TablestoreVectorStore(
self,
embedding: Embeddings,
*,
endpoint: Optional[str| Name | Type |
|---|---|
| embedding | Embeddings |
| endpoint | Optional[str] |
| instance_name | Optional[str] |
| access_key_id | Optional[str] |
| access_key_secret | Optional[str] |
| table_name | Optional[str] |
| index_name | Optional[str] |
| text_field | Optional[str] |
| vector_field | Optional[str] |
| vector_dimension | int |
| vector_metric_type | Optional[str] |
| metadata_mappings | Optional[List[Any]] |
Tablestore vector store.
To use, you should have the tablestore python package installed.
Example:
.. code-block:: python
import os
from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import TablestoreVectorStore import tablestore
embeddings = OpenAIEmbeddings() store = TablestoreVectorStore( embeddings, endpoint=os.getenv("end_point"), instance_name=os.getenv("instance_name"), access_key_id=os.getenv("access_key_id"), access_key_secret=os.getenv("access_key_secret"), vector_dimension=512, # metadata mapping is used to filter non-vector fields. metadata_mappings=[ tablestore.FieldSchema( "type", tablestore.FieldType.KEYWORD, index=True, enable_sort_and_agg=True ), tablestore.FieldSchema( "time", tablestore.FieldType.LONG, index=True, enable_sort_and_agg=True ), ] )