lakehouse/crates/aibridge/src/client.rs

use lru::LruCache;
use reqwest::Client;
use serde::{Deserialize, Serialize};
use std::num::NonZeroUsize;
use std::sync::Mutex;
use std::sync::atomic::{AtomicU64, Ordering};
use std::sync::Arc;
use std::time::Duration;

/// HTTP client for Ollama (post-2026-05-02 — sidecar dropped).
///
/// `base_url` was historically the Python sidecar at `:3200`, which
/// pass-through-proxied to Ollama at `:11434`. The sidecar added zero
/// logic on the hot path (embed.py + generate.py + rerank.py +
/// admin.py = ~120 LOC of pure Ollama wrappers), so this client now
/// talks to Ollama directly and the sidecar process can be retired.
///
/// What stayed Python: `lab_ui.py` + `pipeline_lab.py` (~888 LOC of
/// dev-mode Streamlit-shape UIs) — those aren't on the runtime hot
/// path and continue running for prompt experimentation.
///
/// `generate()` has two transport modes:
/// - When `gateway_url` is None (default), posts directly to Ollama's
///   `${base_url}/api/generate`.
/// - When `gateway_url` is `Some(url)`, posts to `${url}/v1/chat`
///   with `provider="ollama"` so the call appears in `/v1/usage` and
///   Langfuse traces.
///
/// `embed()`, `rerank()`, and admin methods always go direct to
/// Ollama — no `/v1` equivalent for those surfaces yet.
///
/// Phase 44 part 2 (2026-04-27): the gateway URL is wired in by
/// callers that want observability (vectord modules); it's left
/// unset by callers that ARE the gateway internals (avoids self-loops
/// + redundant hops).
/// Per-text embed cache key. We key on (model, text) so different
/// model selections produce distinct cache lines — a query embedded
/// under nomic-embed-text-v2-moe must NOT collide with the same
/// query under nomic-embed-text v1.
#[derive(Eq, PartialEq, Hash, Clone)]
struct EmbedCacheKey {
    model: String,
    text: String,
}

/// Default LRU cache size — 4096 entries × ~6KB per 768-d f64
/// vector ≈ 24MB. Sized for typical staffing-domain repetition
/// (coordinator workflows have query repetition rates around 70-90%
/// per session). Tunable via [aibridge].embed_cache_size in the
/// config; 0 disables the cache entirely.
const DEFAULT_EMBED_CACHE_SIZE: usize = 4096;

#[derive(Clone)]
pub struct AiClient {
    client: Client,
    base_url: String,
    gateway_url: Option<String>,
    /// Closes the 63× perf gap with Go side. Mirrors the shape of
    /// Go's internal/embed/cached.go::CachedProvider — same
    /// (model, text) → vector caching, same nil-disable semantics.
    /// None = caching disabled (cache_size=0); Some = bounded LRU.
    embed_cache: Option<Arc<Mutex<LruCache<EmbedCacheKey, Vec<f64>>>>>,
    /// Hit / miss counters for /admin observability + load-test
    /// validation. Atomic so Clone'd AiClients share the same counts.
    embed_cache_hits: Arc<AtomicU64>,
    embed_cache_misses: Arc<AtomicU64>,
    /// Pinned at construction time so the EmbedResponse can carry
    /// dimension consistently even when every text was a cache hit
    /// (no fresh upstream call to learn the dim from). Set on first
    /// successful Ollama embed; checked on every cache hit.
    cached_dim: Arc<AtomicU64>,
}

// -- Request/Response types --

#[derive(Serialize, Deserialize)]
pub struct EmbedRequest {
    pub texts: Vec<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub model: Option<String>,
}

#[derive(Deserialize, Serialize, Clone)]
pub struct EmbedResponse {
    pub embeddings: Vec<Vec<f64>>,
    pub model: String,
    pub dimensions: usize,
}

#[derive(Clone, Serialize, Deserialize)]
pub struct GenerateRequest {
    pub prompt: String,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub model: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub system: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub temperature: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub max_tokens: Option<u32>,
    /// Phase 21 — per-call opt-out of hidden reasoning. Thinking models
    /// (qwen3.5, gpt-oss, etc) burn tokens on reasoning before the
    /// visible response starts; setting this to `false` on hot-path
    /// JSON emitters avoids empty returns when the budget is tight.
    /// Sidecar forwards this to Ollama's `think` parameter; if the
    /// sidecar drops an unknown field the request still succeeds.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub think: Option<bool>,
}

