langchain_community.vectorstores.surrealdb.SurrealDBStore¶

class langchain_community.vectorstores.surrealdb.SurrealDBStore(embedding_function: Embeddings, **kwargs: Any)[source]¶

SurrealDB as Vector Store.

To use, you should have the surrealdb python package installed.

Parameters
  • embedding_function – Embedding function to use.

  • dburl – SurrealDB connection url

  • ns – surrealdb namespace for the vector store. (default: “langchain”)

  • db – surrealdb database for the vector store. (default: “database”)

  • collection – surrealdb collection for the vector store. (default: “documents”)

  • db_pass ((optional) db_user and) – surrealdb credentials

Example

from langchain_community.vectorstores.surrealdb import SurrealDBStore
from langchain_community.embeddings import HuggingFaceEmbeddings

embedding_function = HuggingFaceEmbeddings()
dburl = "ws://localhost:8000/rpc"
ns = "langchain"
db = "docstore"
collection = "documents"
db_user = "root"
db_pass = "root"

sdb = SurrealDBStore.from_texts(
        texts=texts,
        embedding=embedding_function,
        dburl,
        ns, db, collection,
        db_user=db_user, db_pass=db_pass)

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding_function, **kwargs)

aadd_documents(documents, **kwargs)

Run more documents through the embeddings and add to the vectorstore.

aadd_texts(texts[, metadatas])

Add list of text along with embeddings to the vector store asynchronously

add_documents(documents, **kwargs)

Run more documents through the embeddings and add to the vectorstore.

add_texts(texts[, metadatas])

Add list of text along with embeddings to the vector store

adelete([ids])

Delete by document ID asynchronously.

afrom_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

afrom_texts(texts, embedding[, metadatas])

Create SurrealDBStore from list of text asynchronously

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])

Run similarity search on query asynchronously

asimilarity_search_by_vector(embedding[, k])

Run similarity search on query embedding asynchronously

asimilarity_search_with_relevance_scores(query)

Run similarity search asynchronously and return relevance scores

asimilarity_search_with_score(query[, k])

Run similarity search asynchronously and return distance scores

delete([ids])

Delete by document ID.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Create SurrealDBStore from list of text

initialize()

Initialize connection to surrealdb database and authenticate if credentials are provided

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])

Run similarity search on query

similarity_search_by_vector(embedding[, k])

Run similarity search on query embedding

similarity_search_with_relevance_scores(query)

Run similarity search synchronously and return relevance scores

similarity_search_with_score(query[, k])

Run similarity search synchronously and return distance scores

__init__(embedding_function: Embeddings, **kwargs: Any) None[source]¶
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][source]¶

Add list of text along with embeddings to the vector store asynchronously

Parameters

texts (Iterable[str]) – collection of text to add to the database

Returns

List of ids for the newly inserted documents

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, **kwargs: Any) List[str][source]¶

Add list of text along with embeddings to the vector store

Parameters

texts (Iterable[str]) – collection of text to add to the database

Returns

List of ids for the newly inserted documents

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

Delete by document ID asynchronously.

Parameters
  • ids – List of ids to delete.

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

Returns

True if deletion is successful, False otherwise.

Return type

Optional[bool]

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) SurrealDBStore[source]¶

Create SurrealDBStore from list of text asynchronously

Parameters
  • texts (List[str]) – list of text to vectorize and store

  • embedding (Optional[Embeddings]) – Embedding function.

  • dburl (str) – SurrealDB connection url (default: “ws://localhost:8000/rpc”)

  • ns (str) – surrealdb namespace for the vector store. (default: “langchain”)

  • db (str) – surrealdb database for the vector store. (default: “database”)

  • collection (str) – surrealdb collection for the vector store. (default: “documents”)

  • db_pass ((optional) db_user and) – surrealdb credentials

Returns

SurrealDBStore object initialized and ready for use.

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¶

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

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.

Run similarity search on query asynchronously

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar to the query

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

Run similarity search on query embedding asynchronously

Parameters
  • embedding (List[float]) – Query embedding

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar to the query

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

Run similarity search asynchronously and return relevance scores

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar along with relevance scores

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

Run similarity search asynchronously and return distance scores

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar along with relevance distance scores

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

Delete by document ID.

Parameters
  • ids – List of ids to delete.

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

Returns

True if deletion is successful, False otherwise.

Return type

Optional[bool]

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, **kwargs: Any) SurrealDBStore[source]¶

Create SurrealDBStore from list of text

Parameters
  • texts (List[str]) – list of text to vectorize and store

  • embedding (Optional[Embeddings]) – Embedding function.

  • dburl (str) – SurrealDB connection url

  • ns (str) – surrealdb namespace for the vector store. (default: “langchain”)

  • db (str) – surrealdb database for the vector store. (default: “database”)

  • collection (str) – surrealdb collection for the vector store. (default: “documents”)

  • db_pass ((optional) db_user and) – surrealdb credentials

Returns

SurrealDBStore object initialized and ready for use.

async initialize() None[source]¶

Initialize connection to surrealdb database and authenticate if credentials are provided

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.

Run similarity search on query

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar to the query

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document][source]¶

Run similarity search on query embedding

Parameters
  • embedding (List[float]) – Query embedding

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar to the query

similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]][source]¶

Run similarity search synchronously and return relevance scores

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar along with relevance scores

similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]][source]¶

Run similarity search synchronously and return distance scores

Parameters
  • query (str) – Query

  • k (int) – Number of results to return. Defaults to 4.

Returns

List of Documents most similar along with relevance distance scores