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https://github.com/wisdgod/cursor-api.git
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v0.1.3-rc.3
This commit is contained in:
wisdgod
403
src/chat/adapter.rs
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403
src/chat/adapter.rs
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@@ -0,0 +1,403 @@
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use base64::{engine::general_purpose::STANDARD as BASE64, Engine as _};
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use image::guess_format;
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use prost::Message as _;
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use uuid::Uuid;
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use crate::app::{
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constant::EMPTY_STRING,
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model::{AppConfig, VisionAbility},
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lazy::DEFAULT_INSTRUCTIONS,
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};
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use super::{
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aiserver::v1::{
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conversation_message, image_proto, ConversationMessage, ExplicitContext, GetChatRequest,
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ImageProto, ModelDetails,
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},
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constant::{ERR_UNSUPPORTED_GIF, ERR_UNSUPPORTED_IMAGE_FORMAT, LONG_CONTEXT_MODELS},
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model::{Message, MessageContent, Role},
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};
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async fn process_chat_inputs(inputs: Vec<Message>) -> (String, Vec<ConversationMessage>) {
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// 收集 system 指令
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let instructions = inputs
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.iter()
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.filter(|input| input.role == Role::System)
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.map(|input| match &input.content {
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MessageContent::Text(text) => text.clone(),
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MessageContent::Vision(contents) => contents
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.iter()
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.filter_map(|content| {
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if content.content_type == "text" {
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content.text.clone()
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} else {
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None
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}
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})
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.collect::<Vec<String>>()
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.join("\n"),
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})
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.collect::<Vec<String>>()
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.join("\n\n");
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// 使用默认指令或收集到的指令
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let instructions = if instructions.is_empty() {
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DEFAULT_INSTRUCTIONS.clone()
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} else {
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instructions
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};
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// 过滤出 user 和 assistant 对话
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let mut chat_inputs: Vec<Message> = inputs
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.into_iter()
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.filter(|input| input.role == Role::User || input.role == Role::Assistant)
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.collect();
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// 处理空对话情况
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if chat_inputs.is_empty() {
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return (
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instructions,
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vec![ConversationMessage {
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text: EMPTY_STRING.into(),
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r#type: conversation_message::MessageType::Human as i32,
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attached_code_chunks: vec![],
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codebase_context_chunks: vec![],
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commits: vec![],
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pull_requests: vec![],
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git_diffs: vec![],
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assistant_suggested_diffs: vec![],
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interpreter_results: vec![],
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images: vec![],
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attached_folders: vec![],
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approximate_lint_errors: vec![],
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bubble_id: Uuid::new_v4().to_string(),
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server_bubble_id: None,
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attached_folders_new: vec![],
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lints: vec![],
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user_responses_to_suggested_code_blocks: vec![],
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relevant_files: vec![],
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tool_results: vec![],
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notepads: vec![],
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is_capability_iteration: Some(false),
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capabilities: vec![],
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edit_trail_contexts: vec![],
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suggested_code_blocks: vec![],
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diffs_for_compressing_files: vec![],
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multi_file_linter_errors: vec![],
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diff_histories: vec![],
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recently_viewed_files: vec![],
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recent_locations_history: vec![],
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is_agentic: false,
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file_diff_trajectories: vec![],
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conversation_summary: None,
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}],
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);
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}
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// 如果第一条是 assistant，插入空的 user 消息
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if chat_inputs
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.first()
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.map_or(false, |input| input.role == Role::Assistant)
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{
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chat_inputs.insert(
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0,
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Message {
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role: Role::User,
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content: MessageContent::Text(EMPTY_STRING.into()),
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},
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);
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}
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// 处理连续相同角色的情况
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let mut i = 1;
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while i < chat_inputs.len() {
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if chat_inputs[i].role == chat_inputs[i - 1].role {
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let insert_role = if chat_inputs[i].role == Role::User {
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Role::Assistant
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} else {
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Role::User
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};
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chat_inputs.insert(
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i,
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Message {
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role: insert_role,
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content: MessageContent::Text(EMPTY_STRING.into()),
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},
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);
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}
