fix(stream): robust reasoning/content split + flush un-tagged output

The incremental </think> search missed tags split across tokens, inverting
reasoning/content classification. Detect the first </think> on the full
buffer and track the content boundary as a byte offset. If the model never
closes </think>, flush the buffered text as content so clients always
receive the response. Chat UI now renders reasoning_content too.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

.github/workflows/notify-parent.yml

@@ -0,0 +1,25 @@
+name: Notify Parent Repo
+
+on:
+  push:
+    branches:
+      - master
+  workflow_dispatch:
+
+jobs:
+  dispatch:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Trigger root monorepo build
+        uses: peter-evans/repository-dispatch@v3
+        with:
+          token: ${{ secrets.DISPATCH_TOKEN }}
+          repository: asepharyana/asepharyana-hub
+          event-type: submodule-updated
+          client-payload: |
+            {
+              "service": "llm-api",
+              "ref": "${{ github.ref }}",
+              "sha": "${{ github.sha }}",
+              "actor": "${{ github.actor }}"
+            }

Cargo.lock

@@ -568,6 +568,7 @@ dependencies = [
  "chrono",
  "futures",
  "llama-cpp-2",
+ "minijinja",
  "serde",
  "serde_json",
  "thiserror 1.0.69",
@@ -615,12 +616,28 @@ version = "2.8.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "cf8baf1c55e62ffcace7a9f06f4bd9cd3f0c4beb022d3b367256b91b87513d98"
 
+[[package]]
+name = "memo-map"
+version = "0.3.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "38d1115007560874e373613744c6fba374c17688327a71c1476d1a5954cc857b"
+
 [[package]]
 name = "mime"
 version = "0.3.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6877bb514081ee2a7ff5ef9de3281f14a4dd4bceac4c09388074a6b5df8a139a"
 
+[[package]]
+name = "minijinja"
+version = "2.21.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cb3d648e68cea56d9858d535ee28f9538404e2dd8cb08ed0bd05dca379477f39"
+dependencies = [
+ "memo-map",
+ "serde",
+]
+
 [[package]]
 name = "minimal-lexical"
 version = "0.2.1"

Cargo.toml

@@ -6,6 +6,7 @@ edition = "2021"
 [dependencies]
 # LLM inference
 llama-cpp-2 = "0.1"
+minijinja = "2"
 
 # HTTP server
 axum = { version = "0.8", features = ["json"] }

README.md

@@ -2,7 +2,7 @@
 
 OpenAI-compatible LLM inference server using `llama-cpp-2` (Rust).
 
-**Model:** MiniCPM-V-4.6 Q4_K_M (505 MB)  
+**Model:** MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking (Q8_0)  
 **Engine:** llama.cpp via `llama-cpp-2` crate  
 **Domain:** [ai.asepharyana.my.id](https://ai.asepharyana.my.id)
 
@@ -10,7 +10,7 @@ OpenAI-compatible LLM inference server using `llama-cpp-2` (Rust).
 
 ### `GET /health`
 ```json
-{"status": "ok", "model": "minicpm-v-4.6-q4_k_m"}
+{"status": "ok", "model": "minicpm5-1b-fable5-v2-thinking"}
 ```
 
 ### `GET /v1/models`
@@ -19,11 +19,13 @@ OpenAI-compatible model listing.
 ### `POST /v1/chat/completions`
 OpenAI-compatible chat completions.
 
+The server serves a single model and rejects unknown model ids with `400`:
+
 ```bash
 curl https://ai.asepharyana.my.id/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "minicpm-v-4.6",
+    "model": "minicpm5-1b-fable5-v2-thinking",
     "messages": [{"role": "user", "content": "Hello!"}],
     "max_tokens": 100
   }'
@@ -42,14 +44,37 @@ MODEL_PATH=/path/to/model.gguf ./target/release/llm-api
 ./target/release/llm-api
 ```
 
-## Docker
+### Environment variables
+
+| Var | Default | Description |
+|-----|---------|-------------|
+| `MODEL_PATH` | `/models/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-Q8_0.gguf` | GGUF model file |
+| `API_KEY` | *(empty = auth off)* | Bearer token required on `/v1/chat/completions` |
+| `SERVER_PORT` | `4010` | Listen port |
+| `RUST_LOG` | `info` | Log level |
+| `N_CTX` / `N_BATCH` / `N_THREADS` | `8192` / `512` / `4` | llama.cpp context/batch/threads |
+
+### Smoke test (setelah deploy)
 
 ```bash
-docker compose -f docker-compose.yml up -d
+./scripts/smoke-test.sh http://127.0.0.1:4010        # tanpa auth
+./scripts/smoke-test.sh https://ai.asepharyana.my.id "$API_KEY"
 ```
 
+## Deploy (Nix + systemd)
+
+```bash
+nix build .#default --impure --option sandbox false
+# GitHub Actions: nix copy ssh://vps → systemctl restart llm-api
+```
+
+> **Legacy (2026-08-02):** Docker compose dihapus dari produksi. Deploy sekarang Nix+systemd.
+
 ## Benchmark
 
+> *Historic* (MiniCPM-V-4.6). Kept for reference; numbers predate the current
+> MiniCPM5-1B Thinking model.
+
 | Framework | Model Size | tok/s | vs PyTorch |
 |-----------|-----------|-------|------------|
 | PyTorch BF16 | 2.48 GB | 0.97 | 1.0x |
@@ -57,6 +82,5 @@ docker compose -f docker-compose.yml up -d
 
 ## Infrastructure
 
-- Traefik router: `ai.asepharyana.my.id` → `llm-api:8080`
-- Network: `app-shared-net`
-- Docker Compose: see `llm-api.yml`
+- Caddy reverse proxy: `ai.asepharyana.my.id` → `127.0.0.1:4010`
+- systemd unit `llm-api`, deploy Nix via GitHub Actions

scripts/smoke-test.sh

@@ -0,0 +1,104 @@
+#!/usr/bin/env bash
+# Smoke test untuk llm-api — jalankan di VPS setelah deploy.
+#
+# Memverifikasi alur inti:
+#   /health, /v1/models, chat non-streaming, chat streaming (SSE + [DONE]),
+#   penolakan model tidak dikenal (400), dan request dengan tools.
+#
+# Usage:
+#   ./scripts/smoke-test.sh [BASE_URL] [API_KEY]
+#   BASE_URL default: http://127.0.0.1:4010
+set -euo pipefail
+
+BASE_URL="${1:-http://127.0.0.1:4010}"
+API_KEY="${2:-}"
+MODEL_ID="minicpm5-1b-fable5-v2-thinking"
+
+AUTH=()
+if [[ -n "$API_KEY" ]]; then
+  AUTH=(-H "Authorization: Bearer $API_KEY")
+fi
+
+pass=0
+fail=0
+check() {
+  local name="$1" ok="$2"
+  if [[ "$ok" == "0" ]]; then
+    echo "  PASS: $name"
+    pass=$((pass + 1))
+  else
+    echo "  FAIL: $name"
+    fail=$((fail + 1))
+  fi
+}
+
+echo "== 1. Health =="
+health=$(curl -sf "$BASE_URL/health") || { echo "FAIL: /health unreachable"; exit 1; }
+echo "$health" | jq -e ".status == \"ok\" and .model == \"$MODEL_ID\"" >/dev/null
+check "health melaporkan $MODEL_ID" $?
+
+echo "== 2. Models =="
+models=$(curl -sf "$BASE_URL/v1/models")
+echo "$models" | jq -e ".data[0].id == \"$MODEL_ID\"" >/dev/null
+check "models mencantumkan $MODEL_ID" $?
+
+echo "== 3. Chat non-streaming =="
+resp=$(curl -sf "${AUTH[@]}" -H "Content-Type: application/json" \
+  -d "{\"model\":\"$MODEL_ID\",\"messages\":[{\"role\":\"user\",\"content\":\"Say hi\"}],\"max_tokens\":64}" \
+  "$BASE_URL/v1/chat/completions")
+echo "$resp" | jq -e '.choices[0].message.content | type == "string"' >/dev/null
+check "non-streaming mengembalikan content" $?
+echo "$resp" | jq -e '.usage.total_tokens > 0' >/dev/null
+check "non-streaming berisi usage" $?
+
+echo "== 4. Chat streaming =="
+stream=$(curl -sfN "${AUTH[@]}" -H "Content-Type: application/json" \
+  -d "{\"model\":\"$MODEL_ID\",\"messages\":[{\"role\":\"user\",\"content\":\"Count from 1 to 5\"}],\"stream\":true,\"max_tokens\":64}" \
+  "$BASE_URL/v1/chat/completions")
+
+echo "$stream" | grep -q '\[DONE\]'
+check "stream diakhiri [DONE]" $?
+
+echo "$stream" | grep -q '"finish_reason":"stop"\|"finish_reason":"length"'
+check "stream punya finish_reason" $?
+
+# Gabungkan semua delta content untuk memastikan output tidak kosong
+# dan tidak bocor markup (mis. <tool_call> / <|im_end|>).
+joined=$(echo "$stream" | grep '^data: ' | sed 's/^data: //' \
+  | grep -v '\[DONE\]' \
+  | jq -r 'select((.choices? // []) | length > 0) | .choices[0].delta.content // empty' 2>/dev/null \
+  | tr -d '\n')
+if [[ -z "$joined" ]]; then
+  # Sebagian output mungkin semua ber-label reasoning_content; cek keduanya.
+  joined=$(echo "$stream" | grep '^data: ' | sed 's/^data: //' \
+    | grep -v '\[DONE\]' \
+    | jq -r 'select((.choices? // []) | length > 0) | (.choices[0].delta.content // .choices[0].delta.reasoning_content) // empty' 2>/dev/null \
+    | tr -d '\n')
+fi
+[[ -n "$joined" ]]
+check "stream menghasilkan teks" $?
+if [[ -n "$joined" ]]; then
+  if [[ "$joined" == *"<"* ]]; then
+    check "tidak ada markup bocor di stream" 1
+  else
+    check "tidak ada markup bocor di stream" 0
+  fi
+fi
+
+echo "== 5. Model tidak dikenal ditolak =="
+status=$(curl -s -o /dev/null -w "%{http_code}" "${AUTH[@]}" -H "Content-Type: application/json" \
+  -d '{"model":"minicpm-v-4.6","messages":[{"role":"user","content":"hi"}]}' \
+  "$BASE_URL/v1/chat/completions")
+[[ "$status" == "400" ]]
+check "unknown model -> 400" $?
+
+echo "== 6. Request dengan tools diterima =="
+status2=$(curl -s -o /dev/null -w "%{http_code}" "${AUTH[@]}" -H "Content-Type: application/json" \
+  -d "{\"model\":\"$MODEL_ID\",\"messages\":[{\"role\":\"user\",\"content\":\"What's the weather in Jakarta?\"}],\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_weather\",\"description\":\"Get weather\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}}}}}],\"max_tokens\":128}" \
+  "$BASE_URL/v1/chat/completions")
+[[ "$status2" == "200" ]]
+check "tools request sukses (200)" $?
+
+echo
+echo "Result: $pass passed, $fail failed"
+[[ $fail -eq 0 ]]

