`langchain_community.vectorstores.cassandra`.Cassandra¶

class langchain_community.vectorstores.cassandra.Cassandra(embedding: Embeddings, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ttl_seconds: Optional[int] = None, *, body_index_options: Optional[List[Tuple[str, Any]]] = None, setup_mode: SetupMode = SetupMode.SYNC)[source]¶

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_community.vectorstores import Cassandra
from langchain_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)

Parameters

embedding (Embeddings) – Embedding function to use.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
setup_mode (SetupMode) – mode used to create the Cassandra table (SYNC, ASYNC or OFF).

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(embedding[, session, keyspace, ...])	Apache Cassandra(R) for vector-store workloads.
`aadd_documents`(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas, ids, ...])	Run more texts through the embeddings and add to the vectorstore.
`aclear`()	Empty the table.
`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 IDs.
`adelete_by_document_id`(document_id)	Delete by document ID.
`adelete_collection`()	Just an alias for aclear (to better align with other VectorStore implementations).
`afrom_documents`(documents, embedding, *[, ...])	Create a Cassandra vectorstore from a document list.
`afrom_texts`(texts, embedding[, metadatas, ...])	Create a Cassandra vectorstore from raw texts.
`amax_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. Defaults to 0.5. :param filter: Filter on the metadata to apply. :param body_search: Document textual search terms to apply. Only supported by Astra DB at the moment.
`amax_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. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :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. Defaults to 0.5. :param filter: Filter on the metadata to apply. :param body_search: Document textual search terms to apply. Only supported by Astra DB at the moment.
`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, filter, ...])	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`(query[, k, ...])	Return docs most similar to query.
`asimilarity_search_with_score_by_vector`(...)	Return docs most similar to embedding vector.
`asimilarity_search_with_score_id`(query[, k, ...])	Return docs most similar to query.
`asimilarity_search_with_score_id_by_vector`(...)	Return docs most similar to embedding vector.
`clear`()	Empty the table.
`delete`([ids])	Delete by vector IDs.
`delete_by_document_id`(document_id)	Delete by 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. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :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. Defaults to 0.5. :param filter: Filter on the metadata to apply. :param body_search: Document textual search terms to apply. Only supported by Astra DB at the moment.
`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. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :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. Defaults to 0.5. :param filter: Filter on the metadata to apply. :param body_search: Document textual search terms to apply. Only supported by Astra DB at the moment.
`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, ...])	Return docs most similar to query.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to embedding vector.
`similarity_search_with_score_id`(query[, k, ...])	Return docs most similar to query.
`similarity_search_with_score_id_by_vector`(...)	Return docs most similar to embedding vector.

__init__(embedding: Embeddings, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ttl_seconds: Optional[int] = None, *, body_index_options: Optional[List[Tuple[str, Any]]] = None, setup_mode: SetupMode = SetupMode.SYNC) → None[source]¶

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_community.vectorstores import Cassandra
from langchain_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)

Parameters

embedding (Embeddings) – Embedding function to use.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
setup_mode (SetupMode) – mode used to create the Cassandra table (SYNC, ASYNC or OFF).

Return type

None

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.
documents (List[Document]) –
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, ids: Optional[List[str]] = None, concurrency: 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 list of metadatas.
ids (Optional[List[str]]) – Optional list of IDs.
concurrency (int) – Number of concurrent queries to the database. Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

async aclear() → None[source]¶

Empty the table.

Return type: None

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.
documents (List[Document]) –
kwargs (Any) –

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 list of metadatas.
ids (Optional[List[str]]) – Optional list of IDs.
batch_size (int) – Number of concurrent requests to send to the server.
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶

Delete by vector IDs.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) –

Returns

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

Return type

Optional[bool]

async adelete_by_document_id(document_id: str) → None[source]¶

Delete by document ID.

Parameters: document_id (str) – the document ID to delete.
Return type: None

async adelete_collection() → None[source]¶

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

Return type: None

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, concurrency: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any) → CVST[source]¶

Create a Cassandra vectorstore from a document list.

Parameters

documents (List[Document]) – Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the documents.
concurrency (int) – Number of concurrent queries to send to the database. Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added documents.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
kwargs (Any) –

Returns

a Cassandra vectorstore.

Return type

CVST

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, concurrency: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any) → CVST[source]¶

Create a Cassandra vectorstore from raw texts.

Parameters

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the texts.
concurrency (int) – Number of concurrent queries to send to the database. Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
kwargs (Any) –

Returns

a Cassandra vectorstore.

Return type

CVST

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[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 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. Defaults to 0.5.

Parameters

filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

async amax_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, body_search: Optional[Union[str, List[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. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm.

Defaults to 20.

Parameters

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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
embedding (List[float]) –
k (int) –
fetch_k (int) –
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

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
kwargs (Any) –

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.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
kwargs (Any) –

Returns

List of Document, the most similar to the query vector.

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
kwargs (Any) –

Returns

List of Document, the 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], asynchronously.

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(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float]]

async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][source]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float]]

async asimilarity_search_with_score_id(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float, str]]

async asimilarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][source]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float, str]]

clear() → None[source]¶

Empty the table.

Return type: None

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

Delete by vector IDs.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) –

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 by document ID.

Parameters: document_id (str) – the document ID to delete.
Return type: None

delete_collection() → None[source]¶

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

Return type: None

classmethod from_documents(documents: List[Document], embedding: Embeddings, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any) → CVST[source]¶

Create a Cassandra vectorstore from a document list.

Parameters

documents (List[Document]) – Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the documents.
batch_size (int) – Number of concurrent requests to send to the server. Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added documents.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
kwargs (Any) –

Returns

a Cassandra vectorstore.

Return type

CVST

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any) → CVST[source]¶

Create a Cassandra vectorstore from raw texts.

Parameters

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
session (Optional[Session]) – Cassandra driver session. If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space. If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the texts.
batch_size (int) – Number of concurrent requests to send to the server. Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
kwargs (Any) –

Returns

a Cassandra vectorstore.

Return type

CVST

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[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 query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm.

Defaults to 20.

Parameters

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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
query (str) –
k (int) –
fetch_k (int) –
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, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[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. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm.

Defaults to 20.

Parameters

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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
embedding (List[float]) –
k (int) –
fetch_k (int) –
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) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
kwargs (Any) –

Returns

List of Document, the most similar to the query vector.

Return type

List[Document]

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

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.
kwargs (Any) –

Returns

List of Document, the most similar to the query vector.

Return type

List[Document]

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 (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]]

similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float]]

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

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float]]

similarity_search_with_score_id(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float, str]]

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

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply. Only supported by Astra DB at the moment.

Returns

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

Return type

List[Tuple[Document, float, str]]

langchain_community.vectorstores.cassandra.Cassandra¶

Examples using Cassandra¶

`langchain_community.vectorstores.cassandra`.Cassandra¶