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)[source]¶
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)Run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
add_texts
(texts[, metadatas, ids])Add texts to the index.
adelete
([ids])Delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
asimilarity_search
(query[, k])Return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score
(*args, **kwargs)Run similarity search with distance asynchronously.
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.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
similarity_search
(query[, k, filters])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.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k, filters])Return docs most similar to query, along with scores.
- __init__(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)[source]¶
- Parameters
index (VectorSearchIndex) –
embedding (Optional[Embeddings]) –
text_column (Optional[str]) –
columns (Optional[List[str]]) –
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
documents (List[Document]) –
kwargs (Any) –
- Returns
List of IDs of the added texts.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] ¶
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
List[str]
- add_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
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[Any]] = None, **kwargs: Any) List[str] [source]¶
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] ¶
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete.
**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 ¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- async classmethod afrom_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]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
VST
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] ¶
Return docs selected using the maximal marginal relevance.
- Parameters
query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- 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] ¶
Return docs selected using the maximal marginal relevance.
- Parameters
embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- Return type
List[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever ¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
search_kwargs (Optional[Dict]) –
Keyword arguments to pass to the search function. Can include things like:
k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) –
- Returns
Retriever class for VectorStore.
- Return type
Examples:
# Retrieve more documents with higher diversity # Useful if your dataset has many similar documents docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25} ) # Fetch more documents for the MMR algorithm to consider # But only return the top 5 docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50} ) # Only retrieve documents that have a relevance score # Above a certain threshold docsearch.as_retriever( search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8} ) # Only get the single most similar document from the dataset docsearch.as_retriever(search_kwargs={'k': 1}) # Use a filter to only retrieve documents from a specific paper docsearch.as_retriever( search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}} )
- async asearch(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- Parameters
query (str) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
- Parameters
query (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]] ¶
Run similarity search with distance asynchronously.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
List[Tuple[Document, float]]
- delete(ids: Optional[List[Any]] = None, **kwargs: Any) Optional[bool] [source]¶
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]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST [source]¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
VST
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filters: Optional[Any] = 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 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to 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, filters: Optional[Any] = 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 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
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 specified search type.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, filters: Optional[Any] = 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
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, filters: Optional[Any] = 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to 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, filters: Optional[Any] = None, **kwargs: Any) List[Tuple[Document, float]] [source]¶
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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to 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, filters: Optional[Any] = 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) –
- Returns
List of Documents most similar to the embedding and score for each.
- Return type
List[Tuple[Document, float]]