src/application/chat/mod.rs

@@ -1,3 +1,6 @@
 pub mod use_cases;
 
-pub use use_cases::{build_prompt, build_sampler, clean_text, parse_tool_calls, SamplerParams};
+pub use use_cases::{
+    build_prompt, build_sampler, clean_text, parse_tool_calls, split_stream_chunk, strip_markup,
+    validate_model, SamplerParams,
+};

src/application/chat/templates/chat_template.jinja

@@ -0,0 +1,44 @@
+{{- bos_token }}
+{%- if tools %}
+    {%- set tool_definitions %}
+        {{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+        {%- for tool in tools %}
+            {{- "\n" }}
+            {{- tool | tojson }}
+        {%- endfor %}
+        {{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally.\n- When calling a function, wrap each call in <tool_call> tags:\n<tool_call>\n<function=name>\n<parameter=key>value</parameter>\n</function>\n</tool_call>' }}
+    {%- endset %}
+
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' + tool_definitions }}
+    {%- else %}
+        {{- tool_definitions }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = message.content %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {#- Tool-call XML from history is pre-embedded in content by build_prompt. #}
+        {{- '<|im_start|>assistant\n' + content + '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {{- '<|im_start|>user\n<tool_response>\n' + content + '\n</tool_response><|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined %}
+        {%- if enable_thinking is false %}
+            {{- '<think>\n\n</think>\n\n' }}
+        {%- elif enable_thinking is true %}
+            {{- '<think>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}

src/application/chat/use_cases.rs

@@ -1,97 +1,119 @@
 //! Chat completion use cases.
 //!
-//! Orchestrates prompt building, sampler construction, and output parsing.
-//! These are pure functions with no framework dependencies.
+//! Orchestrates prompt building using the model's baked-in Jinja template
+//! via the `minijinja` crate, sampler construction, and output parsing.
+
+use std::collections::HashMap;
 
 use llama_cpp_2::sampling::LlamaSampler;
+use minijinja::{Environment, Value};
 
-use crate::domain::entity::{ChatMessage, ChatRequest, ToolCall, ToolCallFunction, ToolDef};
+use crate::domain::entity::{ChatMessage, ChatRequest, ToolCall, ToolCallFunction};
 
-/// Build a prompt string from conversation messages and optional tool definitions.
+/// Build a prompt from messages using the project's Jinja chat template.
 ///
-/// Uses ChatML format with `<|im_start|>` / `<|im_end|>` delimiters. Tool
-/// definitions are injected into the first system message.
-pub fn build_prompt(messages: &[ChatMessage], tools: &Option<Vec<ToolDef>>) -> String {
-    let mut prompt = String::new();
+/// Renders the template (see `templates/chat_template.jinja`) via minijinja,
+/// passing the message history, optional tool definitions, and the
+/// generation-prompt switches. The template owns the `<s>` BOS token, so
+/// tokenization must NOT add another one (`AddBos::Never`).
+pub fn build_prompt(
+    messages: &[ChatMessage],
+    tools: &Option<Vec<crate::domain::entity::ToolDef>>,
+) -> Result<String, String> {
+    let template_str = include_str!("templates/chat_template.jinja");
 
-    for (i, msg) in messages.iter().enumerate() {
-        match msg.role.as_str() {
-            "system" => {
-                let mut content = msg.content.clone().unwrap_or_default();
-                // Inject tools into the system message (first occurrence)
-                if i == 0 {
-                    if let Some(tools_list) = tools {
-                        if !tools_list.is_empty() {
-                            let mut tools_text = String::from(
-                                "\n\n# Tools\n\nYou have access to the following functions:\n\n<tools>",
-                            );
-                            for tool in tools_list {
-                                tools_text.push('\n');
-                                tools_text.push_str(
-                                    &serde_json::to_string(tool).unwrap_or_default(),
-                                );
-                            }
-                            tools_text.push_str(
-                                "\n</tools>\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>",
-                            );
-                            content.push_str(&tools_text);
-                        }
-                    }
-                }
-                prompt.push_str(&format!("<|im_start|>system\n{}<|im_end|>\n", content));
-            }
-            "user" => {
-                let content = msg.content.as_deref().unwrap_or("");
-                if msg.tool_call_id.is_some() || msg.name.is_some() {
-                    prompt.push_str(&format!(
-                        "<|im_start|>user\n<tool_response>\n{}\n</tool_response><|im_end|>\n",
+    let mut env = Environment::new();
+    env.add_template("chat", template_str)
+        .map_err(|e| format!("Template add error: {e}"))?;
+
+    // Register tojson filter (safe: Rust serde_json defaults to ensure_ascii=false)
+    env.add_filter("tojson", |value: &Value| -> String {
+        serde_json::to_string(value).unwrap_or_default()
+    });
+
+    let tmpl = env
+        .get_template("chat")
+        .map_err(|e| format!("Template get error: {e}"))?;
+
+    // Build messages as serde_json::Value for minijinja.
+    // Assistant tool calls from history are embedded directly into the content
+    // as XML (in the exact format the model is told to emit) — the template
+    // cannot accumulate `set` variables across a loop, so this is built here.
+    let mut msgs_val: Vec<Value> = Vec::new();
+    for msg in messages {
+        let mut m: HashMap<String, Value> = HashMap::new();
+        m.insert("role".into(), Value::from(msg.role.clone()));
+
+        let mut content = msg.content.clone().unwrap_or_default();
+
+        if msg.role == "assistant" {
+            if let Some(tcs) = &msg.tool_calls {
+                for tc in tcs {
+                    if tc.call_type == "function" {
                         content
-                    ));
-                } else {
-                    prompt.push_str(&format!("<|im_start|>user\n{}<|im_end|>\n", content));
-                }
-            }
-            "assistant" => {
-                let content = msg.content.as_deref().unwrap_or("");
-                if let Some(tcs) = &msg.tool_calls {
-                    let mut asst = format!("<|im_start|>assistant\n{}", content);
-                    for tc in tcs {
+                            .push_str(&format!("\n<tool_call>\n<function={}>\n", tc.function.name));
                         let args: serde_json::Value =
                             serde_json::from_str(&tc.function.arguments).unwrap_or_default();
-                        asst.push_str(&format!(
-                            "<tool_call>\n<function={}>\n",
-                            tc.function.name
-                        ));
                         if let Some(obj) = args.as_object() {
                             for (k, v) in obj {
-                                let val = match v {
+                                // Strings stay raw (no JSON quotes) so they round-trip
+                                // through parse_tool_calls unchanged.
+                                let rendered = match v {
                                     serde_json::Value::String(s) => s.clone(),
-                                    other => serde_json::to_string(other).unwrap_or_default(),
+                                    other => other.to_string(),
                                 };
-                                asst.push_str(&format!("<parameter={}>\n{}\n</parameter>\n", k, val));
+                                content
+                                    .push_str(&format!("<parameter={k}>{rendered}</parameter>\n"));
                             }
                         }
-                        asst.push_str("</function>\n</tool_call>");
+                        content.push_str("</function>\n</tool_call>");
                     }
-                    asst.push_str("<|im_end|>\n");
-                    prompt.push_str(&asst);
-                } else {
-                    prompt.push_str(&format!(
-                        "<|im_start|>assistant\n{}<|im_end|>\n",
-                        content
-                    ));
                 }
             }
-            _ => {
-                let content = msg.content.as_deref().unwrap_or("");
-                prompt.push_str(&format!("<|im_start|>user\n{}<|im_end|>\n", content));
-            }
+        }
+
+        // NOTE: the template wraps tool-role content in <tool_response>; do not
+        // wrap here or it would be double-wrapped.
+        m.insert("content".into(), Value::from(content));
+
+        msgs_val.push(Value::from(m));
+    }
+
+    // BOS token for sentencepiece / unigram models (template-owned).
+    let bos_token: &str = "<s>";
+
+    // Build context
+    let mut ctx: HashMap<String, Value> = HashMap::new();
+    ctx.insert("bos_token".into(), Value::from(bos_token));
+    ctx.insert("messages".into(), Value::from(msgs_val));
+    ctx.insert("add_generation_prompt".into(), Value::from(true));
+    ctx.insert("enable_thinking".into(), Value::from(true));
+
+    // Tool definitions (optional) — previously dead, now actually rendered.
+    if let Some(tools) = tools {
+        if !tools.is_empty() {
+            let tools_val: Vec<Value> = tools.iter().map(Value::from_serialize).collect();
+            ctx.insert("tools".into(), Value::from(tools_val));
         }
     }
 
