langchain_community.vectorstores.singlestoredb
.SingleStoreDB¶
- class langchain_community.vectorstores.singlestoredb.SingleStoreDB(embedding: Embeddings, *, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = 'metadata', vector_field: str = 'vector', pool_size: int = 5, max_overflow: int = 10, timeout: float = 30, **kwargs: Any)[source]¶
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.
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
- 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.
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”.
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.
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:
from langchain_community.embeddings 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_community.embeddings 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:
from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import SingleStoreDB os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db' vectorstore = SingleStoreDB(OpenAIEmbeddings())
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(embedding, *[, distance_strategy, ...])Initialize with necessary components.
aadd_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
add_texts
(texts[, metadatas, embeddings])Add more texts to the vectorstore.
adelete
([ids])Delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
asimilarity_search
(query[, k])Return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score
(*args, **kwargs)Run similarity search with distance asynchronously.
delete
([ids])Delete by vector ID or other criteria.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas, ...])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. .. rubric:: Example.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
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.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k, filter])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', 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
- 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.
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”.
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.
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:
from langchain_community.embeddings 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_community.embeddings 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:
from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import SingleStoreDB os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db' vectorstore = SingleStoreDB(OpenAIEmbeddings())
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
- 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.
- add_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
- Returns
List of IDs of the added texts.
- 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.
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
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 – List of ids to delete.
**kwargs – 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.
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Return VectorStore initialized from texts and embeddings.
- async amax_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.
- async amax_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.
- as_retriever(**kwargs: Any) VectorStoreRetriever ¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
search_kwargs (Optional[Dict]) –
Keyword arguments to pass to the search function. Can include things like:
k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
- Returns
Retriever class for VectorStore.
- Return type
Examples:
# Retrieve more documents with higher diversity # Useful if your dataset has many similar documents docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25} ) # Fetch more documents for the MMR algorithm to consider # But only return the top 5 docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50} ) # Only retrieve documents that have a relevance score # Above a certain threshold docsearch.as_retriever( search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8} ) # Only get the single most similar document from the dataset docsearch.as_retriever(search_kwargs={'k': 1}) # Use a filter to only retrieve documents from a specific paper docsearch.as_retriever( search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}} )
- async asearch(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- 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], asynchronously.
0 is dissimilar, 1 is most similar.
- Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
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)
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]] ¶
Run similarity search with distance asynchronously.
- delete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] ¶
Delete by vector ID or other criteria.
- Parameters
ids – List of ids to delete.
**kwargs – 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.
- 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', 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. .. rubric:: Example
- 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 – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
- Returns
List of Documents selected by maximal marginal relevance.
- 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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
- Returns
List of Documents selected by maximal marginal relevance.
- search(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **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.
- Returns
A list of documents that are most similar to the query text.
- Return type
List[Document]
Examples
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
- Returns
List of Documents most similar to the query vector.
- similarity_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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
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)
- similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) List[Tuple[Document, float]] [source]¶
Return docs most similar to query. Uses cosine similarity.
- Parameters
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.
- Returns
List of Documents most similar to the query and score for each