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# `langchain_community.vectorstores.starrocks`.StarRocks

class langchain_community.vectorstores.starrocks.StarRocks(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any)

  StarRocks vector store.

  You need a pymysql python package, and a valid account to connect to StarRocks.

  Right now StarRocks has only implemented cosine_similarity function to compute distance between two vectors. And there is no vector inside right now, so we have to iterate all vectors and compute spatial distance.

  For more information, please visit

    [StarRocks official site](https://www.starrocks.io/) [StarRocks github](https://github.com/StarRocks/starrocks)

  StarRocks Wrapper to LangChain

  embedding_function (Embeddings): config (StarRocksSettings): Configuration to StarRocks Client

  Attributes

  `embeddings` | Access the query embedding object if available.
  `metadata_column` |

  Methods

  `__init__`(embedding[, config]) | StarRocks Wrapper to LangChain
  `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, batch_size, ids]) | Insert 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.
  `drop`() | Helper function: Drop data
  `escape_str`(value) |
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding[, metadatas, ...]) | Create StarRocks 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, where_str]) | Perform a similarity search with StarRocks
  `similarity_search_by_vector`(embedding[, k, ...]) | Perform a similarity search with StarRocks by vectors
  `similarity_search_with_relevance_scores`(query) | Perform a similarity search with StarRocks
  `similarity_search_with_score`(*args, **kwargs) | Run similarity search with distance.

  Parameters

    - embedding (Embeddings) –

    - config (Optional[StarRocksSettings]) –

    - kwargs (Any) –

  __init__(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any) → None

    StarRocks Wrapper to LangChain

    embedding_function (Embeddings): config (StarRocksSettings): Configuration to StarRocks Client

    Parameters

      - embedding (Embeddings) –

      - config (Optional[StarRocksSettings]) –

      - kwargs (Any) –

    Return type

      None

  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, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str]

    Insert more texts through the embeddings and add to the VectorStore.

    Parameters

      - texts (Iterable[str]) – Iterable of strings to add to the VectorStore.

      - ids (Optional[Iterable[str]]) – Optional list of ids to associate with the texts.

      - batch_size (int) – Batch size of insertion

      - metadata – Optional column data to be inserted

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

      - kwargs (Any) –

    Returns

      List of ids from adding the texts into the VectorStore.

    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]

  drop() → None

    Helper function: Drop data

    Return type

      None

  escape_str(value: str) → str
    Parameters

      value (str) –

    Return type

      str

  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[Any, Any]]] = None, config: Optional[StarRocksSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → StarRocks

    Create StarRocks wrapper with existing texts

    Parameters

      - embedding_function (Embeddings) – Function to extract text embedding

      - texts (Iterable[str]) – List or tuple of strings to be added

      - config (StarRocksSettings, Optional) – StarRocks configuration

      - text_ids (Optional[Iterable], optional) – IDs for the texts. Defaults to None.

      - batch_size (int, optional) – Batchsize when transmitting data to StarRocks. Defaults to 32.

      - metadata (List[dict], optional) – metadata to texts. Defaults to None.

      - embedding (Embeddings) –

      - metadatas (Optional[List[Dict[Any, Any]]]) –

      - kwargs (Any) –

    Returns

      StarRocks Index

    Return type

      StarRocks

  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, where_str: Optional[str] = None, **kwargs: Any) → List[Document]

    Perform a similarity search with StarRocks

    Parameters

      - query (str) – query string

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

      - where_str (Optional[str], optional) – where condition string. Defaults to None.

      - NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

      - kwargs (Any) –

    Returns

      List of Documents

    Return type

      List[Document]

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

    Perform a similarity search with StarRocks by vectors

    Parameters

      - query (str) – query string

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

      - where_str (Optional[str], optional) – where condition string. Defaults to None.

      - NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

      - embedding (List[float]) –

      - kwargs (Any) –

    Returns

      List of (Document, similarity)

    Return type

      List[Document]

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

    Perform a similarity search with StarRocks

    Parameters

      - query (str) – query string

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

      - where_str (Optional[str], optional) – where condition string. Defaults to None.

      - NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

      - kwargs (Any) –

    Returns

      List of documents

    Return type

      List[Document]

  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 StarRocks

- StarRocks

- load text splitter and split docs into snippets of text