#[derive(Deserialize, Serialize, Clone)]
pub struct GenerateResponse {
    pub text: String,
    pub model: String,
    pub tokens_evaluated: Option<u64>,
    pub tokens_generated: Option<u64>,
}

#[derive(Serialize, Deserialize)]
pub struct RerankRequest {
    pub query: String,
    pub documents: Vec<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub model: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub top_k: Option<usize>,
}

#[derive(Deserialize, Serialize, Clone)]
pub struct ScoredDocument {
    pub index: usize,
    pub text: String,
    pub score: f64,
}

#[derive(Deserialize, Serialize, Clone)]
pub struct RerankResponse {
    pub results: Vec<ScoredDocument>,
    pub model: String,
}

impl AiClient {
    pub fn new(base_url: &str) -> Self {
        Self::with_embed_cache(base_url, DEFAULT_EMBED_CACHE_SIZE)
    }

    /// Constructs an AiClient with an explicit embed-cache size.
    /// Pass 0 to disable the cache entirely (matches Go-side
    /// CachedProvider's nil-cache semantics).
    pub fn with_embed_cache(base_url: &str, cache_size: usize) -> Self {
        let client = Client::builder()
            .timeout(Duration::from_secs(120))
            .build()
            .expect("failed to build HTTP client");
        let embed_cache = if cache_size > 0 {
            // SAFETY: cache_size > 0 just verified, NonZeroUsize::new
            // returns Some.
            let cap = NonZeroUsize::new(cache_size).expect("cache_size > 0");
            Some(Arc::new(Mutex::new(LruCache::new(cap))))
        } else {
            None
        };
        Self {
            client,
            base_url: base_url.trim_end_matches('/').to_string(),
            gateway_url: None,
            embed_cache,
            embed_cache_hits: Arc::new(AtomicU64::new(0)),
            embed_cache_misses: Arc::new(AtomicU64::new(0)),
            cached_dim: Arc::new(AtomicU64::new(0)),
        }
    }

    /// Cache hit/miss/size snapshot. Useful for /admin endpoints +
    /// load-test validation ("did the cache fire as expected?").
    pub fn embed_cache_stats(&self) -> (u64, u64, usize) {
        let hits = self.embed_cache_hits.load(Ordering::Relaxed);
        let misses = self.embed_cache_misses.load(Ordering::Relaxed);
        let len = self
            .embed_cache
            .as_ref()
            .map(|c| c.lock().map(|g| g.len()).unwrap_or(0))
            .unwrap_or(0);
        (hits, misses, len)
    }

    /// Same as `new`, but every `generate()` is routed through
    /// `${gateway_url}/v1/chat` (provider=ollama) for observability.
    /// Use this for callers OUTSIDE the gateway. Inside the gateway
    /// itself, prefer `new()` — calling /v1/chat from /v1/chat works
    /// (no infinite loop, ollama_arm doesn't use AiClient) but adds
    /// a wasted localhost hop.
    pub fn new_with_gateway(base_url: &str, gateway_url: &str) -> Self {
        let mut c = Self::new(base_url);
        c.gateway_url = Some(gateway_url.trim_end_matches('/').to_string());
        c
    }

    /// Reachability + version check. Hits Ollama's `/api/version`,
    /// returns a sidecar-shaped envelope so callers reading
    /// `.status` / `.ollama_url` don't break across the
    /// pre-/post-2026-05-02 cutover.
    pub async fn health(&self) -> Result<serde_json::Value, String> {
        let resp = self.client
            .get(format!("{}/api/version", self.base_url))
            .send()
            .await
            .map_err(|e| format!("ollama unreachable: {e}"))?;
        let body: serde_json::Value = resp.json().await
            .map_err(|e| format!("invalid response: {e}"))?;
        Ok(serde_json::json!({
            "status": "ok",
            "ollama_url": &self.base_url,
            "ollama_version": body.get("version"),
        }))
    }

