Aerospike#

class langchain_community.vectorstores.aerospike.Aerospike(client: Client, embedding: Embeddings | Callable, namespace: str, index_name: str | None = None, vector_key: str = '_vector', text_key: str = '_text', id_key: str = '_id', set_name: str | None = None, distance_strategy: DistanceStrategy | VectorDistanceMetric | None = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]#

Aerospike vector store.

To use, you should have the aerospike_vector_search python package installed.

Initialize with Aerospike client.

Parameters:

client (Client) – Aerospike client.
embedding (Union[Embeddings, Callable]) – Embeddings object or Callable (deprecated) to embed text.
namespace (str) – Namespace to use for storing vectors. This should match
index_name (Optional[str]) – Name of the index previously created in Aerospike. This
vector_key (str) – Key to use for vector in metadata. This should match the key used during index creation.
text_key (str) – Key to use for text in metadata.
id_key (str) – Key to use for id in metadata.
set_name (Optional[str]) – Default set name to use for storing vectors.
distance_strategy (Optional[Union[DistanceStrategy, VectorDistanceMetric]]) – Distance strategy to use for similarity search This should match the distance strategy used during index creation.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(client, embedding, namespace[, ...])	Initialize with Aerospike client.
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas, ids])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, ids, set_name, ...])	Run more texts through the embeddings and add to the vectorstore.
`adelete`([ids])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas, ids])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Async return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`convert_distance_strategy`(distance_strategy)	Convert Aerospikes distance strategy to langchains DistanceStrategy enum.
`delete`([ids, set_name])	Delete by vector ID or other criteria.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts, embedding[, metadatas, ...])	This is a user friendly interface that:
`get_by_ids`(ids, /)	Get documents by their IDs.
`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 a specified search type.
`similarity_search`(query[, k, metadata_keys, ...])	Return aerospike documents most similar to query.
`similarity_search_by_vector`(embedding[, k, ...])	Return docs most similar to embedding vector.
`similarity_search_by_vector_with_score`(embedding)	Return aerospike documents most similar to embedding, along with scores.
`similarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1].
`similarity_search_with_score`(query[, k, ...])	Return aerospike documents most similar to query, along with scores.

__init__(client: Client, embedding: Embeddings | Callable, namespace: str, index_name: str | None = None, vector_key: str = '_vector', text_key: str = '_text', id_key: str = '_id', set_name: str | None = None, distance_strategy: DistanceStrategy | VectorDistanceMetric | None = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]#

Initialize with Aerospike client.

Parameters:

client (Client) – Aerospike client.
embedding (Union[Embeddings, Callable]) – Embeddings object or Callable (deprecated) to embed text.
namespace (str) – Namespace to use for storing vectors. This should match
index_name (Optional[str]) – Name of the index previously created in Aerospike. This
vector_key (str) – Key to use for vector in metadata. This should match the key used during index creation.
text_key (str) – Key to use for text in metadata.
id_key (str) – Key to use for id in metadata.
set_name (Optional[str]) – Default set name to use for storing vectors.
distance_strategy (Optional[Union[DistanceStrategy, VectorDistanceMetric]]) – Distance strategy to use for similarity search This should match the distance strategy used during index creation.

async aadd_documents(documents: list[Document], **kwargs: Any) → list[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

list[str]

async aadd_texts(texts: Iterable[str], metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) → list[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

list[str]

add_documents(documents: list[Document], **kwargs: Any) → list[str]#

Add or update documents in the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

list[str]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = None, set_name: str | None = None, embedding_chunk_size: int = 1000, index_name: str | None = None, wait_for_index: bool = True, **kwargs: Any) → List[str][source]#

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

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts.
ids (List[str] | None) – Optional list of ids to associate with the texts.
set_name (str | None) – Optional aerospike set name to add the texts to.
batch_size – Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size (int) – Chunk size to use when embedding the texts.
index_name (str | None) – Optional aerospike index name used for waiting for index completion. If not provided, the default index_name will be used.
wait_for_index (bool) – If True, wait for the all the texts to be indexed before returning. Requires index_name to be provided. Defaults to True.
kwargs (Any) – Additional keyword arguments to pass to the client upsert call.

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[str]

async adelete(ids: list[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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#

Async 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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (list[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list of IDs associated with the texts.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → list[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → list[Document]#

Async 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.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
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]

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]#

Async 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. Default is 20.
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) – Arguments to pass to the search method.

