71 lines
2.2 KiB
Python
71 lines
2.2 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, Dict
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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__(
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self,
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mediator: SupabaseMediator,
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user_id: str,
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session_id: str = str(uuid.uuid4()),
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metadata: Dict = {},
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):
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self.mediator: SupabaseMediator = mediator
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self.user_id = user_id
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self.session_id = session_id
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self.metadata: Dict = metadata
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# @sentry_sdk.trace
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def add_message(
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self,
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message_type: str,
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message: BaseMessage,
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) -> None:
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self.mediator.add_message(self.session_id, message_type, message.content)
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# @sentry_sdk.trace
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def get_messages(self, message_type: str) -> List[BaseMessage]:
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return self.mediator.get_messages(self.session_id, message_type)
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# @sentry_sdk.trace
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def delete(self) -> None:
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self.mediator.delete_session(self.session_id)
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# @sentry_sdk.trace
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# def restart(self) -> None:
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# self.delete()
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# representation = self.mediator.add_session(
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# user_id=self.user_id, location_id=self.location_id
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# )
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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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# @sentry_sdk.trace
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def add_texts(self, texts: List[str]) -> None:
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metadatas = [
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{"session_id": self.session_id, "user_id": self.user_id}
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for _ in range(len(texts))
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]
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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(
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query=query, k=match_count, filter={"user_id": self.user_id}
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)
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