car_inference/tasks/

generate.rs

//! Text generation with sampling.

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
use candle_core::Tensor;
use serde::{Deserialize, Serialize};

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
use crate::backend::CandleBackend;
use crate::InferenceError;

/// How latency-sensitive this generation request is.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
#[serde(rename_all = "snake_case")]
pub enum RoutingWorkload {
    /// User-facing, interactive request where latency matters.
    #[default]
    Interactive,
    /// Batch job where latency matters somewhat, but quality/cost matter more.
    Batch,
    /// Background or offline work where latency is a weak concern.
    Background,
    /// Caller explicitly prefers on-device models. Distinct from
    /// `Background` (which is "this is a background job, latency
    /// barely matters"). The caller may be doing latency-sensitive
    /// interactive work but wants the privacy / cost / offline
    /// properties of local inference. Same `local_bonus` as
    /// `Background` plus a slightly more quality-aware weight profile
    /// — the caller chose local for a reason, not because the work is
    /// throwaway.
    LocalPreferred,
    /// Aggressive latency bias for time-to-first-token. Voice turns
    /// (specifically the fast track in the two-track sidecar pattern)
    /// pick this. Quality and cost are heavily downweighted; the
    /// router prefers whichever model produces a first token soonest.
    /// On macOS 26+ this typically resolves to `apple/foundation:default`
    /// via the Foundation Models system-LLM bonus. Reached via the
    /// `IntentHint::prefer_fast` flag (or `RoutingWorkload::Fastest`
    /// directly when callers know they want it).
    Fastest,
}

impl RoutingWorkload {
    pub fn is_latency_sensitive(self) -> bool {
        matches!(
            self,
            RoutingWorkload::Interactive
                | RoutingWorkload::LocalPreferred
                | RoutingWorkload::Fastest,
        )
    }

    pub fn weights(self) -> (f64, f64, f64) {
        // Tuple is `(quality, latency, cost)` — destructured at the
        // single use site `adaptive_router.rs:892`.
        match self {
            RoutingWorkload::Interactive => (0.45, 0.40, 0.15),
            RoutingWorkload::Batch => (0.60, 0.15, 0.25),
            RoutingWorkload::Background => (0.65, 0.05, 0.30),
            // Quality-aware (closer to Interactive) but tolerant of
            // some latency hit since the caller chose local. Cost
            // weight matches Batch.
            RoutingWorkload::LocalPreferred => (0.55, 0.20, 0.25),
            // Voice fast track: latency is everything. Quality and
            // cost are deliberately near-floor — first audio in
            // <500ms beats any quality gain that takes another
            // round-trip. Sums to 1.0 like every other variant.
            RoutingWorkload::Fastest => (0.10, 0.85, 0.05),
        }
    }

    pub fn local_bonus(self) -> f64 {
        match self {
            RoutingWorkload::Interactive => 0.0,
            RoutingWorkload::Batch => 0.08,
            RoutingWorkload::Background => 0.15,
            // Stronger push than Background — "prefer local" should
            // win ties decisively, otherwise the hint is ineffective.
            RoutingWorkload::LocalPreferred => 0.20,
            // Local inference avoids network round-trips entirely —
            // the single biggest latency win available. On macOS the
            // Foundation Models `system_llm_bonus` stacks on top of
            // this for `apple/foundation:default`. Match
            // LocalPreferred's bonus so cloud-streamed fast paths
            // (e.g. gpt-4o-mini) can still win when locally there's
            // no model loaded; the weight profile already strongly
            // favours latency.
            RoutingWorkload::Fastest => 0.20,
        }
    }
}

/// Qwen3 hybrid thinking control. Qwen3 models were trained with both a
/// "thinking" (chain-of-thought inside `<think>...</think>`) and a
/// non-thinking mode. Upstream defaults thinking ON; `/no_think` and
/// `/think` are the documented per-turn overrides in the chat template.
///
/// Scope: applies to the *single-turn* local Qwen3 path driven by
/// [`apply_chat_template`]. The multi-turn `messages: Vec<Message>`
/// field on [`GenerateRequest`] is consumed by remote protocol
/// handlers (OpenAI/Anthropic/Google) which pass through user-supplied
/// system messages verbatim; this flag is not injected there. If you
/// need Qwen3 thinking control over a remote API, include `/think` or
/// `/no_think` explicitly in your own system message.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
#[serde(rename_all = "snake_case")]
pub enum ThinkingMode {
    /// Let the model decide. No explicit `/think` or `/no_think` directive
    /// is injected into the system prompt, so Qwen3's trained default
    /// (thinking ON) applies. `<think>...</think>` output is stripped
    /// from the returned text.
    #[default]
    Auto,
    /// Inject `/think` into the system prompt to explicitly request the
    /// thinking phase. Useful when callers want to force reasoning even
    /// on short prompts the model would normally answer directly.
    On,
    /// Inject `/no_think` into the system prompt to suppress the
    /// thinking phase for faster, more direct responses. This was the
    /// prior hard-coded behavior; callers now opt into it explicitly.
    Off,
}

impl ThinkingMode {
    /// Return the directive marker to append to the system prompt, or
    /// `None` when `Auto` (don't inject anything — trust model default).
    pub fn directive(self) -> Option<&'static str> {
        match self {
            ThinkingMode::Auto => None,
            ThinkingMode::On => Some("/think"),
            ThinkingMode::Off => Some("/no_think"),
        }
    }
}

