langchain.vectorstores.rocksetdb.Rockset

class langchain.vectorstores.rocksetdb.Rockset(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')

Bases: VectorStore

Wrapper arpund Rockset vector database.

To use, you should have the rockset python package installed. Note that to use this, the collection being used must already exist in your Rockset instance. You must also ensure you use a Rockset ingest transformation to apply VECTOR_ENFORCE on the column being used to store embedding_key in the collection. See: https://rockset.com/blog/introducing-vector-search-on-rockset/ for more details

Everything below assumes commons Rockset workspace.

Example

from langchain.vectorstores import Rockset
from langchain.embeddings.openai import OpenAIEmbeddings
import rockset

# Make sure you use the right host (region) for your Rockset instance
# and APIKEY has both read-write access to your collection.

rs = rockset.RocksetClient(host=rockset.Regions.use1a1, api_key="***")
collection_name = "langchain_demo"
embeddings = OpenAIEmbeddings()
vectorstore = Rockset(rs, collection_name, embeddings,
    "description", "description_embedding")

Initialize with Rockset client. :param client: Rockset client object :param collection: Rockset collection to insert docs / query :param embeddings: Langchain Embeddings object to use to generate

embedding for given text.

Parameters

• text_key – column in Rockset collection to use to store the text

• embedding_key – column in Rockset collection to use to store the embedding. Note: We must apply VECTOR_ENFORCE() on this column via Rockset ingest transformation.

Methods

__init__(client, embeddings, ...[, workspace])	Initialize with Rockset client. :param client: Rockset client object :param collection: Rockset collection to insert docs / query :param embeddings: Langchain Embeddings object to use to generate embedding for given text. :param text_key: column in Rockset collection to use to store the text :param embedding_key: column in Rockset collection to use to store the embedding. Note: We must apply VECTOR_ENFORCE() on this column via Rockset ingest transformation.
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, batch_size])	Run more texts through the embeddings and add to the vectorstore
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.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
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.
asimilarity_search_with_relevance_scores(query)	Return docs most similar to query.
delete([ids])	Delete by vector ID or other criteria.
delete_texts(ids)	Delete a list of docs from the Rockset collection
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])	Create Rockset wrapper with existing texts.
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 specified search type.
similarity_search(query[, k, distance_func, ...])	Same as similarity_search_with_relevance_scores but doesn't return the scores.
similarity_search_by_vector(embedding[, k, ...])	Accepts a query_embedding (vector), and returns documents with similar embeddings.
similarity_search_by_vector_with_relevance_scores(...)	Accepts a query_embedding (vector), and returns documents with similar embeddings along with their relevance scores.
similarity_search_with_relevance_scores(query)	Perform a similarity search with Rockset
similarity_search_with_score(*args, **kwargs)	Run similarity search with distance.

Attributes

embeddings

Access the query embedding object if available.

class DistanceFunction(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

order_by() → str

COSINE_SIM = 'COSINE_SIM'

DOT_PRODUCT = 'DOT_PRODUCT'

EUCLIDEAN_DIST = 'EUCLIDEAN_DIST'

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.

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.

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.

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]

Run more texts through the embeddings and add to the vectorstore

Args:

texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. batch_size: Send documents in batches to rockset.

Returns

List of ids from adding the texts into the vectorstore.

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

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

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.

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.

as_retriever(**kwargs: Any) → VectorStoreRetriever

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

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

Return type

Optional[bool]

delete_texts(ids: List[str]) → None

Delete a list of docs from the Rockset collection

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Any = None, collection_name: str = '', text_key: str = '', embedding_key: str = '', ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → Rockset

Create Rockset wrapper with existing texts. This is intended as a quicker way to get started.

max_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.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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 – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

similarity_search(query: str, k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Document]

Same as similarity_search_with_relevance_scores but doesn’t return the scores.

similarity_search_by_vector(embedding: List[float], k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Document]

Accepts a query_embedding (vector), and returns documents with similar embeddings.

similarity_search_by_vector_with_relevance_scores(embedding: List[float], k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

Accepts a query_embedding (vector), and returns documents with similar embeddings along with their relevance scores.

similarity_search_with_relevance_scores(query: str, k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

Perform a similarity search with Rockset

Parameters

• query (str) – Text to look up documents similar to.

• distance_func (DistanceFunction) – how to compute distance between two vectors in Rockset.

• k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

• where_str (Optional[str], optional) – Metadata filters supplied as a SQL where condition string. Defaults to None. eg. “price<=70.0 AND brand=’Nintendo’”

• NOTE – Please do not let end-user to fill this and always be aware of SQL injection.

Returns

List of documents with their relevance score

Return type

List[Tuple[Document, float]]

similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance.

property embeddings: langchain.embeddings.base.Embeddings

Access the query embedding object if available.

Examples using Rockset

• Rockset