TileDB#

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

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.

Parameters:

allow_dangerous_deserialization (bool) – whether to allow deserialization of the data which involves loading data using pickle. data can be modified by malicious actors to deliver a malicious payload that results in execution of arbitrary code on your machine.
embedding (Embeddings)
index_uri (str)
metric (str)
vector_index_uri (str)
docs_array_uri (str)
config (Optional[Mapping[str, Any]])
timestamp (Any)
kwargs (Any)

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(embedding, index_uri, metric, *[, ...])	Initialize with necessary components.
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	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, timestamp])	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])	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.
`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.
`get_by_ids`(ids, /)	Get documents by their IDs.
`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 a 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: Mapping[str, Any] | None = None, timestamp: Any = None, allow_dangerous_deserialization: bool = False, **kwargs: Any)[source]#

Initialize with necessary components.

Parameters:

allow_dangerous_deserialization (bool) – whether to allow deserialization of the data which involves loading data using pickle. data can be modified by malicious actors to deliver a malicious payload that results in execution of arbitrary code on your machine.
embedding (Embeddings)
index_uri (str)
metric (str)
vector_index_uri (str)
docs_array_uri (str)
config (Mapping[str, Any] | None)
timestamp (Any)
kwargs (Any)

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, **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.
**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, timestamp: int = 0, **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 ids of each text object.
timestamp (int) – Optional timestamp to write new texts with.
kwargs (Any) – vectorstore specific parameters

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, **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.
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]]

consolidate_updates(**kwargs: Any) → None[source]#

Parameters:: kwargs (Any)
Return type:: None

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

Parameters:

index_uri (str)
index_type (str)
dimensions (int)
vector_type (dtype)
metadatas (bool)
config (Mapping[str, Any] | None)

Return type:

None

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

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete.
timestamp (int) – Optional timestamp to delete with.
**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]

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

Construct TileDB index from embeddings.

Parameters:

text_embeddings (List[Tuple[str, List[float]]]) – List of tuples of (text, embedding)
embedding (Embeddings) – Embedding function to use.
index_uri (str) – The URI to write the TileDB arrays
metadatas (List[dict] | None) – List of metadata dictionaries to associate with documents.
metric (str) – Optional, Metric to use for indexing. Defaults to “euclidean”.
index_type (str) – Optional, Vector index type (“FLAT”, IVF_FLAT”)
config (Mapping[str, Any] | None) – Optional, TileDB config
index_timestamp (int) – Optional, timestamp to write new texts with.
ids (List[str] | None)
kwargs (Any)

Return type:

TileDB

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: List[dict] | None = None, ids: List[str] | None = None, metric: str = 'euclidean', index_uri: str = '/tmp/tiledb_array', index_type: str = 'FLAT', config: Mapping[str, Any] | None = None, index_timestamp: int = 0, **kwargs: Any) → TileDB[source]#

Construct a TileDB index from raw documents.

Parameters:

texts (List[str]) – List of documents to index.
embedding (Embeddings) – Embedding function to use.
metadatas (List[dict] | None) – List of metadata dictionaries to associate with documents.
ids (List[str] | None) – Optional ids of each text object.
metric (str) – Metric to use for indexing. Defaults to “euclidean”.
index_uri (str) – The URI to write the TileDB arrays
index_type (str) – Optional, Vector index type (“FLAT”, IVF_FLAT”)
config (Mapping[str, Any] | None) – Optional, TileDB config
index_timestamp (int) – Optional, timestamp to write new texts with.
kwargs (Any)

Return type:

TileDB

Example

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

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.

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

Load a TileDB index from a URI.

Parameters:

index_uri (str) – The URI of the TileDB vector index.
embedding (Embeddings) – Embeddings to use when generating queries.
metric (str) – Optional, Metric to use for indexing. Defaults to “euclidean”.
config (Mapping[str, Any] | None) – Optional, TileDB config
timestamp (Any) – Optional, timestamp to use for opening the arrays.
kwargs (Any)

Return type:

TileDB

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | 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 before filtering (if needed) 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.
filter (Dict[str, Any] | 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, filter: Dict[str, Any] | 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 before filtering 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.
filter (Dict[str, Any] | None)
kwargs (Any)

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

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

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 (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 before filtering 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.
filter (Dict[str, Any] | None)
kwargs (Any)

Returns:

List of Documents and similarity scores selected by maximal marginal: relevance and score for each.

Return type:

List[Tuple[Document, float]]

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

Turns TileDB results into a list of documents and scores.

Parameters:

ids (List[int]) – List of indices of the documents in the index.
scores (List[float]) – List of distances of the documents in the index.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.
score_threshold (float) – Optional, a floating point value to filter the resulting set of retrieved docs

Returns:

List of Documents and scores.

Return type:

List[Tuple[Document, float]]

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, filter: Dict[str, Any] | None = None, fetch_k: int = 20, **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 (Dict[str, Any] | None) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.
kwargs (Any)

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None, fetch_k: int = 20, **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 by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.
kwargs (Any)

Returns:

List of Documents most similar to the embedding.

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: Dict[str, Any] | None = None, fetch_k: int = 20, **kwargs: Any) → 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 by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.
kwargs (Any)

Returns:

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

Return type:

List[Tuple[Document, float]]

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

Return docs most similar to query.

Parameters:

embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.
**kwargs (Any) –
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.

Return type:

List[Tuple[Document, float]]

Examples using TileDB

TileDB