    /// Embed with per-text LRU caching. Mirrors Go-side
    /// CachedProvider behavior: cache key is (model, text);
    /// cache-hit texts skip the sidecar; cache-miss texts batch
    /// into a single sidecar call; results are interleaved in the
    /// caller's input order.
    ///
    /// Closes ~95% of the load-test perf gap vs Go side (loadgen
    /// 2026-05-01: Rust 128 RPS → with cache ≥ 7000 RPS expected
    /// for warm-cache workloads). Cold-cache behavior unchanged
    /// (every text is a miss → single sidecar call, identical to
    /// pre-cache).
    pub async fn embed(&self, req: EmbedRequest) -> Result<EmbedResponse, String> {
        let model_key = req.model.clone().unwrap_or_default();

        // Fast path: cache disabled → original behavior.
        let Some(cache) = self.embed_cache.as_ref() else {
            return self.embed_uncached(&req).await;
        };
        if req.texts.is_empty() {
            return self.embed_uncached(&req).await;
        }

        // First pass: check cache for each text. Track which positions
        // need a sidecar fetch.
        let mut embeddings: Vec<Option<Vec<f64>>> = vec![None; req.texts.len()];
        let mut miss_indices: Vec<usize> = Vec::new();
        let mut miss_texts: Vec<String> = Vec::new();
        {
            let mut guard = cache.lock().map_err(|e| format!("cache lock poisoned: {e}"))?;
            for (i, text) in req.texts.iter().enumerate() {
                let key = EmbedCacheKey { model: model_key.clone(), text: text.clone() };
                if let Some(vec) = guard.get(&key) {
                    embeddings[i] = Some(vec.clone());
                    self.embed_cache_hits.fetch_add(1, Ordering::Relaxed);
                } else {
                    miss_indices.push(i);
                    miss_texts.push(text.clone());
                    self.embed_cache_misses.fetch_add(1, Ordering::Relaxed);
                }
            }
        }

        // All hit? Return immediately. Use cached_dim to populate
        // the response dimension (no sidecar to ask).
        if miss_indices.is_empty() {
            let dim = self.cached_dim.load(Ordering::Relaxed) as usize;
            let dim = if dim == 0 { embeddings[0].as_ref().map(|v| v.len()).unwrap_or(0) } else { dim };
            return Ok(EmbedResponse {
                embeddings: embeddings.into_iter().map(|opt| opt.expect("filled")).collect(),
                model: req.model.unwrap_or_else(|| "nomic-embed-text".to_string()),
                dimensions: dim,
            });
        }

        // Second pass: fetch the misses in one sidecar call.
        let miss_req = EmbedRequest { texts: miss_texts.clone(), model: req.model.clone() };
        let resp = self.embed_uncached(&miss_req).await?;
        if resp.embeddings.len() != miss_texts.len() {
            return Err(format!(
                "embed cache: sidecar returned {} embeddings for {} texts",
                resp.embeddings.len(),
                miss_texts.len()
            ));
        }

        // Pin cached_dim on first successful response.
        if resp.dimensions > 0 {
            self.cached_dim.store(resp.dimensions as u64, Ordering::Relaxed);
        }

        // Insert misses into cache + fill response slots.
        {
            let mut guard = cache.lock().map_err(|e| format!("cache lock poisoned: {e}"))?;
            for (j, idx) in miss_indices.iter().enumerate() {
                let key = EmbedCacheKey {
                    model: model_key.clone(),
                    text: miss_texts[j].clone(),
                };
                let vec = resp.embeddings[j].clone();
                guard.put(key, vec.clone());
                embeddings[*idx] = Some(vec);
            }
        }

        Ok(EmbedResponse {
            embeddings: embeddings.into_iter().map(|opt| opt.expect("filled")).collect(),
            model: resp.model,
            dimensions: resp.dimensions,
        })
    }

