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This PR introduces a significant update to the Toolbox configuration file format, which is one of the primary **breaking changes** required for the implementation of the Advanced Control Plane. # Summary of Changes The configuration schema has been updated to enforce resource isolation and facilitate atomic, incremental updates. * Resource Isolation: Resource definitions are now separated into individual blocks, using a distinct structure for each resource type (Source, Tool, Toolset, etc.). This improves readability, management, and auditing of configuration files. * Field Name Modification: Internal field names have been modified to align with declarative methodologies. Specifically, the configuration now separates kind (general resource type, e.g., Source) from type (specific implementation, e.g., Postgres). # User Impact Existing tools.yaml configuration files are now in an outdated format. Users must eventually update their files to the new YAML format. # Mitigation & Compatibility Backward compatibility is maintained during this transition to ensure no immediate user action is required for existing files. * Immediate Backward Compatibility: The source code includes a pre-processing layer that automatically detects outdated configuration files (v1 format) and converts them to the new v2 format under the hood. * [COMING SOON] Migration Support: The new toolbox migrate subcommand will be introduced to allow users to automatically convert their old configuration files to the latest format. # Example Example for config file v2: ``` kind: sources name: my-pg-instance type: cloud-sql-postgres project: my-project region: my-region instance: my-instance database: my_db user: my_user password: my_pass --- kind: authServices name: my-google-auth type: google clientId: testing-id --- kind: tools name: example_tool type: postgres-sql source: my-pg-instance description: some description statement: SELECT * FROM SQL_STATEMENT; parameters: - name: country type: string description: some description --- kind: tools name: example_tool_2 type: postgres-sql source: my-pg-instance description: returning the number one statement: SELECT 1; --- kind: toolsets name: example_toolset tools: - example_tool ``` --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Averi Kitsch <akitsch@google.com>
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title, type, weight, description
| title | type | weight | description |
|---|---|---|---|
| MindsDB Tools | docs | 1 | MindsDB tools that enable SQL queries across hundreds of datasources and ML models. |
About
MindsDB is the most widely adopted AI federated database that enables you to query hundreds of datasources and ML models through a single SQL interface. The following tools work with MindsDB databases:
- mindsdb-execute-sql - Execute SQL queries directly on MindsDB
- mindsdb-sql - Execute parameterized SQL queries on MindsDB
These tools leverage MindsDB's capabilities to:
- Connect to Multiple Datasources: Query databases, APIs, file systems, and more through SQL
- Cross-Datasource Operations: Perform joins and analytics across different data sources
- ML Model Integration: Use trained ML models as virtual tables for predictions
- Unstructured Data Processing: Query documents, images, and other unstructured data as structured tables
- Real-time Predictions: Get real-time predictions from ML models through SQL
- API Translation: Automatically translate SQL queries into REST APIs, GraphQL, and native protocols
Supported Datasources
MindsDB automatically translates your SQL queries into the appropriate APIs for hundreds of datasources:
Business Applications
- Salesforce: Query leads, opportunities, accounts, and custom objects
- Jira: Access issues, projects, workflows, and team data
- GitHub: Query repositories, commits, pull requests, and issues
- Slack: Access channels, messages, and team communications
- HubSpot: Query contacts, companies, deals, and marketing data
Databases & Storage
- MongoDB: Query NoSQL collections as structured tables
- PostgreSQL/MySQL: Standard relational databases
- Redis: Key-value stores and caching layers
- Elasticsearch: Search and analytics data
- S3/Google Cloud Storage: File storage and data lakes
Communication & Email
- Gmail/Outlook: Query emails, attachments, and metadata
- Microsoft Teams: Team communications and files
- Discord: Server data and message history
Analytics & Monitoring
- Google Analytics: Website traffic and user behavior
- Mixpanel: Product analytics and user events
- Datadog: Infrastructure monitoring and logs
- Grafana: Time-series data and metrics
Example Use Cases
Cross-Datasource Analytics
-- Join Salesforce opportunities with GitHub activity
SELECT
s.opportunity_name,
s.amount,
g.repository_name,
COUNT(g.commits) as commit_count
FROM salesforce.opportunities s
JOIN github.repositories g ON s.account_id = g.owner_id
WHERE s.stage = 'Closed Won';
Email & Communication Analysis
-- Analyze email patterns with Slack activity
SELECT
e.sender,
e.subject,
s.channel_name,
COUNT(s.messages) as message_count
FROM gmail.emails e
JOIN slack.messages s ON e.sender = s.user_name
WHERE e.date >= '2024-01-01';
ML Model Predictions
-- Use ML model to predict customer churn
SELECT
customer_id,
customer_name,
predicted_churn_probability,
recommended_action
FROM customer_churn_model
WHERE predicted_churn_probability > 0.8;
Since MindsDB implements the MySQL wire protocol, these tools are functionally compatible with MySQL tools while providing access to MindsDB's advanced federated database capabilities.
Working Configuration Example
Here's a complete working configuration that has been tested:
kind: sources
name: my-pg-source
type: mindsdb
host: 127.0.0.1
port: 47335
database: files
user: mindsdb
---
kind: tools
name: mindsdb-execute-sql
type: mindsdb-execute-sql
source: my-pg-source
description: |
Execute SQL queries directly on MindsDB database.
Use this tool to run any SQL statement against your MindsDB instance.
Example: SELECT * FROM my_table LIMIT 10