langchain.storage.encoder_backed.EncoderBackedStore¶
class langchain.storage.encoder_backed.EncoderBackedStore(store: BaseStore[str, Any], key_encoder: Callable[[K], str], value_serializer: Callable[[V], bytes], value_deserializer: Callable[[Any], V])[source]¶
Wraps a store with key and value encoders/decoders.
Examples that uses JSON for encoding/decoding:
import json
def key_encoder(key: int) -> str:
    return json.dumps(key)
def value_serializer(value: float) -> str:
    return json.dumps(value)
def value_deserializer(serialized_value: str) -> float:
    return json.loads(serialized_value)
# Create an instance of the abstract store
abstract_store = MyCustomStore()
# Create an instance of the encoder-backed store
store = EncoderBackedStore(
    store=abstract_store,
    key_encoder=key_encoder,
    value_serializer=value_serializer,
    value_deserializer=value_deserializer
)
# Use the encoder-backed store methods
store.mset([(1, 3.14), (2, 2.718)])
values = store.mget([1, 2])  # Retrieves [3.14, 2.718]
store.mdelete([1, 2])  # Deletes the keys 1 and 2
Initialize an EncodedStore.
Methods
__init__(store, key_encoder, ...)
Initialize an EncodedStore.
mdelete(keys)
Delete the given keys and their associated values.
mget(keys)
Get the values associated with the given keys.
mset(key_value_pairs)
Set the values for the given keys.
yield_keys(*[, prefix])
Get an iterator over keys that match the given prefix.