cloud-sql-postgres-vectorassist

name	cloud-sql-postgres-vectorassist
description	Use these skills to set up and optimize production-ready vector workloads by simply expressing your intent and performance requirements. Use when this capability is needed.
metadata	{"author":"gemini-cli-extensions"}

Usage

All scripts can be executed using Node.js. Replace <param_name> and <param_value> with actual values.

Bash: node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'

PowerShell: node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'

Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.

Scripts

apply_spec

This tool automatically executes all the SQL recommendations associated with a specific vector specification (spec_id) or table. It runs the necessary commands in the correct sequence to provision the workload, marking each step as applied once successful. Use this tool when the user has reviewed the generated recommendations from a defined (or modified) spec and is ready to apply the changes directly to their database instance to finalize the vector search setup. This tool can be used as a follow-up action after invoking the 'define_spec' or 'modify_spec' tool.

Parameters

Name	Type	Description	Required	Default
spec_id	string	The unique ID of the vector specification to apply.	No
table_name	string	The name of the table to apply the vector specification to (in case of a single spec defined on the table).	No
column_name	string	The text_column_name or vector_column_name of the spec to identify the exact spec in case there are multiple specs defined on a table.	No
schema_name	string	The schema name for the table.	No

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define_spec

This tool defines a new vector specification by capturing the user's intent and requirements for a vector search workload. This generates a complete, ordered set of SQL recommendations required to set up the database, embeddings, and vector indexes. While highly customizable, any optional parameters left unspecified will use internally determined defaults optimized for the specific workload. Use this tool at the very beginning of the vector setup process when a user first wants to configure a table for vector search, generate embeddings, or create a new vector index.

Parameters

Name	Type	Description	Required	Default
table_name	string	Table name on which vector workload needs to be set up.	Yes
schema_name	string	Schema containing the given table.	No
spec_id	string	Unique ID for the vector spec. Auto-generated, if not specified.	No
vector_column_name	string	Column name for the column with vector embeddings.	No
text_column_name	string	Column name for the column with text on which vector search needs to be set up.	No
vector_index_type	string	Type of the vector index to be created (Allowed inputs: 'hnsw', 'ivfflat', 'scann').	No
embeddings_available	boolean	Boolean parameter to know if vector embeddings are already available in the table.	No
num_vectors	integer	Number of vectors expected in the dataset.	No
dimensionality	integer	If vectors are already generated, set to dimension of vectors. If not, set to dimensionality of the embedding_model.	No
embedding_model	string	Optional parameter: Model to be used for generating embeddings. If not provided, it has an internally selected default value.	No

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execute_sql

Use this tool to execute a single SQL statement.

Parameters

Name	Type	Description	Required	Default
sql	string	The sql to execute.	Yes

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generate_query

This tool generates optimized SQL queries for vector search by leveraging the metadata and vector specifications defined in a specific spec_id. It may return a single query or a sequence of multiple SQL queries that can be executed sequentially. Use this tool when a user wants to perform semantic or similarity searches on their data. It serves as the primary actionable tool to invoke for generating the executable SQL required to retrieve relevant results based on vector similarity. The 'execute_sql' tool can be used as a follow-up action after invoking this tool.

Parameters

Name	Type	Description	Required	Default
spec_id	string	Generate the vector query corresponding to this vector spec.	No
table_name	string	Generate the vector query corresponding to this table (in case of a single spec defined on the table).	No
schema_name	string	Schema name for the table related to the vector query generation.	No
column_name	string	text_column_name or vector_column_name of the spec to identify the exact spec in case there are multiple specs defined on a table.	No
search_text	string	Text search for which query needs to be generated. Embeddings are generated using the model defined in the vector spec.	No
search_vector	string	Vector for which query needs to be generated. Only one of search_text or search_vector must be populated.	No
output_column_names	array	Column names to retrieve in the output search query. Defaults to retrieving all columns.	No
top_k	integer	Number of nearest neighbors to be returned in the vector search query. Defaults to 10.	No
filter_expressions	array	Any filter expressions to be applied on the vector search query.	No
target_recall	float	The recall that the user would like to target with the given query. Overrides the spec-level target_recall.

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modify_spec

This tool modifies an existing vector specification (identified by a required spec_id) with new parameters or overrides. Upon modification, it automatically recalculates and refreshes the list of generated SQL recommendations to match the updated requirements. This tool provides a way to modify column(s) in the vector spec before applying and taking action on the recommendations. While highly customizable, any optional parameters left unspecified will use internally determined defaults optimized for the specific workload. Use this tool to modify configurations established via 'define_spec' tool such as adjusting target recall, embedding models, or quantization settings, etc.

Parameters

Name	Type	Description	Required	Default
spec_id	string	Unique ID for the vector spec you want to modify.	Yes
table_name	string	Modify the table name on which vector workload needs to be set up.	No
schema_name	string	Modify the schema containing the given table.	No
vector_column_name	string	Modify the column name for the column with vector embeddings.	No
text_column_name	string	Modify the column name for the column with text on which vector search needs to be set up.	No
vector_index_type	string	Modify the type of the vector index to be created (Allowed inputs: 'hnsw', 'ivfflat', 'scann').	No
embeddings_available	boolean	Modify whether vector embeddings are already available in the table.	No
num_vectors	integer	Modify the number of vectors expected in the dataset.	No
dimensionality	integer	Modify the dimensionality of the vectors or embedding model.	No
embedding_model	string	Modify the model used for generating embeddings.	No
prefilter_column_names

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Source: gemini-cli-extensions/cloud-sql-postgresql — distributed by TomeVault.