langchain_community.vectorstores.tiledb.TileDB

class langchain_community.vectorstores.tiledb.TileDB(embedding: Embeddings, index_uri: str, metric: str, *, vector_index_uri: str = '', docs_array_uri: str = '', config: Optional[Mapping[str, Any]] = None, timestamp: Any = None, **kwargs: Any)

TileDB vector store.

To use, you should have the tiledb-vector-search python package installed.

Example

from langchain_community import TileDB
embeddings = OpenAIEmbeddings()
db = TileDB(embeddings, index_uri, metric)

Initialize with necessary components.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding, index_uri, metric, *[, ...])	Initialize with necessary components.
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, timestamp])	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.
consolidate_updates(**kwargs)
create(index_uri, index_type, dimensions, ...)
delete([ids, timestamp])	Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding, ...)	Construct TileDB index from embeddings.
from_texts(texts, embedding[, metadatas, ...])	Construct a TileDB index from raw documents.
load(index_uri, embedding, *[, metric, ...])	Load a TileDB index from a URI.
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.
max_marginal_relevance_search_with_score_by_vector(...)	Return docs and their similarity scores selected using the maximal marginal
process_index_results(ids, scores, *[, k, ...])	Turns TileDB results into a list of documents and scores.
search(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
similarity_search(query[, k, filter, fetch_k])	Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])	Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query, *[, k, ...])	Return docs most similar to query.
similarity_search_with_score_by_vector(...)	Return docs most similar to query.

__init__(embedding: Embeddings, index_uri: str, metric: str, *, vector_index_uri: str = '', docs_array_uri: str = '', config: Optional[Mapping[str, Any]] = None, timestamp: Any = None, **kwargs: Any)

Initialize with necessary components.

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, timestamp: int = 0, **kwargs: Any) → List[str]

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

Parameters

• texts – Iterable of strings to add to the vectorstore.

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

• ids – Optional ids of each text object.

• timestamp – Optional timestamp to write new texts with.

• kwargs – vectorstore specific parameters

Returns

List of ids from adding the texts into the vectorstore.

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.

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

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) → VectorStoreRetriever

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.

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

Return docs most similar to query.

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

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs 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.

consolidate_updates(**kwargs: Any) → None

classmethod create(index_uri: str, index_type: str, dimensions: int, vector_type: dtype, *, metadatas: bool = True, config: Optional[Mapping[str, Any]] = None) → None

delete(ids: Optional[List[str]] = None, timestamp: int = 0, **kwargs: Any) → Optional[bool]

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• timestamp – Optional timestamp to delete with.

• **kwargs – Other keyword arguments that subclasses might use.

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.

classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, index_uri: str, *, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, metric: str = 'euclidean', index_type: str = 'FLAT', config: Optional[Mapping[str, Any]] = None, index_timestamp: int = 0, **kwargs: Any) → TileDB

Construct TileDB index from embeddings.

Parameters

• text_embeddings – List of tuples of (text, embedding)

• embedding – Embedding function to use.

• index_uri – The URI to write the TileDB arrays

• metadatas – List of metadata dictionaries to associate with documents.

• metric – Optional, Metric to use for indexing. Defaults to “euclidean”.

• index_type – Optional, Vector index type (“FLAT”, IVF_FLAT”)

• config – Optional, TileDB config

• index_timestamp – Optional, timestamp to write new texts with.

Example

from langchain_community import TileDB
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = TileDB.from_embeddings(text_embedding_pairs, embeddings)

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, metric: str = 'euclidean', index_uri: str = '/tmp/tiledb_array', index_type: str = 'FLAT', config: Optional[Mapping[str, Any]] = None, index_timestamp: int = 0, **kwargs: Any) → TileDB

Construct a TileDB index from raw documents.

Parameters

• texts – List of documents to index.

• embedding – Embedding function to use.

• metadatas – List of metadata dictionaries to associate with documents.

• ids – Optional ids of each text object.

• metric – Metric to use for indexing. Defaults to “euclidean”.

• index_uri – The URI to write the TileDB arrays

• index_type – Optional, Vector index type (“FLAT”, IVF_FLAT”)

• config – Optional, TileDB config

• index_timestamp – Optional, timestamp to write new texts with.

Example

from langchain_community import TileDB
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = TileDB.from_texts(texts, embeddings)

classmethod load(index_uri: str, embedding: Embeddings, *, metric: str = 'euclidean', config: Optional[Mapping[str, Any]] = None, timestamp: Any = None, **kwargs: Any) → TileDB

Load a TileDB index from a URI.

Parameters

• index_uri – The URI of the TileDB vector index.

• embedding – Embeddings to use when generating queries.

• metric – Optional, Metric to use for indexing. Defaults to “euclidean”.

• config – Optional, TileDB config

• timestamp – Optional, timestamp to use for opening the arrays.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch before filtering (if needed) to pass to MMR algorithm.

• 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.

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, Any]] = None, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch before filtering to pass to MMR algorithm.

• 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.

Returns

List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs and their similarity scores selected using the maximal marginal

relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch before filtering to pass to MMR algorithm.

• 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.

Returns

List of Documents and similarity scores selected by maximal marginal

relevance and score for each.

process_index_results(ids: List[int], scores: List[float], *, k: int = 4, filter: Optional[Dict[str, Any]] = None, score_threshold: float = 1.7976931348623157e+308) → List[Tuple[Document, float]]

Turns TileDB results into a list of documents and scores.

Parameters

• ids – List of indices of the documents in the index.

• scores – List of distances of the documents in the index.

• k – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.

• score_threshold – Optional, a floating point value to filter the resulting set of retrieved docs

Returns

List of Documents and scores.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

• fetch_k – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

Returns

List of Documents most similar to the query.

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

• fetch_k – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

Returns

List of Documents most similar to the embedding.

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, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

• fetch_k – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

Returns

List of documents most similar to the query text with Distance as float. Lower score represents more similarity.

similarity_search_with_score_by_vector(embedding: List[float], *, k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• embedding – Embedding vector to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.

• fetch_k – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• **kwargs –

  kwargs to be passed to similarity search. Can include: nprobe: Optional, number of partitions to check if using IVF_FLAT index score_threshold: Optional, a floating point value to filter the

  resulting set of retrieved docs

Returns

List of documents most similar to the query text and distance in float for each. Lower score represents more similarity.