mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-03 12:52:39 +02:00
fix(chat): enforce pinned model quota flow and reset stale pins
This commit is contained in:
Anish Sarkar
parent
41849fe10f
commit
835bd9f65d
2 changed files with 88 additions and 44 deletions
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@ -3,7 +3,7 @@ import logging
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from fastapi import APIRouter, Depends, HTTPException
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from langchain_core.messages import HumanMessage
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from pydantic import BaseModel as PydanticBaseModel
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from sqlalchemy import func
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from sqlalchemy import func, update
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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@ -15,6 +15,7 @@ from app.agents.new_chat.tools.update_memory import MEMORY_HARD_LIMIT, _save_mem
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from app.config import config
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from app.db import (
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ImageGenerationConfig,
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NewChatThread,
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NewLLMConfig,
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Permission,
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SearchSpace,
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@ -790,9 +791,31 @@ async def update_llm_preferences(
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# Update preferences
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update_data = preferences.model_dump(exclude_unset=True)
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previous_agent_llm_id = search_space.agent_llm_id
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for key, value in update_data.items():
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setattr(search_space, key, value)
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agent_llm_changed = (
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"agent_llm_id" in update_data
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and update_data["agent_llm_id"] != previous_agent_llm_id
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)
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if agent_llm_changed:
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await session.execute(
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update(NewChatThread)
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.where(NewChatThread.search_space_id == search_space_id)
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.values(
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pinned_llm_config_id=None,
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pinned_auto_mode=None,
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pinned_at=None,
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)
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)
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logger.info(
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"Cleared auto model pins for search_space_id=%s after agent_llm_id change (%s -> %s)",
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search_space_id,
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previous_agent_llm_id,
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update_data["agent_llm_id"],
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)
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await session.commit()
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await session.refresh(search_space)
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@ -56,6 +56,7 @@ from app.db import (
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shielded_async_session,
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)
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from app.prompts import TITLE_GENERATION_PROMPT
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from app.services.auto_model_pin_service import resolve_or_get_pinned_llm_config_id
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from app.services.chat_session_state_service import (
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clear_ai_responding,
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set_ai_responding,
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@ -1456,6 +1457,21 @@ async def stream_new_chat(
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agent_config: AgentConfig | None = None
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_t0 = time.perf_counter()
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try:
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llm_config_id = (
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await resolve_or_get_pinned_llm_config_id(
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session,
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thread_id=chat_id,
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search_space_id=search_space_id,
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user_id=user_id,
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selected_llm_config_id=llm_config_id,
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)
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).resolved_llm_config_id
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except ValueError as pin_error:
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yield streaming_service.format_error(str(pin_error))
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yield streaming_service.format_done()
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return
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if llm_config_id >= 0:
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# Positive ID: Load from NewLLMConfig database table
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agent_config = await load_agent_config(
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@ -1491,12 +1507,11 @@ async def stream_new_chat(
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llm_config_id,
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)
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# Premium quota reservation — applies to explicitly premium configs
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# AND Auto mode (which may route to premium models).
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# Premium quota reservation for pinned premium model only.
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_needs_premium_quota = (
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agent_config is not None
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and user_id
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and (agent_config.is_premium or agent_config.is_auto_mode)
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and agent_config.is_premium
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)
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if _needs_premium_quota:
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import uuid as _uuid
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@ -1519,16 +1534,18 @@ async def stream_new_chat(
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)
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_premium_reserved = reserve_amount
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if not quota_result.allowed:
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if agent_config.is_premium:
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yield streaming_service.format_error(
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"Premium token quota exceeded. Please purchase more tokens to continue using premium models."
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)
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yield streaming_service.format_done()
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return
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# Auto mode: quota exhausted but we can still proceed
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# (the router may pick a free model). Reset reservation.
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_premium_request_id = None
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_premium_reserved = 0
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logging.getLogger(__name__).info(
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"premium_quota_blocked_pinned_model thread_id=%s search_space_id=%s user_id=%s resolved_config_id=%s",
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chat_id,
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search_space_id,
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user_id,
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llm_config_id,
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)
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yield streaming_service.format_error(
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"Premium token quota exceeded for this pinned model. Select a free model or re-select Auto (Fastest) to repin."
