langchain.vectorstores.starrocks.StarRocks

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

Bases: VectorStore

Wrapper around StarRocks vector database

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

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.
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.
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.

Attributes

embeddings	Access the query embedding object if available.
metadata_column

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, 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 of strings to add to the VectorStore.

• ids – Optional list of ids to associate with the texts.

• batch_size – Batch size of insertion

• metadata – Optional column data to be inserted

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]

drop() → None

Helper function: Drop data

escape_str(value: str) → str

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[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.

Returns

StarRocks Index

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, 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.

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.

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.

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.

property embeddings: langchain.embeddings.base.Embeddings

Access the query embedding object if available.

property metadata_column: str

Examples using StarRocks

• StarRocks