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i += 1;
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}
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// 确保最后一条是 user
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if chat_inputs
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.last()
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.map_or(false, |input| input.role == Role::Assistant)
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{
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chat_inputs.push(Message {
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role: Role::User,
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content: MessageContent::Text(EMPTY_STRING.into()),
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});
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}
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// 转换为 proto messages
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let mut messages = Vec::new();
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for input in chat_inputs {
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let (text, images) = match input.content {
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MessageContent::Text(text) => (text, vec![]),
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MessageContent::Vision(contents) => {
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let mut text_parts = Vec::new();
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let mut images = Vec::new();
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for content in contents {
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match content.content_type.as_str() {
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"text" => {
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if let Some(text) = content.text {
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text_parts.push(text);
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}
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}
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"image_url" => {
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if let Some(image_url) = &content.image_url {
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let url = image_url.url.clone();
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let result =
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tokio::spawn(async move { fetch_image_data(&url).await });
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if let Ok(Ok((image_data, dimensions))) = result.await {
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images.push(ImageProto {
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data: image_data,
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dimension: dimensions,
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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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(text_parts.join("\n"), images)
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}
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};
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messages.push(ConversationMessage {
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text,
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r#type: if input.role == Role::User {
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conversation_message::MessageType::Human as i32
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} else {
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conversation_message::MessageType::Ai as i32
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},
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attached_code_chunks: vec![],
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codebase_context_chunks: vec![],
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commits: vec![],
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pull_requests: vec![],
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git_diffs: vec![],
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assistant_suggested_diffs: vec![],
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interpreter_results: vec![],
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images,
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attached_folders: vec![],
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approximate_lint_errors: vec![],
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bubble_id: Uuid::new_v4().to_string(),
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server_bubble_id: None,
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attached_folders_new: vec![],
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lints: vec![],
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user_responses_to_suggested_code_blocks: vec![],
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relevant_files: vec![],
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tool_results: vec![],
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notepads: vec![],
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is_capability_iteration: Some(false),
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capabilities: vec![],
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edit_trail_contexts: vec![],
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suggested_code_blocks: vec![],
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diffs_for_compressing_files: vec![],
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multi_file_linter_errors: vec![],
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diff_histories: vec![],
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recently_viewed_files: vec![],
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recent_locations_history: vec![],
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is_agentic: false,
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file_diff_trajectories: vec![],
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conversation_summary: None,
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});
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}
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(instructions, messages)
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}
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async fn fetch_image_data(
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url: &str,
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) -> Result<(Vec<u8>, Option<image_proto::Dimension>), Box<dyn std::error::Error + Send + Sync>> {
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// 在进入异步操作前获取并释放锁
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let vision_ability = AppConfig::get_vision_ability();
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match vision_ability {
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VisionAbility::None => Err("图片功能已禁用".into()),
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VisionAbility::Base64 => {
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if !url.starts_with("data:image/") {
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return Err("仅支持 base64 编码的图片".into());
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}
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process_base64_image(url)
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}
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VisionAbility::All => {
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if url.starts_with("data:image/") {
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process_base64_image(url)
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} else {
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process_http_image(url).await
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}
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}
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}
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}
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// 处理 base64 编码的图片
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fn process_base64_image(
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url: &str,
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) -> Result<(Vec<u8>, Option<image_proto::Dimension>), Box<dyn std::error::Error + Send + Sync>> {
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let parts: Vec<&str> = url.split("base64,").collect();
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if parts.len() != 2 {
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return Err("无效的 base64 图片格式".into());
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}
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// 检查图片格式
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let format = parts[0].to_lowercase();
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if !format.contains("png")
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&& !format.contains("jpeg")
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&& !format.contains("jpg")
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&& !format.contains("webp")
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&& !format.contains("gif")
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{
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return Err(ERR_UNSUPPORTED_IMAGE_FORMAT.into());
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}
