Search Vector Embeddings¤
Python Plugin
This operator is part of a Python Plugin Package. In order to use it, you need to install it, e.g. with cmemc.
This workflow task search for the top-k metadata stored into Postgres Vector Store.
The incoming embedding entities are used to retrieve the nearest top-k vectors in the collection stored in the Postgres Vector Store. It is possible to specify which paths are going to be used for searching as well as which Postgres Vector Store and collection name.
The task uses the embeddings from the path configured with the Embedding Query Path
parameter (embedding_query_path, default value: _embedding) to search over the collection.
The results are provided in the output path configured with the Search Result Path parameter
(search_result_path, default value: _search_result).
The results in this output are structured like this:
Parameter¤
Database Host¤
The hostname of the postgres database service.
- Datatype:
string - Default Value:
pgvector
Database Port¤
The port number of the postgres database service.
- Datatype:
Long - Default Value:
5432
Database User¤
The account name used to login to the postgres database service.
- Datatype:
string - Default Value:
pgvector
Database Password¤
The password of the database account.
- Datatype:
password - Default Value:
None
Database Name¤
The database name.
- Datatype:
string - Default Value:
pgvector
Collection Name¤
The name of the collection that will be used for search.
- Datatype:
string - Default Value:
None
Search Result Path¤
The path containing the search result in the output entities.
- Datatype:
string - Default Value:
_search_result
Embedding Query Path¤
The path containing the embedding to be used for searching.
- Datatype:
string - Default Value:
_embedding
Top-k¤
The number of entries to be returned in the search result.
- Datatype:
Long - Default Value:
10
Distance Strategy¤
The distance strategy to use. (default: COSINE)
- Datatype:
enumeration - Default Value:
COSINE