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 model_name = "sentence-transformers/all-mpnet-base-v2" embedding_function = HuggingFaceEmbeddings(model_name=model_name) 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)Async 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)Add or update documents in 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)Async return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Create SurrealDBStore from list of text asynchronously
aget_by_ids
(ids, /)Async get documents by their IDs.
amax_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Async return docs most similar to query using a specified search type.
asimilarity_search
(query[, k, filter])Run similarity search on query asynchronously
asimilarity_search_by_vector
(embedding[, k, ...])Run similarity search on query embedding asynchronously
Run similarity search asynchronously and return relevance scores
asimilarity_search_with_score
(query[, k, filter])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
get_by_ids
(ids, /)Get documents by their IDs.
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.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using a specified search type.
similarity_search
(query[, k, filter])Run similarity search on query
similarity_search_by_vector
(embedding[, k, ...])Run similarity search on query embedding
Run similarity search synchronously and return relevance scores
similarity_search_with_score
(query[, k, filter])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] ¶
Async run more documents through the embeddings and add to the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of IDs does not match the number of documents.
- 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] ¶
Add or update documents in the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of ids does not match the number of documents.
- 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 ¶
Async return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- 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
- async aget_by_ids(ids: Sequence[str], /) List[Document] ¶
Async get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever ¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
**kwargs (Any) –
Keyword arguments to pass to the search function. Can include: 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
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] ¶
Async return docs most similar to query using a specified search type.
- Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- 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
- get_by_ids(ids: Sequence[str], /) List[Document] ¶
Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- 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, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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 a specified search type.
- Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
Run similarity search on query
- Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –
- Returns
List of Documents most similar along with relevance distance scores
- Return type
List[Tuple[Document, float]]