-    // Generation prompt: non-thinking mode
-    prompt.push_str("<|im_start|>assistant\n<think>\n\n</think>\n\n");
-    prompt
+    // Render
+    let result = tmpl
+        .render(&ctx)
+        .map_err(|e| format!("Template render error: {e}"))?;
+
+    Ok(result)
+}
+
+/// Validate that the requested model matches the single served model.
+pub fn validate_model(model: &str) -> Result<(), String> {
+    if model != crate::config::MODEL_ID {
+        return Err(format!(
+            "Unknown model '{model}'. Available: {}",
+            crate::config::MODEL_ID
+        ));
+    }
+    Ok(())
 }
 
 /// Parameters for building a [`LlamaSampler`] chain.
@@ -135,7 +157,6 @@ pub fn build_sampler(params: &SamplerParams) -> LlamaSampler {
     let seed = params.seed;
     let mut samplers: Vec<LlamaSampler> = Vec::new();
 
-    // Repetition/frequency/presence penalties
     let repeat = repeat_penalty.unwrap_or(1.0);
     let freq = frequency_penalty.unwrap_or(0.0);
     let present = presence_penalty.unwrap_or(0.0);
@@ -143,22 +164,18 @@ pub fn build_sampler(params: &SamplerParams) -> LlamaSampler {
         samplers.push(LS::penalties(64, repeat, freq, present));
     }
 
-    // top_k
     if let Some(k) = top_k {
         samplers.push(LS::top_k(k as i32));
     }
 
-    // top_p
     if let Some(p) = top_p {
         samplers.push(LS::top_p(p, 1));
     }
 
-    // min_p
     if let Some(p) = min_p {
         samplers.push(LS::min_p(p, 1));
     }
 
-    // Temperature + final selector
     let temp = temperature.unwrap_or(0.0);
     if temp <= 0.0 {
         samplers.push(LS::greedy());
@@ -173,19 +190,36 @@ pub fn build_sampler(params: &SamplerParams) -> LlamaSampler {
     LlamaSampler::chain_simple(samplers)
 }
 
-
 // ═══════════════════════════════════════════════════════════════
 //  TEXT PROCESSING
 // ═══════════════════════════════════════════════════════════════
 
-/// Remove special tokens from generated text.
-pub fn clean_text(text: &str) -> String {
-    text.replace("<|im_end|>", "")
-        .replace("<|im_start|>", "")
-        .replace("<think>", "")
-        .replace("</think>", "")
-        .trim()
-        .to_string()
+/// Remove special tokens from generated text and separate reasoning.
+///
+/// For thinking models, returns (reasoning, cleaned_answer).
+pub fn clean_text(text: &str) -> (String, String) {
+    let text = text.replace("<|im_end|>", "").replace("<|im_start|>", "");
+
+    // Separate reasoning (between <think>/</think>) from answer
+    let text = text.trim();
+    let (reasoning, answer) = if let Some(close_idx) = text.find("</think>") {
+        let reasoning = text[..close_idx]
+            .trim()
+            .trim_start_matches("<think>")
+            .trim()
+            .to_string();
+        let answer = text[close_idx + 8..].trim().to_string();
+        (reasoning, answer)
+    } else if text.contains("<think>") {
+        // Still thinking — everything is reasoning
+        let reasoning = text.trim_start_matches("<think>").trim().to_string();
+        (reasoning, String::new())
+    } else {
+        (String::new(), text.to_string())
+    };
+
+    let answer = answer.replace("<think>", "").replace("</think>", "");
+    (reasoning, answer.trim().to_string())
 }
 
 /// Parse tool calls from generated text in the format:
@@ -240,9 +274,9 @@ pub fn parse_tool_calls(text: &str) -> (String, Vec<ToolCall>) {
 
         for line in lines {
             let line = line.trim();
-            if let Some(param) =
-                line.strip_prefix("<parameter=")
-                    .and_then(|s| s.strip_suffix('>'))
+            if let Some(param) = line
+                .strip_prefix("<parameter=")
+                .and_then(|s| s.strip_suffix('>'))
             {
                 if let Some(p) = current_param.take() {
                     args_map.insert(
@@ -264,9 +298,11 @@ pub fn parse_tool_calls(text: &str) -> (String, Vec<ToolCall>) {
                 continue;
             }
         }
-        // Save last param
         if let Some(p) = current_param.take() {
-            args_map.insert(p, serde_json::Value::String(current_value.trim().to_string()));
+            args_map.insert(
+                p,
+                serde_json::Value::String(current_value.trim().to_string()),
+            );
         }
 
         let args_json = serde_json::Value::Object(args_map).to_string();
@@ -283,12 +319,354 @@ pub fn parse_tool_calls(text: &str) -> (String, Vec<ToolCall>) {
         idx = end;
     }
 
-    // Remove tool_call blocks from the text
     clean = clean.replace("<tool_call>", "").replace("</tool_call>", "");
-    // XML-like tags are already fully parsed; remaining text is the content
     clean = clean.trim().to_string();
-    // Strip remaining XML tags that aren't part of clean
-    let cleaned = clean_text(&clean);
+    let (_reasoning, cleaned) = clean_text(&clean);
 
