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refactor(api): move llm quota deduction to app graph layer (#32786)

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-LAN-
2026-03-01 23:25:36 +08:00
committed by GitHub
parent fa4b8910c8
commit ef2b5d6107
16 changed files with 434 additions and 95 deletions
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14
api/core/plugin/backwards_invocation/model.py
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@ -2,6 +2,7 @@ import tempfile
from binascii import hexlify, unhexlify
from collections.abc import Generator
from core.app.llm import deduct_llm_quota
from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
from core.model_manager import ModelManager
from core.model_runtime.entities.llm_entities import (
@ -29,7 +30,6 @@ from core.plugin.entities.request import (
)
from core.tools.entities.tool_entities import ToolProviderType
from core.tools.utils.model_invocation_utils import ModelInvocationUtils
from core.workflow.nodes.llm import llm_utils
from models.account import Tenant
@ -63,16 +63,14 @@ class PluginModelBackwardsInvocation(BaseBackwardsInvocation):
def handle() -> Generator[LLMResultChunk, None, None]:
for chunk in response:
if chunk.delta.usage:
llm_utils.deduct_llm_quota(
tenant_id=tenant.id, model_instance=model_instance, usage=chunk.delta.usage
)
deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=chunk.delta.usage)
chunk.prompt_messages = []
yield chunk
return handle()
else:
if response.usage:
llm_utils.deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=response.usage)
deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=response.usage)
def handle_non_streaming(response: LLMResult) -> Generator[LLMResultChunk, None, None]:
yield LLMResultChunk(
@ -126,16 +124,14 @@ class PluginModelBackwardsInvocation(BaseBackwardsInvocation):
def handle() -> Generator[LLMResultChunkWithStructuredOutput, None, None]:
for chunk in response:
if chunk.delta.usage:
llm_utils.deduct_llm_quota(
tenant_id=tenant.id, model_instance=model_instance, usage=chunk.delta.usage
)
deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=chunk.delta.usage)
chunk.prompt_messages = []
yield chunk
return handle()
else:
if response.usage:
llm_utils.deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=response.usage)
deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=response.usage)
def handle_non_streaming(
response: LLMResultWithStructuredOutput,