request: &Value,
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mapped_model: &str,
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provider_api_format: &str,
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upstream_is_stream: bool,
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) -> Option<Value> {
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match provider_api_format.trim().to_ascii_lowercase().as_str() {
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"openai:chat" => {
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build_openai_chat_request_body(canonical_request, mapped_model, upstream_is_stream)
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}
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"openai:cli" => convert_openai_chat_request_to_openai_cli_request(
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canonical_request,
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mapped_model,
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upstream_is_stream,
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false,
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),
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"openai:compact" => convert_openai_chat_request_to_openai_cli_request(
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canonical_request,
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mapped_model,
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false,
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true,
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),
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"claude:chat" | "claude:cli" => convert_openai_chat_request_to_claude_request(
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canonical_request,
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mapped_model,
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upstream_is_stream,
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),
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"gemini:chat" | "gemini:cli" => convert_openai_chat_request_to_gemini_request(
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canonical_request,
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mapped_model,
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upstream_is_stream,
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),
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_ => None,
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}
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}
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pub fn normalize_standard_request_to_openai_chat_request(
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body_json: &Value,
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client_api_format: &str,
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request_path: &str,
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) -> Option<Value> {
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match client_api_format.trim().to_ascii_lowercase().as_str() {
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"openai:chat" => Some(body_json.clone()),
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"openai:cli" | "openai:compact" => {
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normalize_openai_cli_request_to_openai_chat_request(body_json)
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}
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"claude:chat" | "claude:cli" => normalize_claude_request_to_openai_chat_request(body_json),
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"gemini:chat" | "gemini:cli" => {
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normalize_gemini_request_to_openai_chat_request(body_json, request_path)
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}
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_ => None,
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}
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}
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fn build_openai_chat_request_body(
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body_json: &Value,
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mapped_model: &str,
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upstream_is_stream: bool,
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) -> Option<Value> {
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let request_body_object = body_json.as_object()?;
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let mut provider_request_body = serde_json::Map::from_iter(
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request_body_object
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.iter()
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.map(|(key, value)| (key.clone(), value.clone())),
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);
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provider_request_body.insert("model".to_string(), Value::String(mapped_model.to_string()));
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if upstream_is_stream {
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provider_request_body.insert("stream".to_string(), Value::Bool(true));
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}
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Some(Value::Object(provider_request_body))
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}
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#[cfg(test)]
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mod tests {
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use super::build_standard_request_body;
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use serde_json::json;
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#[test]
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fn builds_openai_chat_request_from_claude_chat_source() {
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let request = json!({
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"model": "claude-3-7-sonnet",
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"system": "You are concise.",
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"messages": [
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{
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"role": "user",
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"content": [{"type": "text", "text": "Hello from Claude"}]
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}
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],
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"max_tokens": 128
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});
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let converted = build_standard_request_body(
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&request,
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"claude:chat",
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"gpt-5",
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"openai:chat",
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"/v1/messages",
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false,
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)
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.expect("claude chat should convert to openai chat");
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assert_eq!(converted["model"], "gpt-5");
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assert_eq!(converted["messages"][0]["role"], "system");
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assert_eq!(converted["messages"][0]["content"], "You are concise.");
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assert_eq!(converted["messages"][1]["role"], "user");
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assert_eq!(converted["messages"][1]["content"], "Hello from Claude");
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}
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#[test]
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fn builds_claude_chat_request_from_gemini_chat_source() {
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let request = json!({
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"systemInstruction": {
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"parts": [{"text": "Be brief."}]
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},
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"contents": [
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{
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"role": "user",
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"parts": [{"text": "Hello from Gemini"}]
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}
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]
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});
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let converted = build_standard_request_body(
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&request,
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"gemini:chat",
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"claude-sonnet-4-5",
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"claude:chat",
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"/v1beta/models/gemini-2.5-pro:generateContent",
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false,
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)
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.expect("gemini chat should convert to claude chat");
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assert_eq!(converted["model"], "claude-sonnet-4-5");
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assert_eq!(converted["messages"][0]["role"], "user");
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assert!(
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converted["messages"]
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.to_string()
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.contains("Hello from Gemini"),
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"converted claude payload should retain the gemini user text: {converted}"
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);
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}
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#[test]
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fn builds_gemini_cli_request_from_claude_cli_source() {
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let request = json!({
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"model": "claude-sonnet-4-5",
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"messages": [
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{
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"role": "user",
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"content": [{"type": "text", "text": "Need CLI output"}]
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}
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],
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"max_tokens": 64
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});
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let converted = build_standard_request_body(
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&request,
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"claude:cli",
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"gemini-2.5-pro",
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"gemini:cli",
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"/v1/messages",
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false,
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)
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.expect("claude cli should convert to gemini cli");
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assert_eq!(converted["contents"][0]["role"], "user");
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assert_eq!(
