langchain.vectorstores.cassandra.Cassandra

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

Wrapper around Apache Cassandra(R) for vector-store workloads.

To use it, you need a recent installation of the cassio library and a Cassandra cluster / Astra DB instance supporting vector capabilities.

Visit the cassio.org website for extensive quickstarts and code examples.

Example

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

embeddings = OpenAIEmbeddings()
session = ...             # create your Cassandra session object
keyspace = 'my_keyspace'  # the keyspace should exist already
table_name = 'my_vector_store'
vectorstore = Cassandra(embeddings, session, keyspace, table_name)

Attributes

embeddings

Access the query embedding object if available.

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.

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.

amax_marginal_relevance_search_by_vector(...)

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.

asimilarity_search_with_relevance_scores(query)

Return docs and relevance scores in the range [0, 1], asynchronously.

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance asynchronously.

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, filter])

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, filter])

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.

__init__(embedding: Embeddings, session: Session, keyspace: str, table_name: str, ttl_seconds: Optional[int] = None) None[source]
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][source]

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 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.

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

VectorStoreRetriever

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.

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.

clear() None[source]

Empty the collection.

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

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[source]
delete_collection() None[source]

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[source]

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[source]

Create a Cassandra vectorstore from raw texts.

No support for specifying text IDs

Returns

a Cassandra vectorstore.

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, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document][source]

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.

Return docs most similar to query.

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document][source]

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[str, str]] = None) List[Tuple[Document, float]][source]

Run similarity search with distance.

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None) List[Tuple[Document, float]][source]

Return docs most similar to embedding vector.

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, filter: Optional[Dict[str, str]] = None) List[Tuple[Document, float, str]][source]
similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None) List[Tuple[Document, float, str]][source]

Return docs most similar to embedding vector.

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.

Examples using Cassandra