MariaDB Vector Store

MariaDB Vector Store uses MariaDB 11.7+ native vector storage and similarity search capabilities to store and query document embeddings.

Categories: Artificial Intelligence

Type: mariaDbVectorStore/v1

Connections

Version: 1

custom

Properties

Name	Label	Type	Description	Required
url	JDBC URL	STRING	MariaDB JDBC connection URL (e.g., jdbc:mariadb://localhost:3306/mydb).	true
username	Username	STRING	MariaDB database username.	true
password	Password	STRING	MariaDB database password.	true
tableName	Table Name	STRING	Name of the database table used to store vector embeddings.	false
schemaName	Schema Name	STRING	Database schema name. If not specified, the default schema is used.	false
distanceType	Distance Type	STRING Options COSINE, EUCLIDEAN	Distance function used for vector similarity comparison.	false
dimensions	Dimensions	INTEGER	Number of dimensions for the vector embeddings. If not specified, inferred from the embedding model.	false
initializeSchema	Initialize Schema	BOOLEAN Options true, false	Whether to create the vector store table automatically if it does not exist.	false

Actions

Delete Documents

Name: delete

Delete documents from the vector store by metadata

Properties

Name	Label	Type	Description	Required
metadataFilter	Metadata Filter	ARRAY Items [{}]	List of metadata key-value pairs to filter by. Entries within a group are ANDed; groups are ORed.	false

Example JSON Structure

{
  "label" : "Delete Documents",
  "name" : "delete",
  "parameters" : {
    "metadataFilter" : [ { } ]
  },
  "type" : "mariaDbVectorStore/v1/delete"
}

Output

This action does not produce any output.

Load Documents

Name: load

Loads documents into the vector store using LLM embeddings.

Properties

Name	Label	Type	Description	Required
metadataFilter	Metadata Filter	ARRAY Items [{}]	List of metadata key-value pairs to filter by. Entries within a group are ANDed; groups are ORed.	false

Example JSON Structure

{
  "label" : "Load Documents",
  "name" : "load",
  "parameters" : {
    "metadataFilter" : [ { } ]
  },
  "type" : "mariaDbVectorStore/v1/load"
}

Output

This action does not produce any output.

Search Documents

Name: search

Query documents from the vector store using LLM embeddings.

Properties

Name	Label	Type	Description	Required
query	Query	STRING	The query to be executed.	true
metadataFilter	Metadata Filter	ARRAY Items [{}]	List of metadata key-value pairs to filter by. Entries within a group are ANDed; groups are ORed.	false
topK	Top K	INTEGER	The top 'k' similar results to return.	false
similarityThreshold	Similarity Threshold	NUMBER	Similarity threshold score to filter the search response by. Only documents with similarity score equal or greater than the threshold will be returned. A threshold value of 0 means any similarity is accepted. A threshold value of 1 means an exact match is required.	false

Example JSON Structure

{
  "label" : "Search Documents",
  "name" : "search",
  "parameters" : {
    "query" : "",
    "metadataFilter" : [ { } ],
    "topK" : 1,
    "similarityThreshold" : 0.0
  },
  "type" : "mariaDbVectorStore/v1/search"
}

Output

The output for this action is dynamic and may vary depending on the input parameters. To determine the exact structure of the output, you need to execute the action.

Update Documents

Name: update

Updates documents in the vector store by deleting existing ones matching the metadata filter and loading new ones using LLM embeddings.

Properties

Name	Label	Type	Description	Required
metadataFilter	Metadata Filter	ARRAY Items [{}]	List of metadata key-value pairs to filter by. Entries within a group are ANDed; groups are ORed.	false

Example JSON Structure

{
  "label" : "Update Documents",
  "name" : "update",
  "parameters" : {
    "metadataFilter" : [ { } ]
  },
  "type" : "mariaDbVectorStore/v1/update"
}

Output

This action does not produce any output.

MariaDB Vector Store

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