add KV-cache and move system promt to the config

config.yml

@@ -1,6 +1,13 @@
 # LLM backend: "local" or "lmstudio"
 llm:
   backend: local        # change to "lmstudio" to use LM Studio instead
+  max_cache_tokens: 4096  # max KV-cache size per session (tokens); 0 to disable caching
+
+  system_prompt: >-
+    You are a helpful voice assistant. Keep your responses concise and natural
+    for spoken conversation. Respond in 1-3 short sentences.
+    Do not use markdown, bullet points, code blocks, emojis, or any
+    formatting that doesn't work in speech.
 
   # Settings used only when backend = "lmstudio"
   lmstudio:

server/llm.py

@@ -1,32 +1,38 @@
+import copy
+import dataclasses
 import logging
 import threading
 from typing import AsyncIterator
 
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
 
 from server.audio_utils import split_sentences
 
 log = logging.getLogger(__name__)
 
+@dataclasses.dataclass
+class KVCacheState:
+    """Per-session KV-cache persisted across generate() calls."""
+    past_key_values: DynamicCache | None
+    cached_token_count: int
+    cached_messages: list[dict]  # snapshot of messages when cache was built
+
 
 class LLMEngine:
-    """Wraps Qwen3 for conversation generation."""
+    """Wraps Qwen3 for conversation generation with persistent KV-cache."""
 
-    SYSTEM_PROMPT = (
+    def __init__(self, model: AutoModelForCausalLM, tokenizer: AutoTokenizer, system_prompt: str,
-        "You are a helpful voice assistant. Keep your responses concise and natural "
+                 max_cache_tokens: int = 4096):
-        "for spoken conversation. Respond in 1-3 short sentences. "
-        "Do not use markdown, bullet points, code blocks, emojis, or any "
-        "formatting that doesn't work in speech."
-    )
-
-    def __init__(self, model: AutoModelForCausalLM, tokenizer: AutoTokenizer):
         self.model = model
         self.tokenizer = tokenizer
+        self.system_prompt = system_prompt
+        self.max_cache_tokens = max_cache_tokens
+        self._generate_lock = threading.Lock()
 
     def _build_inputs(self, messages: list[dict]):
         """Build input token ids using the model's chat template."""
-        chat_messages = [{"role": "system", "content": self.SYSTEM_PROMPT}]
+        chat_messages = [{"role": "system", "content": self.system_prompt}]
         for msg in messages:
             chat_messages.append({"role": msg["role"], "content": msg["content"]})
 
@@ -38,36 +44,93 @@ class LLMEngine:
         )
         return self.tokenizer(text, return_tensors="pt").to(self.model.device)
 
-    def generate(self, messages: list[dict], max_new_tokens: int = 256) -> str:
+    def _validate_cache(self, messages: list[dict], cache_state: KVCacheState | None) -> DynamicCache | None:
-        """Generate a complete response (blocking)."""
+        """Return past_key_values if the cache is valid for the given messages, else None."""
+        if cache_state is None or cache_state.past_key_values is None:
+            return None
+        if self.max_cache_tokens and cache_state.cached_token_count > self.max_cache_tokens:
+            log.info("KV-cache exceeds max size, discarding.")
+            return None
+        cached = cache_state.cached_messages
+        # The current messages must start with the cached messages as a prefix
+        if len(cached) > len(messages):
+            return None
+        for cached_msg, current_msg in zip(cached, messages):
+            if cached_msg["role"] != current_msg["role"] or cached_msg["content"] != current_msg["content"]:
+                return None
+        return cache_state.past_key_values
+
+    def generate(
+        self,
+        messages: list[dict],
+        max_new_tokens: int = 256,
+        cache_state: KVCacheState | None = None,
+    ) -> tuple[str, KVCacheState]:
+        """Generate a complete response (blocking). Returns (response, updated_cache_state)."""
+        with self._generate_lock:
             inputs = self._build_inputs(messages)
-        input_len = inputs["input_ids"].shape[1]
+            input_ids = inputs["input_ids"]
+            input_len = input_ids.shape[1]
+
+            past_kv = self._validate_cache(messages, cache_state)
+            cached_len = cache_state.cached_token_count if past_kv is not None else 0
+
+            log.info(
+                f"KV-cache: {cached_len}/{input_len} tokens cached, "
+                f"processing {input_len - cached_len} new tokens"
+            )
 