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)
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yield streaming_service.format_done()
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return
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if not llm:
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yield streaming_service.format_error("Failed to create LLM instance")
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@ -1961,28 +1978,20 @@ async def stream_new_chat(
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)
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# Finalize premium quota with actual tokens.
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# For Auto mode, only count tokens from calls that used premium models.
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if _premium_request_id and user_id:
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try:
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from app.services.token_quota_service import TokenQuotaService
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if agent_config and agent_config.is_auto_mode:
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from app.services.llm_router_service import LLMRouterService
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actual_premium_tokens = LLMRouterService.compute_premium_tokens(
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accumulator.calls
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)
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else:
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actual_premium_tokens = accumulator.grand_total
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async with shielded_async_session() as quota_session:
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await TokenQuotaService.premium_finalize(
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db_session=quota_session,
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user_id=UUID(user_id),
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request_id=_premium_request_id,
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actual_tokens=actual_premium_tokens,
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actual_tokens=accumulator.grand_total,
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reserved_tokens=_premium_reserved,
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)
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_premium_request_id = None
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_premium_reserved = 0
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except Exception:
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logging.getLogger(__name__).warning(
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"Failed to finalize premium quota for user %s",
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@ -2175,6 +2184,21 @@ async def stream_resume_chat(
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agent_config: AgentConfig | None = None
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_t0 = time.perf_counter()
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try:
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llm_config_id = (
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await resolve_or_get_pinned_llm_config_id(
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session,
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thread_id=chat_id,
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search_space_id=search_space_id,
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user_id=user_id,
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selected_llm_config_id=llm_config_id,
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)
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).resolved_llm_config_id
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except ValueError as pin_error:
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yield streaming_service.format_error(str(pin_error))
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yield streaming_service.format_done()
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return
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if llm_config_id >= 0:
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agent_config = await load_agent_config(
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session=session,
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@ -2208,7 +2232,7 @@ async def stream_resume_chat(
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_resume_needs_premium = (
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agent_config is not None
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and user_id
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and (agent_config.is_premium or agent_config.is_auto_mode)
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and agent_config.is_premium
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)
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if _resume_needs_premium:
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import uuid as _uuid
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@ -2231,14 +2255,18 @@ async def stream_resume_chat(
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)
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_resume_premium_reserved = reserve_amount
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if not quota_result.allowed:
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if agent_config.is_premium:
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yield streaming_service.format_error(
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"Premium token quota exceeded. Please purchase more tokens to continue using premium models."
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)
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yield streaming_service.format_done()
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return
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_resume_premium_request_id = None
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_resume_premium_reserved = 0
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logging.getLogger(__name__).info(
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"premium_quota_blocked_pinned_model thread_id=%s search_space_id=%s user_id=%s resolved_config_id=%s",
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chat_id,
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search_space_id,
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user_id,
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llm_config_id,
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)
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yield streaming_service.format_error(
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"Premium token quota exceeded for this pinned model. Select a free model or re-select Auto (Fastest) to repin."
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)
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yield streaming_service.format_done()
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return
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if not llm:
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yield streaming_service.format_error("Failed to create LLM instance")
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@ -2370,23 +2398,16 @@ async def stream_resume_chat(
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try:
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from app.services.token_quota_service import TokenQuotaService
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if agent_config and agent_config.is_auto_mode:
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from app.services.llm_router_service import LLMRouterService
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actual_premium_tokens = LLMRouterService.compute_premium_tokens(
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accumulator.calls
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)
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else:
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actual_premium_tokens = accumulator.grand_total
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async with shielded_async_session() as quota_session:
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await TokenQuotaService.premium_finalize(
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db_session=quota_session,
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user_id=UUID(user_id),
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request_id=_resume_premium_request_id,
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actual_tokens=actual_premium_tokens,
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actual_tokens=accumulator.grand_total,
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reserved_tokens=_resume_premium_reserved,
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)
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_resume_premium_request_id = None
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_resume_premium_reserved = 0
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except Exception:
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logging.getLogger(__name__).warning(
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"Failed to finalize premium quota for user %s (resume)",
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