@ -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 ]
volodymyr-memsql
3 months ago
committed by
GitHub