"""Module contains code for a cache backed embedder.
The cache backed embedder is a wrapper around an embedder that caches
embeddings in a key-value store. The cache is used to avoid recomputing
embeddings for the same text.
The text is hashed and the hash is used as the key in the cache.
"""
from __future__ import annotations
import hashlib
import json
import uuid
from functools import partial
from typing import Callable, List, Optional, Sequence, Union, cast
from langchain_core.embeddings import Embeddings
from langchain_core.stores import BaseStore, ByteStore
from langchain_core.utils.iter import batch_iterate
from langchain.storage.encoder_backed import EncoderBackedStore
NAMESPACE_UUID = uuid.UUID(int=1985)
def _hash_string_to_uuid(input_string: str) -> uuid.UUID:
"""Hash a string and returns the corresponding UUID."""
hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()
return uuid.uuid5(NAMESPACE_UUID, hash_value)
def _key_encoder(key: str, namespace: str) -> str:
"""Encode a key."""
return namespace + str(_hash_string_to_uuid(key))
def _create_key_encoder(namespace: str) -> Callable[[str], str]:
"""Create an encoder for a key."""
return partial(_key_encoder, namespace=namespace)
def _value_serializer(value: Sequence[float]) -> bytes:
"""Serialize a value."""
return json.dumps(value).encode()
def _value_deserializer(serialized_value: bytes) -> List[float]:
"""Deserialize a value."""
return cast(List[float], json.loads(serialized_value.decode()))
[docs]class CacheBackedEmbeddings(Embeddings):
"""Interface for caching results from embedding models.
The interface allows works with any store that implements
the abstract store interface accepting keys of type str and values of list of
floats.
If need be, the interface can be extended to accept other implementations
of the value serializer and deserializer, as well as the key encoder.
Note that by default only document embeddings are cached. To cache query
embeddings too, pass in a query_embedding_store to constructor.
Examples:
.. code-block: python
from langchain.embeddings import CacheBackedEmbeddings
from langchain.storage import LocalFileStore
from langchain_community.embeddings import OpenAIEmbeddings
store = LocalFileStore('./my_cache')
underlying_embedder = OpenAIEmbeddings()
embedder = CacheBackedEmbeddings.from_bytes_store(
underlying_embedder, store, namespace=underlying_embedder.model
)
# Embedding is computed and cached
embeddings = embedder.embed_documents(["hello", "goodbye"])
# Embeddings are retrieved from the cache, no computation is done
embeddings = embedder.embed_documents(["hello", "goodbye"])
"""
[docs] def __init__(
self,
underlying_embeddings: Embeddings,
document_embedding_store: BaseStore[str, List[float]],
*,
batch_size: Optional[int] = None,
query_embedding_store: Optional[BaseStore[str, List[float]]] = None,
) -> None:
"""Initialize the embedder.
Args:
underlying_embeddings: the embedder to use for computing embeddings.
document_embedding_store: The store to use for caching document embeddings.
batch_size: The number of documents to embed between store updates.
query_embedding_store: The store to use for caching query embeddings.
If None, query embeddings are not cached.
"""
super().__init__()
self.document_embedding_store = document_embedding_store
self.query_embedding_store = query_embedding_store
self.underlying_embeddings = underlying_embeddings
self.batch_size = batch_size
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of texts.
The method first checks the cache for the embeddings.
If the embeddings are not found, the method uses the underlying embedder
to embed the documents and stores the results in the cache.
Args:
texts: A list of texts to embed.
Returns:
A list of embeddings for the given texts.
"""
vectors: List[Union[List[float], None]] = self.document_embedding_store.mget(
texts
)
all_missing_indices: List[int] = [
i for i, vector in enumerate(vectors) if vector is None
]
for missing_indices in batch_iterate(self.batch_size, all_missing_indices):
missing_texts = [texts[i] for i in missing_indices]
missing_vectors = self.underlying_embeddings.embed_documents(missing_texts)
self.document_embedding_store.mset(
list(zip(missing_texts, missing_vectors))
)
for index, updated_vector in zip(missing_indices, missing_vectors):
vectors[index] = updated_vector
return cast(
List[List[float]], vectors
) # Nones should have been resolved by now
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of texts.