     (cleaned, tool_calls)
 }
+
+// ═══════════════════════════════════════════════════════════════
+//  STREAMING CHUNK SPLITTING
+// ═══════════════════════════════════════════════════════════════
+
+/// Remove special tokens and tool-call XML markup from a text fragment.
+///
+/// Handles both fixed tags (`<|im_end|>`, `<think>`, `<tool_call>`, …) and the
+/// attribute-bearing openers used by this model's tool format (`<function=…>`,
+/// `<parameter=…>`), even when a tag straddles a token boundary.
+pub fn strip_markup(text: &str) -> String {
+    let mut out = String::with_capacity(text.len());
+    let mut rest = text;
+
+    while !rest.is_empty() {
+        let Some(idx) = rest.find('<') else {
+            out.push_str(rest);
+            break;
+        };
+        out.push_str(&rest[..idx]);
+        let tail = &rest[idx..];
+
+        // Fixed tags (no attribute content).
+        let fixed = [
+            "<|im_start|>",
+            "<|im_end|>",
+            "<think>",
+            "</think>",
+            "<tool_call>",
+            "</tool_call>",
+            "</function>",
+            "</parameter>",
+        ];
+        if let Some(tag) = fixed.iter().find(|t| tail.starts_with(**t)) {
+            rest = &tail[tag.len()..];
+            continue;
+        }
+
+        // Attribute-bearing openers: <function=…> / <parameter=…>.
+        if let Some(attr) = tail
+            .strip_prefix("<function=")
+            .or_else(|| tail.strip_prefix("<parameter="))
+        {
+            if let Some(end) = attr.find('>') {
+                rest = &attr[end + 1..];
+                continue;
+            }
+        }
+
+        // Not a known tag — keep this char and advance one UTF-8 char.
+        let ch = tail.chars().next().expect("non-empty tail");
+        out.push(ch);
+        rest = &tail[ch.len_utf8()..];
+    }
+
+    out
+}
+
+/// Compute `(reasoning, content)` deltas for an incremental streamed fragment.
+///
+/// * `new_text` — the not-yet-emitted fragment (`text_buf[sent_len..]`).
+/// * `sent_len` — byte offset in the full buffer where `new_text` begins.
+/// * `content_start` — byte offset in the full buffer where the content phase
+///   begins (immediately after the first `</think>`); `None` while still
+///   thinking.
+///
+/// The boundary is a position in the *full* buffer, not a string search in the
+/// fragment — this stays correct even when `</think>` is split across tokens.
+/// Whitespace inside a fragment is preserved; only the edges around the
+/// boundary are trimmed. A stray second `</think>` is stripped, not re-split.
+pub fn split_stream_chunk(
+    new_text: &str,
+    sent_len: usize,
+    content_start: Option<usize>,
+) -> (Option<String>, String) {
+    match content_start {
+        // Still thinking — everything is reasoning.
+        None => {
+            let cleaned = strip_markup(new_text);
+            let reasoning = if cleaned.is_empty() {
+                None
+            } else {
+                Some(cleaned)
+            };
+            (reasoning, String::new())
+        }
+        // Boundary already emitted — everything is content.
+        Some(cs) if cs <= sent_len => (None, strip_markup(new_text)),
+        // Boundary falls inside this fragment (or beyond it, defensively).
+        Some(cs) => {
+            let rel = (cs - sent_len).min(new_text.len());
+            let (before, after) = new_text.split_at(rel);
+            let mut before = strip_markup(before);
+            let mut after = strip_markup(after);
+
+            while before.ends_with(['\n', ' ', '\t']) {
+                before.pop();
+            }
+            while after.starts_with(['\n', ' ', '\t']) {
+                after.remove(0);
+            }
+
+            let reasoning = if before.is_empty() {
+                None
+            } else {
+                Some(before)
+            };
+            (reasoning, after)
+        }
+    }
+}
+
+// ═══════════════════════════════════════════════════════════════
+//  TESTS
+// ═══════════════════════════════════════════════════════════════
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::domain::entity::{
+        ChatMessage, ToolCallFunction, ToolCallResponse, ToolDef, ToolFunction,
+    };
+
+    fn msg(role: &str, content: &str) -> ChatMessage {
+        ChatMessage {
+            role: role.into(),
+            content: Some(content.into()),
+            tool_calls: None,
+            tool_call_id: None,
+            name: None,
+        }
+    }
+
+    fn tool_def() -> ToolDef {
+        ToolDef {
+            tool_type: "function".into(),
+            function: ToolFunction {
+                name: "get_weather".into(),
+                description: "Get weather for a city".into(),
+                parameters: serde_json::json!({
+                    "type": "object",
+                    "properties": {
+                        "city": { "type": "string" }
+                    },
+                    "required": ["city"]
+                }),
+            },
+        }
+    }
+
+    // ── split_stream_chunk ──
+
+    #[test]
+    fn split_chunk_before_think_is_reasoning() {
+        let (reasoning, content) = split_stream_chunk("Hello ", 0, None);
+        assert_eq!(reasoning.as_deref(), Some("Hello "));
+        assert_eq!(content, "");
+    }
+
+    #[test]
+    fn split_chunk_preserves_internal_spaces() {
+        // Regression: trimming every fragment used to eat inter-word spaces.
+        let (r1, _) = split_stream_chunk("Hello", 0, None);
+        let (r2, _) = split_stream_chunk(" world", 5, None);
+        assert_eq!(format!("{}{}", r1.unwrap(), r2.unwrap()), "Hello world");
+    }
+
+    #[test]
+    fn split_chunk_boundary_trims_only_edges() {
+        // text_buf = "question\n</think>\n\nAnswer "; boundary right after the
+        // tag at byte 17.
+        let (reasoning, content) = split_stream_chunk("question\n</think>\n\nAnswer ", 0, Some(17));
+        assert_eq!(reasoning.as_deref(), Some("question"));
+        assert_eq!(content, "Answer ");
+    }
+
+    #[test]
+    fn split_chunk_after_think_is_content() {
+        let (reasoning, content) = split_stream_chunk(" answer", 0, Some(0));
+        assert_eq!(reasoning, None);
+        assert_eq!(content, " answer");
+    }
+
+    #[test]
+    fn split_chunk_stray_think_tag_is_stripped_not_split() {
+        // A second </think> (already past the boundary) must not restart
+        // reasoning classification.
+        let (reasoning, content) = split_stream_chunk("...</think>more", 0, Some(0));
+        assert_eq!(reasoning, None);
+        assert_eq!(content, "...more");
+    }
+
+    #[test]
+    fn split_chunk_boundary_straddling_tokens() {
+        // `</think>` split as "</think" + ">" across two fragments: the boundary
+        // is detected on the full buffer, so the answer still becomes content.
+        let (r1, _) = split_stream_chunk("reasoning...</think", 0, None);
+        assert!(r1.is_some());
+        let (r2, c2) = split_stream_chunk("\n\n2 + 2 = 4.", 18, Some(18));
+        assert_eq!(r2, None);
+        assert_eq!(c2, "\n\n2 + 2 = 4.");
+    }
+
+    #[test]
+    fn split_chunk_strips_special_and_markup() {
+        // boundary at byte 30 (right after "</think>").
+        let (reasoning, content) = split_stream_chunk(
+            "<|im_end|><think>Hello</think>\n<tool_call><function=get_weather>",
+            0,
+            Some(30),
+        );
+        assert_eq!(reasoning.as_deref(), Some("Hello"));
+        assert_eq!(content, "");
+    }
+
+    // ── clean_text ──
+
+    #[test]
+    fn clean_text_splits_reasoning_and_answer() {
+        let (reasoning, answer) =
+            clean_text("<think>Let me think\nabout it</think>\nThe answer is 42.");
+        assert_eq!(reasoning, "Let me think\nabout it");
+        assert_eq!(answer, "The answer is 42.");
+    }
+
+    #[test]
+    fn clean_text_no_think_returns_answer() {
+        let (reasoning, answer) = clean_text("Just an answer");
+        assert_eq!(reasoning, "");
+        assert_eq!(answer, "Just an answer");
+    }
+
+    // ── parse_tool_calls ──
+
+    #[test]
+    fn parse_single_tool_call() {
+        let text = "I'll look that up.\n<tool_call>\n<function=get_weather>\n<parameter=city>Jakarta</parameter>\n</function>\n</tool_call>";
+        let (cleaned, calls) = parse_tool_calls(text);
+        assert_eq!(calls.len(), 1);
+        assert_eq!(calls[0].function.name, "get_weather");
+        assert!(calls[0].function.arguments.contains("Jakarta"));
+        assert!(!cleaned.contains("<tool_call>"));
+    }
+
+    #[test]
+    fn parse_multiple_tool_calls() {
+        let text = "<tool_call>\n<function=a>\n<parameter=x>1</parameter>\n</function>\n</tool_call>\n<tool_call>\n<function=b>\n<parameter=y>2</parameter>\n</function>\n</tool_call>";
+        let (_cleaned, calls) = parse_tool_calls(text);
+        assert_eq!(calls.len(), 2);
+        assert_eq!(calls[0].function.name, "a");
+        assert_eq!(calls[1].function.name, "b");
+    }
+
+    #[test]
+    fn parse_malformed_tool_call_returns_empty() {
+        let (cleaned, calls) = parse_tool_calls("no calls here");
+        assert!(calls.is_empty());
+        assert_eq!(cleaned, "no calls here");
+    }
+
+    // ── validate_model ──
+
+    #[test]
+    fn validate_model_accepts_served_id() {
+        assert!(validate_model(crate::config::MODEL_ID).is_ok());
+    }
+
+    #[test]
+    fn validate_model_rejects_unknown_id() {
+        assert!(validate_model("minicpm-v-4.6").is_err());
+    }
+
+    // ── build_sampler (smoke — no model required) ──
+
+    #[test]
+    fn build_sampler_constructs_for_common_params() {
+        let params = SamplerParams {
+            temperature: Some(0.8),
+            top_p: Some(0.9),
+            top_k: Some(40),
+            min_p: Some(0.05),
+            repeat_penalty: Some(1.1),
+            frequency_penalty: Some(0.0),
+            presence_penalty: Some(0.0),
+            seed: Some(42),
+        };
+        let _ = build_sampler(&params);
+
+        let greedy = SamplerParams {
+            temperature: Some(0.0),
+            ..params
+        };
+        let _ = build_sampler(&greedy);
+    }
+
+    // ── build_prompt ──
+
+    #[test]
+    fn build_prompt_renders_messages() {
+        let messages = vec![msg("system", "You are helpful."), msg("user", "Hi!")];
+        let prompt = build_prompt(&messages, &None).unwrap();
+        assert!(prompt.starts_with("<s>"));
+        assert!(prompt.contains("<|im_start|>system\nYou are helpful."));
+        assert!(prompt.contains("<|im_start|>user\nHi!"));
+        assert!(prompt.contains("<|im_start|>assistant\n<think>\n"));
+    }
+
+    #[test]
+    fn build_prompt_includes_tool_definitions() {
+        let messages = vec![msg("user", "What's the weather?")];
+        let prompt = build_prompt(&messages, &Some(vec![tool_def()])).unwrap();
+        assert!(prompt.contains("<tools>"));
+        assert!(prompt.contains("get_weather"));
+        assert!(prompt.contains("Tool usage guidelines"));
+    }
+
+    #[test]
+    fn build_prompt_wraps_tool_response_once() {
+        let messages = vec![msg("user", "Weather?"), msg("tool", "Sunny")];
+        let prompt = build_prompt(&messages, &None).unwrap();
+        assert_eq!(prompt.matches("<tool_response>").count(), 1);
+        assert_eq!(prompt.matches("</tool_response>").count(), 1);
+    }
+
+    #[test]
+    fn build_prompt_renders_assistant_tool_calls_history() {
+        let assistant = ChatMessage {
+            role: "assistant".into(),
+            content: Some("".into()),
+            tool_calls: Some(vec![ToolCallResponse {
+                id: "call_1".into(),
+                call_type: "function".into(),
+                function: ToolCallFunction {
+                    name: "get_weather".into(),
+                    arguments: r#"{"city":"Jakarta"}"#.into(),
+                },
+            }]),
+            tool_call_id: None,
+            name: None,
+        };
+        let prompt = build_prompt(&[assistant], &None).unwrap();
+        assert!(prompt.contains("<function=get_weather>"));
+        assert!(prompt.contains("<parameter=city>Jakarta</parameter>"));
+    }
+}

src/bootstrap/mod.rs

@@ -32,15 +32,12 @@ impl Application {
     pub async fn build() -> anyhow::Result<Self> {
         // Initialize tracing
         let env_filter = EnvFilter::new(&CONFIG.log_level);
-        tracing_subscriber::fmt()
-            .with_env_filter(env_filter)
-            .init();
+        tracing_subscriber::fmt().with_env_filter(env_filter).init();
         tracing::info!("🚀 LLM API starting up...");
 