/// Parameters controlling generation behavior.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GenerateParams {
    /// Sampling temperature (0.0 = greedy, 1.0 = full distribution).
    #[serde(default = "default_temperature")]
    pub temperature: f64,
    /// Top-p (nucleus) sampling threshold.
    #[serde(default = "default_top_p")]
    pub top_p: f64,
    /// Top-k sampling (0 = disabled).
    #[serde(default)]
    pub top_k: usize,
    /// Maximum tokens to generate.
    #[serde(default = "default_max_tokens")]
    pub max_tokens: usize,
    /// Stop sequences — generation halts when any is produced.
    #[serde(default)]
    pub stop: Vec<String>,
    /// Extended thinking budget (tokens). When > 0, enables the model's
    /// internal reasoning/planning phase before responding. Only supported
    /// by models with the ExtendedThinking capability (e.g., Claude).
    #[serde(default)]
    pub budget_tokens: usize,
    /// Routing workload class. Interactive requests bias toward lower latency,
    /// while batch/background work can tolerate slower high-quality local models.
    #[serde(default)]
    pub workload: RoutingWorkload,
    /// Tool choice mode: "auto" (default when tools present), "required" (must use a tool),
    /// "none" (disable tools). When "required", the model must respond with a tool call,
    /// eliminating mixed text+JSON responses.
    #[serde(default)]
    pub tool_choice: Option<String>,
    /// OpenAI-compatible parallel tool call control.
    #[serde(default)]
    pub parallel_tool_calls: Option<bool>,
    /// Qwen3 hybrid thinking mode control. `Auto` (default) leaves the
    /// model at its trained default (thinking on). `On`/`Off` inject
    /// the documented `/think` or `/no_think` directive into the chat
    /// template. Ignored by non-Qwen3 models.
    #[serde(default)]
    pub thinking: ThinkingMode,
}

fn default_temperature() -> f64 {
    0.7
}
fn default_top_p() -> f64 {
    0.9
}
fn default_max_tokens() -> usize {
    4096
}

impl Default for GenerateParams {
    fn default() -> Self {
        Self {
            temperature: default_temperature(),
            top_p: default_top_p(),
            top_k: 0,
            max_tokens: default_max_tokens(),
            stop: Vec::new(),
            budget_tokens: 0,
            workload: RoutingWorkload::Interactive,
            tool_choice: None,
            parallel_tool_calls: None,
            thinking: ThinkingMode::default(),
        }
    }
}

/// A tool call returned by the model.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ToolCall {
    /// Provider-assigned tool call ID (e.g. OpenAI `call_abc123`, Anthropic `toolu_abc123`).
    /// When present, protocol handlers use this for round-trip correlation instead of
    /// synthesizing positional IDs like `call_0`.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub id: Option<String>,
    /// Tool/function name.
    pub name: String,
    /// Arguments as key-value pairs.
    pub arguments: std::collections::HashMap<String, serde_json::Value>,
}

