`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 (Embeddings) – 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
kwargs (Any) –

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]¶

Parameters

embedding_function (Embeddings) –
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][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
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Returns

List of ids for the newly inserted documents

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, **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
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Returns

List of ids for the newly inserted documents

Return type

List[str]

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

Delete by document ID asynchronously.

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.

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) → 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
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Returns

SurrealDBStore object initialized and ready for use.

Return type

SurrealDBStore

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][source]¶

Run similarity search on query asynchronously

Parameters

query (str) – Query
k (int) – Number of results to return. Defaults to 4.
kwargs (Any) –

Returns

List of Documents most similar to the query

Return type

List[Document]

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.
kwargs (Any) –

Returns

List of Documents most similar to the query

Return type

List[Document]

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.
kwargs (Any) –

Returns

List of Documents most similar along with relevance scores

Return type

List[Tuple[Document, float]]

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.
kwargs (Any) –

Returns

List of Documents most similar along with relevance distance scores

Return type

List[Tuple[Document, float]]

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

Delete by document ID.

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.

Return type

Optional[bool]

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, **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
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Returns

SurrealDBStore object initialized and ready for use.

Return type

SurrealDBStore

async initialize() → None[source]¶

Initialize connection to surrealdb database and authenticate if credentials are provided

Return type: None

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, **kwargs: Any) → List[Document][source]¶

Run similarity search on query

Parameters

query (str) – Query
k (int) – Number of results to return. Defaults to 4.
kwargs (Any) –

Returns

List of Documents most similar to the query

Return type

List[Document]

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.
kwargs (Any) –

Returns

List of Documents most similar to the query

Return type

List[Document]

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.
kwargs (Any) –

Returns

List of Documents most similar along with relevance scores

Return type

List[Tuple[Document, float]]

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.
kwargs (Any) –

Returns

List of Documents most similar along with relevance distance scores

Return type

List[Tuple[Document, float]]

Examples using SurrealDBStore¶

%pip install –upgrade –quiet surrealdb langchain langchain-community

langchain_community.vectorstores.surrealdb.SurrealDBStore¶

Examples using SurrealDBStore¶

`langchain_community.vectorstores.surrealdb`.SurrealDBStore¶