         // Load model (fail-fast)
-        let engine = LlamaEngine::load().map_err(|e| {
-            anyhow::anyhow!("Failed to initialize LLM engine: {e}")
-        })?;
+        let engine = LlamaEngine::load()
+            .map_err(|e| anyhow::anyhow!("Failed to initialize LLM engine: {e}"))?;
         let engine = Arc::new(engine);
 
         let state = Arc::new(AppState { engine });
@@ -51,10 +48,7 @@ impl Application {
         // Bind listener
         let addr = format!("0.0.0.0:{}", CONFIG.server_port);
         let listener = TcpListener::bind(&addr).await?;
-        tracing::info!(
-            "Server listening on {}",
-            listener.local_addr()?
-        );
+        tracing::info!("Server listening on {}", listener.local_addr()?);
 
         Ok(Self {
             port: CONFIG.server_port,

src/config/mod.rs

@@ -4,8 +4,16 @@
 
 use std::sync::LazyLock;
 
-const DEFAULT_MODEL_PATH: &str = "/models/MiniCPM-V-4.6-Q4_K_M.gguf";
-pub const MODEL_ID: &str = "minicpm-v-4.6";
+const DEFAULT_MODEL_PATH: &str = "/models/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-Q8_0.gguf";
+pub const MODEL_ID: &str = "minicpm5-1b-fable5-v2-thinking";
+
+/// Read an env var as `T`, falling back to `default` on absence or parse error.
+fn env_or<T: std::str::FromStr>(key: &str, default: T) -> T {
+    std::env::var(key)
+        .ok()
+        .and_then(|v| v.parse().ok())
+        .unwrap_or(default)
+}
 
 /// Application configuration loaded at startup from environment variables.
 #[derive(Debug, Clone)]
@@ -42,11 +50,11 @@ impl AppConfig {
             server_port: std::env::var("SERVER_PORT")
                 .ok()
                 .and_then(|v| v.parse().ok())
-                .unwrap_or(8080),
+                .unwrap_or(4010),
             log_level: std::env::var("RUST_LOG").unwrap_or_else(|_| "info".to_string()),
-            n_ctx: 2048,
-            n_batch: 512,
-            n_threads: 4,
+            n_ctx: env_or("N_CTX", 8192),
+            n_batch: env_or("N_BATCH", 512),
+            n_threads: env_or("N_THREADS", 4),
         }
     }
 }

src/domain/entity/mod.rs

@@ -96,12 +96,15 @@ pub struct Choice {
 #[derive(Serialize)]
 pub struct ResponseMessage {
     pub role: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
     pub content: Option<String>,
     #[serde(skip_serializing_if = "Option::is_none")]
+    pub reasoning_content: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
     pub tool_calls: Option<Vec<ToolCall>>,
 }
 
-#[derive(Serialize)]
+#[derive(Serialize, Clone)]
 pub struct Usage {
     pub prompt_tokens: u32,
     pub completion_tokens: u32,
@@ -136,6 +139,36 @@ pub struct ToolCallResponse {
     pub function: ToolCallFunction,
 }
 
+// ═══════════════════════════════════════════════════════════════
+//  GENERATION FINISH REASON
+// ═══════════════════════════════════════════════════════════════
+
+/// Why a generation run stopped. Produced by the engine and rendered as the
+/// OpenAI `finish_reason` at the presentation layer.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum FinishReason {
+    /// Natural end-of-generation (EOS token) or a stop sequence matched.
+    Stop,
+    /// `max_tokens` exhausted.
+    Length,
+    /// A complete `<tool_call>` block was emitted.
+    ToolCalls,
+    /// Generation aborted early (e.g. client disconnected).
+    Aborted,
+}
+
+impl FinishReason {
+    /// Map to the OpenAI-compatible `finish_reason` string.
+    pub fn as_str(&self) -> &'static str {
+        match self {
+            FinishReason::Stop => "stop",
+            FinishReason::Length => "length",
+            FinishReason::ToolCalls => "tool_calls",
+            FinishReason::Aborted => "stop",
+        }
+    }
+}
+
 // ═══════════════════════════════════════════════════════════════
 //  SSE (STREAMING) TYPES
 // ═══════════════════════════════════════════════════════════════
@@ -148,6 +181,59 @@ pub struct SseChunk {
     pub created: i64,
     pub model: String,
     pub choices: Vec<SseChoice>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub usage: Option<Usage>,
+}
+
+impl SseChunk {
+    /// A delta chunk carrying partial reasoning/content/tool-call output.
+    pub fn delta(id: String, created: i64, model: String, delta: SseDelta) -> Self {
+        Self {
+            id,
+            object: "chat.completion.chunk".into(),
+            created,
+            model,
+            choices: vec![SseChoice {
+                index: 0,
+                delta,
+                finish_reason: None,
+            }],
+            usage: None,
+        }
+    }
+
+    /// The final chunk carrying the `finish_reason` (no token deltas).
+    pub fn finish(id: String, created: i64, model: String, finish_reason: &str) -> Self {
+        Self {
+            id,
+            object: "chat.completion.chunk".into(),
+            created,
+            model,
+            choices: vec![SseChoice {
+                index: 0,
+                delta: SseDelta {
+                    role: None,
+                    content: None,
+                    tool_calls: None,
+                    reasoning_content: None,
+                },
+                finish_reason: Some(finish_reason.into()),
+            }],
+            usage: None,
+        }
+    }
+
+    /// A trailing chunk with token usage and empty choices (OpenAI convention).
+    pub fn usage(id: String, created: i64, model: String, usage: Usage) -> Self {
+        Self {
+            id,
+            object: "chat.completion.chunk".into(),
+            created,
+            model,
+            choices: vec![],
+            usage: Some(usage),
+        }
+    }
 }
 
 #[derive(Serialize)]
@@ -165,6 +251,8 @@ pub struct SseDelta {
     #[serde(skip_serializing_if = "Option::is_none")]
     pub content: Option<String>,
     #[serde(skip_serializing_if = "Option::is_none")]
+    pub reasoning_content: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
     pub tool_calls: Option<Vec<ToolCall>>,
 }
 

src/infrastructure/llama/engine.rs

@@ -4,6 +4,7 @@
 //! lifetime transmute), tokenization, and generation.
 
 use std::num::NonZeroU32;
+use std::sync::Mutex;
 
 use llama_cpp_2::context::params::LlamaContextParams;
 use llama_cpp_2::context::LlamaContext;
@@ -14,10 +15,10 @@ use llama_cpp_2::model::{AddBos, LlamaModel};
 use llama_cpp_2::sampling::LlamaSampler;
 use llama_cpp_2::token::LlamaToken;
 use llama_cpp_2::TokenToStringError;
-use tokio::sync::Mutex;
 use tracing::info;
 
 use crate::config::CONFIG;
+use crate::domain::entity::FinishReason;
 use crate::domain::LlmError;
 
 // ── Thread-safe wrapper for raw llama.cpp context ──
@@ -95,17 +96,28 @@ impl CtxInner {
 
 // ── LlamaEngine ──
 
+/// Outcome of a generation run.
+pub struct GenerationOutcome {
+    /// Generated tokens (stop-sequence and EOG tokens are excluded).
+    pub tokens: Vec<LlamaToken>,
+    /// Accumulated decoded text (raw, before markup/special-token cleaning).
+    pub text: String,
+    /// Why generation stopped.
+    pub finish: FinishReason,
+}
+
 /// Safe interface to a llama.cpp model and inference context.
 ///
 /// All access to the underlying context is serialized through a `Mutex`,
 /// so only one generation can happen at a time. This is intentional —
-/// the model is designed for sequential inference.
+/// the model is designed for sequential inference. Generation is synchronous
+/// and must be invoked from the tokio blocking pool (`spawn_blocking`).
 pub struct LlamaEngine {
     /// The loaded model (read-only after load, safe to share).
     pub model: LlamaModel,
 
     /// The inference context (single-threaded access via Mutex).
-    pub ctx: Mutex<CtxInner>,
+    ctx: Mutex<CtxInner>,
 }
 
 impl LlamaEngine {
@@ -117,17 +129,17 @@ impl LlamaEngine {
     /// cannot be created.
     pub fn load() -> Result<Self, LlmError> {
         info!("Initializing llama backend...");
-        let backend = LlamaBackend::init().map_err(|e| {
-            LlmError::Model(format!("Backend init failed: {e}"))
-        })?;
+        let backend = LlamaBackend::init()
+            .map_err(|e| LlmError::Model(format!("Backend init failed: {e}")))?;
 