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:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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]#

Async return docs most similar to query using a specified search type.

Parameters:

query (str) – Input text.
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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → list[Document]#

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

list[Document]

async asimilarity_search_by_vector(embedding: list[float], k: int = 4, **kwargs: Any) → list[Document]#

Async 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.
**kwargs (Any) – Arguments to pass to the search method.

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]]#

Async 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]]#

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

list[tuple[Document, float]]

static convert_distance_strategy(distance_strategy: VectorDistanceMetric | DistanceStrategy) → DistanceStrategy[source]#

Convert Aerospikes distance strategy to langchains DistanceStrategy enum. This is a convenience method to allow users to pass in the same distance metric used to create the index.

Parameters:: distance_strategy (Union[VectorDistanceMetric, DistanceStrategy])
Return type:: DistanceStrategy

delete(ids: List[str] | None = None, set_name: str | None = None, **kwargs: Any) → bool | None[source]#

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete.
**kwargs (Any) – Other keyword arguments to pass to client delete call.
set_name (str | None)
**kwargs

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:

documents (list[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, client: Client = None, namespace: str = 'test', index_name: str | None = None, ids: List[str] | None = None, embeddings_chunk_size: int = 1000, client_kwargs: dict | None = None, **kwargs: Any) → Aerospike[source]#

This is a user friendly interface that:

Embeds text.
Converts the texts into documents.
Adds the documents to a provided Aerospike index

This is intended to be a quick way to get started.

Example

from langchain_community.vectorstores import Aerospike
from langchain_openai import OpenAIEmbeddings
from aerospike_vector_search import Client, HostPort

client = Client(seeds=HostPort(host="localhost", port=5000))
aerospike = Aerospike.from_texts(
    ["foo", "bar", "baz"],
    embedder,
    client,
    "namespace",
    index_name="index",
    vector_key="vector",
    distance_strategy=MODEL_DISTANCE_CALC,
)

Parameters:

texts (List[str])
embedding (Embeddings)
metadatas (Optional[List[dict]])
client (Client)
namespace (str)
index_name (Optional[str])
ids (Optional[List[str]])
embeddings_chunk_size (int)
client_kwargs (Optional[dict])
kwargs (Any)

Return type:

Aerospike

get_by_ids(ids: Sequence[str], /) → list[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata_keys: List[str] | None = None, index_name: str | None = 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.

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.
index_name (str | None) – Name of the index to search.
metadata_keys (List[str] | None)
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, metadata_keys: List[str] | None = None, index_name: str | None = 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.

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.
metadata_keys (List[str] | None) – List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None.
index_name (str | None) – Optional name of the index to search. Overrides the default index_name.
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 a specified search type.

Parameters:

query (str) – Input text
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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

similarity_search(query: str, k: int = 4, metadata_keys: List[str] | None = None, index_name: str | None = None, **kwargs: Any) → List[Document][source]#

Return aerospike documents most similar to query.

Parameters:

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
metadata_keys (List[str] | None) – List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None.
index_name (str | None) – Optional name of the index to search. Overrides the default index_name.
kwargs (Any)

Returns:

List of Documents most similar to the query and score for each

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, metadata_keys: List[str] | None = None, index_name: str | None = 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.
metadata_keys (List[str] | None) – List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None.
index_name (str | None) – Name of the index to search. Overrides the default index_name.
kwargs (Any) – Additional keyword arguments to pass to the search method.

Returns:

List of Documents most similar to the query vector.

Return type:

List[Document]

similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, metadata_keys: List[str] | None = None, index_name: str | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Return aerospike documents most similar to embedding, along with scores.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
metadata_keys (List[str] | None) – List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None.
index_name (str | None) – Name of the index to search. Overrides the default index_name.
kwargs (Any) – Additional keyword arguments to pass to the client vector_search method.

Returns:

List of Documents most similar to the query and associated scores.

Return type:

List[Tuple[Document, float]]

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, metadata_keys: List[str] | None = None, index_name: str | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Return aerospike documents most similar to query, along with scores.

Parameters:

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
metadata_keys (List[str] | None) – List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None.
index_name (str | None) – Name of the index to search. Overrides the default index_name.
kwargs (Any) – Additional keyword arguments to pass to the search method.

Returns:

List of Documents most similar to the query and associated scores.

Return type:

List[Tuple[Document, float]]

Examples using Aerospike

Aerospike