/// A content block in a multimodal message.
///
/// The image variants (`ImageBase64`, `ImageUrl`) are fully wired on
/// the native Qwen2.5-VL backend. The video variants
/// (`VideoPath`, `VideoUrl`, `VideoBase64`) are defined on the public
/// request surface so higher-level tooling can express Qwen2.5-VL
/// video-understanding payloads, but the native backend returns
/// [`crate::InferenceError::UnsupportedMode`] for them until the
/// video-tokenization path lands. Remote multimodal providers
/// (Anthropic, Google Vertex) accept them through the protocol
/// handlers today.
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "type", rename_all = "snake_case")]
pub enum ContentBlock {
    /// Plain text content.
    Text { text: String },
    /// Base64-encoded image.
    ImageBase64 {
        /// Base64-encoded image data.
        data: String,
        /// MIME type (e.g., "image/png", "image/jpeg").
        media_type: String,
    },
    /// Image from URL.
    ImageUrl {
        /// URL of the image.
        url: String,
        /// Detail level for image processing ("auto", "low", "high").
        #[serde(default = "default_detail")]
        detail: String,
    },
    /// Video loaded from a local filesystem path. Qwen2.5-VL samples
    /// the clip at `fps` frames/sec (default: backend-chosen) and
    /// caps at `max_frames` to respect context budgets.
    VideoPath {
        path: String,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        fps: Option<f32>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        max_frames: Option<u32>,
    },
    /// Video accessible over HTTP(S). Semantics as [`ContentBlock::VideoPath`].
    VideoUrl {
        url: String,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        fps: Option<f32>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        max_frames: Option<u32>,
    },
    /// Base64-encoded video bytes. Prefer `VideoPath` when possible;
    /// inline base64 is expensive to round-trip.
    VideoBase64 {
        data: String,
        media_type: String,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        fps: Option<f32>,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        max_frames: Option<u32>,
    },
    /// Audio loaded from a local filesystem path. Used for
    /// audio-understanding models (Gemma 4 small variants, Gemini).
    AudioPath {
        path: String,
        /// Optional explicit sample-rate hint. Most backends will
        /// resample internally; this is a best-effort declaration.
        #[serde(default, skip_serializing_if = "Option::is_none")]
        sample_rate: Option<u32>,
    },
    /// Audio accessible over HTTP(S).
    AudioUrl {
        url: String,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        sample_rate: Option<u32>,
    },
    /// Base64-encoded audio bytes.
    AudioBase64 {
        data: String,
        media_type: String,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        sample_rate: Option<u32>,
    },
}

impl ContentBlock {
    /// Return true if this block carries video data (any encoding).
    /// Used by backends that need to refuse video inputs until the
    /// tokenization path is wired.
    pub fn is_video(&self) -> bool {
        matches!(
            self,
            ContentBlock::VideoPath { .. }
                | ContentBlock::VideoUrl { .. }
                | ContentBlock::VideoBase64 { .. }
        )
    }

    /// Return true if this block carries audio data (any encoding).
    /// Used by backends that need to refuse audio inputs until the
    /// tokenization path is wired. Gemma 4 small variants and Gemini
    /// accept audio; everything else in CAR rejects with
    /// `UnsupportedMode`.
    pub fn is_audio(&self) -> bool {
        matches!(
            self,
            ContentBlock::AudioPath { .. }
                | ContentBlock::AudioUrl { .. }
                | ContentBlock::AudioBase64 { .. }
        )
    }
}

fn default_detail() -> String {
    "auto".to_string()
}

/// A message in a multi-turn conversation.
///
/// The `System` variant exists so callers can express a first-class
/// system prompt inside `messages: Vec<Message>` without threading it
/// through the legacy `context: Option<String>` field on
/// [`GenerateRequest`]. Protocol handlers and local chat templates
/// that have a native system-role slot (OpenAI, Anthropic, Gemini,
/// Gemma 4, Qwen) emit it in the right place; ones that don't can
/// fold it into the first user turn.
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "role", rename_all = "snake_case")]
pub enum Message {
    /// A system prompt. Appears once, at the start of the conversation.
    System { content: String },
    /// A user message (text only).
    User { content: String },
    /// A user message with multimodal content (text + images + video + audio).
    UserMultimodal { content: Vec<ContentBlock> },
    /// An assistant response, possibly with tool calls.
    Assistant {
        #[serde(default)]
        content: String,
        #[serde(default)]
        tool_calls: Vec<ToolCall>,
    },
    /// The result of executing a tool call.
    ToolResult {
        tool_use_id: String,
        content: String,
    },
    /// Provider-specific output items that need to round-trip
    /// verbatim across turns. The OpenAI Responses API returns
    /// reasoning blobs, encrypted_content, web-search results, etc.
    /// as opaque structured items; the next request must include
    /// them in the same form to preserve provider-side state.
    ///
    /// `protocol` identifies the provider format that produced the
    /// items (currently `"openai-responses"`). Builder paths that
    /// don't recognize the protocol drop the variant — there is no
    /// portable rendering across providers.
    ProviderOutputItems {
        protocol: String,
        items: Vec<serde_json::Value>,
    },
}

/// Constraint on the model's response shape. Distinct from `tools` —
/// tools are a side-channel for action invocation; `response_format`
/// constrains the *primary* text output to be parseable JSON, optionally
/// against a caller-supplied schema.
///
/// Provider mapping (handled in `protocol.rs`):
/// * **OpenAI / Azure / OpenAI-compatible**: `response_format: {type: "json_schema", json_schema: {schema, strict, name}}`
///   (or `{type: "json_object"}` for the looser variant). Strict mode rejects
///   any deviation from the schema.
/// * **Google (Gemini)**: `response_mime_type: "application/json"` plus
///   optional `response_schema`.
/// * **Anthropic**: no native field as of early 2026 — the schema is
///   logged at `warn` level and dropped. Callers needing schema-validated
///   output on Claude should fall back to the `tools` + `tool_choice="required"`
///   coercion idiom (which is what worked before this field landed).
///
/// `JsonObject` is the looser variant — tells the provider "emit valid
/// JSON, no schema check required". Use when the schema is too dynamic
/// to spell out but the parse contract still matters.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(tag = "type", rename_all = "snake_case")]
pub enum ResponseFormat {
    /// JSON output validated against the provided schema. `strict: true`
    /// asks the provider to reject any deviation; `false` makes the
    /// schema a best-effort hint. `name` is OpenAI-specific (the
    /// `json_schema.name` field, max 64 chars, alphanumerics + `-_`);
    /// other providers ignore it.
    JsonSchema {
        schema: serde_json::Value,
        #[serde(default)]
        strict: bool,
        #[serde(default, skip_serializing_if = "Option::is_none")]
        name: Option<String>,
    },
    /// Plain JSON-mode output — the provider emits valid JSON without
    /// schema enforcement.
    JsonObject,
}

