66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
"""
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Below is an implementation of a basic LRUcache that utilizes the built
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in OrderedDict data structure.
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"""
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from collections import OrderedDict
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import uuid
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from typing import List, Dict
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class Session:
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"Wrapper Class for storing contexts between channels. Using an object to pass by reference avoid additional cache hits"
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def __init__(self, user_id: str, session_id: str = str(uuid.uuid4()), location_id: str = "web", metadata: Dict = {}):
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self.user_id: str = user_id
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self.session_id: str = session_id
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self.location_id: str = location_id
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self.metadata: Dict = metadata
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def add_message(self, message_type: str, message: str,) -> None:
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self.mediator.add_message(self.session_id, self.user_id, message_type, message)
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def messages(self, message_type: str) -> List[str]:
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return self.mediator.messages(self.session_id, self.user_id, message_type)
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def delete(self) -> None:
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self.mediator.delete_session(self.session_id)
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def restart(self) -> None:
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self.delete()
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representation = self.mediator.add_session(user_id=self.user_id, location_id=self.location_id)
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self.session_id: str = representation["id"]
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self.metadata = representation["metadata"]
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# vector DB fn
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def add_texts(self, texts: List[str]) -> None:
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metadatas = [{"session_id": self.session_id, "user_id": self.user_id} for _ in range(len(texts))]
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self.mediator.vector_table.add_texts(texts, metadatas)
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# vector DB fn
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def similarity_search(self, query: str, match_count: int = 5) :
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return self.mediator.vector_table.similarity_search(query=query, k=match_count, filter={"user_id": self.user_id})
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class LRUCache:
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def __init__(self, capacity: int):
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self.capacity = capacity
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self.cache = OrderedDict()
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def get(self, key: str):
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if key not in self.cache:
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return None
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# Move the accessed key to the end to indicate it was recently used
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self.cache.move_to_end(key)
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return self.cache[key]
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def put(self, key: str, value: Session):
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if key in self.cache:
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# If the key already exists, move it to the end and update the value
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self.cache.move_to_end(key)
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else:
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if len(self.cache) >= self.capacity:
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# If the cache is full, remove the least recently used key-value pair (the first item in the OrderedDict)
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self.cache.popitem(last=False)
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# Add or update the key-value pair at the end of the OrderedDict
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self.cache[key] = value
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