Skip to main content

Data Analysis Tools

Solace Agent Mesh (SAM) includes a suite of optional built-in tools that enable agents to perform data analysis tasks directly on artifacts. These tools provide functionality for SQL querying, JQ transformations, and Plotly chart generation.

Setup and Configuration​

Enable the data analysis tool group in the agent's app_config.yml file.

# In your agent's app_config:
tools:
  - tool_type: builtin-group
    group_name: "data_analysis"

# Optional: Configure tool behavior
data_tools_config:
  sqlite_memory_threshold_mb: 100
  max_result_preview_rows: 50
  max_result_preview_bytes: 4096

Available Tools​

query_data_with_sql​

Enterprise Only

This feature is available in the Enterprise Edition only.

Executes a SQL query against data stored in a CSV or SQLite artifact.

  • Parameters:
    • input_filename (str): The filename of the input artifact (for example, 'data.csv', 'mydatabase.sqlite'). Supports versioning (for example, 'data.csv:2').
    • sql_query (str): The SQL query string to execute.
    • output_format (str, optional): The desired format for the output artifact ('csv' or 'json'). Defaults to 'csv'.
  • Behavior:
    • For CSV Input: The tool loads the CSV data into a temporary in-memory SQLite database table named data and executes the query against it.
    • For SQLite Input: The tool connects directly to the specified SQLite database artifact in read-only mode.
  • Returns: A dictionary containing the execution status, a preview of the query result, and the output_filename where the full result set is stored.

create_sqlite_db​

Enterprise Only

This feature is available in the Enterprise Edition only.

Converts a CSV or JSON artifact into a persistent SQLite database artifact. This is the recommended approach for executing multiple queries on the same dataset, as it avoids repeated parsing of the source file.

  • Parameters:
    • input_filename (str): The filename of the input CSV or JSON artifact.
    • output_db_filename (str): The desired filename for the output SQLite database artifact (for example, 'queryable_dataset.sqlite').
    • table_name (str, optional): The name of the table to be created within the SQLite database. Defaults to 'data'.
  • Returns: A dictionary confirming the successful creation of the database artifact and providing its output_filename.

transform_data_with_jq​

Enterprise Only

This feature is available in the Enterprise Edition only.

Applies a JQ expression to transform data from a JSON, YAML, or CSV artifact.

  • Parameters:
    • input_filename (str): The filename of the input artifact.
    • jq_expression (str): The JQ filter or transformation expression string (for example, '.users[] | {name, id}').
  • Returns: A dictionary containing the execution status, a preview of the transformed data, and the output_filename where the full JSON result is stored.

create_chart_from_plotly_config​

Generates a static chart image (for example, PNG, JPG, SVG) from a Plotly configuration provided as a string.

  • Parameters:
    • config_content (str): A JSON or YAML formatted string representing the Plotly figure dictionary.
    • config_format (str): Specifies whether config_content is 'json' or 'yaml'.
    • output_filename (str): The desired filename for the output image artifact (for example, 'sales_chart.png').
    • output_format (str, optional): The desired image format ('png', 'jpeg', 'svg', etc.). Defaults to 'png'.
  • Returns: A dictionary confirming the chart's creation and providing its output_filename.

Example Workflow: Querying a Large CSV​

The following workflow demonstrates an efficient method for analyzing a large CSV file:

  1. User Request: "I need to run several queries on large_data.csv."
  2. Agent Strategy: The agent determines that converting the CSV to a SQLite database is more performant for subsequent queries.
  3. Agent Call 1: The agent calls create_sqlite_db to convert large_data.csv into a new artifact, queryable_data.sqlite.
  4. Agent Response: "The data has been prepared for querying. What is your first question?"
  5. User Request: "Find all records where the category is 'Sales'."
  6. Agent Call 2: The agent calls query_data_with_sql, targeting the queryable_data.sqlite artifact.
  7. Agent Response: The agent provides the results of the query.
  8. User Request: "Now, find the average amount for the 'Marketing' category."
  9. Agent Call 3: The agent calls query_data_with_sql again on the same queryable_data.sqlite artifact, avoiding the overhead of reprocessing the original CSV file.

Technical Considerations​

Result Handling​

  • Previews: For query_data_with_sql and transform_data_with_jq, the tools return a truncated preview of the result directly to the LLM for immediate context.
  • Full Results: The complete, untruncated result sets are always saved as new artifacts. The LLM is provided with the filename and version of these artifacts.
  • Accessing Full Results: To utilize the full results, the agent can employ file management tools (load_artifact) or Dynamic Embeds («artifact_content:...»).

Security​

  • SQL Execution: Queries against existing SQLite artifacts are performed in read-only mode to prevent data modification. Queries against temporary databases generated from CSVs are isolated.
  • JQ Execution: JQ expressions are executed within a sandboxed Python library, not via shell execution.
  • Resource Usage: Complex queries or transformations can be resource-intensive. Monitor performance and resource consumption accordingly.