feat(tui): add usage overlay and sidebar for displaying usage statistics and tasks

feat(tui): implement status bar with connection and turn state indicators
feat(tui): create workflow panel for agent status and progress visualization
feat(web): introduce web frontend interface with static file serving
feat(ws): add WebSocket interface for real-time communication and session management
This commit is contained in:
asepharyana
2026-07-20 09:04:57 +07:00
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//! Provider-facing DTOs: chat completion request, response, streaming types,
//! and the SSE stream parser.
//!
//! # Flow
//!
//! 1. **Request** — [`ChatRequest`] is built with model, messages, tools,
//! streaming options and sent to the LLM provider.
//! 2. **Response** — Non-streaming responses arrive as [`ChatResponse`] with
//! [`Choice`]s containing the full [`ChatMessage`](super::message::ChatMessage).
//! 3. **Streaming** — SSE chunks are fed into [`SseParser::feed`] which yields
//! [`StreamEvent`]s: token/text, reasoning, tool-call deltas, usage, done.
//!
//! # Components
//!
//! - `ChatRequest` / `StreamOptions` / `ToolDef` / `ToolFunctionDef` — outbound
//! - `ChatResponse` / `Choice` / `Delta` / `TokenUsage` — non-streaming inbound
//! - `StreamEvent` — one atomic streaming event (Token, Reasoning,
//! ToolCallDelta, Usage, Done, Error)
//! - `SseParser` — incremental SSE frame parser: `feed()` → `Vec<StreamEvent>`
use serde::{Deserialize, Serialize};
use serde_json::Value;
use tracing;
// ---------------------------------------------------------------------------
// Chat request / response
// ---------------------------------------------------------------------------
/// Outbound chat completion request body sent to an OpenAI/Anthropic-compatible
/// provider.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChatRequest {
/// Model identifier, e.g. `"anthropic/claude-opus-4-8"`.
pub model: String,
/// Full message history (system + user + assistant + tool turns).
pub messages: Vec<super::message::ChatMessage>,
/// Maximum number of output tokens.
#[serde(skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<u32>,
/// Sampling temperature (0.0 2.0).
#[serde(skip_serializing_if = "Option::is_none")]
pub temperature: Option<f32>,
/// Tool definitions available to the model.
#[serde(skip_serializing_if = "Option::is_none")]
pub tools: Option<Vec<ToolDef>>,
/// Controls which (if any) function is called by the model.
#[serde(skip_serializing_if = "Option::is_none")]
pub tool_choice: Option<Value>,
/// Whether to use SSE streaming (`true`) or a single response.
#[serde(skip_serializing_if = "Option::is_none")]
pub stream: Option<bool>,
/// Nucleus sampling threshold.
#[serde(skip_serializing_if = "Option::is_none")]
pub top_p: Option<f32>,
/// Sequences where the model should stop generation.
#[serde(skip_serializing_if = "Option::is_none")]
pub stop: Option<Vec<String>>,
/// Additional streaming options (e.g. `include_usage`).
#[serde(skip_serializing_if = "Option::is_none")]
pub stream_options: Option<StreamOptions>,
}
/// Streaming options for the request; `include_usage` asks the provider to
/// emit a final usage chunk in the SSE stream.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StreamOptions {
pub include_usage: bool,
}
/// Wire format for a single tool definition sent to the provider.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ToolDef {
/// The tool type discriminator, e.g. `"function"`.
#[serde(rename = "type")]
pub type_: String,
/// The function definition (name, description, JSON schema).
pub function: ToolFunctionDef,
}
/// Name, description, and JSON schema parameters for a tool definition.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ToolFunctionDef {
/// The function name the model may invoke.
pub name: String,
/// Human-readable description of what the function does.
pub description: String,
/// JSON Schema object describing the expected arguments.
pub parameters: Value,
}
/// Non-streaming chat completion response returned by the provider.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChatResponse {
/// Unique response identifier from the provider.
pub id: String,
/// Object type, e.g. `"chat.completion"`.
pub object: Option<String>,
/// Model identifier that produced this response.
pub model: String,
/// One or more completion candidates.
pub choices: Vec<Choice>,
/// Token usage statistics (prompt, completion, total).
pub usage: Option<TokenUsage>,
/// Unix-timestamp of response creation.
pub created: Option<i64>,
}
/// One completion candidate within a `ChatResponse.choices` list.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Choice {
/// Zero-based index of this choice in the candidate list.
pub index: u32,
/// Full message (non-streaming response).