         // Backend must outlive model and context. We leak it to achieve 'static
         // lifetime since the engine lives for the program lifetime.
         let backend: &'static LlamaBackend = Box::leak(Box::new(backend));
 
         info!("Loading model: {}", CONFIG.model_path);
-        let model = LlamaModel::load_from_file(backend, &CONFIG.model_path, &LlamaModelParams::default())
-            .map_err(|e| LlmError::Model(format!("Failed to load model: {e}")))?;
+        let model =
+            LlamaModel::load_from_file(backend, &CONFIG.model_path, &LlamaModelParams::default())
+                .map_err(|e| LlmError::Model(format!("Failed to load model: {e}")))?;
         info!("  Vocab: {}", model.n_vocab());
         info!("  Params: {}", model.n_params());
         info!("  Layers: {}", model.n_layer());
@@ -156,9 +168,12 @@ impl LlamaEngine {
     }
 
     /// Tokenize a prompt string into tokens.
+    ///
+    /// `AddBos::Never`: the chat template already prepends the `<s>` BOS token,
+    /// so adding another here would produce a double BOS.
     pub fn tokenize(&self, prompt: &str) -> Result<Vec<LlamaToken>, LlmError> {
         self.model
-            .str_to_token(prompt, AddBos::Always)
+            .str_to_token(prompt, AddBos::Never)
             .map_err(|e| LlmError::Model(format!("Tokenization failed: {e}")))
     }
 
@@ -177,37 +192,28 @@ impl LlamaEngine {
         String::from_utf8(bytes).unwrap_or_default()
     }
 
-    /// Decode multiple tokens to a single string.
-    pub fn decode_tokens(&self, tokens: &[LlamaToken]) -> String {
-        let mut out = String::with_capacity(tokens.len() * 4);
-        for &token in tokens {
-            out.push_str(&self.decode_token(token));
-        }
-        out
-    }
-
-    /// Check if a token is an end-of-generation token.
-    pub fn is_eog(&self, token: LlamaToken) -> bool {
-        self.model.is_eog_token(token)
-    }
-
-    /// Return a reference to the context mutex for advanced operations.
-    pub fn ctx(&self) -> &Mutex<CtxInner> {
-        &self.ctx
-    }
-
-    /// Generate tokens (non-streaming) and return output tokens, cleaned text, and tool calls.
+    /// Generate tokens and invoke `on_token` for each one.
     ///
-    /// Locks the context mutex, prefill the prompt, then iterates sampling + decoding
-    /// until EOG, max_tokens, stop sequence, or tool call completion.
-    pub async fn generate(
+    /// Synchronous (CPU-bound) — call from `spawn_blocking`. Locks the context,
+    /// prefills the prompt, then iterates sampling + decoding until EOG,
+    /// `max_tokens`, a stop sequence, or a complete `<tool_call>` block.
+    ///
+    /// `on_token` is invoked for every generated token (after leading-EOG
+    /// skipping, before it is decoded into the KV cache) and may return `false`
+    /// to abort early (e.g. the streaming client disconnected).
+    pub fn generate(
         &self,
         input_tokens: &[LlamaToken],
-        sampler: &mut SendSampler,
+        sampler: &mut LlamaSampler,
         max_tokens: u32,
         stop: &[String],
-    ) -> Result<(Vec<LlamaToken>, String), LlmError> {
-        let mut inner = self.ctx.lock().await;
+        enable_tool_detection: bool,
+        on_token: &mut dyn FnMut(LlamaToken, &str) -> bool,
+    ) -> Result<GenerationOutcome, LlmError> {
+        let mut inner = self
+            .ctx
+            .lock()
+            .map_err(|_| LlmError::Internal("context mutex poisoned".into()))?;
         inner.clear();
         inner
             .prefill(input_tokens)
@@ -215,49 +221,66 @@ impl LlamaEngine {
 
         let mut output: Vec<LlamaToken> = Vec::new();
         let mut text_buf = String::new();
-        let mut stop_now = false;
+        let mut finish = FinishReason::Length;
 
         let mut current = inner.sample(sampler);
 
+        // Skip leading EOG tokens (like a stray <|im_end|> right after the prompt)
+        while output.is_empty() && self.model.is_eog_token(current) {
+            let pos = input_tokens.len() as i32 + output.len() as i32;
+            inner
+                .decode(current, pos)
+                .map_err(|e| LlmError::Model(format!("Decode: {e}")))?;
+            current = inner.sample(sampler);
+        }
+
         for _ in 0..max_tokens {
             if self.model.is_eog_token(current) {
+                finish = FinishReason::Stop;
+                break;
+            }
+
+            let piece = self.decode_token(current);
+
+            // Check stop sequences *before* committing, so the stop tokens never
+            // leak into the output text or the stream.
+            if stop
+                .iter()
+                .any(|s| !s.is_empty() && format!("{text_buf}{piece}").contains(s))
+            {
+                finish = FinishReason::Stop;
                 break;
             }
 
             let pos = input_tokens.len() as i32 + output.len() as i32;
             output.push(current);
-
-            let piece = self.decode_token(current);
             text_buf.push_str(&piece);
 
-            // Check stop sequences
-            for s in stop {
-                if text_buf.contains(s) {
-                    stop_now = true;
-                    break;
-                }
-            }
-            if stop_now {
-                break;
-            }
-
-            // Check for tool_call block completion
-            if text_buf.contains("<tool_call>") {
-                let close_count = text_buf.matches("</tool_call>").count();
-                let open_count = text_buf.matches("<tool_call>").count();
-                if open_count > 0 && close_count >= open_count {
+            // Complete <tool_call> block emitted?
+            if enable_tool_detection && text_buf.contains("<tool_call>") {
+                let open = text_buf.matches("<tool_call>").count();
+                let close = text_buf.matches("</tool_call>").count();
+                if close >= open {
+                    finish = FinishReason::ToolCalls;
                     break;
                 }
             }
 
-            if let Err(e) = inner.decode(current, pos) {
-                tracing::info!("  Decode error: {e}");
+            if !on_token(current, &piece) {
+                finish = FinishReason::Aborted;
                 break;
             }
 
+            inner
+                .decode(current, pos)
+                .map_err(|e| LlmError::Model(format!("Decode: {e}")))?;
             current = inner.sample(sampler);
         }
 
-        Ok((output, text_buf))
+        Ok(GenerationOutcome {
+            tokens: output,
+            text: text_buf,
+            finish,
+        })
     }
 }

src/presentation/handler/chat-ui/index.html

@@ -72,6 +72,13 @@
     display: flex; align-items: center; gap: 6px;
   }
   .msg .tool-call::before { content: '\1F527'; }
+  .msg .reasoning {
+    font-size: 12px; font-style: italic;
+    color: var(--text2);
+    white-space: pre-wrap; word-break: break-word;
+    border-bottom: 1px solid var(--border);
+    padding-bottom: 8px; margin-bottom: 8px;
+  }
   .msg.error {
     background: #2a1818; border-color: #4a2828; color: #f08080;
   }
@@ -168,7 +175,7 @@ async function send() {
       method: 'POST',
       headers: { 'Content-Type': 'application/json' },
       body: JSON.stringify({
-        model: '',
+        model: 'minicpm5-1b-fable5-v2-thinking',
         messages: messages.slice(-5),  // keep context window manageable
         stream: true,
         max_tokens: 1024,
@@ -186,8 +193,33 @@ async function send() {
     const decoder = new TextDecoder();
     let buffer = '';
     let full = '';
+    let reasoning = '';
     let toolCalls = null;
 
+    // Re-render the assistant bubble: reasoning (muted) above the answer.
+    function render() {
+      el.innerHTML = '';
+      if (reasoning) {
+        const r = document.createElement('div');
+        r.className = 'reasoning';
+        r.textContent = reasoning;
+        el.appendChild(r);
+      }
+      if (full) {
+        const c = document.createElement('div');
+        c.textContent = full;
+        el.appendChild(c);
+      }
+      if (toolCalls?.length) {
+        for (const tc of toolCalls) {
+          const t = document.createElement('div');
+          t.className = 'tool-call';
+          t.textContent = 'Calling tool: ' + (tc.function?.name || 'tool');
+          el.appendChild(t);
+        }
+      }
+    }
+
     while (true) {
       const { done, value } = await reader.read();
       if (done) break;
@@ -206,18 +238,20 @@ async function send() {
           const delta = chunk.choices?.[0]?.delta;
           const finish = chunk.choices?.[0]?.finish_reason;
 
+          if (delta?.reasoning_content) {
+            reasoning += delta.reasoning_content;
+            render();
+          }
           if (delta?.content) {
             full += delta.content;
-            el.textContent = full;
+            render();
           }
           if (delta?.tool_calls) {
             toolCalls = delta.tool_calls;
+            render();
           }
-          if (finish === 'tool_calls' && toolCalls) {
-            const t = document.createElement('div');
-            t.className = 'tool-call';
-            t.textContent = 'Calling tool: ' + toolCalls.map(tc => tc.function?.name).join(', ');
-            el.appendChild(t);
+          if (finish === 'tool_calls') {
+            render();
           }
         } catch (e) { /* skip malformed chunk */ }
       }

src/presentation/handler/chat.rs

@@ -1,11 +1,18 @@
 //! Chat completions endpoint — streaming and non-streaming.
+//!
+//! Both paths share the same synchronous generation core
+//! ([`LlamaEngine::generate`]), which runs on the tokio blocking pool via
+//! `spawn_blocking` so worker threads are not hogged by CPU-bound inference.
+//! The streaming path forwards each generated token into an SSE channel.
 