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let image_data = BASE64.decode(parts[1])?;
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// 检查是否为动态 GIF
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if format.contains("gif") {
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if let Ok(frames) = gif::DecodeOptions::new().read_info(std::io::Cursor::new(&image_data)) {
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if frames.into_iter().count() > 1 {
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return Err(ERR_UNSUPPORTED_GIF.into());
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}
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}
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}
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// 获取图片尺寸
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let dimensions = if let Ok(img) = image::load_from_memory(&image_data) {
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Some(image_proto::Dimension {
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width: img.width() as i32,
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height: img.height() as i32,
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})
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} else {
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None
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};
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Ok((image_data, dimensions))
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}
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// 处理 HTTP 图片 URL
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async fn process_http_image(
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url: &str,
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) -> Result<(Vec<u8>, Option<image_proto::Dimension>), Box<dyn std::error::Error + Send + Sync>> {
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let response = reqwest::get(url).await?;
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let image_data = response.bytes().await?.to_vec();
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let format = guess_format(&image_data)?;
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// 检查图片格式
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match format {
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image::ImageFormat::Png | image::ImageFormat::Jpeg | image::ImageFormat::WebP => {
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// 这些格式都支持
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}
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image::ImageFormat::Gif => {
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if let Ok(frames) =
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gif::DecodeOptions::new().read_info(std::io::Cursor::new(&image_data))
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{
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if frames.into_iter().count() > 1 {
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return Err(ERR_UNSUPPORTED_GIF.into());
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}
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}
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}
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_ => return Err(ERR_UNSUPPORTED_IMAGE_FORMAT.into()),
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}
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// 获取图片尺寸
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let dimensions = if let Ok(img) = image::load_from_memory_with_format(&image_data, format) {
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Some(image_proto::Dimension {
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width: img.width() as i32,
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height: img.height() as i32,
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})
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} else {
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None
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};
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Ok((image_data, dimensions))
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}
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pub async fn encode_chat_message(
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inputs: Vec<Message>,
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model_name: &str,
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) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
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// 在进入异步操作前获取并释放锁
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let enable_slow_pool = {
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if AppConfig::get_slow_pool() {
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Some(true)
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} else {
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None
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}
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};
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let (instructions, messages) = process_chat_inputs(inputs).await;
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let explicit_context = if !instructions.trim().is_empty() {
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Some(ExplicitContext {
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context: instructions,
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repo_context: None,
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})
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} else {
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None
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};
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let chat = GetChatRequest {
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current_file: None,
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conversation: messages,
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repositories: vec![],
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explicit_context,
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workspace_root_path: None,
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code_blocks: vec![],
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model_details: Some(ModelDetails {
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model_name: Some(model_name.to_string()),
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api_key: None,
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enable_ghost_mode: None,
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azure_state: None,
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enable_slow_pool,
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openai_api_base_url: None,
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}),
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documentation_identifiers: vec![],
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request_id: Uuid::new_v4().to_string(),
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linter_errors: None,
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summary: None,
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summary_up_until_index: None,
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allow_long_file_scan: None,
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is_bash: None,
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conversation_id: Uuid::new_v4().to_string(),
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can_handle_filenames_after_language_ids: None,
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use_web: None,
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quotes: vec![],
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debug_info: None,
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workspace_id: None,
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external_links: vec![],
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commit_notes: vec![],
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long_context_mode: if LONG_CONTEXT_MODELS.contains(&model_name) {
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Some(true)
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} else {
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None
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},
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is_eval: None,
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desired_max_tokens: None,
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context_ast: None,
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is_composer: None,
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runnable_code_blocks: None,
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should_cache: None,
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};
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let mut encoded = Vec::new();
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chat.encode(&mut encoded)?;
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let len_prefix = format!("{:010x}", encoded.len()).to_uppercase();
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let content = hex::encode_upper(&encoded);
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Ok(hex::decode(len_prefix + &content)?)
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}
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