             with torch.no_grad():
-            output_ids = self.model.generate(
+                outputs = self.model.generate(
-                **inputs,
+                    input_ids=input_ids,
+                    attention_mask=inputs.get("attention_mask"),
+                    past_key_values=past_kv,
                     max_new_tokens=max_new_tokens,
                     temperature=0.7,
                     top_p=0.9,
                     do_sample=True,
                     repetition_penalty=1.2,
+                    return_dict_in_generate=True,
+                    use_cache=True,
                 )
 
             # Decode only the generated tokens (skip prompt)
-        new_ids = output_ids[0][input_len:]
+            new_ids = outputs.sequences[0][input_len:]
             response = self.tokenizer.decode(new_ids, skip_special_tokens=True).strip()
             log.info(f"LLM response: {response}")
-        return response
+
+            # Build updated cache state: messages now include the assistant response
+            new_messages = copy.deepcopy(messages) + [{"role": "assistant", "content": response}]
+            new_cache = KVCacheState(
+                past_key_values=outputs.past_key_values,
+                cached_token_count=outputs.sequences.shape[1],
+                cached_messages=new_messages,
+            )
+            return response, new_cache
+
+    def trim_cache(self, cache_state: KVCacheState, messages: list[dict]) -> KVCacheState | None:
+        """Trim cache to match the actual conversation history (e.g. after barge-in)."""
+        if cache_state is None or cache_state.past_key_values is None:
+            return None
+        inputs = self._build_inputs(messages)
+        target_len = inputs["input_ids"].shape[1]
+        if target_len >= cache_state.cached_token_count:
+            return cache_state
+        cache_state.past_key_values.crop(target_len)
+        cache_state.cached_token_count = target_len
+        cache_state.cached_messages = copy.deepcopy(messages)
+        return cache_state
 
     async def generate_sentences(
         self,
         messages: list[dict],
         cancel_event: threading.Event | None = None,
+        cache_state: KVCacheState | None = None,
     ) -> AsyncIterator[str]:
         """Generate response and yield it sentence by sentence for TTS pipelining."""
         import asyncio
 
-        response = await asyncio.to_thread(self.generate, messages)
+        response = await asyncio.to_thread(self.generate, messages, 256, cache_state)
 
         if cancel_event and cancel_event.is_set():
             return
@@ -83,25 +146,23 @@ class LLMEngine:
             yield remainder
 
 
-SYSTEM_PROMPT = (
-    "You are a helpful voice assistant. Keep your responses concise and natural "
-    "for spoken conversation. Respond in 1-3 short sentences. "
-    "Do not use markdown, bullet points, code blocks, emojis, or any "
-    "formatting that doesn't work in speech."
-)
-
-
 class LMStudioEngine:
     """LLM engine that delegates to an LM Studio server via its OpenAI-compatible API."""
 
-    def __init__(self, base_url: str, model: str):
+    def __init__(self, base_url: str, model: str, system_prompt: str):
         self.base_url = base_url.rstrip("/")
         self.model = model
+        self.system_prompt = system_prompt
 
-    def generate(self, messages: list[dict], max_new_tokens: int = 256) -> str:
+    def generate(
+        self,
+        messages: list[dict],
+        max_new_tokens: int = 256,
+        cache_state: KVCacheState | None = None,
+    ) -> tuple[str, None]:
         import requests
 
