google__adk-docs/docs/integrations/spanner.md

catalog_title, catalog_description, catalog_icon, catalog_tags

catalog_title	catalog_description	catalog_icon	catalog_tags
Spanner Tools	Interact with Spanner to retrieve data, search, and execute SQL	/integrations/assets/spanner.png	data google

Google Cloud Spanner tool for ADK

Supported in ADKPython v1.11.0Experimental

Google Cloud Spanner is a fully managed, distributed database with support for SQL and vector search. The ADK Spanner tools let your agent explore database schemas, run SQL queries, and perform vector similarity search against your Spanner data.

!!! example "Experimental" This feature is experimental and may be updated in future releases.

Available tools

The SpannerToolset provides the following tools:

• list_table_names: Fetches table names present in a GCP Spanner database.
• list_table_indexes: Fetches table indexes present in a GCP Spanner database.
• list_table_index_columns: Fetches table index columns present in a GCP Spanner database.
• list_named_schemas: Fetches named schema for a Spanner database.
• get_table_schema: Fetches Spanner database table schema and metadata information.
• execute_sql: Runs a SQL query in Spanner database and fetch the result.
• similarity_search: Similarity search in Spanner using a text query.

Use with agent

--8<-- "examples/python/snippets/tools/built-in-tools/spanner.py"

Vector similarity search

The vector_store_similarity_search tool enables agents to perform semantic searches against a Spanner table configured as a vector store. This capability is essential for building contextually aware RAG applications; it allows AI models to retrieve database context based on semantic meaning rather than exact keyword matches. By configuring SpannerVectorStoreSettings, your agents can better understand the intent behind user queries and ground their responses in the most relevant Spanner data.

The following example configures a Spanner table as a vector store and wires the vector_store_similarity_search tool into a RAG agent:

from google.adk.agents import LlmAgent
from google.adk.tools.spanner import SpannerCredentialsConfig, SpannerToolset
from google.adk.tools.spanner.settings import (
    Capabilities,
    SpannerToolSettings,
    SpannerVectorStoreSettings,
)

# 1. Define Spanner tool config with vector store settings
my_vector_store_settings = SpannerVectorStoreSettings(
    project_id="your-gcp-project",
    instance_id="your-spanner-instance",
    database_id="your-database",
    table_name="my_products",
    content_column="productDescription",
    embedding_column="productDescriptionEmbedding",
    vector_length=768,
    vertex_ai_embedding_model_name="text-embedding-005",
    selected_columns=["productId", "productName", "productDescription"],
    nearest_neighbors_algorithm="EXACT_NEAREST_NEIGHBORS",
    top_k=3,
    distance_type="COSINE",
    additional_filter="inventoryCount > 0",
)

my_tool_settings = SpannerToolSettings(
    capabilities=[Capabilities.DATA_READ],
    vector_store_settings=my_vector_store_settings,
)

# 2. Initialize the Spanner toolset
credentials_config = SpannerCredentialsConfig()
my_spanner_toolset = SpannerToolset(
    credentials_config=credentials_config,
    spanner_tool_settings=my_tool_settings,
    tool_filter=["vector_store_similarity_search"],
)

# 3. Use the toolset in your RAG agent
my_rag_agent = LlmAgent(
    model="gemini-flash-latest",
    name="product_search_agent",
    instruction="""
    You are a helpful assistant that answers user questions by finding similar products.
    1. Always use the `vector_store_similarity_search` tool to find relevant product information.
    2. If no relevant information is found, state that no matching products were found.
    3. Present the relevant product details clearly in your response.
    """,
    tools=[my_spanner_toolset],
)

Configuration

The SpannerVectorStoreSettings class used above defines how vector_store_similarity_search operates. It accepts the following parameters:

Required parameters

• project_id: Your Google Cloud Project ID required for authentication context.
• instance_id: The Spanner instance ID.
• database_id: The Spanner database ID.
• table_name: The Spanner table containing the vector embeddings.
• embedding_column: The ARRAY<FLOAT> or ARRAY<DOUBLE> column where the vector embeddings are stored.
• content_column: The column containing the original text or content to be retrieved.
• vector_length: The dimensionality of your embedding vectors that must match your model.
• vertex_ai_embedding_model_name: The model used to generate the embeddings, for example "text-embedding-005".

Optional parameters

• selected_columns: A list of columns you can include in the search results, such as metadata or identifiers.
• nearest_neighbors_algorithm: The algorithm you use for the search, such as EXACT_NEAREST_NEIGHBORS and APPROXIMATE_NEAREST_NEIGHBORS.
  • num_leaves_to_search: Number of index leaf nodes searched. Only used with APPROXIMATE_NEAREST_NEIGHBORS.
  • vector_search_index_settings: Vector index settings. Only required with APPROXIMATE_NEAREST_NEIGHBORS.
• top_k: The number of nearest neighbors to retrieve per query.
• distance_type: The distance metric used for similarity calculation, such as COSINE or EUCLIDEAN.
• additional_filter: An optional SQL filter string to apply during the search, for example: "inventoryCount > 0".