Add support of temperature and max tokens parameters

app/api.py

@@ -9,7 +9,7 @@ from flask import Flask, request, jsonify, Response, stream_with_context
 from flask_cors import CORS
 from flask_socketio import SocketIO
 from workflows import graph_history_manager, run_agent_streaming
-from llm_client import MODELS, get_llm  # Добавляем импорт списка моделей
+from llm_client import DEFAULT_TEMPERATURE, MODELS, get_llm  # Добавляем импорт списка моделей
 
 api = Flask(__name__)
 CORS(
@@ -229,6 +229,8 @@ def chat_stream():
     system_prompt = data.get("system_prompt")
     model = data.get("model")
     cache_folder = data.get("cache_folder")
+    temperature = data.get("temperature", DEFAULT_TEMPERATURE)
+    max_tokens = data.get("max_tokens")
     # Необязательный параметр для существующего узла ассистента при регенерации
     existing_assistant_node_id = data.get("existing_assistant_node_id")
 
@@ -257,7 +259,7 @@ def chat_stream():
                 for chunk_data in run_agent_streaming(graph_id, user_node_id,
                                                       assistant_node_id,
                                                       system_prompt, model,
-                                                      cache_folder):
+                                                      cache_folder, temperature, max_tokens):
                     if chunk_data.get("type") == "chunk":
                         accumulated_content += chunk_data.get("content", "")
                         yield f"data: {json.dumps(chunk_data)}\n\n"
@@ -328,6 +330,8 @@ def regenerate_message():
     model = data.get("model")
     system_prompt = data.get("system_prompt")
     cache_folder = data.get("cache_folder")
+    temperature = data.get("temperature", DEFAULT_TEMPERATURE)
+    max_tokens = data.get("max_tokens")
 
     if not graph_id or not node_id:
         return jsonify({"error": "Не указаны graph_id или node_id."}, 400)
@@ -362,7 +366,9 @@ def regenerate_message():
             "graph_id": graph_id,
             "model": model,
             "system_prompt": system_prompt,
-            "cache_folder": cache_folder
+            "cache_folder": cache_folder,
+            "temperature": temperature,
+            "max_tokens": max_tokens
         })
     except Exception as e:
         print(f"Ошибка при регенерации сообщения: {e}")

app/llm_client.py

@@ -3,7 +3,7 @@
 включая OpenAI (Gemini) и MistralAI, обеспечивая унифицированный интерфейс.
 """
 
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional
 from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
 
 from openai import OpenAI
@@ -14,6 +14,8 @@ from mistralai import Mistral
 # Пустой словарь для локальных моделей (Ollama)
 LOCAL_MODELS: Dict[str, Any] = {}
 
+DEFAULT_TEMPERATURE = 1.0
+
 # Конфигурация внешних моделей
 MODELS: Dict[str, Dict[str, Any]] = {
     "gemini-2.0-flash": {
@@ -215,7 +217,7 @@ class CustomLLM:
             print(f"Ошибка при вызове OpenAI API: {e}")
             raise
 
-    def invoke(self, messages: List[Any]) -> str:
+    def invoke(self, messages: List[Any], temperature: float = DEFAULT_TEMPERATURE, max_tokens: Optional[int] = None) -> str:
         """
         Отправляет запрос к LLM и возвращает ответ.
         Messages могут быть строкой или списком объектов Langchain Message.
@@ -252,10 +254,15 @@ class CustomLLM:
                         })
 
             try:
+                kwargs = {"temperature": temperature}
+                if max_tokens:
+                    kwargs["max_tokens"] = int(max_tokens)
+                    
                 response = self.client.chat.completions.create(
                     model=self.model_name,
                     messages=openai_messages,
                     stream=self.stream,
+                    **kwargs
                 )
 
                 if self.stream:
@@ -309,8 +316,13 @@ class CustomLLM:
                         })
 
             try:
+                kwargs = {"temperature": temperature}
+                if max_tokens:
+                    kwargs["max_tokens"] = int(max_tokens)
+                    
                 response = self.client.chat.stream(model=self.model_name,
-                                                   messages=mistralai_messages)
+                                                   messages=mistralai_messages,
+                                                   **kwargs)
 
                 if self.stream:
                     collected_messages = []
@@ -336,7 +348,7 @@ class CustomLLM:
                 raise
         return ""
 
-    def stream2(self, messages: List[Any]):
+    def stream2(self, messages: List[Any], temperature: float = DEFAULT_TEMPERATURE, max_tokens: Optional[int] = None):
         """
         Генератор для стриминга ответа от LLM.
         Возвращает чанки контента по мере их получения.
@@ -345,10 +357,15 @@ class CustomLLM:
             openai_messages = self._prepare_openai_messages(messages)
 
             try:
+                kwargs = {"temperature": temperature}
+                if max_tokens:
+                    kwargs["max_tokens"] = int(max_tokens)
+
                 response = self.client.chat.completions.create(
                     model=self.model_name,
                     messages=openai_messages,
                     stream=True,
+                    **kwargs
                 )
 
                 for chunk in response:
@@ -368,8 +385,13 @@ class CustomLLM:
             mistralai_messages = self._prepare_mistralai_messages(messages)
 
             try:
+                kwargs = {"temperature": temperature}
+                if max_tokens:
+                    kwargs["max_tokens"] = int(max_tokens)
+                    
                 response = self.client.chat.stream(model=self.model_name,
-                                                   messages=mistralai_messages)
+                                                   messages=mistralai_messages,
+                                                   **kwargs)
 
                 for chunk in response:
                     delta = chunk.data.choices[0].delta

app/title_generator.py

@@ -7,7 +7,7 @@ import sqlite3
 import threading
 import time
 from typing import Any, Dict, Optional
-from llm_client import get_llm
+from llm_client import DEFAULT_TEMPERATURE, get_llm
 from langchain_core.messages import SystemMessage, HumanMessage
 import uuid
 
