33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
from .lpmmconfig import global_config
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from .embedding_store import EmbeddingManager
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from .llm_client import LLMClient
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from .utils.dyn_topk import dyn_select_top_k
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class MemoryActiveManager:
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def __init__(
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self,
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embed_manager: EmbeddingManager,
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llm_client_embedding: LLMClient,
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):
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self.embed_manager = embed_manager
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self.embedding_client = llm_client_embedding
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def get_activation(self, question: str) -> float:
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"""获取记忆激活度"""
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# 生成问题的Embedding
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question_embedding = self.embedding_client.send_embedding_request("text-embedding", question)
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# 查询关系库中的相似度
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rel_search_res = self.embed_manager.relation_embedding_store.search_top_k(question_embedding, 10)
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# 动态过滤阈值
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rel_scores = dyn_select_top_k(rel_search_res, 0.5, 1.0)
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if rel_scores[0][1] < global_config["qa"]["params"]["relation_threshold"]:
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# 未找到相关关系
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return 0.0
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# 计算激活度
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activation = sum([item[2] for item in rel_scores]) * 10
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return activation
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