},
      "table": {
         "title": "Table",
         "default": "langchain",
         "env_names": "{'myscale_table'}",
         "type": "string"
      },
      "metric": {
         "title": "Metric",
         "default": "cosine",
         "env_names": "{'myscale_metric'}",
         "type": "string"
      }
   },
   "additionalProperties": false
}
Config
env_file: str = .env
env_file_encoding: str = utf-8
env_prefix: str = myscale_
Fields
column_map (Dict[str, str])
database (str)
host (str)
index_param (Optional[Dict[str, str]])
index_type (str)
metric (str)
password (Optional[str])
port (int)
table (str)
username (Optional[str])
field column_map: Dict[str, str] = {'id': 'id', 'metadata': 'metadata', 'text': 'text', 'vector': 'vector'}#
field database: str = 'default'#
field host: str = 'localhost'#
field index_param: Optional[Dict[str, str]] = None#
field index_type: str = 'IVFFLAT'#
field metric: str = 'cosine'#
field password: Optional[str] = None#
field port: int = 8443#
field table: str = 'langchain'#
field username: Optional[str] = None#
class langchain.vectorstores.OpenSearchVectorSearch(opensearch_url: str, index_name: str, embedding_function: langchain.embeddings.base.Embeddings, **kwargs: Any)[source]#
Wrapper around OpenSearch as a vector database.
Example
from langchain import OpenSearchVectorSearch
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html