@@ -17,6 +17,9 @@ DEFAULT_VOICE_LLM_NAME = "gemini-2.0-flash-lite" # "gemini-3.0-flash-openrouter"
 MAX_TITLE_GENERATION_CONTENT_LENGTH = 5000 # Максимальное количество символов для генерации заголовков
 QUEUE_POLLING_INTERVAL = 4
 
+TITLE_TEMPERATURE = 0.1
+MAX_TITLE_LENGTH = 100
+
 from app.voice_service import VOICE_COMMANDS_RESPONSE_TO_STORE
 
 class TitleGenerator:
@@ -203,7 +206,7 @@ class TitleGenerator:
                 HumanMessage(content=first_user_message)
             ]
             
-            title = self.llm.invoke(llm_messages)
+            title = self.llm.invoke(llm_messages, temperature=TITLE_TEMPERATURE, max_tokens=MAX_TITLE_LENGTH)
             title = title.strip()[:80]  # Ограничиваем длину
             
             # Сохраняем заголовок
@@ -257,7 +260,7 @@ class TitleGenerator:
                 HumanMessage(content=content)
             ]
             
-            title = self.llm.invoke(llm_messages)
+            title = self.llm.invoke(llm_messages, temperature=TITLE_TEMPERATURE, max_tokens=MAX_TITLE_LENGTH)
             title = title.strip()[:40]  # Ограничиваем длину
             
             # Сохраняем заголовок
@@ -379,12 +382,19 @@ class TitleGenerator:
     # 3. Добавьте метод обработки регулярной задачи
     def _process_voice_regular(self, text, prompt):
         try:
+            import app.api as api
+            settings = api.current_obsidian_settings
+            temp = float(settings.get('temperatureVoice', DEFAULT_TEMPERATURE))
+            max_t = settings.get('maxTokensVoiceRegular')
+            if max_t == "" or max_t == 0: max_t = None
+            elif max_t is not None: max_t = int(max_t)
+
             print(f"🎤 Анализ транскрибации (Prompt 1)...")
             llm_messages = [
                 SystemMessage(content=prompt),
                 HumanMessage(content=f"Последний фрагмент диалога для анализа: {text}")
             ]
-            response = self.voice_llm.invoke(llm_messages)
+            response = self.voice_llm.invoke(llm_messages, temperature=temp, max_tokens=max_t)
             
             import app.api as api
             new_entry = {
@@ -401,12 +411,19 @@ class TitleGenerator:
     # 4. Поправьте _process_voice_command (запись команды в Response 2)
     def _process_voice_command(self, text, prompt):
         try:
+            import app.api as api
+            settings = api.current_obsidian_settings
+            temp = float(settings.get('temperatureVoice', DEFAULT_TEMPERATURE))
+            max_t = settings.get('maxTokensVoiceCommands')
+            if max_t == "" or max_t == 0: max_t = None
+            elif max_t is not None: max_t = int(max_t)
+
             print(f"🎤 Обработка голосовой команды (Prompt 2)...")
             llm_messages = [
                 SystemMessage(content=prompt),
                 HumanMessage(content=f"Выполни команду из транскрибации: {text}")
             ]
-            response = self.voice_llm.invoke(llm_messages)
+            response = self.voice_llm.invoke(llm_messages, temperature=temp, max_tokens=max_t)
             
             import app.api as api
             # Форматируем как Markdown для рендеринга на фронте

app/workflows.py

@@ -10,7 +10,7 @@ from graph_history_manager import GraphHistoryManager
 from models import AgentState
 from nodes import parse_command_node, execute_command_node, call_llm_node, generate_images_node, analyze_image_node, get_meet_subtitles_node, get_teams_subtitles_node, summarize_history_node, handle_error_node, help_node
 import uuid  # Добавлено для генерации UUID
-from llm_client import get_llm
+from llm_client import DEFAULT_TEMPERATURE, get_llm
 
 import os
 import base64
@@ -100,7 +100,9 @@ def run_agent_streaming(graph_id: str,
                         assistant_node_id: str,
                         system_prompt: Optional[str] = None,
                         model: Optional[str] = None,
-                        cache_folder: Optional[str] = None):
+                        cache_folder: Optional[str] = None,
+                        temperature: float = DEFAULT_TEMPERATURE,
+                        max_tokens: Optional[int] = None):
     """Генератор для стримингового ответа LLM с поддержкой вложений."""
     try:
         loaded_graph_data = graph_history_manager.get_graph(
@@ -163,7 +165,7 @@ def run_agent_streaming(graph_id: str,
 
         # Стриминг ответа
         llm = get_llm(model or "gemini-2.5-flash")
-        for chunk in llm.stream2(messages_for_llm):
+        for chunk in llm.stream2(messages_for_llm, temperature=temperature, max_tokens=max_tokens):
             content = chunk.content if hasattr(chunk,
                                                'content') else str(chunk)
             yield {"type": "chunk", "content": content}