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community: add SingleStoreDB semantic cache (#23218)

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This PR adds a `SingleStoreDBSemanticCache` class that implements a
cache based on SingleStoreDB vector store, integration tests, and a
notebook example.

Additionally, this PR contains minor changes to SingleStoreDB vector
store:
 - change add texts/documents methods to return a list of inserted ids
 - implement delete(ids) method to delete documents by list of ids
 - added drop() method to drop a correspondent database table
- updated integration tests to use and check functionality implemented
above


CC: @baskaryan, @hwchase17

---------

Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
This commit is contained in:
volodymyr-memsql
2024-07-05 16:26:06 +03:00
committed by GitHub
parent bb597b1286
commit a4eb6d0fb1
8 changed files with 423 additions and 31 deletions
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220
libs/community/langchain_community/cache.py
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@@ -58,6 +58,7 @@ from langchain_community.vectorstores.azure_cosmos_db import (
CosmosDBSimilarityType,
CosmosDBVectorSearchType,
)
from langchain_community.vectorstores.utils import DistanceStrategy
try:
from sqlalchemy.orm import declarative_base
@@ -84,6 +85,7 @@ from langchain_community.vectorstores import (
OpenSearchVectorSearch as OpenSearchVectorStore,
)
from langchain_community.vectorstores.redis import Redis as RedisVectorstore
from langchain_community.vectorstores.singlestoredb import SingleStoreDB
logger = logging.getLogger(__file__)
@@ -2373,3 +2375,221 @@ class OpenSearchSemanticCache(BaseCache):
if index_name in self._cache_dict:
self._cache_dict[index_name].delete_index(index_name=index_name)
del self._cache_dict[index_name]
class SingleStoreDBSemanticCache(BaseCache):
"""Cache that uses SingleStore DB as a backend"""
def __init__(
self,
embedding: Embeddings,
*,
cache_table_prefix: str = "cache_",
search_threshold: float = 0.2,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
cache_table_prefix (str, optional): Prefix for the cache table name.
Defaults to "cache_".
search_threshold (float, optional): The minimum similarity score for
a search result to be considered a match. Defaults to 0.2.
Following arguments pertrain to the SingleStoreDB vector store:
distance_strategy (DistanceStrategy, optional):
Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
two vectors. This metric considers the geometric distance in
the vector space, and might be more suitable for embeddings
that rely on spatial relationships. This metric is not
compatible with the WEIGHTED_SUM search strategy.
content_field (str, optional): Specifies the field to store the content.
Defaults to "content".
metadata_field (str, optional): Specifies the field to store metadata.
Defaults to "metadata".
vector_field (str, optional): Specifies the field to store the vector.
Defaults to "vector".
id_field (str, optional): Specifies the field to store the id.
Defaults to "id".
use_vector_index (bool, optional): Toggles the use of a vector index.
Works only with SingleStoreDB 8.5 or later. Defaults to False.
If set to True, vector_size parameter is required to be set to
a proper value.
vector_index_name (str, optional): Specifies the name of the vector index.
Defaults to empty. Will be ignored if use_vector_index is set to False.
vector_index_options (dict, optional): Specifies the options for
the vector index. Defaults to {}.
Will be ignored if use_vector_index is set to False. The options are:
index_type (str, optional): Specifies the type of the index.
Defaults to IVF_PQFS.
For more options, please refer to the SingleStoreDB documentation:
https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/
vector_size (int, optional): Specifies the size of the vector.
Defaults to 1536. Required if use_vector_index is set to True.
Should be set to the same value as the size of the vectors
stored in the vector_field.
Following arguments pertain to the connection pool:
pool_size (int, optional): Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertain to the database connection:
host (str, optional): Specifies the hostname, IP address, or URL for the
database connection. The default scheme is "mysql".
user (str, optional): Database username.
password (str, optional): Database password.
port (int, optional): Database port. Defaults to 3306 for non-HTTP
connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional): Database name.
Additional optional arguments provide further customization over the
database connection:
pure_python (bool, optional): Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file containing the SSL
certificate.
ssl_ca (str, optional): Specifies the path of the file containing the SSL
certificate authority.
ssl_cipher (str, optional): Sets the SSL cipher list.
ssl_disabled (bool, optional): Disables SSL usage.
ssl_verify_cert (bool, optional): Verifies the server's certificate.
Automatically enabled if ``ssl_ca`` is specified.
ssl_verify_identity (bool, optional): Verifies the server's identity.
conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
results_type (str, optional): Determines the structure of the query results:
tuples, namedtuples, dicts.
results_format (str, optional): Deprecated. This option has been renamed to
results_type.
Examples:
Basic Usage:
.. code-block:: python
import langchain
from langchain.cache import SingleStoreDBSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = SingleStoreDBSemanticCache(
embedding=OpenAIEmbeddings(),
host="https://user:password@127.0.0.1:3306/database"
)
Advanced Usage:
.. code-block:: python
import langchain
from langchain.cache import SingleStoreDBSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = = SingleStoreDBSemanticCache(
embeddings=OpenAIEmbeddings(),
use_vector_index=True,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
"""
self._cache_dict: Dict[str, SingleStoreDB] = {}
self.embedding = embedding
self.cache_table_prefix = cache_table_prefix
self.search_threshold = search_threshold
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
def _index_name(self, llm_string: str) -> str:
hashed_index = _hash(llm_string)
return f"{self.cache_table_prefix}{hashed_index}"
def _get_llm_cache(self, llm_string: str) -> SingleStoreDB:
index_name = self._index_name(llm_string)
# return vectorstore client for the specific llm string
if index_name not in self._cache_dict:
self._cache_dict[index_name] = SingleStoreDB(
embedding=self.embedding,
table_name=index_name,
**self.connection_kwargs,
)
return self._cache_dict[index_name]
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
llm_cache = self._get_llm_cache(llm_string)
generations: List = []
# Read from a Hash
results = llm_cache.similarity_search_with_score(
query=prompt,
k=1,
)
if results:
for document_score in results:
if (
document_score[1] > self.search_threshold
and llm_cache.distance_strategy == DistanceStrategy.DOT_PRODUCT
) or (
document_score[1] < self.search_threshold
and llm_cache.distance_strategy
== DistanceStrategy.EUCLIDEAN_DISTANCE
):
generations.extend(loads(document_score[0].metadata["return_val"]))
return generations if generations else None
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
for gen in return_val:
if not isinstance(gen, Generation):
raise ValueError(
"SingleStoreDBSemanticCache only supports caching of "
f"normal LLM generations, got {type(gen)}"
)
llm_cache = self._get_llm_cache(llm_string)
metadata = {
"llm_string": llm_string,
"prompt": prompt,
"return_val": dumps([g for g in return_val]),
}
llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
def clear(self, **kwargs: Any) -> None:
"""Clear semantic cache for a given llm_string."""
index_name = self._index_name(kwargs["llm_string"])
if index_name in self._cache_dict:
self._cache_dict[index_name].drop()
del self._cache_dict[index_name]