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refactor: consolidate LLM runtime model state on ModelInstance (#32746)

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Signed-off-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
-LAN-
2026-03-01 02:29:32 +08:00
committed by GitHub
parent 48d8667c4f
commit 962df17a15
20 changed files with 375 additions and 324 deletions
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62
api/core/prompt/prompt_transform.py
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@ -4,45 +4,83 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.model_runtime.entities.message_entities import PromptMessage
from core.model_runtime.entities.model_entities import ModelPropertyKey
from core.model_runtime.entities.model_entities import AIModelEntity, ModelPropertyKey
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
class PromptTransform:
def _resolve_model_runtime(
self,
*,
model_config: ModelConfigWithCredentialsEntity | None = None,
model_instance: ModelInstance | None = None,
) -> tuple[ModelInstance, AIModelEntity]:
if model_instance is None:
if model_config is None:
raise ValueError("Either model_config or model_instance must be provided.")
model_instance = ModelInstance(
provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
)
model_instance.credentials = model_config.credentials
model_instance.parameters = model_config.parameters
model_instance.stop = model_config.stop
model_schema = model_instance.model_type_instance.get_model_schema(
model=model_instance.model_name,
credentials=model_instance.credentials,
)
if model_schema is None:
if model_config is None:
raise ValueError("Model schema not found for the provided model instance.")
model_schema = model_config.model_schema
return model_instance, model_schema
def _append_chat_histories(
self,
memory: TokenBufferMemory,
memory_config: MemoryConfig,
prompt_messages: list[PromptMessage],
model_config: ModelConfigWithCredentialsEntity,
*,
model_config: ModelConfigWithCredentialsEntity | None = None,
model_instance: ModelInstance | None = None,
) -> list[PromptMessage]:
rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
rest_tokens = self._calculate_rest_token(
prompt_messages,
model_config=model_config,
model_instance=model_instance,
)
histories = self._get_history_messages_list_from_memory(memory, memory_config, rest_tokens)
prompt_messages.extend(histories)
return prompt_messages
def _calculate_rest_token(
self, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
self,
prompt_messages: list[PromptMessage],
*,
model_config: ModelConfigWithCredentialsEntity | None = None,
model_instance: ModelInstance | None = None,
) -> int:
model_instance, model_schema = self._resolve_model_runtime(
model_config=model_config,
model_instance=model_instance,
)
model_parameters = model_instance.parameters
rest_tokens = 2000
model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
model_context_tokens = model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
if model_context_tokens:
model_instance = ModelInstance(
provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
)
curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
max_tokens = 0
for parameter_rule in model_config.model_schema.parameter_rules:
for parameter_rule in model_schema.parameter_rules:
if parameter_rule.name == "max_tokens" or (
parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
):
max_tokens = (
model_config.parameters.get(parameter_rule.name)
or model_config.parameters.get(parameter_rule.use_template or "")
model_parameters.get(parameter_rule.name)
or model_parameters.get(parameter_rule.use_template or "")
) or 0
rest_tokens = model_context_tokens - max_tokens - curr_message_tokens