from __future__ import annotations
import json
import re
from enum import Enum
from typing import (
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
Type,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
from sqlalchemy.pool import QueuePool
from langchain_community.vectorstores.utils import DistanceStrategy
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.DOT_PRODUCT
ORDERING_DIRECTIVE: dict = {
DistanceStrategy.EUCLIDEAN_DISTANCE: "",
DistanceStrategy.DOT_PRODUCT: "DESC",
}
[docs]class SingleStoreDB(VectorStore):
"""`SingleStore DB` vector store.
The prerequisite for using this class is the installation of the ``singlestoredb``
Python package.
The SingleStoreDB vectorstore can be created by providing an embedding function and
the relevant parameters for the database connection, connection pool, and
optionally, the names of the table and the fields to use.
"""
class SearchStrategy(str, Enum):
"""Enumerator of the Search strategies for searching in the vectorstore."""
VECTOR_ONLY = "VECTOR_ONLY"
TEXT_ONLY = "TEXT_ONLY"
FILTER_BY_TEXT = "FILTER_BY_TEXT"
FILTER_BY_VECTOR = "FILTER_BY_VECTOR"
WEIGHTED_SUM = "WEIGHTED_SUM"
def _get_connection(self: SingleStoreDB) -> Any:
try:
import singlestoredb as s2
except ImportError:
raise ImportError(
"Could not import singlestoredb python package. "
"Please install it with `pip install singlestoredb`."
)
return s2.connect(**self.connection_kwargs)
[docs] def __init__(
self,
embedding: Embeddings,
*,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
id_field: str = "id",
use_vector_index: bool = False,
vector_index_name: str = "",
vector_index_options: Optional[dict] = None,
vector_size: int = 1536,
use_full_text_search: bool = False,
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
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.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
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.
use_full_text_search (bool, optional): Toggles the use a full-text index
on the document content. Defaults to False. If set to True, the table
will be created with a full-text index on the content field,
and the simularity_search method will all using TEXT_ONLY,
FILTER_BY_TEXT, FILTER_BY_VECTOR, and WIGHTED_SUM search strategies.
If set to False, the simularity_search method will only allow
VECTOR_ONLY search strategy.
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
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="https://user:password@127.0.0.1:3306/database"
)
Advanced Usage:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
Using environment variables:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(OpenAIEmbeddings())
Using vector index:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
use_vector_index=True,
)
Using full-text index:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
use_full_text_search=True,
)
"""
self.embedding = embedding
self.distance_strategy = distance_strategy
self.table_name = self._sanitize_input(table_name)
self.content_field = self._sanitize_input(content_field)
self.metadata_field = self._sanitize_input(metadata_field)
self.vector_field = self._sanitize_input(vector_field)
self.id_field = self._sanitize_input(id_field)
self.use_vector_index = bool(use_vector_index)
self.vector_index_name = self._sanitize_input(vector_index_name)
self.vector_index_options = dict(vector_index_options or {})
self.vector_index_options["metric_type"] = self.distance_strategy
self.vector_size = int(vector_size)
self.use_full_text_search = bool(use_full_text_search)
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
# Add program name and version to connection attributes.
if "conn_attrs" not in self.connection_kwargs:
self.connection_kwargs["conn_attrs"] = dict()
self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
self.connection_kwargs["conn_attrs"]["_connector_version"] = "2.1.0"
# Create connection pool.
self.connection_pool = QueuePool(
self._get_connection,
max_overflow=max_overflow,
pool_size=pool_size,
timeout=timeout,
)
self._create_table()
@property
def embeddings(self) -> Embeddings:
return self.embedding
def _sanitize_input(self, input_str: str) -> str:
# Remove characters that are not alphanumeric or underscores
return re.sub(r"[^a-zA-Z0-9_]", "", input_str)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
return self._max_inner_product_relevance_score_fn
def _create_table(self: SingleStoreDB) -> None:
"""Create table if it doesn't exist."""