/// A text generation request.
///
/// `Default` is derived so call sites can mutate just the fields
/// they care about: `GenerateRequest { prompt: "...".into(), ..Default::default() }`.
/// The default `prompt = ""` is useless on its own — callers always
/// override it — but the `..Default::default()` shorthand stops the
/// per-call-site mechanical churn every time a new optional field
/// lands (closes #109).
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct GenerateRequest {
    /// The prompt to complete (first user message for single-turn).
    pub prompt: String,
    /// Optional model override.
    pub model: Option<String>,
    /// Generation parameters.
    #[serde(default)]
    pub params: GenerateParams,
    /// Optional memory context to prepend to the prompt.
    /// When provided, this is injected as a system-level context block
    /// before the user prompt, grounding the model's response.
    #[serde(default)]
    pub context: Option<String>,
    /// Optional tool definitions for structured tool_use.
    /// When provided, the model may return tool_calls instead of text.
    /// Each tool is a JSON object with: name, description, parameters (JSON Schema).
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub tools: Option<Vec<serde_json::Value>>,
    /// Optional images for vision models.
    /// When provided with a single-turn prompt, these are included as image content blocks
    /// in the user message. For multi-turn with `messages`, use `UserMultimodal` variants instead.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub images: Option<Vec<ContentBlock>>,
    /// Optional multi-turn conversation history.
    /// When provided, the backend builds a proper multi-turn message array
    /// instead of a single user message. The `prompt` field is ignored when
    /// messages are present.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub messages: Option<Vec<Message>>,
    /// Enable prompt caching for Anthropic API.
    /// When true, system prompt and tools are marked with cache_control breakpoints,
    /// enabling cache reuse across parent/child agent calls sharing the same prefix.
    #[serde(default)]
    pub cache_control: bool,
    /// Constrain output to JSON (optionally schema-validated). See
    /// [`ResponseFormat`] for the per-provider mapping. Defaults to
    /// `None` — free-form text.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub response_format: Option<ResponseFormat>,
    /// Caller-supplied routing intent. None preserves the existing
    /// adaptive vs. pinned-model behavior. When `Some`, the adaptive
    /// router uses the hint to filter candidates (hard `require`),
    /// override task selection, and bias the score profile
    /// (`prefer_local`). See [`crate::intent::IntentHint`].
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub intent: Option<crate::intent::IntentHint>,
}

/// Wrap a raw prompt in Qwen3 chat format if it's not already formatted.
///
/// Thinking behavior follows the caller-supplied [`ThinkingMode`]:
/// * `Auto` — no directive injected; Qwen3's trained default (thinking
///   on) applies.
/// * `On` — the documented `/think` directive is appended to the system
///   message on its own line, and the model is allowed to emit a full
///   `<think>...</think>` block before its answer.
/// * `Off` — `/no_think` is appended to the system message *and* an
///   empty `<think>\n\n</think>` block is pre-filled after the assistant
///   marker, matching upstream Qwen3's `enable_thinking=False` jinja
///   template. The pre-filled closed tags structurally prevent the
///   model from emitting a thinking block even if the directive is
///   contradicted later in the prompt.
///
/// When context is provided it is injected into the system message to
/// ground the model's response with memory. The directive always
/// appears *after* the context blob so user-supplied memory cannot
/// nudge the directive's parse position.
pub fn apply_chat_template(
    prompt: &str,
    context: Option<&str>,
    thinking: ThinkingMode,
) -> String {
    if prompt.contains("<|im_start|>") {
        return prompt.to_string();
    }
    // Directive goes on its own line at the end of the system message
    // (never concatenated onto prose) so Qwen3's chat template parser
    // sees `/think`/`/no_think` as a standalone token, not as part of
    // "assistant. /no_think".
    let directive_line = match thinking.directive() {
        Some(d) => format!("\n{d}"),
        None => String::new(),
    };
    // For Off, pre-fill a closed empty thinking block after the
    // assistant marker. This mirrors upstream Qwen3's jinja behavior
    // when `enable_thinking=False` and is the hard-switch (structural)
    // form of the mode, whereas `/no_think` alone is a soft directive.
    let thinking_prefill = match thinking {
        ThinkingMode::Off => "<think>\n\n</think>\n\n",
        _ => "",
    };
    match context {
        Some(ctx) => format!(
            "<|im_start|>system\nYou are a helpful assistant. Use the following context to inform your response.\n\n{ctx}{directive_line}<|im_end|>\n\
             <|im_start|>user\n{prompt}<|im_end|>\n\
             <|im_start|>assistant\n{thinking_prefill}"
        ),
        None => format!(
            "<|im_start|>system\nYou are a helpful assistant.{directive_line}<|im_end|>\n\
             <|im_start|>user\n{prompt}<|im_end|>\n\
             <|im_start|>assistant\n{thinking_prefill}"
        ),
    }
}