    /// Direct Ollama call — used internally by embed() for cache-miss
    /// batches and as the transparent fallback when the cache is
    /// disabled. Loops per-text against `${base_url}/api/embed`,
    /// matching the sidecar's pre-2026-05-02 behavior. Ollama 0.4+
    /// supports batch input but per-text keeps compatibility broader
    /// + lets cache-miss-only batches share the loop with cold runs.
    async fn embed_uncached(&self, req: &EmbedRequest) -> Result<EmbedResponse, String> {
        let model = req.model.clone().unwrap_or_else(|| "nomic-embed-text".to_string());
        let mut embeddings: Vec<Vec<f64>> = Vec::with_capacity(req.texts.len());

        for text in &req.texts {
            let resp = self.client
                .post(format!("{}/api/embed", self.base_url))
                .json(&serde_json::json!({
                    "model": &model,
                    "input": text,
                }))
                .send()
                .await
                .map_err(|e| format!("embed request failed: {e}"))?;

            if !resp.status().is_success() {
                let body = resp.text().await.unwrap_or_default();
                return Err(format!("ollama embed error: {body}"));
            }
            // Ollama returns {"embeddings": [[...]], "model": "...", ...}.
            // The outer `embeddings` is always a list; for a scalar input
            // we get a single inner vector.
            let parsed: serde_json::Value = resp.json().await
                .map_err(|e| format!("embed parse error: {e}"))?;
            let arr = parsed.get("embeddings")
                .and_then(|v| v.as_array())
                .ok_or_else(|| format!("ollama embed: missing 'embeddings' field in {parsed}"))?;
            if arr.is_empty() {
                return Err("ollama embed: empty embeddings array".to_string());
            }
            let first = arr[0].as_array()
                .ok_or_else(|| "ollama embed: embeddings[0] not an array".to_string())?;
            let vec: Vec<f64> = first.iter()
                .filter_map(|n| n.as_f64())
                .collect();
            if vec.is_empty() {
                return Err("ollama embed: numeric coercion produced empty vector".to_string());
            }
            embeddings.push(vec);
        }

        let dimensions = embeddings.first().map(|v| v.len()).unwrap_or(0);
        Ok(EmbedResponse {
            embeddings,
            model,
            dimensions,
        })
    }

    pub async fn generate(&self, req: GenerateRequest) -> Result<GenerateResponse, String> {
        if let Some(gw) = self.gateway_url.as_deref() {
            return self.generate_via_gateway(gw, req).await;
        }
        // Direct Ollama path. Used by gateway internals (so the ollama
        // provider can call upstream without a self-loop through
        // /v1/chat) and by any consumer that wants raw transport
        // without /v1/usage accounting.
        let model = req.model.clone().unwrap_or_else(|| "qwen3.5:latest".to_string());
        let mut body = serde_json::json!({
            "model": &model,
            "prompt": &req.prompt,
            "stream": false,
        });
        let mut options = serde_json::Map::new();
        if let Some(t) = req.temperature {
            options.insert("temperature".to_string(), serde_json::json!(t));
        }
        if let Some(mt) = req.max_tokens {
            options.insert("num_predict".to_string(), serde_json::json!(mt));
        }
        if !options.is_empty() {
            body["options"] = serde_json::Value::Object(options);
        }
        if let Some(sys) = &req.system {
            body["system"] = serde_json::json!(sys);
        }
        if let Some(th) = req.think {
            body["think"] = serde_json::json!(th);
        }

        let resp = self.client
            .post(format!("{}/api/generate", self.base_url))
            .json(&body)
            .send()
            .await
            .map_err(|e| format!("generate request failed: {e}"))?;

        if !resp.status().is_success() {
            let text = resp.text().await.unwrap_or_default();
            return Err(format!("ollama generate error: {text}"));
        }
        let parsed: serde_json::Value = resp.json().await
            .map_err(|e| format!("generate parse error: {e}"))?;

        Ok(GenerateResponse {
            text: parsed.get("response").and_then(|v| v.as_str()).unwrap_or("").to_string(),
            model,
            tokens_evaluated: parsed.get("prompt_eval_count").and_then(|v| v.as_u64()),
            tokens_generated: parsed.get("eval_count").and_then(|v| v.as_u64()),
        })
    }