#[serde(skip_serializing_if = "Option::is_none")]
pub message: Option<super::message::ChatMessage>,
/// Incremental delta (streaming response).
#[serde(skip_serializing_if = "Option::is_none")]
pub delta: Option<Delta>,
/// Why the model stopped: `"stop"`, `"tool_calls"`, `"length"`, etc.
#[serde(skip_serializing_if = "Option::is_none")]
pub finish_reason: Option<String>,
}
/// Incremental delta emitted in a streaming SSE chunk.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Delta {
/// Role being set for the first streaming chunk.
#[serde(skip_serializing_if = "Option::is_none")]
pub role: Option<super::message::Role>,
/// Incremental text content delta.
#[serde(skip_serializing_if = "Option::is_none")]
pub content: Option<String>,
/// Incremental tool-call delta (partial name/arguments).
#[serde(skip_serializing_if = "Option::is_none")]
pub tool_calls: Option<Vec<super::tool_call::ToolCall>>,
}
/// Token counts and optional cost breakdown for a single completion request.
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct TokenUsage {
/// Tokens consumed by the prompt (input).
pub prompt_tokens: u32,
/// Tokens consumed by the completion (output).
pub completion_tokens: u32,
/// Sum of prompt + completion tokens.
pub total_tokens: u32,
/// Estimated cost for prompt tokens (provider-specific).
#[serde(skip_serializing_if = "Option::is_none")]
pub prompt_tokens_cost: Option<f64>,
/// Estimated cost for completion tokens (provider-specific).
#[serde(skip_serializing_if = "Option::is_none")]
pub completion_tokens_cost: Option<f64>,
}
// ---------------------------------------------------------------------------
// SSE streaming
// ---------------------------------------------------------------------------
/// One atomic event extracted from an LLM streaming response stream.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StreamEvent {
/// An incremental text token.
Token(String),
/// An incremental reasoning token (Anthropic `reasoning_content`).
Reasoning(String),
/// An incremental tool-call delta (partial ID, name, or arguments).
ToolCallDelta {
/// Tool-call index (multiple calls in one response).
index: usize,
/// Optional tool-call ID (usually in the first delta for a call).
id: Option<String>,
/// Optional function name (usually in the first delta for a call).
name: Option<String>,
/// Partial JSON arguments delta for this tool call.
arguments_delta: String,
},
/// Final usage chunk with token counts.
Usage {
prompt_tokens: u64,
completion_tokens: u64,
total_tokens: u64,
},
/// Stream complete (all tokens have been delivered).
Done,
/// A stream-level error occurred.
Error(String),
}
/// Buffered SSE frame parser that accumulates raw `data:` lines and
/// flushes a `StreamEvent` on each blank-line boundary.
pub struct SseParser {
/// Leftover bytes from the last chunk that did not end with `\n`.
buffer: String,
/// The current `event:` type (set by `event:` lines, cleared on flush).
event_type: Option<String>,
/// Accumulated `data:` lines for the current event frame.
data_lines: Vec<String>,
}
impl SseParser {
/// Create a new parser with an empty buffer.
pub fn new() -> Self {
SseParser {
buffer: String::new(),
event_type: None,
data_lines: Vec::new(),
}
}
/// Feed a raw SSE chunk and produce any completed events.
///
/// Flow: append chunk to buffer → scan for '\n' → strip '\r' → on
/// blank line, call `flush_event` to parse the accumulated data →
/// on `event:` line, store the event type → on `data:` line, append
/// to data accumulator → continue until buffer exhausted.
///
/// Edge case: a chunk may split mid-line; the remainder stays in the
/// buffer for the next `feed()` call.
///
/// Return: all `StreamEvent`s completed by this chunk.
pub fn feed(&mut self, chunk: &str) -> Vec<StreamEvent> {
self.buffer.push_str(chunk);
let mut events = Vec::new();
while let Some(line_end) = self.buffer.find('\n') {
let line = self.buffer[..line_end].trim_end_matches('\r').to_string();
self.buffer = self.buffer[line_end + 1..].to_string();
if line.is_empty() {
events.extend(self.flush_event());
} else if let Some(ty) = line.strip_prefix("event: ") {
self.event_type = Some(ty.trim().to_string());
} else if let Some(data) = line.strip_prefix("data:") {
let data = data.trim_start().to_string();
self.data_lines.push(data);
}
}
events
}
/// Flush the current buffered `data:` lines as one or more `StreamEvent`s.
