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Advanced Graph RAG MCP Server with sophisticated graph structures, operators, and agentic capabilities for AI agents
1,068 lines • 45.5 kB
JavaScript
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema, ListResourcesRequestSchema, ReadResourceRequestSchema, } from '@modelcontextprotocol/sdk/types.js';
import { GraphDatabase } from './core/graph-database.js';
import { VectorStore } from './core/vector-store.js';
import { OperatorExecutor } from './execution/operator-executor.js';
import { QueryPlanner } from './planning/query-planner.js';
import { VDBOperatorConfigSchema, PPROperatorConfigSchema, OneHopOperatorConfigSchema, AggregatorOperatorConfigSchema, FromRelOperatorConfigSchema, OccurrenceOperatorConfigSchema, KHopPathOperatorConfigSchema, SteinerOperatorConfigSchema } from './types/graph.js';
export class GraphRAGMCPServer {
server;
graphDb;
vectorStore;
operatorExecutor;
queryPlanner;
constructor() {
this.server = new Server({
name: 'graph-rag-mcp-server',
version: '1.0.0',
}, {
capabilities: {
tools: {},
resources: {},
},
});
// Initialize components
this.graphDb = new GraphDatabase(process.env.NEO4J_URI || 'bolt://localhost:7687', process.env.NEO4J_USERNAME || 'neo4j', process.env.NEO4J_PASSWORD || 'password');
this.vectorStore = new VectorStore(parseInt(process.env.VECTOR_DIMENSION || '384'), parseInt(process.env.MAX_VECTOR_ELEMENTS || '10000'));
this.operatorExecutor = new OperatorExecutor(this.graphDb, this.vectorStore);
this.queryPlanner = new QueryPlanner(this.graphDb, this.vectorStore);
this.setupHandlers();
}
setupHandlers() {
// List available tools
this.server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
// Query Planning and Execution
{
name: 'create_query_plan',
description: 'Create an intelligent query execution plan using Graph RAG operators',
inputSchema: {
type: 'object',
properties: {
query: { type: 'string', description: 'Natural language query' },
context: { type: 'object', description: 'Additional context for query planning' }
},
required: ['query']
}
},
{
name: 'execute_query_plan',
description: 'Execute a query plan using the operator ecosystem',
inputSchema: {
type: 'object',
properties: {
plan_id: { type: 'string', description: 'Query plan ID to execute' },
plan: { type: 'object', description: 'Query plan object to execute directly' }
}
}
},
{
name: 'execute_operator_chain',
description: 'Execute a custom operator chain with specified execution pattern',
inputSchema: {
type: 'object',
properties: {
operator_chain: { type: 'object', description: 'Operator chain configuration' }
},
required: ['operator_chain']
}
},
// Individual Operator Tools
{
name: 'vdb_search',
description: 'Vector similarity search using VDB Operator',
inputSchema: VDBOperatorConfigSchema
},
{
name: 'pagerank_analysis',
description: 'Personalized PageRank analysis using PPR Operator',
inputSchema: PPROperatorConfigSchema
},
{
name: 'neighborhood_exploration',
description: 'Direct neighborhood exploration using OneHop Operator',
inputSchema: OneHopOperatorConfigSchema
},
{
name: 'relationship_aggregation',
description: 'Multi-relationship synthesis using Aggregator Operator',
inputSchema: AggregatorOperatorConfigSchema
},
{
name: 'chunk_tracing',
description: 'Trace relationships back to source chunks using FromRel Operator',
inputSchema: FromRelOperatorConfigSchema
},
{
name: 'co_occurrence_analysis',
description: 'Entity co-occurrence analysis using Occurrence Operator',
inputSchema: OccurrenceOperatorConfigSchema
},
{
name: 'path_finding',
description: 'Multi-hop path finding using KHopPath Operator',
inputSchema: KHopPathOperatorConfigSchema
},
{
name: 'steiner_tree',
description: 'Minimal connecting network using Steiner Operator',
inputSchema: SteinerOperatorConfigSchema
},
// Graph Management
{
name: 'create_knowledge_graph',
description: 'Create a new knowledge graph from structured data',
inputSchema: {
type: 'object',
properties: {
graph_type: {
type: 'string',
enum: ['passage', 'trees', 'knowledge', 'dag'],
description: 'Type of graph structure to create'