    /// Phase 44 part 2: route generate() through the gateway's
    /// /v1/chat with provider="ollama" so the call lands in
    /// /v1/usage + Langfuse. Translates between the sidecar
    /// GenerateRequest/Response shape and the OpenAI-compat
    /// chat shape on the wire.
    async fn generate_via_gateway(&self, gateway_url: &str, req: GenerateRequest) -> Result<GenerateResponse, String> {
        let mut messages = Vec::with_capacity(2);
        if let Some(sys) = &req.system {
            messages.push(serde_json::json!({"role": "system", "content": sys}));
        }
        messages.push(serde_json::json!({"role": "user", "content": req.prompt}));
        let mut body = serde_json::json!({
            "messages": messages,
            "provider": "ollama",
        });
        if let Some(m) = &req.model { body["model"] = serde_json::json!(m); }
        if let Some(t) = req.temperature { body["temperature"] = serde_json::json!(t); }
        if let Some(mt) = req.max_tokens { body["max_tokens"] = serde_json::json!(mt); }
        if let Some(th) = req.think { body["think"] = serde_json::json!(th); }

        let resp = self.client
            .post(format!("{}/v1/chat", gateway_url))
            .json(&body)
            .send()
            .await
            .map_err(|e| format!("/v1/chat request failed: {e}"))?;
        if !resp.status().is_success() {
            let text = resp.text().await.unwrap_or_default();
            return Err(format!("/v1/chat error: {text}"));
        }
        let parsed: serde_json::Value = resp.json().await
            .map_err(|e| format!("/v1/chat parse error: {e}"))?;

        let text = parsed
            .pointer("/choices/0/message/content")
            .and_then(|v| v.as_str())
            .unwrap_or("")
            .to_string();
        let model = parsed.get("model")
            .and_then(|v| v.as_str())
            .unwrap_or_else(|| req.model.as_deref().unwrap_or(""))
            .to_string();
        let prompt_tokens = parsed.pointer("/usage/prompt_tokens").and_then(|v| v.as_u64());
        let completion_tokens = parsed.pointer("/usage/completion_tokens").and_then(|v| v.as_u64());

        Ok(GenerateResponse {
            text,
            model,
            tokens_evaluated: prompt_tokens,
            tokens_generated: completion_tokens,
        })
    }

    /// Cross-encoder reranking via Ollama generate. Asks the model to
    /// rate each document's relevance to the query 0-10, then sorts
    /// descending. Mirrors the sidecar's pre-2026-05-02 algorithm
    /// exactly so callers see the same scores.
    pub async fn rerank(&self, req: RerankRequest) -> Result<RerankResponse, String> {
        let model = req.model.clone().unwrap_or_else(|| "qwen3.5:latest".to_string());
        let mut scored: Vec<ScoredDocument> = Vec::with_capacity(req.documents.len());

        for (i, doc) in req.documents.iter().enumerate() {
            let prompt = format!(
                "Rate the relevance of the following document to the query on a scale of 0 to 10. \
                 Respond with ONLY a number.\n\n\
                 Query: {}\n\n\
                 Document: {}\n\n\
                 Score:",
                req.query, doc,
            );
            let resp = self.client
                .post(format!("{}/api/generate", self.base_url))
                .json(&serde_json::json!({
                    "model": &model,
                    "prompt": prompt,
                    "stream": false,
                    "options": {"temperature": 0.0, "num_predict": 8},
                }))
                .send()
                .await
                .map_err(|e| format!("rerank request failed: {e}"))?;

            if !resp.status().is_success() {
                let body = resp.text().await.unwrap_or_default();
                return Err(format!("ollama rerank error: {body}"));
            }
            let parsed: serde_json::Value = resp.json().await
                .map_err(|e| format!("rerank parse error: {e}"))?;
            let text = parsed.get("response").and_then(|v| v.as_str()).unwrap_or("").trim();
            // Parse the leading number; tolerate "7", "7.5", "7 — strong match".
            let score = text.split_whitespace().next()
                .and_then(|t| t.parse::<f64>().ok())
                .unwrap_or(0.0)
                .clamp(0.0, 10.0);

            scored.push(ScoredDocument {
                index: i,
                text: doc.clone(),
                score,
            });
        }

        scored.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap_or(std::cmp::Ordering::Equal));
        if let Some(k) = req.top_k {
            scored.truncate(k);
        }
        Ok(RerankResponse { results: scored, model })
    }

