Tony20100/langchain_full huggingface dataset:
to False, the simularity_search method will only allow                              VECTOR_ONLY search strategy. :type use_full_text_search: bool, optional :param pool_size: Determines the number of active connections in                   the pool. Defaults to 5. :type pool_size: int, optional :param max_overflow: Determines the maximum number of connections                      allowed beyond the pool_size. Defaults to 10. :type max_overflow: int, optional :param timeout: Specifies the maximum wait time in seconds for                 establishing a connection. Defaults to 30. :type timeout: float, optional :param Additional optional arguments provide further customization over the: :param database connection: :param pure_python: Toggles the connector mode. If True,                     operates in pure Python mode. :type pure_python: bool, optional :param local_infile: Allows local file uploads. :type local_infile: bool, optional :param charset: Specifies the character set for string values. :type charset: str, optional :param ssl_key: Specifies the path of the file containing the SSL                 key. :type ssl_key: str, optional :param ssl_cert: Specifies the path of the file containing the SSL                  certificate. :type ssl_cert: str, optional :param ssl_ca: Specifies the path of the file containing the SSL
:param ssl_ca: Specifies the path of the file containing the SSL                certificate authority. :type ssl_ca: str, optional :param ssl_cipher: Sets the SSL cipher list. :type ssl_cipher: str, optional :param ssl_disabled: Disables SSL usage. :type ssl_disabled: bool, optional :param ssl_verify_cert: Verifies the server's certificate. Automatically enabled if ssl_ca is specified. :type ssl_verify_cert: bool, optional :param ssl_verify_identity: Verifies the server's identity. :type ssl_verify_identity: bool, optional :param conv: A dictionary of data conversion              functions. :type conv: dict[int, Callable], optional :param credential_type: Specifies the type of authentication to                         use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO. :type credential_type: str, optional :param autocommit: Enables autocommits. :type autocommit: bool, optional :param results_type: Determines the structure of the query results:                      tuples, namedtuples, dicts. :type results_type: str, optional :param results_format: Deprecated. This option has been renamed to                        results_type. :type results_format: str, optional.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter, ...])
Returns the most similar indexed documents to the query text.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
__init__(embedding: Embeddings, *, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, 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)[source]¶
Initialize with necessary components.
Parameters
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
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
EUCLIDEAN_DISTANCE: Computes the Euclidean distance betweentwo 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.
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 (Following arguments pertain to the connection) – 
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.
connection (database) – 
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.
the (Additional optional arguments provide further customization over) – 
connection – 
pure_python (bool, optional) – Toggles the connector mode. If True,
operates in pure Python mode.
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.
kwargs (Any) – 
Examples
Basic Usage:
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:
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
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:
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:
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:
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_images(uris: List[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]¶
Run images through the embeddings and add to the vectorstore.
Parameters
List[str] (uris) – 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.
uris (List[str]) – 
kwargs (Any) – 
Returns
empty list
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]¶
Add more texts to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
Parameters
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.
kwargs (Any) – 
Returns
empty list
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, 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[source]¶
Create a SingleStoreDB vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new table for the embeddings in SingleStoreDB.
Adds the documents to the newly created table.
This is intended to be a quick way to get started.
:param texts: List of texts to add to the vectorstore.
:type texts: List[str]
:param embedding: A text embedding model.
:type embedding: Embeddings
:param metadatas: Optional list of metadatas.
Defaults to None.
Parameters
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
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.
the (Additional optional arguments provide further customization over) – 
connection (database) – 
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.
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.
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]], optional) – 
kwargs (Any) – 
Return type
SingleStoreDB
Example
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_texts(
    texts,
    OpenAIEmbeddings(),
    host="username:password@localhost:3306/database"
)
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
similarity_search(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][source]¶
Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Parameters
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 andsearches by vector similarity. This option is only available if
use_full_text_search is True.
SearchStrategy.FILTER_BY_VECTOR: Filters by vector similarity andsearches 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 andvector similarity. This option is only available if
use_full_text_search is True and distance_strategy is DOT_PRODUCT.
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.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
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