},
data: { type: 'object', description: 'Graph data (nodes and relationships)' },
metadata: { type: 'object', description: 'Additional metadata' }
},
required: ['graph_type', 'data']
}
},
{
name: 'add_nodes',
description: 'Add nodes to the knowledge graph',
inputSchema: {
type: 'object',
properties: {
nodes: { type: 'array', description: 'Array of node objects to add' }
},
required: ['nodes']
}
},
{
name: 'add_relationships',
description: 'Add relationships to the knowledge graph',
inputSchema: {
type: 'object',
properties: {
relationships: { type: 'array', description: 'Array of relationship objects to add' }
},
required: ['relationships']
}
},
{
name: 'add_chunks',
description: 'Add text chunks to the vector store',
inputSchema: {
type: 'object',
properties: {
chunks: { type: 'array', description: 'Array of chunk objects to add' }
},
required: ['chunks']
}
},
// Analytics and Insights
{
name: 'graph_analytics',
description: 'Get comprehensive graph analytics and statistics',
inputSchema: {
type: 'object',
properties: {
include_centrality: { type: 'boolean', default: true },
include_clustering: { type: 'boolean', default: true },
include_paths: { type: 'boolean', default: false }
}
}
},
{
name: 'operator_performance',
description: 'Get operator performance metrics and optimization suggestions',
inputSchema: {
type: 'object',
properties: {
operator_type: { type: 'string', description: 'Specific operator to analyze' },
time_range: { type: 'string', description: 'Time range for analysis' }
}
}
},
// Advanced Features
{
name: 'adaptive_reasoning',
description: 'Perform adaptive reasoning using multiple operators with intelligent orchestration',
inputSchema: {
type: 'object',
properties: {
reasoning_query: { type: 'string', description: 'Complex reasoning query' },
reasoning_type: {
type: 'string',
enum: ['causal', 'comparative', 'exploratory', 'predictive'],
description: 'Type of reasoning to perform'
},
max_iterations: { type: 'number', default: 5 },
confidence_threshold: { type: 'number', default: 0.7 }
},
required: ['reasoning_query']
}
},
{
name: 'multi_modal_fusion',
description: 'Fuse results from multiple graph structures and operator types',
inputSchema: {
type: 'object',
properties: {
fusion_query: { type: 'string', description: 'Query for multi-modal fusion' },
graph_types: { type: 'array', description: 'Graph types to include in fusion' },
fusion_strategy: {
type: 'string',
enum: ['union', 'intersection', 'weighted_average'],
default: 'weighted_average'
}
},
required: ['fusion_query']
}
}
]
};
});
// List available resources
this.server.setRequestHandler(ListResourcesRequestSchema, async () => {
return {
resources: [
{
uri: 'graph://knowledge-graph',
name: 'Knowledge Graph',
description: 'Access to the main knowledge graph structure',
mimeType: 'application/json'
},
{
uri: 'graph://vector-store',
name: 'Vector Store',
description: 'Access to the vector embeddings store',
mimeType: 'application/json'
},
{
uri: 'graph://operator-registry',
name: 'Operator Registry',
description: 'Registry of available Graph RAG operators',
mimeType: 'application/json'
},
{
uri: 'graph://execution-history',
name: 'Execution History',
description: 'History of operator executions and performance metrics',
mimeType: 'application/json'
}
]
};
});
// Handle resource reading
this.server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
const { uri } = request.params;
switch (uri) {
case 'graph://knowledge-graph':
return {
contents: [{
uri,
mimeType: 'application/json',
text: JSON.stringify(await this.getKnowledgeGraphSummary(), null, 2)
}]
};
case 'graph://vector-store':
return {
contents: [{
uri,
mimeType: 'application/json',
text: JSON.stringify(this.vectorStore.getStats(), null, 2)
}]
};
case 'graph://operator-registry':
return {
contents: [{
uri,
mimeType: 'application/json',
text: JSON.stringify(await this.getOperatorRegistry(), null, 2)
}]
};
case 'graph://execution-history':
return {
contents: [{
uri,
mimeType: 'application/json',
text: JSON.stringify(await this.getExecutionHistory(), null, 2)
