datacontract-editor/embed.html at main · datacontract/datacontract-editor · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Data Contract Editor - Embed Example</title>
    <link rel="stylesheet" href="./dist/datacontract-editor.css">
    <style>
        body {
            margin: 0;
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
            'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
            sans-serif;
        }
        html, body {
            height: 100%;
        }
        #editor {
            height: 100%;
            width: 100%;
        }
    </style>
</head>
<body>
<div id="editor"></div>

<script type="module">
    // Import the ESM build
    import { init } from './dist/datacontract-editor.es.js';

    // Function to load initial YAML content
    async function loadInitialYaml() {
        return `apiVersion: "v3.1.0"
kind: "DataContract"
id: embed-example
version: "1.0.0"
status: draft
servers:
  - server: production
    type: snowflake
    account: my-account
    database: ANALYTICS_DB
    schema: PUBLIC
`;
    }

    // Team and domain lists
    const teams = [
        { id: 'team-1', name: 'Data Engineering Team' },
        { id: 'team-2', name: 'Analytics Team' },
        { id: 'team-3', name: 'Platform Team' }
    ];

    const domains = [
        { id: 'logistics', name: 'Logistics' },
        { id: 'finance', name: 'Finance' },
        { id: 'sales', name: 'Sales' },
        { id: 'marketing', name: 'Marketing' }
    ];

    // =========================================================================
    // Mock Definition Search Callback
    // =========================================================================

    // =========================================================================
    // Custom AI Tools - MCP-like extensibility
    // =========================================================================

    /**
     * Example: Business Glossary Lookup Tool
     * Allows AI to look up business term definitions
     */
    const businessGlossaryTool = {
        name: 'lookupBusinessTerm',
        description: 'Look up the definition of a business term from the company glossary. Use this when you need to understand what a field or concept means in the business context.',
        parameters: {
            type: 'object',
            properties: {
                term: {
                    type: 'string',
                    description: 'The business term to look up'
                }
            },
            required: ['term']
        },
        handler: async ({ term }) => {
            // Simulated glossary - in production, this would call your API
            const glossary = {
                'revenue': 'Total income generated from sales before any deductions',
                'mrr': 'Monthly Recurring Revenue - predictable revenue from subscriptions',
                'churn': 'Rate at which customers stop using the service',
                'ltv': 'Lifetime Value - total revenue expected from a customer',
                'cac': 'Customer Acquisition Cost - cost to acquire a new customer',
                'arpu': 'Average Revenue Per User',
                'nps': 'Net Promoter Score - customer loyalty metric',
            };

            const result = glossary[term.toLowerCase()];
            if (result) {
                return { term, definition: result, found: true };
            }
            return { term, found: false, message: `Term "${term}" not found in glossary` };
        }
    };

    /**
     * Example: Data Quality Validation Tool
     * Allows AI to validate data contract against quality rules
     */
    const validateQualityTool = {
        name: 'validateDataQuality',
        description: 'Validate the data contract against enterprise data quality standards. Returns a list of quality issues and recommendations.',
        parameters: {
            type: 'object',
            properties: {
                checkType: {
                    type: 'string',
                    enum: ['all', 'naming', 'documentation', 'schema'],
                    description: 'Type of quality check to perform'
                }
            },
            required: ['checkType']
        },
        handler: async ({ checkType }, context) => {
            // Access the current YAML from context
            const yaml = context.yaml;
            const issues = [];

            // Simulated quality checks
            if (checkType === 'all' || checkType === 'naming') {
                if (yaml.includes('field1') || yaml.includes('col1')) {
                    issues.push({
                        type: 'naming',
                        severity: 'warning',
                        message: 'Generic field names detected. Use descriptive business names.'
                    });
                }
            }

            if (checkType === 'all' || checkType === 'documentation') {
                if (!yaml.includes('description:')) {
                    issues.push({
                        type: 'documentation',
                        severity: 'error',
                        message: 'Missing descriptions. All fields should have descriptions.'
                    });
                }
            }

            if (checkType === 'all' || checkType === 'schema') {
                if (!yaml.includes('type:')) {
                    issues.push({
                        type: 'schema',
                        severity: 'warning',
                        message: 'Some fields may be missing type definitions.'
                    });
                }
            }

            return {
                checkType,
                issueCount: issues.length,
                issues,
                passed: issues.filter(i => i.severity === 'error').length === 0
            };
        }
    };

    /**
     * Example: Test Runner Tool
     * Allows AI to execute data contract tests
     */
    const runTestsTool = {
        name: 'runContractTests',
        description: 'Execute tests against the data contract to verify it meets quality and schema requirements. Returns test results.',
        parameters: {
            type: 'object',
            properties: {
                server: {
                    type: 'string',
                    description: 'Optional server name to test against'
                },
                testType: {
                    type: 'string',
                    enum: ['schema', 'quality', 'all'],
                    description: 'Type of tests to run'
                }
            },
            required: []
        },
        handler: async ({ server, testType = 'all' }, context) => {
            // In production, this would call your test API
            // For demo, we'll use the editor's built-in test runner
            try {
                const store = context.editorConfig;
                const testUrl = store?.tests?.dataContractCliApiServerUrl || 'https://api.datacontract.com';

                // Simulated test result
                return {
                    testType,
                    server: server || 'default',
                    results: {
                        passed: 8,
                        failed: 2,
                        skipped: 1,
                        total: 11
                    },
                    summary: 'Most tests passed. 2 quality checks need attention.',
                    testUrl
                };
            } catch (error) {
                return { error: error.message };
            }
        }
    };

