DatabricksVectorSearch#

class langchain_databricks.vectorstores.DatabricksVectorSearch(index_name: str, endpoint: str | None = None, embedding: Embeddings | None = None, text_column: str | None = None, columns: List[str] | None = None)[source]#

Deprecated since version 0.1.2: Use databricks_langchain.DatabricksVectorSearch It will be removed in None==1.0.0.

Databricks vector store integration.

Setup:

Install langchain-databricks and databricks-vectorsearch python packages.

pip install -U langchain-databricks databricks-vectorsearch

If you don’t have a Databricks Vector Search endpoint already, you can create one by following the instructions here: https://docs.databricks.com/en/generative-ai/create-query-vector-search.html

If you are outside Databricks, set the Databricks workspace hostname and personal access token to environment variables:

export DATABRICKS_HOSTNAME="https://your-databricks-workspace"
export DATABRICKS_TOKEN="your-personal-access-token"

Key init args — indexing params:

index_name: The name of the index to use. Format: “catalog.schema.index”. endpoint: The name of the Databricks Vector Search endpoint. If not specified,
the endpoint name is automatically inferred based on the index name.
Note

If you are using databricks-vectorsearch version < 0.35, the endpoint parameter is required when initializing the vector store.
vector_store = DatabricksVectorSearch(
    endpoint="<your-endpoint-name>",
    index_name="<your-index-name>",
    ...
)
embedding: The embedding model.
Required for direct-access index or delta-sync index with self-managed embeddings.

text_column: 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: The list of column names to get when doing the search.
Defaults to [primary_key, text_column].

Instantiate:

DatabricksVectorSearch supports two types of indexes:

Delta Sync Index automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.

Direct Vector Access Index supports direct read and write of vectors and metadata. The user is responsible for updating this table using the REST API or the Python SDK.

Also for delta-sync index, you can choose to use Databricks-managed embeddings or self-managed embeddings (via LangChain embeddings classes).

If you are using a delta-sync index with Databricks-managed embeddings:
from langchain_databricks.vectorstores import DatabricksVectorSearch

vector_store = DatabricksVectorSearch(
    index_name="<your-index-name>"
)
If you are using a direct-access index or a delta-sync index with self-managed embeddings, you also need to provide the embedding model and text column in your source table to use for the embeddings:
from langchain_openai import OpenAIEmbeddings

vector_store = DatabricksVectorSearch(
    index_name="<your-index-name>",
    embedding=OpenAIEmbeddings(),
    text_column="document_content"
)

Add Documents:
Delete Documents:: Note

The delete method is only supported for direct-access index.
Search:
Search with filter:
Search with score:
Async:
Use as Retriever:

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(index_name[, endpoint, embedding, ...])
`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, ids])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Async return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`delete`([ids])	Delete documents from the 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.

Parameters:

index_name (str)
endpoint (Optional[str])
embedding (Optional[Embeddings])
text_column (Optional[str])
columns (Optional[List[str]])

__init__(index_name: str, endpoint: str | None = None, embedding: Embeddings | None = None, text_column: str | None = None, columns: List[str] | None = None)[source]#

Parameters:

index_name (str)
endpoint (str | None)
embedding (Embeddings | None)
text_column (str | None)
columns (List[str] | None)

async aadd_documents(documents: list[Document], **kwargs: Any) → list[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

list[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str][source]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids – Optional list
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

add_documents(documents: list[Document], **kwargs: Any) → list[str]#

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

Add texts to the index.

Note

This method is only supported for a direct-access index.

Parameters:

texts (Iterable[str]) – List of texts to add.
metadatas (List[Dict] | None) – List of metadata for each text. Defaults to None.
ids (List[Any] | None) – 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: list[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.

Returns:

True if deletion is successful, False otherwise, None if not implemented.

Return type:

Optional[bool]

async classmethod afrom_documents(documents: list[Document], embedding: Embeddings, **kwargs: Any) → VST#

Async return VectorStore initialized from documents and embeddings.

Parameters:

documents (list[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (list[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list of IDs associated with the texts.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → list[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

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

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][source]#

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][source]#

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][source]#

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]][source]#

Async run similarity search with distance.

Parameters:

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

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

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

Delete documents from the index.

Note

This method is only supported for a direct-access index.

Parameters:

ids (List[Any] | None) – List of ids of documents to delete.
kwargs (Any)

Returns:

True if successful.

Return type:

bool | None

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: List[Dict] | None = None, **kwargs: Any) → VST[source]#

Return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (List[Dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids – Optional list of IDs associated with the texts.
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]

Added in version 0.2.11.

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

Note

This method is not supported for index with Databricks-managed embeddings.

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 (Dict[str, Any] | None) – Filters to apply to the query. Defaults to None.
query_type (str | None) – 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: Any | None = None, *, query_type: str | 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.

Note

This method is not supported for index with Databricks-managed embeddings.

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 (Any | None) – Filters to apply to the query. Defaults to None.
query_type (str | None) – 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: Dict[str, Any] | None = None, *, query_type: str | None = None, **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) – Filters to apply to the query. Defaults to None.
query_type (str | None) – 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: Any | None = None, *, query_type: str | None = None, query: str | None = None, **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 (Any | None) – Filters to apply to the query. Defaults to None.
query_type (str | None) – The type of this query. Supported values are “ANN” and “HYBRID”.
query (str | None)
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: Any | None = None, *, query_type: str | None = None, query: str | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

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

Note

This method is not supported for index with Databricks-managed embeddings.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Any | None) – Filters to apply to the query. Defaults to None.
query_type (str | None) – The type of this query. Supported values are “ANN” and “HYBRID”.
query (str | None)
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: Dict[str, Any] | None = None, *, query_type: str | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

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 (Dict[str, Any] | None) – Filters to apply to the query. Defaults to None.
query_type (str | None) – 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]]

Examples using DatabricksVectorSearch

Databricks Vector Search