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91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
from typing import Any, Optional
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_manager import ModelInstance
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from core.model_runtime.entities.message_entities import PromptMessage
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from core.model_runtime.entities.model_entities import ModelPropertyKey
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from core.prompt.entities.advanced_prompt_entities import MemoryConfig
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class PromptTransform:
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def _append_chat_histories(
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self,
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memory: TokenBufferMemory,
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memory_config: MemoryConfig,
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prompt_messages: list[PromptMessage],
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model_config: ModelConfigWithCredentialsEntity,
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) -> list[PromptMessage]:
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rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
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histories = self._get_history_messages_list_from_memory(memory, memory_config, rest_tokens)
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prompt_messages.extend(histories)
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return prompt_messages
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def _calculate_rest_token(
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self, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
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) -> int:
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rest_tokens = 2000
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model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
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if model_context_tokens:
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model_instance = ModelInstance(
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provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
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)
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curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
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max_tokens = 0
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for parameter_rule in model_config.model_schema.parameter_rules:
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if parameter_rule.name == "max_tokens" or (
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parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
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):
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max_tokens = (
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model_config.parameters.get(parameter_rule.name)
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or model_config.parameters.get(parameter_rule.use_template or "")
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) or 0
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rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
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rest_tokens = max(rest_tokens, 0)
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return rest_tokens
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def _get_history_messages_from_memory(
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self,
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memory: TokenBufferMemory,
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memory_config: MemoryConfig,
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max_token_limit: int,
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human_prefix: Optional[str] = None,
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ai_prefix: Optional[str] = None,
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) -> str:
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"""Get memory messages."""
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kwargs: dict[str, Any] = {"max_token_limit": max_token_limit}
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if human_prefix:
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kwargs["human_prefix"] = human_prefix
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if ai_prefix:
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kwargs["ai_prefix"] = ai_prefix
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if memory_config.window.enabled and memory_config.window.size is not None and memory_config.window.size > 0:
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kwargs["message_limit"] = memory_config.window.size
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return memory.get_history_prompt_text(**kwargs)
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def _get_history_messages_list_from_memory(
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self, memory: TokenBufferMemory, memory_config: MemoryConfig, max_token_limit: int
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) -> list[PromptMessage]:
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"""Get memory messages."""
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return list(
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memory.get_history_prompt_messages(
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max_token_limit=max_token_limit,
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message_limit=memory_config.window.size
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if (
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memory_config.window.enabled
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and memory_config.window.size is not None
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and memory_config.window.size > 0
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)
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else None,
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)
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)
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