dimitriev.gs/llm-agent-backend
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Add small fixes due to def voice llm model name

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dimitrievgs 2026-04-30 19:30:51 +03:00
parent d9dc09bca9
commit 19b034e4ea
3 changed files with 11 additions and 7 deletions
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7
app/heartbeat_monitor.py
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@ -191,9 +191,10 @@ class VoiceHeartbeatMonitor(threading.Thread):
print(f"🎤 New Voice Command detected: {cmd}") print(f"🎤 New Voice Command detected: {cmd}")
# ОТПРАВЛЯЕМ В LLM через TitleGenerator # ОТПРАВЛЯЕМ В LLM через TitleGenerator
prompt = f"{(s.get('systemPrompt') or '')}\n\n{(s.get('prompt_commands') or '')}"
self.generator.add_voice_task_to_queue( self.generator.add_voice_task_to_queue(
cmd, cmd,
s['prompt_commands'] # Это путь к .md файлу из настроек prompt
) )
self._processed_hashes.add(cmd_hash) self._processed_hashes.add(cmd_hash)
@ -204,5 +205,7 @@ class VoiceHeartbeatMonitor(threading.Thread):
raw_text = self.vs.get_last_chars(s['voice_context_limit']) raw_text = self.vs.get_last_chars(s['voice_context_limit'])
if not raw_text.strip(): return if not raw_text.strip(): return
prompt = f"{(s.get('systemPrompt') or '')}\n\n{(s.get('prompt_regular') or '')}"
# Добавим новый тип 'voice_regular' вTitleGenerator аналогично 'voice_command' # Добавим новый тип 'voice_regular' вTitleGenerator аналогично 'voice_command'
self.generator.add_voice_regular_task(raw_text, s['prompt_regular']) self.generator.add_voice_regular_task(raw_text, prompt)

2
app/nodes.py
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@ -143,8 +143,6 @@ def execute_command_node(state: AgentState) -> AgentState:
DEFAULT_LLM_NAME = "gemini-2.0-flash" #"gemini-2.5-flash" DEFAULT_LLM_NAME = "gemini-2.0-flash" #"gemini-2.5-flash"
main_llm = get_llm(DEFAULT_LLM_NAME) main_llm = get_llm(DEFAULT_LLM_NAME)
DEFAULT_SUMMARIZATION_LLM_NAME = "mistral-small-latest" # "mistral-small-latest" #"gemini-2.0-flash-r"
def call_llm_node(state: AgentState) -> AgentState: def call_llm_node(state: AgentState) -> AgentState:
""" """

9
app/title_generator.py
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@ -7,11 +7,13 @@ import sqlite3
import threading import threading
import time import time
from typing import Any, Dict, Optional from typing import Any, Dict, Optional
from nodes import DEFAULT_SUMMARIZATION_LLM_NAME
from llm_client import get_llm from llm_client import get_llm
from langchain_core.messages import SystemMessage, HumanMessage from langchain_core.messages import SystemMessage, HumanMessage
import os import os
DEFAULT_SUMMARIZATION_LLM_NAME = "mistral-small-latest" # "mistral-small-latest" #"gemini-2.0-flash-r"
DEFAULT_VOICE_LLM_NAME = "gemini-3.0-flash-openrouter"
MAX_TITLE_GENERATION_CONTENT_LENGTH = 5000 # Максимальное количество символов для генерации заголовков MAX_TITLE_GENERATION_CONTENT_LENGTH = 5000 # Максимальное количество символов для генерации заголовков
class TitleGenerator: class TitleGenerator:
@ -22,6 +24,7 @@ class TitleGenerator:
self._create_table() self._create_table()
self.history_manager = history_manager self.history_manager = history_manager
self.llm = get_llm(DEFAULT_SUMMARIZATION_LLM_NAME) self.llm = get_llm(DEFAULT_SUMMARIZATION_LLM_NAME)
self.voice_llm = get_llm(DEFAULT_VOICE_LLM_NAME)
self.running = False self.running = False
self.thread: Optional[threading.Thread] = None self.thread: Optional[threading.Thread] = None
@ -376,7 +379,7 @@ class TitleGenerator:
SystemMessage(content=prompt), SystemMessage(content=prompt),
HumanMessage(content=f"Последний фрагмент диалога для анализа: {text}") HumanMessage(content=f"Последний фрагмент диалога для анализа: {text}")
] ]
response = self.llm.invoke(llm_messages) response = self.voice_llm.invoke(llm_messages)
import app.api as api import app.api as api
api.voice_inst.responses["regular"] = response # Записываем в Response 1 api.voice_inst.responses["regular"] = response # Записываем в Response 1
@ -392,7 +395,7 @@ class TitleGenerator:
SystemMessage(content=prompt), SystemMessage(content=prompt),
HumanMessage(content=f"Выполни команду из транскрибации: {text}") HumanMessage(content=f"Выполни команду из транскрибации: {text}")
] ]
response = self.llm.invoke(llm_messages) response = self.voice_llm.invoke(llm_messages)
import app.api as api import app.api as api
# Форматируем для вкладки Response 2 # Форматируем для вкладки Response 2