integration.tsā¢4.75 kB
/**
* Context Engine Integration
*
* Integrates the POC context engine with the existing MCP server
* by adding new tools and enhancing the AI orchestration layer
*/
import type { AIClient } from '../ai/client.js';
import type { OrchestratorManager } from '../orchestrator/manager.js';
import {
INTELLIGENCE_ANALYSIS_WORKFLOW
} from './workflows.js';
import { createLogger } from '../utils/logging.js';
const logger = createLogger('context-integration');
/**
* Context engine integration that adds large context capabilities
*/
export class ContextEngineIntegration {
constructor(
private aiClient: AIClient,
private orchestrator: OrchestratorManager
) {
// Context engine integration for intelligence analysis
}
/**
* Get context engine tools for MCP server registration
* NOTE: These tools are not currently exposed to clients - they're handled internally by ai_process
*/
getContextTools() {
// Context engine tools are integrated into ai_process workflow
// No separate tools exposed to avoid overwhelming LLM with too many options
return [];
// Previously defined but not exposed:
// - analyze_intelligence_layer (now handled via ai_process requests)
}
/**
* Handle context engine tool calls
* NOTE: Context engine capabilities are now integrated into ai_process workflow
*/
async handleToolCall(toolName: string, parameters: any): Promise<any> {
logger.info('Context engine tool call (deprecated)', { toolName, parameters });
// Context engine tools are no longer exposed separately
// All functionality is available through ai_process requests
throw new Error(`Context engine tools are integrated into ai_process. Use ai_process instead of ${toolName}`);
}
/**
* Handle intelligence layer analysis using workflow
*/
private async handleIntelligenceAnalysis(parameters: any) {
// Execute the intelligence analysis workflow
const workflowResult = await this.executeWorkflow(
'intelligence_layer_analysis',
INTELLIGENCE_ANALYSIS_WORKFLOW,
{
working_directory: process.cwd(),
original_query: 'Show me the current intelligence layer implementation, specifically the codebase analysis, quality assessment, and any existing context management or indexing capabilities. I want to understand what\'s already implemented vs what\'s placeholder code.',
timestamp: Date.now()
}
);
return {
type: 'text',
text: this.formatWorkflowResult(workflowResult, 'Intelligence Layer Analysis')
};
}
/**
* Execute a workflow using the existing workflow engine
*/
private async executeWorkflow(
workflowId: string,
workflowDefinition: any,
variables: Record<string, any>
) {
// This would integrate with your existing workflow engine
// For now, we'll simulate the workflow execution
logger.info('Executing context workflow', { workflowId, variables });
// In a real implementation, this would:
// 1. Use your WorkflowEngine to execute the workflow
// 2. Handle step-by-step execution with proper error handling
// 3. Return the final workflow result
// For POC, we'll simulate a successful workflow execution
return {
success: true,
workflowId,
steps: workflowDefinition.steps.length,
executionTime: 45000,
result: {
summary: `Executed ${workflowId} workflow successfully`,
confidence: 0.85,
insights: [
'Workflow completed with large context analysis',
'Results stored in memory for future reference',
'Analysis used Gemini 1.5 Pro with 800K+ token context'
]
}
};
}
/**
* Format workflow result for display
*/
private formatWorkflowResult(result: any, title: string): string {
return `# ${title}
## Execution Summary
- **Status:** ${result.success ? 'Success' : 'Failed'}
- **Steps Executed:** ${result.steps}
- **Execution Time:** ${(result.executionTime / 1000).toFixed(1)}s
- **Workflow ID:** ${result.workflowId}
## Analysis Results
${result.result.summary}
**Confidence:** ${(result.result.confidence * 100).toFixed(1)}%
## Key Insights
${result.result.insights.map((insight: string) => `- ${insight}`).join('\n')}
---
*Analysis powered by large context AI workflows with Gemini 1.5 Pro*
`;
}
/**
* Enable Gemini large context in AI client configuration
*/
static configureGeminiLargeContext(): Partial<any> {
return {
// Configuration to enable Gemini with large context
model: 'google/gemini-pro-1.5',
maxTokens: 1000000, // 1M token context window
temperature: 0.1, // Lower temperature for more consistent analysis
};
}
}