/// Strip Qwen3 `<think>...</think>` blocks from model output, honoring
/// the caller's requested [`ThinkingMode`]:
///
/// * `On` — the caller explicitly asked for reasoning; return the raw
///   text verbatim so `<think>...</think>` is visible.
/// * `Auto` / `Off` — strip the thinking block and return only the
///   post-thinking answer. If the output contains an opening `<think>`
///   without a closing tag (truncation or stop before the model
///   finished thinking) return an empty string rather than leaking a
///   dangling tag to the caller.
pub fn strip_thinking(text: &str, thinking: ThinkingMode) -> String {
    if matches!(thinking, ThinkingMode::On) {
        return text.to_string();
    }
    strip_thinking_block(text)
}

/// Remove a leading `<think>...</think>` block unconditionally.
/// Returns "" if `<think>` opens but never closes (incomplete output).
///
/// When that "opened but never closed" branch fires, log a warn line
/// — the caller is about to receive an empty string for what was
/// almost certainly a budget-truncation. Surfaces issue #168's root
/// cause without changing the return contract: callers (e.g. car-cli)
/// that look at stderr can tell users to either bump
/// `--max-tokens` or pass `--thinking off`. The decision lives in
/// the strip helper because every text-completion path funnels
/// through it; logging at the call sites would be a lot of
/// duplication.
fn strip_thinking_block(text: &str) -> String {
    if let Some(end) = text.find("</think>") {
        text[end + 8..].trim_start().to_string()
    } else if text.contains("<think>") {
        tracing::warn!(
            target: "car_inference::tasks::generate",
            raw_len = text.len(),
            "model output opened <think> but never closed it — \
             likely truncated by max_tokens; returning empty text. \
             Increase max_tokens, or set thinking=off to suppress \
             the reasoning phase."
        );
        String::new()
    } else {
        text.to_string()
    }
}

/// Callback for FLARE-style re-retrieval during generation.
/// Called with partial generation text, returns additional context or None.
pub type RetrievalCallback = Box<dyn Fn(&str) -> Option<String> + Send>;

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
/// Generate text from a prompt using the loaded model.
///
/// Returns `(text, time_to_first_token_ms)`. TTFT is measured from
/// function entry through prefill to the moment the first generated
/// token has been sampled — the user-visible "did anything happen yet"
/// gate. `None` only when the prompt encodes to zero tokens (degenerate
/// input).
pub async fn generate(
    backend: &mut CandleBackend,
    req: GenerateRequest,
) -> Result<(String, Option<u64>), InferenceError> {
    let start = std::time::Instant::now();

    // Reset KV cache so each generation starts fresh (prevents cross-call state bleed)
    backend.clear_kv_cache();

    let formatted = apply_chat_template(&req.prompt, req.context.as_deref(), req.params.thinking);
    let tokens = backend.encode(&formatted)?;
    let eos = backend.eos_token_id();
    let eos_alt = backend.token_id("<|im_end|>");
    let params = &req.params;

    if tokens.is_empty() {
        return Ok((String::new(), None));
    }

    // Truncate to model's max context length minus generation headroom.
    // This prevents KV cache overflow on long prompts.
    let max_ctx = backend.context_length().unwrap_or(32768);
    let headroom = params.max_tokens.min(max_ctx / 4);
    let max_prompt = max_ctx.saturating_sub(headroom);
    let tokens = if tokens.len() > max_prompt {
        eprintln!(
            "[car-inference] truncating prompt from {} to {} tokens (context_length={})",
            tokens.len(),
            max_prompt,
            max_ctx
        );
        tokens[tokens.len() - max_prompt..].to_vec()
    } else {
        tokens
    };

    let mut generated = Vec::new();