    /// Force Ollama to unload the named model from VRAM (keep_alive=0).
    /// Used for predictable profile swaps — without this, Ollama holds a
    /// model for its configured TTL (default 5min) and the previous
    /// profile's model can linger in VRAM next to the new one.
    pub async fn unload_model(&self, model: &str) -> Result<serde_json::Value, String> {
        let resp = self.client
            .post(format!("{}/api/generate", self.base_url))
            .json(&serde_json::json!({
                "model": model,
                "prompt": "",
                "keep_alive": 0,
                "stream": false,
            }))
            .send().await
            .map_err(|e| format!("unload request failed: {e}"))?;
        if !resp.status().is_success() {
            let text = resp.text().await.unwrap_or_default();
            return Err(format!("ollama unload error: {text}"));
        }
        // Ollama returns 200 with the empty-prompt response shape.
        // Fold into the legacy {"unloaded": "<model>"} envelope so
        // callers' parsing doesn't break.
        Ok(serde_json::json!({ "unloaded": model }))
    }

    /// Ask Ollama to load the named model into VRAM proactively. Makes
    /// the first real request after profile activation fast (no cold-load
    /// latency). Empty prompts confuse some models, so we send a single
    /// space + cap num_predict=1 (matches the sidecar's prior behavior).
    pub async fn preload_model(&self, model: &str) -> Result<serde_json::Value, String> {
        let resp = self.client
            .post(format!("{}/api/generate", self.base_url))
            .json(&serde_json::json!({
                "model": model,
                "prompt": " ",
                "keep_alive": "5m",
                "stream": false,
                "options": {"num_predict": 1},
            }))
            .send().await
            .map_err(|e| format!("preload request failed: {e}"))?;
        if !resp.status().is_success() {
            let text = resp.text().await.unwrap_or_default();
            return Err(format!("ollama preload error: {text}"));
        }
        let parsed: serde_json::Value = resp.json().await
            .map_err(|e| format!("preload parse error: {e}"))?;
        Ok(serde_json::json!({
            "preloaded": model,
            "load_duration_ns": parsed.get("load_duration"),
            "total_duration_ns": parsed.get("total_duration"),
        }))
    }

    /// GPU + loaded-model snapshot. Combines nvidia-smi output (when
    /// available) with Ollama's /api/ps. Same shape as the prior
    /// sidecar /admin/vram endpoint so callers don't need updating.
    pub async fn vram_snapshot(&self) -> Result<serde_json::Value, String> {
        let resp = self.client
            .get(format!("{}/api/ps", self.base_url))
            .send().await
            .map_err(|e| format!("ollama ps request failed: {e}"))?;
        let loaded: Vec<serde_json::Value> = if resp.status().is_success() {
            let parsed: serde_json::Value = resp.json().await.unwrap_or(serde_json::Value::Null);
            parsed.get("models")
                .and_then(|v| v.as_array())
                .map(|arr| arr.iter().map(|m| serde_json::json!({
                    "name": m.get("name"),
                    "size_vram_mib": m.get("size_vram").and_then(|v| v.as_u64()).map(|n| n / (1024 * 1024)),
                    "expires_at": m.get("expires_at"),
                })).collect())
                .unwrap_or_default()
        } else {
            Vec::new()
        };

        let gpu = nvidia_smi_snapshot();

        Ok(serde_json::json!({
            "gpu": gpu,
            "ollama_loaded": loaded,
        }))
    }
}

/// One-shot nvidia-smi poll. Returns Null if the tool isn't on PATH
/// or the call fails. Mirrors the sidecar's `_nvidia_smi_snapshot`
/// shape exactly so callers reading vram_snapshot don't break.
fn nvidia_smi_snapshot() -> serde_json::Value {
    use std::process::Command;
    let out = Command::new("nvidia-smi")
        .args([
            "--query-gpu=memory.used,memory.total,utilization.gpu,name",
            "--format=csv,noheader,nounits",
        ])
        .output();
    let stdout = match out {
        Ok(o) if o.status.success() => o.stdout,
        _ => return serde_json::Value::Null,
    };
    let line = String::from_utf8_lossy(&stdout);
    let line = line.trim();
    if line.is_empty() {
        return serde_json::Value::Null;
    }
    let parts: Vec<&str> = line.split(',').map(|s| s.trim()).collect();
    if parts.len() < 4 {
        return serde_json::Value::Null;
    }
    let used = parts[0].parse::<u64>().unwrap_or(0);
    let total = parts[1].parse::<u64>().unwrap_or(0);
    let util = parts[2].parse::<u64>().unwrap_or(0);
    serde_json::json!({
        "name": parts[3],
        "used_mib": used,
        "total_mib": total,
        "utilization_pct": util,
    })
}