The method first checks the cache for the embeddings.
If the embeddings are not found, the method uses the underlying embedder
to embed the documents and stores the results in the cache.
Args:
texts: A list of texts to embed.
Returns:
A list of embeddings for the given texts.
"""
vectors: List[
Union[List[float], None]
] = await self.document_embedding_store.amget(texts)
all_missing_indices: List[int] = [
i for i, vector in enumerate(vectors) if vector is None
]
# batch_iterate supports None batch_size which returns all elements at once
# as a single batch.
for missing_indices in batch_iterate(self.batch_size, all_missing_indices):
missing_texts = [texts[i] for i in missing_indices]
missing_vectors = await self.underlying_embeddings.aembed_documents(
missing_texts
)
await self.document_embedding_store.amset(
list(zip(missing_texts, missing_vectors))
)
for index, updated_vector in zip(missing_indices, missing_vectors):
vectors[index] = updated_vector
return cast(
List[List[float]], vectors
) # Nones should have been resolved by now
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed query text.
By default, this method does not cache queries. To enable caching, set the
`cache_query` parameter to `True` when initializing the embedder.
Args:
text: The text to embed.
Returns:
The embedding for the given text.
"""
if not self.query_embedding_store:
return self.underlying_embeddings.embed_query(text)
(cached,) = self.query_embedding_store.mget([text])
if cached is not None:
return cached
vector = self.underlying_embeddings.embed_query(text)
self.query_embedding_store.mset([(text, vector)])
return vector
[docs] async def aembed_query(self, text: str) -> List[float]:
"""Embed query text.
By default, this method does not cache queries. To enable caching, set the
`cache_query` parameter to `True` when initializing the embedder.
Args:
text: The text to embed.
Returns:
The embedding for the given text.
"""
if not self.query_embedding_store:
return await self.underlying_embeddings.aembed_query(text)
(cached,) = await self.query_embedding_store.amget([text])
if cached is not None:
return cached
vector = await self.underlying_embeddings.aembed_query(text)
await self.query_embedding_store.amset([(text, vector)])
return vector
[docs] @classmethod
def from_bytes_store(
cls,
underlying_embeddings: Embeddings,
document_embedding_cache: ByteStore,
*,
namespace: str = "",
batch_size: Optional[int] = None,
query_embedding_cache: Union[bool, ByteStore] = False,
) -> CacheBackedEmbeddings:
"""On-ramp that adds the necessary serialization and encoding to the store.
Args:
underlying_embeddings: The embedder to use for embedding.
document_embedding_cache: The cache to use for storing document embeddings.
*,
namespace: The namespace to use for document cache.
This namespace is used to avoid collisions with other caches.
For example, set it to the name of the embedding model used.
batch_size: The number of documents to embed between store updates.
query_embedding_cache: The cache to use for storing query embeddings.
True to use the same cache as document embeddings.
False to not cache query embeddings.
"""
namespace = namespace
key_encoder = _create_key_encoder(namespace)
document_embedding_store = EncoderBackedStore[str, List[float]](
document_embedding_cache,
key_encoder,
_value_serializer,
_value_deserializer,
)
if query_embedding_cache is True:
query_embedding_store = document_embedding_store
elif query_embedding_cache is False:
query_embedding_store = None
else:
query_embedding_store = EncoderBackedStore[str, List[float]](
query_embedding_cache,
key_encoder,
_value_serializer,
_value_deserializer,
)
return cls(
underlying_embeddings,
document_embedding_store,
batch_size=batch_size,
query_embedding_store=query_embedding_store,
)
