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68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
# coding=utf-8
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"""
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@project: maxkb
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@Author：虎
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@file： embedding_config.py
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@date：2023/10/23 16:03
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@desc:
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"""
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import threading
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import time
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from common.cache.mem_cache import MemCache
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_lock = threading.Lock()
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class ModelManage:
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cache = MemCache('model', {})
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up_clear_time = time.time()
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@staticmethod
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def get_model(_id, get_model):
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model_instance = ModelManage.cache.get(_id)
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if model_instance is None:
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with _lock:
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model_instance = ModelManage.cache.get(_id)
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if model_instance is None:
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model_instance = get_model(_id)
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ModelManage.cache.set(_id, model_instance, timeout=60 * 60 * 8)
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else:
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if model_instance.is_cache_model():
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ModelManage.cache.touch(_id, timeout=60 * 60 * 8)
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else:
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model_instance = get_model(_id)
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ModelManage.cache.set(_id, model_instance, timeout=60 * 60 * 8)
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ModelManage.clear_timeout_cache()
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return model_instance
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@staticmethod
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def clear_timeout_cache():
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if time.time() - ModelManage.up_clear_time > 60 * 60:
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threading.Thread(target=lambda: ModelManage.cache.clear_timeout_data()).start()
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ModelManage.up_clear_time = time.time()
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@staticmethod
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def delete_key(_id):
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if ModelManage.cache.has_key(_id):
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ModelManage.cache.delete(_id)
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class VectorStore:
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from embedding.vector.pg_vector import PGVector
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from embedding.vector.base_vector import BaseVectorStore
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instance_map = {
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'pg_vector': PGVector,
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}
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instance = None
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@staticmethod
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def get_embedding_vector() -> BaseVectorStore:
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from embedding.vector.pg_vector import PGVector
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if VectorStore.instance is None:
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from smartdoc.const import CONFIG
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vector_store_class = VectorStore.instance_map.get(CONFIG.get("VECTOR_STORE_NAME"),
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PGVector)
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VectorStore.instance = vector_store_class()
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return VectorStore.instance
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