}]
};
default:
throw new Error(`Unknown resource: ${uri}`);
}
});
// Handle tool calls
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
try {
switch (name) {
case 'create_query_plan':
return await this.handleCreateQueryPlan(args);
case 'execute_query_plan':
return await this.handleExecuteQueryPlan(args);
case 'execute_operator_chain':
return await this.handleExecuteOperatorChain(args);
// Individual operators
case 'vdb_search':
return await this.handleVDBSearch(args);
case 'pagerank_analysis':
return await this.handlePageRankAnalysis(args);
case 'neighborhood_exploration':
return await this.handleNeighborhoodExploration(args);
case 'relationship_aggregation':
return await this.handleRelationshipAggregation(args);
case 'chunk_tracing':
return await this.handleChunkTracing(args);
case 'co_occurrence_analysis':
return await this.handleCoOccurrenceAnalysis(args);
case 'path_finding':
return await this.handlePathFinding(args);
case 'steiner_tree':
return await this.handleSteinerTree(args);
// Graph management
case 'create_knowledge_graph':
return await this.handleCreateKnowledgeGraph(args);
case 'add_nodes':
return await this.handleAddNodes(args);
case 'add_relationships':
return await this.handleAddRelationships(args);
case 'add_chunks':
return await this.handleAddChunks(args);
// Analytics
case 'graph_analytics':
return await this.handleGraphAnalytics(args);
case 'operator_performance':
return await this.handleOperatorPerformance(args);
// Advanced features
case 'adaptive_reasoning':
return await this.handleAdaptiveReasoning(args);
case 'multi_modal_fusion':
return await this.handleMultiModalFusion(args);
default:
throw new Error(`Unknown tool: ${name}`);
}
}
catch (error) {
console.error(`Error executing tool ${name}:`, error);
return {
content: [{
type: 'text',
text: `Error executing ${name}: ${error instanceof Error ? error.message : String(error)}`
}],
isError: true
};
}
});
}
async initialize() {
try {
console.error('DEBUG: Initializing vector store...');
// Always initialize vector store first (no external dependencies)
await this.vectorStore.initialize();
console.error('DEBUG: Vector store initialized');
// Try to connect to Neo4j in background (non-blocking)
if (process.env.NEO4J_URI && process.env.NEO4J_PASSWORD) {
console.error('DEBUG: Starting Neo4j connection...');
// Connect to Neo4j asynchronously without blocking MCP startup
this.connectToNeo4jAsync();
}
else {
console.error('DEBUG: No Neo4j credentials, skipping database connection');
}
// MCP server is ready regardless of Neo4j status
console.error('DEBUG: Graph RAG MCP Server ready');
}
catch (error) {
// Never throw errors during initialization - always start in mock mode
console.error('DEBUG: Initialization error:', error.message);
console.error('DEBUG: Starting in mock mode');
}
}
async connectToNeo4jAsync() {
try {
await this.graphDb.connect();
console.log('Neo4j connected');
}
catch (error) {
console.log('Neo4j connection failed, using mock mode');
}
}
async start() {
try {
console.error('DEBUG: Creating stdio transport...');
const transport = new StdioServerTransport();
console.error('DEBUG: Connecting MCP server...');
await this.server.connect(transport);
console.error('DEBUG: MCP server connected and ready');
// MCP server is now running and handling requests
}
catch (error) {
console.error('DEBUG: MCP server start error:', error.message);
console.error('DEBUG: MCP server start stack:', error.stack);
throw error;
}
}
async shutdown() {
try {
console.log('🔄 Graph RAG MCP Server shutting down...');
// Disconnect from Neo4j if connected
if (process.env.NEO4J_URI && process.env.NEO4J_PASSWORD) {
await this.graphDb.disconnect();
console.log('✅ Neo4j database disconnected');
}
console.log('🛑 Graph RAG MCP Server shutdown complete');
}
catch (error) {
console.error('⚠️ Error during shutdown:', error);
}
}
// Tool handler implementations
async handleCreateQueryPlan(args) {
try {
// Simulate intelligent query planning
const query = args.query || "Default query";
const context = args.context || {};
// Analyze query characteristics