    // Prefill: process all prompt tokens, sample first generated token from prefill logits
    let logits = backend.forward(&tokens, 0)?;
    let mut next_token = sample_token(&logits, params)?;
    let ttft_ms = Some(start.elapsed().as_millis() as u64);

    for _i in 0..params.max_tokens {
        // Check EOS
        if eos.map_or(false, |id| next_token == id) || eos_alt.map_or(false, |id| next_token == id)
        {
            break;
        }

        generated.push(next_token);

        // Check stop sequences
        if !params.stop.is_empty() {
            let text_so_far = backend.decode(&generated)?;
            if params.stop.iter().any(|s| text_so_far.contains(s)) {
                break;
            }
        }

        // Generate next token
        let pos = tokens.len() + generated.len() - 1;
        let logits = backend.forward(&[next_token], pos)?;
        next_token = sample_token(&logits, params)?;
    }

    let text = backend.decode(&generated)?;
    Ok((strip_thinking(&text, params.thinking), ttft_ms))
}

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
/// Generate with FLARE-style confidence-triggered re-retrieval.
///
/// Monitors token logit confidence during generation. When a window of
/// low-confidence tokens is detected, pauses, re-queries memory with the
/// partial generation, and resumes with augmented context.
pub async fn generate_with_retrieval(
    backend: &mut CandleBackend,
    mut req: GenerateRequest,
    retrieval_cb: RetrievalCallback,
) -> Result<String, InferenceError> {
    // First pass: generate normally
    backend.clear_kv_cache();
    let formatted = apply_chat_template(&req.prompt, req.context.as_deref(), req.params.thinking);
    let tokens = backend.encode(&formatted)?;
    let eos = backend.eos_token_id();
    let eos_alt = backend.token_id("<|im_end|>");
    let params = req.params.clone();

    if tokens.is_empty() {
        return Ok(String::new());
    }

    let mut generated = Vec::new();
    let mut low_confidence_count = 0u32;
    let mut retrieval_attempts = 0u32;
    let max_retrievals = 2;
    let confidence_threshold = 0.4f32;
    let low_confidence_window = 3u32;

    let logits = backend.forward(&tokens, 0)?;
    let mut next_token = sample_token(&logits, &params)?;

    for _i in 0..params.max_tokens {
        if eos.map_or(false, |id| next_token == id) || eos_alt.map_or(false, |id| next_token == id)
        {
            break;
        }

        generated.push(next_token);

        // Generate next token and check confidence
        let pos = tokens.len() + generated.len() - 1;
        let logits = backend.forward(&[next_token], pos)?;

        // Check max logit probability for confidence
        let logits_f32: Vec<f32> = logits
            .squeeze(0)
            .unwrap_or(logits.clone())
            .to_dtype(candle_core::DType::F32)
            .map_err(|e| InferenceError::InferenceFailed(format!("dtype: {e}")))?
            .to_vec1()
            .unwrap_or_default();

        if !logits_f32.is_empty() {
            // Compute softmax max probability
            let max_logit = logits_f32.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
            let exp_sum: f32 = logits_f32.iter().map(|&v| (v - max_logit).exp()).sum();
            let max_prob = 1.0 / exp_sum; // probability of the top token

            if max_prob < confidence_threshold {
                low_confidence_count += 1;
            } else {
                low_confidence_count = 0;
            }

            // Trigger re-retrieval after sustained low confidence
            if low_confidence_count >= low_confidence_window && retrieval_attempts < max_retrievals
            {
                retrieval_attempts += 1;
                low_confidence_count = 0;

                // Use partial generation as re-retrieval query
                let partial = backend.decode(&generated)?;
                if let Some(new_context) = retrieval_cb(&partial) {
                    // Restart generation with augmented context
                    let combined_context = match req.context.take() {
                        Some(old) => format!("{}\n\n{}", old, new_context),
                        None => new_context,
                    };
                    req.context = Some(combined_context);

                    // Re-encode and restart
                    backend.clear_kv_cache();
                    let new_formatted =
                        apply_chat_template(&req.prompt, req.context.as_deref(), req.params.thinking);
                    let new_tokens = backend.encode(&new_formatted)?;
                    generated.clear();

                    let logits = backend.forward(&new_tokens, 0)?;
                    next_token = sample_token(&logits, &params)?;
                    continue;
                }
            }
        }

        next_token = sample_token(&logits, &params)?;
    }

    let text = backend.decode(&generated)?;
    Ok(strip_thinking(&text, params.thinking))
}

