Initial commit: audio-chat with fixes

- Created AGENTS.md with architecture documentation
- Fixed race conditions and async patterns
- Added conversation history to LLM prompts
- Fixed TTS audio shape handling
- Added buffer limits and graceful shutdown
- Fixed client.py with file sending support
- Removed duplicate requirements
- Added .gitignore

engine/llm.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+from config import Config
+import torch
+
+
+class LLMEngine:
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.config = Config()
+
+    def initialize(self):
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        dtype = torch.float16 if device == "cuda" else torch.float32
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.config.LLM_MODEL,
+            trust_remote_code=True,
+        )
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.config.LLM_MODEL,
+            torch_dtype=dtype,
+            device_map="auto",
+            trust_remote_code=True,
+        )
+
+    def generate(self, user_text: str, system_prompt: str = None) -> str:
+        if not self.model:
+            self.initialize()
+
+        if system_prompt is None:
+            system_prompt = "Ты полезный ассистент. Отвечай на русском языке кратко и по делу."
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_text},
+        ]
+
+        text = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+
+        inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=self.config.LLM_MAX_TOKENS,
+                temperature=self.config.LLM_TEMPERATURE,
+                do_sample=True,
+                top_p=0.9,
+                repetition_penalty=1.1,
+            )
+
+        generated = outputs[0][inputs["input_ids"].shape[1]:]
+        response = self.tokenizer.decode(generated, skip_special_tokens=True)
+
+        return response.strip()