10c0cc007b
- Split agent crate into client/, model/, agent/ subdirs - Add billing.rs for token usage recording - Add sync.rs for upstream model sync - EmbedService: Qdrant-backed vector memory for semantic search - ChatService: wire EmbedService for memory lookup, passive skill awareness - ReAct loop: streamline with tokio::select! and proper error handling
83 lines
2.3 KiB
Rust
83 lines
2.3 KiB
Rust
use std::pin::Pin;
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use db::cache::AppCache;
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use db::database::AppDatabase;
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use models::agents::model;
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use models::projects::project;
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use models::repos::repo;
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use models::rooms::{room, room_message};
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use models::users::user;
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use config::AppConfig;
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use std::collections::HashMap;
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use uuid::Uuid;
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/// Maximum recursion rounds for tool-call loops (AI → tool → result → AI).
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/// Previous default of 3 caused frequent silent termination on realistic multi-step queries.
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pub const DEFAULT_MAX_TOOL_DEPTH: usize = 99;
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/// A single chunk from an AI streaming response.
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#[derive(Debug, Clone)]
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pub struct AiStreamChunk {
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pub content: String,
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pub done: bool,
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/// What kind of content this chunk contains — helps the frontend render
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/// thinking, tool calls, and results with different styles.
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pub chunk_type: AiChunkType,
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}
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/// Type of streaming chunk, used by the frontend for rendering.
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#[derive(Debug, Clone, PartialEq, Eq)]
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pub enum AiChunkType {
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/// AI reasoning/thinking text before a tool call or answer.
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Thinking,
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/// Final answer text from the AI.
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Answer,
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/// A tool call is being executed (content = tool name + args summary).
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ToolCall,
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/// Tool execution result (content = result or error).
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ToolResult,
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}
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impl Default for AiChunkType {
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fn default() -> Self {
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Self::Answer
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}
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}
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/// Optional streaming callback: called for each token chunk.
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pub type StreamCallback = Box<
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dyn Fn(AiStreamChunk) -> Pin<Box<dyn std::future::Future<Output = ()> + Send>> + Send + Sync,
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>;
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pub struct AiRequest {
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pub db: AppDatabase,
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pub cache: AppCache,
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pub config: AppConfig,
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pub model: model::Model,
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pub project: project::Model,
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pub sender: user::Model,
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pub room: room::Model,
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pub input: String,
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pub mention: Vec<Mention>,
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pub history: Vec<room_message::Model>,
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pub user_names: HashMap<Uuid, String>,
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pub temperature: f64,
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pub max_tokens: i32,
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pub top_p: f64,
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pub frequency_penalty: f64,
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pub presence_penalty: f64,
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pub think: bool,
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pub tools: Option<Vec<serde_json::Value>>,
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pub max_tool_depth: usize,
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}
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pub enum Mention {
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User(user::Model),
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Repo(repo::Model),
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}
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pub mod context;
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pub mod service;
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pub use context::{AiContextSenderType, RoomMessageContext};
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pub use service::ChatService;
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