Epsilla(
self,
client: Any,
embeddings: Embeddings,
db_path: Optional[str| Name | Type | Description |
|---|---|---|
client* | Any | Epsilla client to connect to. |
embeddings* | Embeddings | Function used to embed the texts. |
db_path | Optional[str] | Default: _LANGCHAIN_DEFAULT_DB_PATHThe path where the database will be persisted. Defaults to "/tmp/langchain-epsilla". |
db_name | Optional[str] | Default: _LANGCHAIN_DEFAULT_DB_NAME |
Wrapper around Epsilla vector database.
As a prerequisite, you need to install pyepsilla package
and have a running Epsilla vector database (for example, through our docker image)
See the following documentation for how to run an Epsilla vector database:
https://epsilla-inc.gitbook.io/epsilladb/quick-start
Example: .. code-block:: python
from langchain_community.vectorstores import Epsilla
from pyepsilla import vectordb
client = vectordb.Client()
embeddings = OpenAIEmbeddings()
db_path = "/tmp/vectorstore"
db_name = "langchain_store"
epsilla = Epsilla(client, embeddings, db_path, db_name)
Give a name to the loaded database. Defaults to "langchain_store".