 use std::convert::Infallible;
 use std::sync::Arc;
 
 use axum::extract::State;
+use axum::http::HeaderMap;
 use axum::response::sse::{Event, KeepAlive, Sse};
-use axum::{Json, response::{IntoResponse, Response}};
+use axum::response::{IntoResponse, Response};
+use axum::Json;
 use chrono::Utc;
 use tokio::sync::mpsc;
 use tokio_stream::wrappers::ReceiverStream;
@@ -13,7 +20,7 @@ use tracing::info;
 
 use crate::application::chat;
 use crate::domain::entity::{
-    ChatRequest, ChatResponse, Choice, ResponseMessage, SseChunk, SseChoice, SseDelta, Usage,
+    ChatRequest, ChatResponse, Choice, FinishReason, ResponseMessage, SseChunk, SseDelta, Usage,
 };
 use crate::infrastructure::llama::SendSampler;
 use crate::presentation::error::AppError;
@@ -24,11 +31,14 @@ pub async fn chat_completions(
     State(state): State<Arc<AppState>>,
     Json(req): Json<ChatRequest>,
 ) -> Result<Response, AppError> {
+    // Strict model validation — reject unknown model ids up front.
+    chat::validate_model(&req.model).map_err(AppError::BadRequest)?;
+
     let max_tokens = req.max_tokens.unwrap_or(256).min(1024);
     let stop = req.stop.clone().unwrap_or_default();
-    let prompt = chat::build_prompt(&req.messages, &req.tools);
+    let prompt = chat::build_prompt(&req.messages, &req.tools).map_err(AppError::LlmError)?;
 
-    // Tokenize
+    // Tokenize (fast — keep on the async thread).
     let input_tokens = state
         .engine
         .tokenize(&prompt)
@@ -36,7 +46,7 @@ pub async fn chat_completions(
 
     let prompt_tokens = input_tokens.len() as u32;
     info!(
-        "  Chat: {} prompt tokens, max_tokens={}, tools={}",
+        "Chat: {} prompt tokens, max_tokens={}, tools={}",
         prompt_tokens,
         max_tokens,
         req.tools.as_ref().is_some_and(|t| !t.is_empty())
@@ -47,7 +57,7 @@ pub async fn chat_completions(
     } else {
         handle_non_streaming(state.clone(), req, max_tokens, stop, input_tokens).await?
     };
-    Ok(response.into_response())
+    Ok(response)
 }
 
 // ── Non-streaming path ──
@@ -64,24 +74,40 @@ async fn handle_non_streaming(
     let prompt_tokens = input_tokens.len() as u32;
     let has_tools = req.tools.as_ref().is_some_and(|t| !t.is_empty());
     let params = chat::SamplerParams::from_request(&req);
-    let mut sampler = SendSampler(chat::build_sampler(&params));
 
-    let (output_tokens, raw_text) = state
-        .engine
-        .generate(&input_tokens, &mut sampler, max_tokens, &stop)
-        .await?;
+    let engine = state.engine.clone();
+    let outcome = tokio::task::spawn_blocking(move || {
+        let mut sampler = SendSampler(chat::build_sampler(&params));
+        engine.generate(
+            &input_tokens,
+            &mut sampler,
+            max_tokens,
+            &stop,
+            has_tools,
+            &mut |_token, _piece| true,
+        )
+    })
+    .await
+    .map_err(|e| AppError::Internal(format!("Generation task panicked: {e}")))?
+    .map_err(AppError::from)?;
 
-    let (output_text, tool_calls) = chat::parse_tool_calls(&chat::clean_text(&raw_text));
-
-    let completion_tokens = output_tokens.len() as u32;
+    let completion_tokens = outcome.tokens.len() as u32;
     info!("  {} generated tokens", completion_tokens);
 
-    let finish_reason = if has_tools && !tool_calls.is_empty() {
-        "tool_calls"
-    } else if completion_tokens < max_tokens {
-        "stop"
+    let (reasoning, cleaned) = chat::clean_text(&outcome.text);
+    let (output_text, tool_calls) = chat::parse_tool_calls(&cleaned);
+
+    let finish_reason = match (outcome.finish, tool_calls.is_empty()) {
+        (FinishReason::ToolCalls, false) => "tool_calls",
+        // Model opened a <tool_call> but never completed it — don't claim a call.
+        (FinishReason::ToolCalls, true) => "stop",
+        (f, _) => f.as_str(),
+    };
+
+    let reasoning_opt = if reasoning.is_empty() {
+        None
     } else {
-        "length"
+        Some(reasoning)
     };
 
     Ok(Json(ChatResponse {
@@ -94,6 +120,7 @@ async fn handle_non_streaming(
             message: ResponseMessage {
                 role: "assistant".into(),
                 content: Some(output_text),
+                reasoning_content: reasoning_opt,
                 tool_calls: if tool_calls.is_empty() {
                     None
                 } else {
@@ -124,187 +151,191 @@ async fn handle_streaming(
     let created = Utc::now().timestamp();
     let has_tools = req.tools.as_ref().is_some_and(|t| !t.is_empty());
     let model_name = req.model.clone();
+    let prompt_tokens = input_tokens.len() as u32;
     let params = chat::SamplerParams::from_request(&req);
+
     let (tx, rx) = mpsc::channel::<Result<Event, Infallible>>(64);
 
-    tokio::spawn(async move {
-        // Role chunk
-        let role_chunk = serde_json::to_string(&SseChunk {
-            id: chat_id.clone(),
-            object: "chat.completion.chunk".into(),
-            created,
-            model: model_name.clone(),
-            choices: vec![SseChoice {
-                index: 0,
-                delta: SseDelta {
-                    role: Some("assistant".into()),
-                    content: None,
-                    tool_calls: None,
-                },
-                finish_reason: None,
-            }],
-        })
-        .unwrap();
-        if tx.send(Ok(Event::default().data(role_chunk))).await.is_err() {
-            return;
-        }
+    // First chunk: announce the assistant role.
+    let role_chunk = SseChunk::delta(
+        chat_id.clone(),
+        created,
+        model_name.clone(),
+        SseDelta {
+            role: Some("assistant".into()),
+            content: None,
+            tool_calls: None,
+            reasoning_content: None,
+        },
+    );
+    let role_event = serde_json::to_string(&role_chunk).unwrap();
+    let _ = tx.send(Ok(Event::default().data(role_event))).await;
 
-        // Build sampler
+    let engine = state.engine.clone();
+    tokio::task::spawn_blocking(move || {
         let mut sampler = SendSampler(chat::build_sampler(&params));
-
-        // Lock context
-        let mut inner = state.engine.ctx().lock().await;
-        inner.clear();
-        if let Err(e) = inner.prefill(&input_tokens) {
-            info!("  Prefill error: {e}");
-            return;
-        }
-
-        let mut count = 0u32;
         let mut text_buf = String::new();
-        let mut current = inner.sample(&mut sampler);
+        let mut sent_len: usize = 0;
+        // Byte offset in text_buf where the content phase begins (right after
+        // the first `</think>`); None while still thinking.
+        let mut content_start: Option<usize> = None;
 
-        loop {
-            if count >= max_tokens {
-                let chunk = serde_json::to_string(&SseChunk {
-                    id: chat_id.clone(),
-                    object: "chat.completion.chunk".into(),
-                    created,
-                    model: model_name.clone(),
-                    choices: vec![SseChoice {
-                        index: 0,
-                        delta: SseDelta {
-                            role: None,
-                            content: None,
-                            tool_calls: None,
-                        },
-                        finish_reason: Some("length".into()),
-                    }],
-                })
-                .unwrap();
-                let _ = tx.send(Ok(Event::default().data(chunk))).await;
-                break;
-            }
+        let outcome = engine.generate(
+            &input_tokens,
+            &mut sampler,
+            max_tokens,
+            &stop,
+            has_tools,
+            &mut |_token, piece| {
+                text_buf.push_str(piece);
 
-            if state.engine.is_eog(current) {
-                let reason = if has_tools && text_buf.contains("<tool_call>") {
-                    "tool_calls"
-                } else {
-                    "stop"
-                };
-                let chunk = serde_json::to_string(&SseChunk {
-                    id: chat_id.clone(),
-                    object: "chat.completion.chunk".into(),
-                    created,
-                    model: model_name.clone(),
-                    choices: vec![SseChoice {
-                        index: 0,
-                        delta: SseDelta {
-                            role: None,
-                            content: None,
-                            tool_calls: None,
-                        },
-                        finish_reason: Some(reason.into()),
-                    }],
-                })
-                .unwrap();
-                let _ = tx.send(Ok(Event::default().data(chunk))).await;
-                break;
-            }
-
-            let piece = state.engine.decode_token(current);
-            let content = chat::clean_text(&piece);
-
-            if !content.is_empty() {
-                let chunk = serde_json::to_string(&SseChunk {
-                    id: chat_id.clone(),
-                    object: "chat.completion.chunk".into(),
-                    created,
-                    model: model_name.clone(),
-                    choices: vec![SseChoice {
-                        index: 0,
-                        delta: SseDelta {
-                            role: None,
-                            content: Some(content.clone()),
-                            tool_calls: None,
-                        },
-                        finish_reason: None,
-                    }],
-                })
-                .unwrap();
-                if tx.send(Ok(Event::default().data(chunk))).await.is_err() {
-                    break;
+                // Robust boundary detection on the *full* buffer — a `</think>`
+                // tag may be split across tokens, which would defeat a search
+                // over the incremental fragment only.
+                if content_start.is_none() {
+                    if let Some(pos) = text_buf.find("</think>") {
+                        content_start = Some(pos + 8);
+                    }
                 }
-            }
 