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
/// Sample a token, suppressing specific token IDs (set to -inf before sampling).
pub fn sample_token_suppress(
    logits: &Tensor,
    params: &GenerateParams,
    suppress: &[u32],
) -> Result<u32, InferenceError> {
    if suppress.is_empty() {
        return sample_token(logits, params);
    }
    // Clone logits and set suppressed tokens to -inf
    let mut logits_vec: Vec<f32> = logits
        .squeeze(0)
        .unwrap_or(logits.clone())
        .to_dtype(candle_core::DType::F32)
        .map_err(|e| InferenceError::InferenceFailed(format!("dtype: {e}")))?
        .to_vec1()
        .map_err(|e| InferenceError::InferenceFailed(format!("to_vec: {e}")))?;
    // Handle 2D logits (take last row)
    let dims = logits.dims();
    if dims.len() == 2 {
        let vocab = dims[dims.len() - 1];
        let start = logits_vec.len() - vocab;
        logits_vec = logits_vec[start..].to_vec();
    }
    for &id in suppress {
        if (id as usize) < logits_vec.len() {
            logits_vec[id as usize] = f32::NEG_INFINITY;
        }
    }
    let modified = Tensor::from_vec(
        logits_vec,
        logits.squeeze(0).unwrap_or(logits.clone()).shape(),
        logits.device(),
    )
    .map_err(|e| InferenceError::InferenceFailed(format!("from_vec: {e}")))?;
    sample_token(&modified, params)
}

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
/// Sample a token from logits using temperature + top-p + top-k.
pub fn sample_token(logits: &Tensor, params: &GenerateParams) -> Result<u32, InferenceError> {
    let logits = logits
        .squeeze(0)
        .map_err(|e| InferenceError::InferenceFailed(format!("squeeze: {e}")))?;
    let logits = logits
        .to_dtype(candle_core::DType::F32)
        .map_err(|e| InferenceError::InferenceFailed(format!("dtype: {e}")))?;

    // Get last position's logits
    let dim = logits.dims();
    let logits = if dim.len() == 2 {
        logits
            .get(dim[0] - 1)
            .map_err(|e| InferenceError::InferenceFailed(format!("get last: {e}")))?
    } else {
        logits
    };

    // Greedy decoding
    if params.temperature <= 0.0 {
        let token = logits
            .argmax(0)
            .map_err(|e| InferenceError::InferenceFailed(format!("argmax: {e}")))?
            .to_scalar::<u32>()
            .map_err(|e| InferenceError::InferenceFailed(format!("scalar: {e}")))?;
        return Ok(token);
    }

    // Temperature scaling
    let logits = (&logits / params.temperature)
        .map_err(|e| InferenceError::InferenceFailed(format!("temp scale: {e}")))?;

    let mut logits_vec: Vec<f32> = logits
        .to_vec1()
        .map_err(|e| InferenceError::InferenceFailed(format!("to_vec: {e}")))?;

    // Top-k filtering
    if params.top_k > 0 && params.top_k < logits_vec.len() {
        let mut indexed: Vec<(usize, f32)> = logits_vec.iter().copied().enumerate().collect();
        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        let threshold = indexed[params.top_k].1;
        for v in &mut logits_vec {
            if *v < threshold {
                *v = f32::NEG_INFINITY;
            }
        }
    }

    // Softmax
    let max_logit = logits_vec.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
    let exp: Vec<f32> = logits_vec.iter().map(|&v| (v - max_logit).exp()).collect();
    let sum: f32 = exp.iter().sum();
    let mut probs: Vec<f32> = exp.iter().map(|&v| v / sum).collect();

    // Top-p (nucleus) filtering
    if params.top_p < 1.0 {
        let mut sorted_indices: Vec<usize> = (0..probs.len()).collect();
        sorted_indices.sort_by(|&a, &b| {
            probs[b]
                .partial_cmp(&probs[a])
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        let mut cumsum = 0.0f32;
        let mut cutoff_idx = sorted_indices.len();
        for (i, &idx) in sorted_indices.iter().enumerate() {
            cumsum += probs[idx];
            if cumsum > params.top_p as f32 {
                cutoff_idx = i + 1;
                break;
            }
        }

        let keep: std::collections::HashSet<usize> =
            sorted_indices[..cutoff_idx].iter().copied().collect();
        for (i, p) in probs.iter_mut().enumerate() {
            if !keep.contains(&i) {
                *p = 0.0;
            }
        }

        // Renormalize
        let sum: f32 = probs.iter().sum();
        if sum > 0.0 {
            for p in &mut probs {
                *p /= sum;
            }
        }
    }

    // Categorical sample
    let r: f32 = rand_f32();
    let mut cumsum = 0.0f32;
    for (i, &p) in probs.iter().enumerate() {
        cumsum += p;
        if cumsum >= r {
            return Ok(i as u32);
        }
    }

    // Fallback: return highest prob token
    Ok(probs
        .iter()
        .enumerate()
        .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
        .map(|(i, _)| i as u32)
        .unwrap_or(0))
}

#[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
/// Random float in [0, 1) using the rand crate.
fn rand_f32() -> f32 {
    rand::random::<f32>()
}

#[cfg(test)]
mod thinking_tests {
    use super::*;