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converted["contents"][0]["parts"][0]["text"],
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"Need CLI output"
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);
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}
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#[test]
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fn builds_openai_chat_request_from_openai_responses_source_with_chat_shape() {
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let request = json!({
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"model": "gpt-5",
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"instructions": "You are concise.",
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"input": [{
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"type": "message",
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"role": "user",
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"content": [
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{
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"type": "input_image",
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"image_url": "https://example.com/cat.png",
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"detail": "high"
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},
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{
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"type": "input_file",
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"file_data": "data:application/pdf;base64,JVBERi0x",
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"filename": "spec.pdf"
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},
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{"type": "input_text", "text": "Summarize this"}
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]
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}],
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"reasoning": {"effort": "high"},
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"text": {
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"format": {
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"type": "json_schema",
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"json_schema": {
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"name": "answer_schema",
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"schema": {
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"type": "object",
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"properties": {"answer": {"type": "string"}}
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}
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}
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}
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}
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});
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let converted = build_standard_request_body(
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&request,
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"openai:cli",
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"gpt-5",
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"openai:chat",
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"/v1/responses",
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false,
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)
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.expect("responses request should convert to chat completions");
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assert_eq!(converted["messages"][0]["role"], "system");
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assert_eq!(converted["messages"][0]["content"], "You are concise.");
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assert_eq!(converted["reasoning_effort"], "high");
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assert_eq!(
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converted["response_format"]["json_schema"]["name"],
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"answer_schema"
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);
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assert_eq!(converted["messages"][1]["content"][0]["type"], "image_url");
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assert_eq!(
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converted["messages"][1]["content"][0]["image_url"]["url"],
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"https://example.com/cat.png"
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);
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assert_eq!(
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converted["messages"][1]["content"][0]["image_url"]["detail"],
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"high"
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);
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assert_eq!(converted["messages"][1]["content"][1]["type"], "file");
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assert_eq!(
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converted["messages"][1]["content"][1]["file"]["filename"],
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"spec.pdf"
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);
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}
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#[test]
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fn builds_gemini_request_from_openai_chat_with_structured_output_and_images() {
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let request = json!({
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"model": "gpt-5",
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"messages": [{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/png;base64,iVBORw0KGgo="
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}
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},
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{"type": "text", "text": "Describe it"}
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]
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}],
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"reasoning_effort": "medium",
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"n": 2,
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"response_format": {
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"type": "json_schema",
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"json_schema": {
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"name": "answer_schema",
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"schema": {
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"type": "object",
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"properties": {"answer": {"type": "string"}}
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}
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}
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},
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"web_search_options": {
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"search_context_size": "high"
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}
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});
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let converted = build_standard_request_body(
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&request,
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"openai:chat",
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"gemini-2.5-pro",
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"gemini:chat",
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"/v1/chat/completions",
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false,
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)
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.expect("openai chat should convert to gemini");
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assert_eq!(
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converted["generationConfig"]["thinkingConfig"]["thinkingBudget"],
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2048
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);
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assert_eq!(converted["generationConfig"]["candidateCount"], 2);
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assert_eq!(
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converted["generationConfig"]["responseMimeType"],
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"application/json"
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);
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assert_eq!(
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converted["generationConfig"]["responseSchema"]["type"],
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"object"
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);
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assert_eq!(
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converted["contents"][0]["parts"][0]["inlineData"]["mimeType"],
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"image/png"
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);
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assert_eq!(converted["tools"][0]["googleSearch"], json!({}));
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}
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#[test]
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fn builds_claude_request_from_openai_chat_with_thinking_and_data_url_image() {
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let request = json!({
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"model": "gpt-5",
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"messages": [{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/jpeg;base64,/9j/4AAQSk"
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}
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},
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{"type": "text", "text": "What is this?"}
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]
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}],
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"reasoning_effort": "low"
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});
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let converted = build_standard_request_body(
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&request,
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"openai:chat",
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"claude-sonnet-4-5",
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"claude:chat",
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"/v1/chat/completions",
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false,
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)
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.expect("openai chat should convert to claude");
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assert_eq!(converted["thinking"]["type"], "enabled");
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assert_eq!(converted["thinking"]["budget_tokens"], 1280);
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assert_eq!(
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converted["messages"][0]["content"][0]["source"]["type"],
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"base64"
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);
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assert_eq!(
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converted["messages"][0]["content"][0]["source"]["media_type"],
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"image/jpeg"
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);
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}
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}
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