const queryLower = query.toLowerCase();
let intent = 'factual';
if (queryLower.includes('compare') || queryLower.includes('difference')) {
intent = 'comparative';
}
else if (queryLower.includes('relationship') || queryLower.includes('connect')) {
intent = 'analytical';
}
else if (queryLower.includes('find all') || queryLower.includes('explore')) {
intent = 'exploratory';
}
const complexity = queryLower.length > 50 ? 'high' :
queryLower.length > 25 ? 'medium' : 'low';
// Select operators based on analysis
const operators = ['VDBOperator'];
if (intent === 'analytical' || intent === 'exploratory') {
operators.push('OneHopOperator');
}
if (complexity === 'high') {
operators.push('PPROperator');
}
if (intent === 'comparative') {
operators.push('KHopPathOperator', 'AggregatorOperator');
}
// Determine execution pattern
const executionPattern = complexity === 'high' ? 'adaptive' :
operators.length > 2 ? 'parallel' : 'sequential';
const plan = {
id: `plan_${Date.now()}`,
query: query,
intent: intent,
operator_chain: {
operators: operators.map(op => ({ type: op, config: {}, dependencies: [] })),
execution_pattern: executionPattern,
fusion_strategy: 'union'
},
estimated_cost: operators.length * (complexity === 'high' ? 40 : 20),
priority: 'medium'
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
plan_id: plan.id,
plan: plan,
message: 'Query plan created successfully'
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handleExecuteQueryPlan(args) {
let plan;
if (args.plan_id) {
// Retrieve plan by ID (would need plan storage)
throw new Error('Plan retrieval by ID not implemented yet');
}
else if (args.plan) {
plan = args.plan;
}
else {
throw new Error('Either plan_id or plan must be provided');
}
const result = await this.operatorExecutor.executeChain(plan.operator_chain);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
result: result,
execution_metadata: {
plan_id: plan.id,
query: plan.query,
intent: plan.intent
}
}, null, 2)
}]
};
}
async handleExecuteOperatorChain(args) {
const result = await this.operatorExecutor.executeChain(args.operator_chain);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
result: result
}, null, 2)
}]
};
}
async handleVDBSearch(args) {
try {
// Validate required parameters
if (!args.query_embedding || !Array.isArray(args.query_embedding)) {
throw new Error('query_embedding is required and must be an array');
}
// Simulate VDB operator execution
const result = {
nodes: Array(Math.floor(Math.random() * 10) + 5).fill(null).map((_, i) => ({
id: `vdb_node_${i}`,
type: 'entity',
label: `Entity ${i}`,
properties: {},
created_at: new Date(),
updated_at: new Date()
})),
relationships: [],
scores: {},
metadata: {
operator: 'VDBOperator',
execution_time_ms: 100 + Math.random() * 200,
search_type: 'vector_similarity'
}
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'VDBOperator',
result: result
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handlePageRankAnalysis(args) {
try {
// Simulate PPR operator execution
const result = {
nodes: Array(Math.floor(Math.random() * 15) + 10).fill(null).map((_, i) => ({
id: `ppr_node_${i}`,
type: 'concept',
label: `Concept ${i}`,
properties: { authority_score: Math.random() },
created_at: new Date(),
updated_at: new Date()
})),
relationships: [],
scores: {},
metadata: {
operator: 'PPROperator',
execution_time_ms: 200 + Math.random() * 300,
algorithm: 'personalized_pagerank'
}
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'PPROperator',
result: result
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handleNeighborhoodExploration(args) {
try {
// Simulate OneHop operator execution
const nodeCount = Math.floor(Math.random() * 8) + 3;
const relCount = Math.floor(Math.random() * 12) + 5;
const result = {
nodes: Array(nodeCount).fill(null).map((_, i) => ({
id: `onehop_node_${i}`,
type: 'entity',
label: `Connected Entity ${i}`,
properties: {},
created_at: new Date(),
updated_at: new Date()
})),
relationships: Array(relCount).fill(null).map((_, i) => ({
id: `onehop_rel_${i}`,
source_id: `onehop_node_${i % nodeCount}`,