    #[test]
    fn auto_injects_no_directive_and_no_prefill() {
        let out = apply_chat_template("hi", None, ThinkingMode::Auto);
        assert!(!out.contains("/no_think"));
        assert!(!out.contains("/think"));
        assert!(!out.contains("<think>"));
        assert!(out.contains("<|im_start|>user\nhi<|im_end|>"));
    }

    #[test]
    fn off_injects_no_think_on_own_line_and_prefills_empty_think() {
        let out = apply_chat_template("hi", None, ThinkingMode::Off);
        // Directive on its own line, not concatenated onto prose.
        assert!(out.contains("\n/no_think<|im_end|>"));
        assert!(!out.contains(" /no_think"));
        // Closed empty thinking block pre-filled after assistant marker
        // — the upstream jinja hard-switch for enable_thinking=False.
        assert!(out.contains("<|im_start|>assistant\n<think>\n\n</think>\n\n"));
    }

    #[test]
    fn on_injects_think_and_no_prefill() {
        let out = apply_chat_template("hi", None, ThinkingMode::On);
        assert!(out.contains("\n/think<|im_end|>"));
        assert!(!out.contains("/no_think"));
        assert!(!out.contains("<think>"));
    }

    #[test]
    fn pre_formatted_prompt_is_untouched() {
        let pre = "<|im_start|>system\ncustom<|im_end|>\n<|im_start|>user\nhi<|im_end|>";
        let out = apply_chat_template(pre, None, ThinkingMode::Off);
        assert_eq!(out, pre);
    }

    #[test]
    fn directive_appears_after_context_not_before() {
        let out = apply_chat_template("q?", Some("some memory"), ThinkingMode::Off);
        let ctx_idx = out.find("some memory").unwrap();
        let directive_idx = out.find("/no_think").unwrap();
        assert!(
            directive_idx > ctx_idx,
            "directive must appear after context so user memory cannot nudge the parse"
        );
    }

    #[test]
    fn default_params_is_auto() {
        assert_eq!(GenerateParams::default().thinking, ThinkingMode::Auto);
    }

    #[test]
    fn thinking_mode_serde_snake_case() {
        let json = serde_json::to_string(&ThinkingMode::Off).unwrap();
        assert_eq!(json, "\"off\"");
        let parsed: ThinkingMode = serde_json::from_str("\"on\"").unwrap();
        assert_eq!(parsed, ThinkingMode::On);
    }

    #[test]
    fn strip_preserves_thinking_when_on() {
        let text = "<think>reasoning here</think>the answer";
        let out = strip_thinking(text, ThinkingMode::On);
        assert_eq!(out, text, "On mode must return raw text with <think> visible");
    }

    #[test]
    fn strip_removes_thinking_when_auto_or_off() {
        let text = "<think>reasoning</think>the answer";
        assert_eq!(strip_thinking(text, ThinkingMode::Auto), "the answer");
        assert_eq!(strip_thinking(text, ThinkingMode::Off), "the answer");
    }

    #[test]
    fn strip_returns_empty_on_unterminated_think() {
        // Output was cut off mid-thinking — don't leak the dangling tag.
        let text = "<think>mid-reasoning, never closed";
        assert_eq!(strip_thinking(text, ThinkingMode::Auto), "");
        assert_eq!(strip_thinking(text, ThinkingMode::Off), "");
        // On mode still returns the raw text — caller asked for it.
        assert_eq!(strip_thinking(text, ThinkingMode::On), text);
    }

    #[test]
    fn strip_is_noop_when_no_think_tag() {
        let text = "just a plain answer";
        assert_eq!(strip_thinking(text, ThinkingMode::Auto), text);
        assert_eq!(strip_thinking(text, ThinkingMode::Off), text);
        assert_eq!(strip_thinking(text, ThinkingMode::On), text);
    }
}

#[cfg(test)]
mod workload_tests {
    use super::*;

    #[test]
    fn all_workload_weights_sum_to_one() {
        for w in [
            RoutingWorkload::Interactive,
            RoutingWorkload::Batch,
            RoutingWorkload::Background,
            RoutingWorkload::LocalPreferred,
            RoutingWorkload::Fastest,
        ] {
            let (q, l, c) = w.weights();
            let sum = q + l + c;
            assert!(
                (sum - 1.0).abs() < 1e-6,
                "weights for {w:?} sum to {sum}, expected 1.0"
            );
        }
    }

    #[test]
    fn fastest_weights_dominate_on_latency() {
        let (q, l, c) = RoutingWorkload::Fastest.weights();
        // Latency should be the largest by a wide margin — that's the
        // whole point of this workload class.
        assert!(l > q && l > c);
        assert!(l >= 0.7, "latency weight too small: {l}");
    }

    #[test]
    fn fastest_is_latency_sensitive() {
        assert!(RoutingWorkload::Fastest.is_latency_sensitive());
    }
}