langchain_community.vectorstores.surrealdb.SurrealDBStore

class langchain_community.vectorstores.surrealdb.SurrealDBStore(embedding_function: Embeddings, **kwargs: Any)

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

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]

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]

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]

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

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.

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

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.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

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]

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]]

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]]

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]

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

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

Initialize connection to surrealdb database and authenticate if credentials are provided

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, **kwargs: Any) → List[Document]

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]

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]]

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]]

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