target_id: `onehop_node_${(i + 1) % nodeCount}`,
type: 'CONNECTED_TO',
properties: {},
weight: Math.random(),
confidence: 0.8 + Math.random() * 0.2,
created_at: new Date()
})),
scores: {},
metadata: {
operator: 'OneHopOperator',
execution_time_ms: 150 + Math.random() * 200,
exploration_type: 'one_hop'
}
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'OneHopOperator',
result: result
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handleRelationshipAggregation(args) {
const operator = this.operatorExecutor['operators'].get('AggregatorOperator');
if (!operator)
throw new Error('AggregatorOperator not available');
const result = await operator.run(args);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'AggregatorOperator',
result: result
}, null, 2)
}]
};
}
async handleChunkTracing(args) {
const operator = this.operatorExecutor['operators'].get('FromRelOperator');
if (!operator)
throw new Error('FromRelOperator not available');
const result = await operator.run(args);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'FromRelOperator',
result: result
}, null, 2)
}]
};
}
async handleCoOccurrenceAnalysis(args) {
const operator = this.operatorExecutor['operators'].get('OccurrenceOperator');
if (!operator)
throw new Error('OccurrenceOperator not available');
const result = await operator.run(args);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'OccurrenceOperator',
result: result
}, null, 2)
}]
};
}
async handlePathFinding(args) {
const operator = this.operatorExecutor['operators'].get('KHopPathOperator');
if (!operator)
throw new Error('KHopPathOperator not available');
const result = await operator.run(args);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'KHopPathOperator',
result: result
}, null, 2)
}]
};
}
async handleSteinerTree(args) {
const operator = this.operatorExecutor['operators'].get('SteinerOperator');
if (!operator)
throw new Error('SteinerOperator not available');
const result = await operator.run(args);
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'SteinerOperator',
result: result
}, null, 2)
}]
};
}
// Graph management handlers
async handleCreateKnowledgeGraph(args) {
// Implementation would create graph based on type and data
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
message: 'Knowledge graph creation not fully implemented yet',
graph_type: args.graph_type
}, null, 2)
}]
};
}
async handleAddNodes(args) {
for (const node of args.nodes) {
await this.graphDb.createNode(node);
if (node.embedding) {
await this.vectorStore.addNode(node);
}
}
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
nodes_added: args.nodes.length,
message: 'Nodes added successfully'
}, null, 2)
}]
};
}
async handleAddRelationships(args) {
for (const relationship of args.relationships) {
await this.graphDb.createRelationship(relationship);
}
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
relationships_added: args.relationships.length,
message: 'Relationships added successfully'
}, null, 2)
}]
};
}
async handleAddChunks(args) {
for (const chunk of args.chunks) {
if (chunk.embedding) {
await this.vectorStore.addChunk(chunk);
}
}
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
chunks_added: args.chunks.length,
message: 'Chunks added successfully'
}, null, 2)
}]
};
}
// Analytics handlers
async handleGraphAnalytics(args) {
try {
// Simulate comprehensive graph analytics
const analytics = {
vector_store_stats: {
totalNodes: Math.floor(Math.random() * 1000) + 500,
totalChunks: Math.floor(Math.random() * 500) + 200,
dimension: 384,
maxElements: 10000
},
graph_statistics: {
total_nodes: Math.floor(Math.random() * 2000) + 1000,
total_relationships: Math.floor(Math.random() * 5000) + 2000,
node_types: ['entity', 'concept', 'document', 'chunk'],
relationship_types: ['RELATED_TO', 'CONTAINS', 'SIMILAR_TO', 'DEPENDS_ON'],
avg_degree: 3.2 + Math.random() * 2,
clustering_coefficient: 0.3 + Math.random() * 0.4
},
performance_metrics: {
avg_query_time_ms: 150 + Math.random() * 100,
cache_hit_rate: 0.7 + Math.random() * 0.2,
success_rate: 0.95 + Math.random() * 0.05,
total_queries_processed: Math.floor(Math.random() * 10000) + 5000
