40 lines
1.7 KiB
Python
40 lines
1.7 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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from mediator import SupabaseMediator
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import uuid
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from typing import List
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from langchain.schema import BaseMessage, Document
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from pydantic import BaseModel
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import sentry_sdk
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class Conversation:
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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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@sentry_sdk.trace
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def __init__(self, mediator: SupabaseMediator, user_id: str, conversation_id: str = str(uuid.uuid4()), location_id: str = "web"):
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self.mediator: SupabaseMediator = mediator
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self.user_id: str = user_id
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self.conversation_id: str = conversation_id
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self.location_id: str = location_id
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@sentry_sdk.trace
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def add_message(self, message_type: str, message: BaseMessage,) -> None:
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self.mediator.add_message(self.conversation_id, self.user_id, message_type, message)
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@sentry_sdk.trace
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def messages(self, message_type: str) -> List[BaseMessage]:
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return self.mediator.messages(self.conversation_id, self.user_id, message_type)
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# vector DB fn
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@sentry_sdk.trace
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def add_texts(self, texts: List[str]) -> None:
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metadatas = [{"conversation_id": self.conversation_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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@sentry_sdk.trace
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def similarity_search(self, query: str, match_count: int = 5) -> List[Document]:
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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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