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
full_text_index = ""
if self.use_full_text_search:
full_text_index = ", FULLTEXT({})".format(self.content_field)
if self.use_vector_index:
index_options = ""
if self.vector_index_options and len(self.vector_index_options) > 0:
index_options = "INDEX_OPTIONS '{}'".format(
json.dumps(self.vector_index_options)
)
cur.execute(
"""CREATE TABLE IF NOT EXISTS {}
({} BIGINT AUTO_INCREMENT PRIMARY KEY, {} LONGTEXT CHARACTER
SET utf8mb4 COLLATE utf8mb4_general_ci, {} VECTOR({}, F32)
NOT NULL, {} JSON, VECTOR INDEX {} ({}) {}{});""".format(
self.table_name,
self.id_field,
self.content_field,
self.vector_field,
self.vector_size,
self.metadata_field,
self.vector_index_name,
self.vector_field,
index_options,
full_text_index,
),
)
else:
cur.execute(
"""CREATE TABLE IF NOT EXISTS {}
({} BIGINT AUTO_INCREMENT PRIMARY KEY, {} LONGTEXT CHARACTER
SET utf8mb4 COLLATE utf8mb4_general_ci, {} BLOB, {} JSON{});
""".format(
self.table_name,
self.id_field,
self.content_field,
self.vector_field,
self.metadata_field,
full_text_index,
),
)
finally:
cur.close()
finally:
conn.close()
[docs] def add_images(
self,
uris: List[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
return_ids: bool = False,
**kwargs: Any,
) -> List[str]:
"""Run images through the embeddings and add to the vectorstore.
Args:
uris List[str]: File path to images.
Each URI will be added to the vectorstore as document content.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional): Optional pre-generated
embeddings. Defaults to None.
Returns:
List[str]: list of document ids added to the vectorstore
if return_ids is True. Otherwise, an empty list.
"""
# Set embeddings
if (
embeddings is None
and self.embedding is not None
and hasattr(self.embedding, "embed_image")
):
embeddings = self.embedding.embed_image(uris=uris)
return self.add_texts(
uris, metadatas, embeddings, return_ids=return_ids, **kwargs
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
return_ids: bool = False,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional): Optional pre-generated
embeddings. Defaults to None.
Returns:
List[str]: list of document ids added to the vectorstore
if return_ids is True. Otherwise, an empty list.
"""
ids: List[str] = []
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
# Write data to singlestore db
for i, text in enumerate(texts):
# Use provided values by default or fallback
metadata = metadatas[i] if metadatas else {}
embedding = (
embeddings[i]
if embeddings
else self.embedding.embed_documents([text])[0]
)
cur.execute(
"""INSERT INTO {}({}, {}, {})
VALUES (%s, JSON_ARRAY_PACK(%s), %s)""".format(
self.table_name,
self.content_field,
self.vector_field,
self.metadata_field,
),
(
text,
"[{}]".format(",".join(map(str, embedding))),
json.dumps(metadata),
),
)
if return_ids:
cur.execute("SELECT LAST_INSERT_ID();")
row = cur.fetchone()
if row:
ids.append(str(row[0]))
if self.use_vector_index or self.use_full_text_search:
cur.execute("OPTIMIZE TABLE {} FLUSH;".format(self.table_name))
finally:
cur.close()
finally:
conn.close()
return ids
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool | None:
"""Delete documents from the vectorstore.
Args:
ids (List[str], optional): List of document ids to delete.
If None, all documents will be deleted. Defaults to None.
Returns:
bool: True if deletion was successful, False otherwise.
"""
if ids is None:
return True
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute(
"DELETE FROM {} WHERE {} IN ({})".format(
self.table_name, self.id_field, ",".join(ids)
)
)
if self.use_vector_index or self.use_full_text_search:
cur.execute("OPTIMIZE TABLE {} FLUSH;".format(self.table_name))
finally:
cur.close()
finally:
conn.close()
return True
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
search_strategy: SearchStrategy = SearchStrategy.VECTOR_ONLY,
filter_threshold: float = 0,
text_weight: float = 0.5,
vector_weight: float = 0.5,
vector_select_count_multiplier: int = 10,
**kwargs: Any,
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filter (dict): A dictionary of metadata fields and values to filter by.