    /**
     * Example: Schema Suggestion Tool
     * Allows AI to get schema suggestions from external catalog
     */
    const schemaSuggestionTool = {
        name: 'getSchemaSuggestions',
        description: 'Get schema suggestions from the enterprise data catalog based on a table or dataset name.',
        parameters: {
            type: 'object',
            properties: {
                tableName: {
                    type: 'string',
                    description: 'Name of the table or dataset to get suggestions for'
                },
                database: {
                    type: 'string',
                    description: 'Optional database name'
                }
            },
            required: ['tableName']
        },
        handler: async ({ tableName, database }) => {
            // Simulated catalog lookup
            const suggestions = {
                'customers': {
                    fields: [
                        { name: 'customer_id', type: 'string', description: 'Unique customer identifier' },
                        { name: 'email', type: 'string', description: 'Customer email address' },
                        { name: 'created_at', type: 'timestamp', description: 'Account creation timestamp' },
                        { name: 'status', type: 'string', description: 'Customer status (active, inactive, churned)' }
                    ],
                    owner: 'Data Engineering Team',
                    lastUpdated: '2024-01-15'
                },
                'orders': {
                    fields: [
                        { name: 'order_id', type: 'string', description: 'Unique order identifier' },
                        { name: 'customer_id', type: 'string', description: 'Reference to customer' },
                        { name: 'total_amount', type: 'decimal', description: 'Order total in cents' },
                        { name: 'order_date', type: 'date', description: 'Date order was placed' }
                    ],
                    owner: 'Analytics Team',
                    lastUpdated: '2024-01-20'
                }
            };

            const key = tableName.toLowerCase();
            if (suggestions[key]) {
                return {
                    tableName,
                    database: database || 'default',
                    ...suggestions[key],
                    found: true
                };
            }
            return { tableName, found: false, message: `No schema found for table "${tableName}"` };
        }
    };

    // Load initial YAML and initialize
    const initialYaml = await loadInitialYaml();

    // Initialize the editor with AI configuration and custom tools
    const editor = init({
        container: '#editor',
        mode: 'EMBEDDED',
        yaml: initialYaml,
        schemaUrl: 'https://raw.githubusercontent.com/bitol-io/open-data-contract-standard/refs/heads/dev/schema/odcs-json-schema-v3.1.0.json',
        initialView: 'form',

        // Callbacks
        onSave: (yaml, { markers, yamlParseError }) => {
            const errors = markers.filter(m => m.severity === 8);
            if (errors.length > 0) {
                alert(`Cannot save: ${errors.length} validation error(s)`);
                return false;
            }
            console.log('Save called with YAML:', yaml);
            alert('Contract saved!');
        },
        onCancel: () => {
            console.log('Cancel clicked');
            alert('Cancel was called!');
        },
        onDelete: () => {
            console.log('Delete clicked');
            alert('Delete was called!');
        },

        // Semantics configuration
        semantics: {
            enabled: true,
						baseUrl: "/embed/definitions.json",
						queryParam: "q",
						pageParam: "page",
						definitionAcceptHeader: "application/json"
        },

        // Test configuration
        tests: {
            enabled: false,
            dataContractCliApiServerUrl: 'https://api.datacontract.com',
        },

        // Teams and domains
        teams: teams,
        domains: domains,

        managedTags: [{ tag: "pii"}, { tag: "example", href: "https://example.com"}],

        // =====================================================================
        // AI Configuration - OpenAI-compatible endpoint
        // =====================================================================
        ai: {
            enabled: true,

            // OpenAI-compatible endpoint (works with OpenAI, Azure, Ollama, OpenRouter, etc.)
            // Azure example: 'https://your-resource.cognitiveservices.azure.com/openai/v1/chat/completions'
            // OpenAI: 'https://api.openai.com/v1/chat/completions'
            // Ollama: 'http://localhost:11434/v1/chat/completions'
            endpoint: 'https://datacontract-editor-ai.cognitiveservices.azure.com/openai/v1/chat/completions',

            // API Key
            apiKey: '<your-api-key>',

            // Model/deployment name
            model: 'model-router',

            // Auth header style: 'bearer' (default) or 'api-key' (Azure style)
            authHeader: 'api-key',

            // Custom headers (optional)
            headers: {
                // 'X-Custom-Header': 'value'
            },

            // Custom tools - MCP-like extensibility
            tools: [
                businessGlossaryTool,
                validateQualityTool,
                runTestsTool,
                schemaSuggestionTool,
            ],
        },

        persistence: 'sessionStorage'
    });

    console.log('Editor initialized with AI tools:', editor);

    // =========================================================================
    // Runtime tool registration example
    // =========================================================================

    // You can also register tools after initialization
    editor.ai.registerTool({
        name: 'getSystemTime',
        description: 'Get the current system time. Useful for adding timestamps.',
        parameters: {
            type: 'object',
            properties: {
                timezone: {
                    type: 'string',
                    description: 'Timezone (e.g., "UTC", "America/New_York")'
                }
            },
            required: []
        },
        handler: async ({ timezone = 'UTC' }) => {
            const now = new Date();
            return {
                iso: now.toISOString(),
                formatted: now.toLocaleString('en-US', { timeZone: timezone }),
                timezone
            };
        }
    });

    // Example using helper templates
    // const lookupTool = editor.ai.helpers.createLookupTool({
    //     name: 'lookupColumn',
    //     description: 'Look up column metadata from the data dictionary',
    //     lookupFn: async (query) => {
    //         // Your lookup logic here
    //         return [{ name: query, type: 'string', description: '...' }];
    //     }
    // });
    // lookupTool.register();

</script>
</body>
</html>