-            text_buf.push_str(&piece);
-
-            // Check stop sequences
-            let mut stop_now = false;
-            for s in &stop {
-                if text_buf.contains(s) {
-                    stop_now = true;
-                    break;
+                let new_text = &text_buf[sent_len..];
+                if new_text.is_empty() {
+                    return true;
                 }
-            }
-            if stop_now {
-                let chunk = serde_json::to_string(&SseChunk {
-                    id: chat_id.clone(),
-                    object: "chat.completion.chunk".into(),
-                    created,
-                    model: model_name.clone(),
-                    choices: vec![SseChoice {
-                        index: 0,
-                        delta: SseDelta {
-                            role: None,
-                            content: None,
-                            tool_calls: None,
-                        },
-                        finish_reason: Some("stop".into()),
-                    }],
-                })
-                .unwrap();
-                let _ = tx.send(Ok(Event::default().data(chunk))).await;
-                break;
-            }
 
-            // Check tool call completeness
-            if has_tools && text_buf.contains("<tool_call>") {
-                let open = text_buf.matches("<tool_call>").count();
-                let close = text_buf.matches("</tool_call>").count();
-                if close >= open {
-                    let chunk = serde_json::to_string(&SseChunk {
-                        id: chat_id.clone(),
-                        object: "chat.completion.chunk".into(),
+                let (reasoning, content) =
+                    chat::split_stream_chunk(new_text, sent_len, content_start);
+
+                if let Some(reasoning) = reasoning {
+                    let chunk = SseChunk::delta(
+                        chat_id.clone(),
                         created,
-                        model: model_name.clone(),
-                        choices: vec![SseChoice {
-                            index: 0,
-                            delta: SseDelta {
+                        model_name.clone(),
+                        SseDelta {
+                            role: None,
+                            content: None,
+                            tool_calls: None,
+                            reasoning_content: Some(reasoning),
+                        },
+                    );
+                    let event = serde_json::to_string(&chunk).unwrap();
+                    if tx.blocking_send(Ok(Event::default().data(event))).is_err() {
+                        return false;
+                    }
+                }
+
+                if !content.is_empty() {
+                    let chunk = SseChunk::delta(
+                        chat_id.clone(),
+                        created,
+                        model_name.clone(),
+                        SseDelta {
+                            role: None,
+                            content: Some(content),
+                            tool_calls: None,
+                            reasoning_content: None,
+                        },
+                    );
+                    let event = serde_json::to_string(&chunk).unwrap();
+                    if tx.blocking_send(Ok(Event::default().data(event))).is_err() {
+                        return false;
+                    }
+                }
+
+                sent_len = text_buf.len();
+                true
+            },
+        );
+
+        match outcome {
+            Ok(outcome) => {
+                let completion_tokens = outcome.tokens.len() as u32;
+                let usage = Usage {
+                    prompt_tokens,
+                    completion_tokens,
+                    total_tokens: prompt_tokens + completion_tokens,
+                };
+
+                // If the model never emitted `</think>`, everything was streamed
+                // as reasoning_content. Flush it as content so the client always
+                // receives the response text.
+                if content_start.is_none() && !text_buf.is_empty() {
+                    let cleaned = chat::strip_markup(&text_buf);
+                    if !cleaned.is_empty() {
+                        let chunk = SseChunk::delta(
+                            chat_id.clone(),
+                            created,
+                            model_name.clone(),
+                            SseDelta {
+                                role: None,
+                                content: Some(cleaned),
+                                tool_calls: None,
+                                reasoning_content: None,
+                            },
+                        );
+                        let event = serde_json::to_string(&chunk).unwrap();
+                        let _ = tx.blocking_send(Ok(Event::default().data(event)));
+                    }
+                }
+
+                // Single-shot tool-calls delta (this model emits whole blocks).
+                let mut sent_tool_calls = false;
+                if outcome.finish == FinishReason::ToolCalls {
+                    let (_cleaned, calls) = chat::parse_tool_calls(&outcome.text);
+                    if !calls.is_empty() {
+                        sent_tool_calls = true;
+                        let chunk = SseChunk::delta(
+                            chat_id.clone(),
+                            created,
+                            model_name.clone(),
+                            SseDelta {
                                 role: None,
                                 content: None,
-                                tool_calls: None,
+                                tool_calls: Some(calls),
+                                reasoning_content: None,
                             },
-                            finish_reason: Some("tool_calls".into()),
-                        }],
-                    })
-                    .unwrap();
-                    let _ = tx.send(Ok(Event::default().data(chunk))).await;
-                    break;
+                        );
+                        let event = serde_json::to_string(&chunk).unwrap();
+                        let _ = tx.blocking_send(Ok(Event::default().data(event)));
+                    }
                 }
-            }
 
-            let pos = input_tokens.len() as i32 + count as i32;
-            if let Err(e) = inner.decode(current, pos) {
-                info!("  Decode error: {e}");
-                break;
+                let finish_reason = match (outcome.finish, sent_tool_calls) {
+                    (FinishReason::ToolCalls, true) => "tool_calls",
+                    (FinishReason::ToolCalls, false) => "stop",
+                    (f, _) => f.as_str(),
+                };
+
+                let finish_chunk =
+                    SseChunk::finish(chat_id.clone(), created, model_name.clone(), finish_reason);
+                let event = serde_json::to_string(&finish_chunk).unwrap();
+                let _ = tx.blocking_send(Ok(Event::default().data(event)));
+
+                let usage_chunk = SseChunk::usage(chat_id, created, model_name, usage);
+                let event = serde_json::to_string(&usage_chunk).unwrap();
+                let _ = tx.blocking_send(Ok(Event::default().data(event)));
+            }
+            Err(e) => {
+                // Surface the error instead of silently truncating the stream.
+                let error_body = serde_json::json!({
+                    "error": {
+                        "message": e.to_string(),
+                        "type": "server_error",
+                    }
+                });
+                let _ = tx.blocking_send(Ok(Event::default().data(error_body.to_string())));
             }
-            count += 1;
-            current = inner.sample(&mut sampler);
         }
+
+        // OpenAI-compatible terminator.
+        let _ = tx.blocking_send(Ok(Event::default().data("[DONE]")));
     });
 
     let stream = ReceiverStream::new(rx);
+    let mut headers = HeaderMap::new();
+    headers.insert("X-Accel-Buffering", "no".parse().unwrap());
+    headers.insert("Cache-Control", "no-cache".parse().unwrap());
+    headers.insert("Connection", "keep-alive".parse().unwrap());
     let sse = Sse::new(stream).keep_alive(KeepAlive::default());
-    Ok(sse.into_response())
+    Ok((headers, sse).into_response())
 }

src/presentation/handler/chat_ui.rs

@@ -9,7 +9,10 @@ const HTML: &str = include_str!("chat-ui/index.html");
 pub async fn chat_ui() -> Response {
     (
         StatusCode::OK,
-        [(header::CONTENT_TYPE, HeaderValue::from_static("text/html; charset=utf-8"))],
+        [(
+            header::CONTENT_TYPE,
+            HeaderValue::from_static("text/html; charset=utf-8"),
+        )],
         HTML,
     )
         .into_response()

src/presentation/handler/health.rs

@@ -8,6 +8,7 @@ use crate::domain::entity::HealthResponse;
 pub async fn health_check() -> Json<HealthResponse> {
     Json(HealthResponse {
         status: "ok".into(),
-        model: format!("{MODEL_ID}-q4_k_m"),
+        // Report the exact model id served by /v1/models (no extra suffix).
+        model: MODEL_ID.into(),
     })
 }

src/presentation/middleware/auth.rs

@@ -4,18 +4,18 @@
 //! Only applied to routes that require authentication.
 
 use axum::extract::Request;
-use axum::http::StatusCode;
 use axum::middleware::Next;
 use axum::response::Response;
 
 use crate::config::CONFIG;
+use crate::presentation::error::AppError;
 
 /// Middleware that validates the Bearer token in the Authorization header.
 ///
 /// If `API_KEY` is not set (empty), authentication is disabled and
 /// all requests pass through. If set, the middleware rejects requests
 /// without a matching token.
-pub async fn auth_middleware(request: Request, next: Next) -> Result<Response, (StatusCode, String)> {
+pub async fn auth_middleware(request: Request, next: Next) -> Result<Response, AppError> {
     let api_key = &CONFIG.api_key;
     if api_key.is_empty() {
         return Ok(next.run(request).await);
@@ -32,8 +32,6 @@ pub async fn auth_middleware(request: Request, next: Next) -> Result<Response, (
         return Ok(next.run(request).await);
     }
 
-    Err((
-        StatusCode::UNAUTHORIZED,
-        "{\"error\":\"unauthorized\",\"message\":\"Invalid API key\"}".into(),
-    ))
+    // AppError renders a JSON body with the correct Content-Type.
+    Err(AppError::Unauthorized)
 }