Default is None.
search_strategy (SearchStrategy): The search strategy to use.
Default is SearchStrategy.VECTOR_ONLY.
Available options are:
- SearchStrategy.VECTOR_ONLY: Searches only by vector similarity.
- SearchStrategy.TEXT_ONLY: Searches only by text similarity. This
option is only available if use_full_text_search is True.
- SearchStrategy.FILTER_BY_TEXT: Filters by text similarity and
searches by vector similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.FILTER_BY_VECTOR: Filters by vector similarity and
searches by text similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.WEIGHTED_SUM: Searches by a weighted sum of text and
vector similarity. This option is only available if
use_full_text_search is True and distance_strategy is DOT_PRODUCT.
filter_threshold (float): The threshold for filtering by text or vector
similarity. Default is 0. This option has effect only if search_strategy
is SearchStrategy.FILTER_BY_TEXT or SearchStrategy.FILTER_BY_VECTOR.
text_weight (float): The weight of text similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_weight (float): The weight of vector similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_select_count_multiplier (int): The multiplier for the number of
vectors to select when using the vector index. Default is 10.
This parameter has effect only if use_vector_index is True and
search_strategy is SearchStrategy.WEIGHTED_SUM or
SearchStrategy.FILTER_BY_TEXT.
The number of vectors selected will
be k * vector_select_count_multiplier.
This is needed due to the limitations of the vector index.
Returns:
List[Document]: A list of documents that are most similar to the query text.
Examples:
Basic Usage:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
results = s2.similarity_search("query text", 1,
{"metadata_field": "metadata_value"})
Different Search Strategies:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_TEXT,
filter_threshold=0.5)
Weighted Sum Search Strategy:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.WEIGHTED_SUM,
text_weight=0.3,
vector_weight=0.7)
"""
docs_and_scores = self.similarity_search_with_score(
query=query,
k=k,
filter=filter,
search_strategy=search_strategy,
filter_threshold=filter_threshold,
text_weight=text_weight,
vector_weight=vector_weight,
vector_select_count_multiplier=vector_select_count_multiplier,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
search_strategy: SearchStrategy = SearchStrategy.VECTOR_ONLY,
filter_threshold: float = 1,
text_weight: float = 0.5,
vector_weight: float = 0.5,
vector_select_count_multiplier: int = 10,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query. Uses cosine similarity.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: A dictionary of metadata fields and values to filter by.
Defaults to None.
search_strategy (SearchStrategy): The search strategy to use.
Default is SearchStrategy.VECTOR_ONLY.
Available options are:
- SearchStrategy.VECTOR_ONLY: Searches only by vector similarity.
- SearchStrategy.TEXT_ONLY: Searches only by text similarity. This
option is only available if use_full_text_search is True.
- SearchStrategy.FILTER_BY_TEXT: Filters by text similarity and
searches by vector similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.FILTER_BY_VECTOR: Filters by vector similarity and
searches by text similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.WEIGHTED_SUM: Searches by a weighted sum of text and
vector similarity. This option is only available if
use_full_text_search is True and distance_strategy is DOT_PRODUCT.
filter_threshold (float): The threshold for filtering by text or vector
similarity. Default is 0. This option has effect only if search_strategy
is SearchStrategy.FILTER_BY_TEXT or SearchStrategy.FILTER_BY_VECTOR.
text_weight (float): The weight of text similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_weight (float): The weight of vector similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_select_count_multiplier (int): The multiplier for the number of
vectors to select when using the vector index. Default is 10.
This parameter has effect only if use_vector_index is True and
search_strategy is SearchStrategy.WEIGHTED_SUM or
SearchStrategy.FILTER_BY_TEXT.
The number of vectors selected will
be k * vector_select_count_multiplier.
This is needed due to the limitations of the vector index.
Returns:
List of Documents most similar to the query and score for each
document.
