langchain_community.vectorstores.kdbai.KDBAI¶

class langchain_community.vectorstores.kdbai.KDBAI(table: Any, embedding: Embeddings, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶

KDB.AI vector store.

See https://kdb.ai.

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

Parameters
  • table (Any) – kdbai_client.Table object to use as storage,

  • embedding (Embeddings) – Any embedding function implementing langchain.embeddings.base.Embeddings interface,

  • distance_strategy (Optional[DistanceStrategy]) – One option from DistanceStrategy.EUCLIDEAN_DISTANCE, DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE.

See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb).

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(table, embedding[, distance_strategy])

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[, batch_size])

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

add_texts(texts[, metadatas, ids, batch_size])

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.

delete([ids])

Delete by vector ID or other criteria.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Not implemented.

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, filter])

Run similarity search from a query string.

similarity_search_by_vector(embedding[, k])

Return docs most similar to embedding vector.

similarity_search_by_vector_with_score(...)

Return 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, filter])

Run similarity search with distance from a query string.

__init__(table: Any, embedding: Embeddings, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Parameters
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: Optional[List[dict]] = 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 (Optional[List[dict]]) – 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], batch_size: int = 32, **kwargs: Any) List[str][source]¶

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

Parameters
  • (List[Document] (documents) – Documents to add to the vectorstore.

  • batch_size (Optional[int]) – Size of batch of documents to insert at once.

  • documents (List[Document]) –

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) List[str][source]¶

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

Parameters
  • texts (Iterable[str]) – Texts to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – List of metadata corresponding to each chunk of text.

  • ids (Optional[List[str]]) – List of IDs corresponding to each chunk of text.

  • batch_size (Optional[int]) – Size of batch of chunks of text to insert at once.

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

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

Async delete by vector ID or other criteria.

Parameters
  • ids (Optional[List[str]]) – 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: Optional[List[dict]] = 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 (Optional[List[dict]]) – 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]

New 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: Optional[List[str]] = None, **kwargs: Any) Optional[bool]¶

Delete by vector ID or other criteria.

Parameters
  • ids (Optional[List[str]]) – 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]

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: Optional[List[dict]] = None, **kwargs: Any) Any[source]¶

Not implemented.

Parameters
  • texts (List[str]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

Any

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]

New in version 0.2.11.

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) – Arguments to pass to the search method.

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, **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 (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]

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]

Run similarity search from a query string.

Parameters
  • query (str) – Query string.

  • k (Optional[int]) – number of neighbors to retrieve.

  • filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html

  • kwargs (Any) –

Returns

List of similar documents.

Return type

List[Document]

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

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]

similarity_search_by_vector_with_score(embedding: List[float], *, k: int = 1, filter: Optional[List] = [], **kwargs: Any) List[Tuple[Document, float]][source]¶

Return documents most similar to embedding, along with scores.

Parameters
  • embedding (List[float]) – query vector.

  • k (Optional[int]) – number of neighbors to retrieve.

  • filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html

  • kwargs (Any) –

Returns

List of similar documents.

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 = 1, filter: Optional[List] = [], **kwargs: Any) List[Tuple[Document, float]][source]¶

Run similarity search with distance from a query string.

Parameters
  • query (str) – Query string.

  • k (Optional[int]) – number of neighbors to retrieve.

  • filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html

  • kwargs (Any) –

Returns

List of similar documents.

Return type

List[Document]

Examples using KDBAI¶