langchain_community.vectorstores.databricks_vector_search.DatabricksVectorSearch

class langchain_community.vectorstores.databricks_vector_search.DatabricksVectorSearch(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)

Databricks Vector Search vector store.

To use, you should have the databricks-vectorsearch python package installed.

Example

from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient

vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(vs_index)

Parameters

• index (VectorSearchIndex) – A Databricks Vector Search index object.

• embedding (Optional[Embeddings]) – The embedding model. Required for direct-access index or delta-sync index with self-managed embeddings.

• text_column (Optional[str]) – The name of the text column to use for the embeddings. Required for direct-access index or delta-sync index with self-managed embeddings. Make sure the text column specified is in the index.

• columns (Optional[List[str]]) – The list of column names to get when doing the search. Defaults to [primary_key, text_column].

Delta-sync index with Databricks-managed embeddings manages the ingestion, deletion, and embedding for you. Manually ingestion/deletion of the documents/texts is not supported for delta-sync index.

If you want to use a delta-sync index with self-managed embeddings, you need to provide the embedding model and text column name to use for the embeddings.

Example

from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings

vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
  index=vs_index,
  embedding=OpenAIEmbeddings(),
  text_column="document_content"
)

If you want to manage the documents ingestion/deletion yourself, you can use a direct-access index.

Example

from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings

vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
  index=vs_index,
  embedding=OpenAIEmbeddings(),
  text_column="document_content"
)
vectorstore.add_texts(
  texts=["text1", "text2"]
)

For more information on Databricks Vector Search, see `Databricks Vector Search documentation: https://docs.databricks.com/en/generative-ai/vector-search.html.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(index, *[, embedding, text_column, ...])
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])	Add texts to the index.
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.
astreaming_upsert(items, /, batch_size, **kwargs)
aupsert(items, /, **kwargs)
delete([ids])	Delete documents from the index.
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
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, query_type])	Return docs 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 docs most similar to embedding vector, 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 docs most similar to query, along with scores.
streaming_upsert(items, /, batch_size, **kwargs)
upsert(items, /, **kwargs)

__init__(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)

Parameters

• index (VectorSearchIndex) –

• embedding (Optional[Embeddings]) –

• text_column (Optional[str]) –

• columns (Optional[List[str]]) –

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], **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: Optional[List[Dict]] = None, ids: Optional[List[Any]] = None, **kwargs: Any) → List[str]

Add texts to the index.

Only support direct-access index.

Parameters

• texts (Iterable[str]) – List of texts to add.

• metadatas (Optional[List[Dict]]) – List of metadata for each text. Defaults to None.

• ids (Optional[List[Any]]) – List of ids for each text. Defaults to None. If not provided, a random uuid will be generated for each text.

• kwargs (Any) –

Returns

List of ids from adding the texts into the index.

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 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]]

astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) → AsyncIterator[UpsertResponse]

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion. Async version of streaming_upsert.

Parameters

• items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.

• batch_size (int) – The size of each batch to upsert.

• kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

AsyncIterator[UpsertResponse]

New in version 0.2.11.

async aupsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore. Async version of upsert.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

• items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

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

Delete documents from the index.

Only support direct-access index.

Parameters

• ids (Optional[List[Any]]) – List of ids of documents to delete.

• kwargs (Any) –

Returns

True if successful.

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) → VST

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

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.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, *, query_type: Optional[str] = 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 (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.

• filter (Optional[Dict[str, Any]]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• 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: Optional[Any] = None, *, query_type: Optional[str] = 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 (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.

• filter (Optional[Any]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• 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, filter: Optional[Dict[str, Any]] = None, *, query_type: Optional[str] = None, **kwargs: Any) → List[Document]

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, Any]]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Any] = None, *, query_type: Optional[str] = None, **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.

• filter (Optional[Any]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding.

Return type

List[Document]

similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, filter: Optional[Any] = None, *, query_type: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to embedding vector, along with scores.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Any]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding and score for each.

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, filter: Optional[Dict[str, Any]] = None, *, query_type: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs 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.

• filter (Optional[Dict[str, Any]]) – Filters to apply to the query. Defaults to None.

• query_type (Optional[str]) – The type of this query. Supported values are “ANN” and “HYBRID”.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding and score for each.

Return type

List[Tuple[Document, float]]

streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) → Iterator[UpsertResponse]

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion.

Parameters

• items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.

• batch_size (int) – The size of each batch to upsert.

• kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

Iterator[UpsertResponse]

New in version 0.2.11.

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

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

• items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

Examples using DatabricksVectorSearch

• Databricks Vector Search