Raises:
ValueError: If the search strategy is not supported with the
distance strategy.
Examples:
Basic Usage:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
results = s2.similarity_search_with_score("query text", 1,
{"metadata_field": "metadata_value"})
Different Search Strategies:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search_with_score("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
filter_threshold=0.5)
Weighted Sum Search Strategy:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search_with_score("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.WEIGHTED_SUM,
text_weight=0.3,
vector_weight=0.7)
"""
if (
search_strategy != SingleStoreDB.SearchStrategy.VECTOR_ONLY
and not self.use_full_text_search
):
raise ValueError(
"""Search strategy {} is not supported
when use_full_text_search is False""".format(search_strategy)
)
if (
search_strategy == SingleStoreDB.SearchStrategy.WEIGHTED_SUM
and self.distance_strategy != DistanceStrategy.DOT_PRODUCT
):
raise ValueError(
"Search strategy {} is not supported with distance strategy {}".format(
search_strategy, self.distance_strategy
)
)
# Creates embedding vector from user query
embedding = []
if search_strategy != SingleStoreDB.SearchStrategy.TEXT_ONLY:
embedding = self.embedding.embed_query(query)
self.embedding.embed_query(query)
conn = self.connection_pool.connect()
result = []
where_clause: str = ""
where_clause_values: List[Any] = []
if filter or search_strategy in [
SingleStoreDB.SearchStrategy.FILTER_BY_TEXT,
SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
]:
where_clause = "WHERE "
arguments = []
if search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_TEXT:
arguments.append(
"MATCH ({}) AGAINST (%s) > %s".format(self.content_field)
)
where_clause_values.append(query)
where_clause_values.append(float(filter_threshold))
if search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR:
condition = "{}({}, JSON_ARRAY_PACK(%s)) ".format(
self.distance_strategy.name
if isinstance(self.distance_strategy, DistanceStrategy)
else self.distance_strategy,
self.vector_field,
)
if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
condition += "< %s"
else:
condition += "> %s"
arguments.append(condition)
where_clause_values.append("[{}]".format(",".join(map(str, embedding))))
where_clause_values.append(float(filter_threshold))
def build_where_clause(
where_clause_values: List[Any],
sub_filter: dict,
prefix_args: Optional[List[str]] = None,
) -> None:
prefix_args = prefix_args or []
for key in sub_filter.keys():
if isinstance(sub_filter[key], dict):
build_where_clause(
where_clause_values, sub_filter[key], prefix_args + [key]
)
else:
arguments.append(
"JSON_EXTRACT_JSON({}, {}) = %s".format(
self.metadata_field,
", ".join(["%s"] * (len(prefix_args) + 1)),
)
)
where_clause_values += prefix_args + [key]
where_clause_values.append(json.dumps(sub_filter[key]))
if filter:
build_where_clause(where_clause_values, filter)
where_clause += " AND ".join(arguments)
try:
cur = conn.cursor()
try:
if (
search_strategy == SingleStoreDB.SearchStrategy.VECTOR_ONLY
or search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_TEXT
):
search_options = ""
if (
self.use_vector_index
and search_strategy
== SingleStoreDB.SearchStrategy.FILTER_BY_TEXT
):
search_options = "SEARCH_OPTIONS '{\"k\":%d}'" % (
k * vector_select_count_multiplier
)
cur.execute(
"""SELECT {}, {}, {}({}, JSON_ARRAY_PACK(%s)) as __score
FROM {} {} ORDER BY __score {}{} LIMIT %s""".format(
self.content_field,
self.metadata_field,
self.distance_strategy.name
if isinstance(self.distance_strategy, DistanceStrategy)
else self.distance_strategy,
self.vector_field,
self.table_name,
where_clause,
search_options,
ORDERING_DIRECTIVE[self.distance_strategy],
),
("[{}]".format(",".join(map(str, embedding))),)
+ tuple(where_clause_values)
+ (k,),
)
elif (
search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR
or search_strategy == SingleStoreDB.SearchStrategy.TEXT_ONLY
):
cur.execute(
"""SELECT {}, {}, MATCH ({}) AGAINST (%s) as __score
