SQLiteVec(
self,
table: str,
connection: Optional[sqlite3.Connection],
embedding: Embeddings| Name | Type |
|---|---|
| table | str |
| connection | Optional[sqlite3.Connection] |
| embedding | Embeddings |
| db_file | str |
SQLite with Vec extension as a vector database.
To use, you should have the sqlite-vec python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import SQLiteVec
from langchain_community.embeddings.openai import OpenAIEmbeddings
...
Add more texts to the vectorstore index. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. kwargs: vectorstore specific parameters
Return docs most similar to query.
Return docs most similar to query.
Return VectorStore initialized from texts and embeddings.
Function that does a dummy embedding to figure out how many dimensions this embedding function returns. Needed for the virtual table DDL.