SurrealDBStore(
self,
embedding_function: Embeddings,
**kwargs: Any = {}
)| Name | Type | Description |
|---|---|---|
embedding_function* | Embeddings | Embedding function to use. |
dburl* | unknown | SurrealDB connection url |
ns* | unknown | surrealdb namespace for the vector store. (default: "langchain") |
db* | unknown | surrealdb database for the vector store. (default: "database") |
collection* | unknown | surrealdb collection for the vector store. (default: "documents") |
(optional) db_user and db_pass* | unknown |
| Name | Type |
|---|---|
| embedding_function | Embeddings |
SurrealDB as Vector Store.
To use, you should have the surrealdb python package installed.
Example:
.. code-block:: python
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)
surrealdb credentials