///
/// Flow: join data lines → handle `[DONE]` sentinel → JSON-parse →
/// emit `Usage` if a usage object is present → else match `event_type`
/// ("message.stop", "message.delta", etc.) → extract content,
/// reasoning, tool-call deltas, or finish-reason from the delta
/// structure (supporting both Anthropic-style top-level delta and
/// OpenAI-style `choices` array).
///
/// Return: 0, 1, or more `StreamEvent`s from the flushed frame.
fn flush_event(&mut self) -> Vec<StreamEvent> {
let data = self.data_lines.join("\n");
self.data_lines.clear();
let event_type = self.event_type.take().unwrap_or_default();
if data.is_empty() || data == "[DONE]" {
if data == "[DONE]" {
return vec![StreamEvent::Done];
}
return vec![];
}
let value: Value = match serde_json::from_str(&data) {
Ok(v) => v,
Err(e) => {
tracing::warn!("[stream] failed to parse chunk: {}", e);
return vec![];
}
};
let mut events = Vec::new();
if let Some(usage) = value.get("usage") {
if !usage.is_null() {
let prompt_tokens = usage
.get("prompt_tokens")
.and_then(Value::as_u64)
.unwrap_or_else(|| {
tracing::warn!("[stream] prompt_tokens missing in usage chunk");
0
});
let completion_tokens = usage
.get("completion_tokens")
.and_then(Value::as_u64)
.unwrap_or_else(|| {
tracing::warn!("[stream] completion_tokens missing in usage chunk");
0
});
let total_tokens = usage
.get("total_tokens")
.and_then(Value::as_u64)
.unwrap_or_else(|| {
tracing::warn!("[stream] total_tokens missing in usage chunk");
prompt_tokens + completion_tokens
});
events.push(StreamEvent::Usage {
prompt_tokens,
completion_tokens,
total_tokens,
});
}
}
let mut other_events = match event_type.as_str() {
"message.stop" => vec![StreamEvent::Done],
"message.delta" | "" => {
let mut d_events = Vec::new();
if let Some(delta) = value.get("delta").or_else(|| value.get("choices")) {
if let Some(choices) = delta.as_array() {
if let Some(choice) = choices.first() {
if let Some(d) = choice.get("delta") {
// Content token
if let Some(content) = d.get("content").and_then(|c| c.as_str()) {
d_events.push(StreamEvent::Token(content.to_string()));
}
// Reasoning token
if let Some(reasoning) =
d.get("reasoning_content").and_then(|r| r.as_str())
{
d_events.push(StreamEvent::Reasoning(reasoning.to_string()));
}
// Tool calls — iterate ALL entries, not just first()
if let Some(tool_calls) =
d.get("tool_calls").and_then(|tc| tc.as_array())
{
for tc in tool_calls {
let index =
tc.get("index").and_then(Value::as_u64).unwrap_or_else(
|| {
tracing::warn!(
"[stream] tool call delta missing index, \
defaulting to 0"
);
0
},
);
let index = usize::try_from(index).unwrap_or(0);
let id = tc
.get("id")
.and_then(|i| i.as_str())
.map(std::string::ToString::to_string);
let name = tc
.get("function")
.and_then(|f| f.get("name"))
.and_then(|n| n.as_str())
.map(std::string::ToString::to_string);
let args_delta = tc
.get("function")
.and_then(|f| f.get("arguments"))
.and_then(|a| a.as_str())
.unwrap_or("")
.to_string();
d_events.push(StreamEvent::ToolCallDelta {
index,
id,
name,
arguments_delta: args_delta,
});
}
}
// Finish reason
if let Some(reason) =
choice.get("finish_reason").and_then(|r| r.as_str())
{
if reason == "stop" || reason == "tool_calls" {
d_events.push(StreamEvent::Done);
}
}
}
}
} else if let Some(content) = delta.get("content").and_then(|c| c.as_str()) {
d_events.push(StreamEvent::Token(content.to_string()));
}
}
d_events
}
_ => vec![],
};
events.append(&mut other_events);
events
}
}
impl Default for SseParser {
fn default() -> Self {
Self::new()
}
}