-        payload_messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+        payload_messages = [{"role": "system", "content": self.system_prompt}]
         payload_messages.extend(messages)
 
         body: dict = {
@@ -121,7 +182,7 @@ class LMStudioEngine:
         resp.raise_for_status()
         response = resp.json()["choices"][0]["message"]["content"].strip()
         log.info(f"LM Studio response: {response}")
-        return response
+        return response, None
 
     async def generate_sentences(
         self,

server/models.py

@@ -68,16 +68,18 @@ class ModelManager:
     def _load_llm(self):
         from server.config import config
 
-        backend = config.get("llm", {}).get("backend", "local")
+        llm_config = config.get("llm", {})
+        backend = llm_config.get("backend", "local")
+        system_prompt = llm_config.get("system_prompt", "You are a helpful assistant.")
 
         if backend == "lmstudio":
             from server.llm import LMStudioEngine
 
-            lms = config.get("llm", {}).get("lmstudio", {})
+            lms = llm_config.get("lmstudio", {})
             url = lms.get("url", "http://host.docker.internal:1234")
             model = lms.get("model", "") or ""
             log.info(f"Using LM Studio backend at {url} (model={model or 'server default'})")
-            self.llm_engine = LMStudioEngine(url, model)
+            self.llm_engine = LMStudioEngine(url, model, system_prompt)
         else:
             log.info("Loading Qwen3-4B (GPTQ 4-bit)...")
             from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -90,7 +92,8 @@ class ModelManager:
                 model_name,
                 device_map=device,
             )
-            self.llm_engine = LLMEngine(model, tokenizer)
+            max_cache_tokens = llm_config.get("max_cache_tokens", 4096)
+            self.llm_engine = LLMEngine(model, tokenizer, system_prompt, max_cache_tokens)
             log.info("Qwen3-4B-GPTQ-Int4 loaded (~2.5GB VRAM).")
 
     def _load_tts(self):

server/pipeline.py

@@ -6,6 +6,7 @@ import threading
 import numpy as np
 
 from server.audio_utils import float32_to_pcm_bytes
+from server.llm import KVCacheState
 from server.models import ModelManager
 from server.vad import StreamingVAD
 
@@ -27,6 +28,7 @@ class ConversationSession:
 
         self.vad: StreamingVAD = models.create_vad()
         self.conversation_history: list[dict] = []
+        self.kv_cache_state: KVCacheState | None = None
         self.cancel_event = threading.Event()
         self.is_responding = False
         self._response_task: asyncio.Task | None = None
@@ -38,6 +40,7 @@ class ConversationSession:
         self.cancel_event.set()
         if self._response_task and not self._response_task.done():
             self._response_task.cancel()
+        self.kv_cache_state = None
 
     async def handle_audio_chunk(self, chunk_16k: np.ndarray):
         utterance = self.vad.process_chunk(chunk_16k)
@@ -91,8 +94,8 @@ class ConversationSession:
         # LLM
         log.info(f"Conversation history ({len(self.conversation_history)} messages): "
                  + str([m['content'][:50] for m in self.conversation_history]))
-        response = await asyncio.to_thread(
+        response, self.kv_cache_state = await asyncio.to_thread(
-            self.models.llm_engine.generate, self.conversation_history
+            self.models.llm_engine.generate, self.conversation_history, 256, self.kv_cache_state
         )
 
         if self.cancel_event.is_set():
@@ -147,12 +150,18 @@ class ConversationSession:
         tts_thread.join(timeout=2.0)
 
         # Save only what was actually spoken
+        was_interrupted = spoken_text.strip() != response.strip()
         if spoken_text.strip():
             self.conversation_history.append(
                 {"role": "assistant", "content": spoken_text.strip()}
             )
+            if was_interrupted and self.kv_cache_state is not None:
+                self.kv_cache_state = self.models.llm_engine.trim_cache(
+                    self.kv_cache_state, self.conversation_history
+                )
         elif self.conversation_history and self.conversation_history[-1]["role"] == "user":
             self.conversation_history.pop()
+            self.kv_cache_state = None
 
         if not self.cancel_event.is_set():
             await self.send_json({"type": "response_text", "text": "", "final": True})