FROM {} {} ORDER BY __score DESC LIMIT %s""".format(
self.content_field,
self.metadata_field,
self.content_field,
self.table_name,
where_clause,
),
(query,) + tuple(where_clause_values) + (k,),
)
elif search_strategy == SingleStoreDB.SearchStrategy.WEIGHTED_SUM:
cur.execute(
"""SELECT {}, {}, __score1 * %s + __score2 * %s as __score
FROM (
SELECT {}, {}, {}, MATCH ({}) AGAINST (%s) as __score1
FROM {} {}) r1 FULL OUTER JOIN (
SELECT {}, {}({}, JSON_ARRAY_PACK(%s)) as __score2
FROM {} {} ORDER BY __score2 {} LIMIT %s
) r2 ON r1.{} = r2.{} ORDER BY __score {} LIMIT %s""".format(
self.content_field,
self.metadata_field,
self.id_field,
self.content_field,
self.metadata_field,
self.content_field,
self.table_name,
where_clause,
self.id_field,
self.distance_strategy.name
if isinstance(self.distance_strategy, DistanceStrategy)
else self.distance_strategy,
self.vector_field,
self.table_name,
where_clause,
ORDERING_DIRECTIVE[self.distance_strategy],
self.id_field,
self.id_field,
ORDERING_DIRECTIVE[self.distance_strategy],
),
(text_weight, vector_weight, query)
+ tuple(where_clause_values)
+ ("[{}]".format(",".join(map(str, embedding))),)
+ tuple(where_clause_values)
+ (k * vector_select_count_multiplier, k),
)
else:
raise ValueError(
"Invalid search strategy: {}".format(search_strategy)
)
for row in cur.fetchall():
doc = Document(page_content=row[0], metadata=row[1])
result.append((doc, float(row[2])))
finally:
cur.close()
finally:
conn.close()
return result
[docs] @classmethod
def from_texts(
cls: Type[SingleStoreDB],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
id_field: str = "id",
use_vector_index: bool = False,
vector_index_name: str = "",
vector_index_options: Optional[dict] = None,
vector_size: int = 1536,
use_full_text_search: bool = False,
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
) -> SingleStoreDB:
"""Create a SingleStoreDB vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new table for the embeddings in SingleStoreDB.
3. Adds the documents to the newly created table.
This is intended to be a quick way to get started.
Args:
texts (List[str]): List of texts to add to the vectorstore.
embedding (Embeddings): A text embedding model.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
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.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
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.
use_full_text_search (bool, optional): Toggles the use a full-text index
on the document content. Defaults to False. If set to True, the table
will be created with a full-text index on the content field,
and the simularity_search method will all using TEXT_ONLY,
FILTER_BY_TEXT, FILTER_BY_VECTOR, and WIGHTED_SUM search strategies.
If set to False, the simularity_search method will only allow
VECTOR_ONLY search strategy.
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.
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.
Example:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_texts(
texts,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
"""
instance = cls(
embedding,
distance_strategy=distance_strategy,
table_name=table_name,
content_field=content_field,
metadata_field=metadata_field,
vector_field=vector_field,
id_field=id_field,
pool_size=pool_size,
max_overflow=max_overflow,
timeout=timeout,
use_vector_index=use_vector_index,
vector_index_name=vector_index_name,
vector_index_options=vector_index_options,
vector_size=vector_size,
use_full_text_search=use_full_text_search,
**kwargs,
)
instance.add_texts(texts, metadatas, embedding.embed_documents(texts), **kwargs)
return instance
[docs] def drop(self) -> None:
"""Drop the table and delete all data from the vectorstore.
Vector store will be unusable after this operation.
"""
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute("DROP TABLE IF EXISTS {}".format(self.table_name))
finally:
cur.close()
finally:
conn.close()
# SingleStoreDBRetriever is not needed, but we keep it for backwards compatibility
SingleStoreDBRetriever = VectorStoreRetriever
