ApertureDB#

class langchain_community.vectorstores.aperturedb.ApertureDB(embeddings: Embeddings, descriptor_set: str = 'langchain', dimensions: int | None = None, engine: str | None = None, metric: str | None = None, log_level: int = 30, properties: Dict | None = None, **kwargs: Any)[source]#

Create a vectorstore backed by ApertureDB

A single ApertureDB instance can support many vectorstores, distinguished by ‘descriptor_set’ name. The descriptor set is created if it does not exist. Different descriptor sets can use different engines and metrics, be supplied by different embedding models, and have different dimensions.

See ApertureDB documentation on AddDescriptorSet https://docs.aperturedata.io/query_language/Reference/descriptor_commands/desc_set_commands/AddDescriptorSet for more information on the engine and metric options.

Parameters:

  • embeddings (Embeddings) – Embeddings object

  • descriptor_set (str, optional) – Descriptor set name. Defaults to “langchain”.

  • dimensions (Optional[int], optional) – Number of dimensions of the embeddings. Defaults to None.

  • engine (str, optional) – Engine to use. Defaults to “HNSW” for new descriptorsets.

  • metric (str, optional) – Metric to use. Defaults to “CS” for new descriptorsets.

  • log_level (int, optional) – Logging level. Defaults to logging.WARN.

  • properties (Optional[Dict])

  • kwargs (Any)

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embeddings[, descriptor_set, ...])

Create a vectorstore backed by ApertureDB

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])

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.

delete([ids])

Delete documents from the vectorstore by id.

delete_vectorstore(descriptor_set)

Deletes a vectorstore and all its data from the database

from_documents(documents, embedding, **kwargs)

Creates a new vectorstore from a list of documents

from_texts(texts, embedding[, metadatas])

Creates a new vectorstore from a list of texts

get_by_ids(ids, /)

Find documents in the vectorstore by id.

list_vectorstores()

Returns a list of all vectorstores in the database

max_marginal_relevance_search(query[, k, ...])

Returns similar documents to the query that also have diversity

max_marginal_relevance_search_by_vector(...)

Returns similar documents to the vector that also have diversity

search(query, search_type, **kwargs)

Return docs most similar to query using a specified search type.

similarity_search(query[, k])

Search for documents similar to the query using the vectorstore

similarity_search_by_vector(embedding[, k])

Returns the k most similar documents to the given embedding vector

similarity_search_with_relevance_scores(query)

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

similarity_search_with_score(query, *args, ...)

Run similarity search with distance.

upsert(items, /, **kwargs)

Insert or update items
__init__(embeddings: Embeddings, descriptor_set: str = 'langchain', dimensions: int | None = None, engine: str | None = None, metric: str | None = None, log_level: int = 30, properties: Dict | None = None, **kwargs: Any) → None[source]#

Create a vectorstore backed by ApertureDB

A single ApertureDB instance can support many vectorstores, distinguished by ‘descriptor_set’ name. The descriptor set is created if it does not exist. Different descriptor sets can use different engines and metrics, be supplied by different embedding models, and have different dimensions.

See ApertureDB documentation on AddDescriptorSet https://docs.aperturedata.io/query_language/Reference/descriptor_commands/desc_set_commands/AddDescriptorSet for more information on the engine and metric options.

Parameters:

  • embeddings (Embeddings) – Embeddings object

  • descriptor_set (str, optional) – Descriptor set name. Defaults to “langchain”.

  • dimensions (Optional[int], optional) – Number of dimensions of the embeddings. Defaults to None.

  • engine (str, optional) – Engine to use. Defaults to “HNSW” for new descriptorsets.

  • metric (str, optional) – Metric to use. Defaults to “CS” for new descriptorsets.

  • log_level (int, optional) – Logging level. Defaults to logging.WARN.

  • properties (Dict | None)

  • kwargs (Any)

Return type:

None

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][source]#

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, **kwargs: Any) → list[str]#

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 associated with the texts.

  • **kwargs (Any) – vectorstore specific parameters. One of the kwargs should be ids which is a list of ids associated with the texts.

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]

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 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 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]]

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

Delete documents from the vectorstore by id.

Parameters:

  • ids (List[str] | None) – List of ids to delete from the vectorstore.

  • kwargs (Any)

Returns:

True if the deletion was successful, False otherwise

Return type:

bool | None

classmethod delete_vectorstore(descriptor_set: str) → None[source]#

Deletes a vectorstore and all its data from the database

Parameters:

descriptor_set (str) – The name of the descriptor set to delete

Return type:

None

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → ApertureDB[source]#

Creates a new vectorstore from a list of documents

Parameters:

  • documents (List[Document]) – List of Document objects

  • embedding (Embeddings) – Embeddings object as for constructing the vectorstore

  • metadatas – Optional list of metadatas associated with the texts.

  • kwargs (Any) – Additional arguments to pass to the constructor

Return type:

ApertureDB

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → ApertureDB[source]#

Creates a new vectorstore from a list of texts

Parameters:

  • texts (List[str]) – List of text strings

  • embedding (Embeddings) – Embeddings object as for constructing the vectorstore

  • metadatas (List[dict] | None) – Optional list of metadatas associated with the texts.

  • kwargs (Any) – Additional arguments to pass to the constructor

Return type:

ApertureDB

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

Find documents in the vectorstore by id.

Parameters:

ids (Sequence[str]) – List of ids to find in the vectorstore.

Returns:

List of Document objects found in the vectorstore.

Return type:

documents

classmethod list_vectorstores() → None[source]#

Returns a list of all vectorstores in the database

Returns:

List of descriptor sets with properties

Return type:

None

Returns similar documents to the query that also have diversity

This algorithm balances relevance and diversity in the search results.

Parameters:

  • query (str) – Query string to search for.

  • k (int) – Number of results to return.

  • fetch_k (int) – Number of results to fetch.

  • lambda_mult (float) – Lambda multiplier for MMR.

  • kwargs (Any)

Returns:

List of Document objects ordered by decreasing similarity/diversty.

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, **kwargs: Any) → List[Document][source]#

Returns similar documents to the vector that also have diversity

This algorithm balances relevance and diversity in the search results.

Parameters:

  • embedding (List[float]) – Embedding vector to search for.

  • k (int) – Number of results to return.

  • fetch_k (int) – Number of results to fetch.

  • lambda_mult (float) – Lambda multiplier for MMR.

  • kwargs (Any)

Returns:

List of Document objects ordered by decreasing similarity/diversty.

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]

Search for documents similar to the query using the vectorstore

Parameters:

  • query (str) – Query string to search for.

  • k (int) – Number of results to return.

  • args (Any)

  • kwargs (Any)

Returns:

List of Document objects ordered by decreasing similarity to the query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]#

Returns the k most similar documents to the given embedding vector

Parameters:

  • embedding (List[float]) – The embedding vector to search for

  • k (int) – The number of similar documents to return

  • kwargs (Any)

Returns:

List of Document objects ordered by decreasing similarity to the query.

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, *args: Any, **kwargs: Any) → List[Tuple[Document, float]][source]#

Run similarity search with distance.

Parameters:

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

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

  • query (str)

  • *args

  • **kwargs

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

upsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse[source]#

Insert or update items

Updating documents is dependent on the documents’ id attribute.

Parameters:

  • items (Sequence[Document]) – List of Document objects to upsert

  • kwargs (Any)

Returns:

UpsertResponse object with succeeded and failed

Return type:

UpsertResponse

Examples using ApertureDB