langchain.vectorstores.cassandra.Cassandra

class langchain.vectorstores.cassandra.Cassandra(embedding: Embeddings, session: Session, keyspace: str, table_name: str, ttl_seconds: Optional[int] = None)

Bases: VectorStore

Wrapper around Cassandra embeddings platform.

There is no notion of a default table name, since each embedding function implies its own vector dimension, which is part of the schema.

Example

from langchain.vectorstores import Cassandra
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
session = ...
keyspace = 'my_keyspace'
vectorstore = Cassandra(embeddings, session, keyspace, 'my_doc_archive')

Methods

__init__(embedding, session, keyspace, ...)
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, ids, ...])	Run more texts through the embeddings and add to the vectorstore.
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.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
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.
asimilarity_search_with_relevance_scores(query)	Return docs most similar to query.
clear()	Empty the collection.
delete([ids])	Delete by vector IDs.
delete_by_document_id(document_id)
delete_collection()	Just an alias for clear (to better align with other VectorStore implementations).
from_documents(documents, embedding[, ...])	Create a Cassandra vectorstore from a document list.
from_texts(texts, embedding[, metadatas, ...])	Create a Cassandra vectorstore from raw texts.
max_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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. Optional.
max_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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.
search(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
similarity_search(query[, k])	Return docs most similar to query.
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])	Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)	Return docs most similar to embedding vector.
similarity_search_with_score_id(query[, k])
similarity_search_with_score_id_by_vector(...)	Return docs most similar to embedding vector.

Attributes

embeddings

Access the query embedding object if available.

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, ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters

• texts (Iterable[str]) – Texts to add to the vectorstore.

• metadatas (Optional[List[dict]], optional) – Optional list of metadatas.

• ids (Optional[List[str]], optional) – Optional list of IDs.

• batch_size (int) – Number of concurrent requests to send to the server.

• ttl_seconds (Optional[int], optional) – Optional time-to-live for the added texts.

Returns

List of IDs of the added texts.

Return type

List[str]

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

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 most similar to query.

clear() → None

Empty the collection.

delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

Delete by vector IDs.

Parameters

ids – List of ids to delete.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

delete_by_document_id(document_id: str) → None

delete_collection() → None

Just an alias for clear (to better align with other VectorStore implementations).

classmethod from_documents(documents: List[Document], embedding: Embeddings, batch_size: int = 16, **kwargs: Any) → CVST

Create a Cassandra vectorstore from a document list.

No support for specifying text IDs

Returns

a Cassandra vectorstore.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, batch_size: int = 16, **kwargs: Any) → CVST

Create a Cassandra vectorstore from raw texts.

No support for specifying text IDs

Returns

a Cassandra vectorstore.

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. :param query: Text to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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. Optional.

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. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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.

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, **kwargs: Any) → List[Document]

Return docs most similar to query.

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) → List[Tuple[Document, float]]

Run similarity search with distance.

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float]]

Return docs most similar to embedding vector.

No support for filter query (on metadata) along with vector search.

Parameters

• embedding (str) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

Returns

List of (Document, score), the most similar to the query vector.

similarity_search_with_score_id(query: str, k: int = 4) → List[Tuple[Document, float, str]]

similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float, str]]

Return docs most similar to embedding vector.

No support for filter query (on metadata) along with vector search.

Parameters

• embedding (str) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

Returns

List of (Document, score, id), the most similar to the query vector.

property embeddings: langchain.embeddings.base.Embeddings

Access the query embedding object if available.

Examples using Cassandra

• Cassandra