`langchain_community.vectorstores.rocksetdb`.Rockset¶

class langchain_community.vectorstores.rocksetdb.Rockset(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')[source]¶

Rockset vector store.

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_community.vectorstores import Rockset
from langchain_community.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 (str) – column in Rockset collection to use to store the text
embedding_key (str) – column in Rockset collection to use to store the embedding. Note: We must apply VECTOR_ENFORCE() on this column via Rockset ingest transformation.
client (Any) –
embeddings (Embeddings) –
collection_name (str) –
workspace (str) –

Attributes

embeddings

Access the query embedding object if available.

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
`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.
`amax_marginal_relevance_search_by_vector`(...)	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.
`asimilarity_search_with_relevance_scores`(query)	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 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.

__init__(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')[source]¶

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 (str) – column in Rockset collection to use to store the text
embedding_key (str) – column in Rockset collection to use to store the embedding. Note: We must apply VECTOR_ENFORCE() on this column via Rockset ingest transformation.
client (Any) –
embeddings (Embeddings) –
collection_name (str) –
workspace (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[str]] = None, batch_size: int = 32, **kwargs: Any) → List[str][source]¶

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.

Parameters

texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
ids (Optional[List[str]]) –
batch_size (int) –
kwargs (Any) –

Return type

List[str]

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

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

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]¶

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[str]] = None, **kwargs: Any) → Optional[bool][source]¶

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]

delete_texts(ids: List[str]) → None[source]¶

Delete a list of docs from the Rockset collection

Parameters: ids (List[str]) –
Return type: None

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, client: Any = None, collection_name: str = '', text_key: str = '', embedding_key: str = '', ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → Rockset[source]¶

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

Parameters

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
client (Any) –
collection_name (str) –
text_key (str) –
embedding_key (str) –
ids (Optional[List[str]]) –
batch_size (int) –
kwargs (Any) –

Return type

Rockset

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 (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.
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, **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.
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, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶

Same as similarity_search_with_relevance_scores but doesn’t return the scores.

Parameters

query (str) –
k (int) –
distance_func (DistanceFunction) –
where_str (Optional[str]) –
kwargs (Any) –

Return type

List[Document]

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][source]¶

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

Parameters

embedding (List[float]) –
k (int) –
distance_func (DistanceFunction) –
where_str (Optional[str]) –
kwargs (Any) –

Return type

List[Document]

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]][source]¶

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

Parameters

embedding (List[float]) –
k (int) –
distance_func (DistanceFunction) –
where_str (Optional[str]) –
kwargs (Any) –

Return type

List[Tuple[Document, float]]

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]][source]¶

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.
kwargs (Any) –

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.

Parameters

args (Any) –
kwargs (Any) –

Return type

List[Tuple[Document, float]]

Examples using Rockset¶

Rockset

langchain_community.vectorstores.rocksetdb.Rockset¶

Examples using Rockset¶

`langchain_community.vectorstores.rocksetdb`.Rockset¶