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utils.py
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utils.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import contextlib
import json
from datetime import datetime
from typing import Any
import numpy as np
from redis.asyncio.client import Redis
from redis.commands.search.document import Document
from redis.commands.search.field import Field as RedisField
from redis.commands.search.field import NumericField, TagField, TextField, VectorField
from semantic_kernel.connectors.memory.azure_ai_search.const import DISTANCE_FUNCTION_MAP
from semantic_kernel.connectors.memory.redis.const import TYPE_MAPPER_VECTOR, RedisCollectionTypes
from semantic_kernel.data.vector_store_model_definition import VectorStoreRecordDefinition
from semantic_kernel.data.vector_store_record_fields import (
VectorStoreRecordDataField,
VectorStoreRecordKeyField,
VectorStoreRecordVectorField,
)
from semantic_kernel.memory.memory_record import MemoryRecord
def get_redis_key(collection_name: str, record_id: str) -> str: # pragma: no cover
"""Returns the Redis key for an element called record_id within collection_name.
Args:
collection_name (str): Name for a collection of embeddings
record_id (str): ID associated with a memory record
Returns:
str: Redis key in the format collection_name:id
"""
return f"{collection_name}:{record_id}"
def split_redis_key(redis_key: str) -> tuple[str, str]: # pragma: no cover
"""Split a Redis key into its collection name and record ID.
Args:
redis_key (str): Redis key
Returns:
tuple[str, str]: Tuple of the collection name and ID
"""
collection, record_id = redis_key.split(":")
return collection, record_id
def serialize_record_to_redis(record: MemoryRecord, vector_type: np.dtype) -> dict[str, Any]: # pragma: no cover