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# `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')

  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')

    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]

    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]

    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.

    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]

  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]

    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

    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: Optional[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.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - client (Optional[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, *, where_str: 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.

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

      - 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.

      - where_str (Optional[str]) – where clause for the sql query

      - 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]

    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]

    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]]

    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]]

    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

- output length: 4