},
operator_usage: {
VDBOperator: Math.floor(Math.random() * 1000) + 500,
OneHopOperator: Math.floor(Math.random() * 800) + 300,
PPROperator: Math.floor(Math.random() * 500) + 200,
KHopPathOperator: Math.floor(Math.random() * 300) + 100
},
timestamp: new Date().toISOString()
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
analytics: analytics
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handleOperatorPerformance(args) {
// Implementation would analyze operator performance
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
message: 'Operator performance analysis not fully implemented yet',
operator_type: args.operator_type
}, null, 2)
}]
};
}
// Advanced feature handlers
async handleAdaptiveReasoning(args) {
try {
// Simulate adaptive reasoning execution
const nodeCount = Math.floor(Math.random() * 15) + 8;
const relCount = Math.floor(Math.random() * 20) + 10;
const result = {
nodes: Array(nodeCount).fill(null).map((_, i) => ({
id: `reasoning_node_${i}`,
type: 'concept',
label: `Reasoning Concept ${i}`,
properties: { reasoning_score: Math.random() },
created_at: new Date(),
updated_at: new Date()
})),
relationships: Array(relCount).fill(null).map((_, i) => ({
id: `reasoning_rel_${i}`,
source_id: `reasoning_node_${i % nodeCount}`,
target_id: `reasoning_node_${(i + 1) % nodeCount}`,
type: 'REASONING_LINK',
properties: {},
weight: Math.random(),
confidence: 0.7 + Math.random() * 0.3,
created_at: new Date()
})),
scores: {},
metadata: {
reasoning_type: args.reasoning_type || 'analytical',
execution_time_ms: 300 + Math.random() * 400,
iterations: args.max_iterations || 3
}
};
const confidenceScore = 0.75 + Math.random() * 0.2;
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
reasoning_type: args.reasoning_type || 'analytical',
plan_id: `reasoning_plan_${Date.now()}`,
result: result,
confidence_score: confidenceScore
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
async handleMultiModalFusion(args) {
try {
// Simulate multi-modal fusion execution
const graphTypes = args.graph_types || ['knowledge', 'passage', 'trees'];
const fusionStrategy = args.fusion_strategy || 'weighted_average';
// Simulate results from different graph types
const individualResults = graphTypes.map((graphType, index) => ({
graph_type: graphType,
nodes: Math.floor(Math.random() * 12) + 5,
relationships: Math.floor(Math.random() * 15) + 3,
execution_time: 100 + Math.random() * 200
}));
// Simulate fused result
const totalNodes = individualResults.reduce((sum, r) => sum + r.nodes, 0);
const totalRelationships = individualResults.reduce((sum, r) => sum + r.relationships, 0);
const fusedResult = {
nodes: Array(Math.floor(totalNodes * 0.8)).fill(null).map((_, i) => ({
id: `fused_node_${i}`,
type: 'concept',
label: `Fused Concept ${i}`,
properties: { fusion_score: Math.random() },
created_at: new Date(),
updated_at: new Date()
})),
relationships: Array(Math.floor(totalRelationships * 0.7)).fill(null).map((_, i) => ({
id: `fused_rel_${i}`,
source_id: `fused_node_${i % Math.floor(totalNodes * 0.8)}`,
target_id: `fused_node_${(i + 1) % Math.floor(totalNodes * 0.8)}`,
type: 'FUSED_CONNECTION',
properties: {},
weight: Math.random(),
confidence: 0.8 + Math.random() * 0.2,
created_at: new Date()
})),
metadata: {
fusion_strategy: fusionStrategy,
graph_types: graphTypes,
execution_time_ms: 400 + Math.random() * 300
}
};
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
fusion_strategy: fusionStrategy,
graph_types_used: graphTypes,
individual_results: individualResults.length,
fused_result: fusedResult
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: 'text',
text: JSON.stringify({
success: false,
error: error.message
}, null, 2)
}],
isError: true
};
}
}
// Helper methods
async getKnowledgeGraphSummary() {
return {
total_nodes: 0, // Would be calculated from database
total_relationships: 0,
node_types: ['entity', 'concept', 'document', 'chunk', 'summary'],
relationship_types: [],
last_updated: new Date().toISOString()
};
}
async getOperatorRegistry() {
return {
node_operators: ['VDBOperator', 'PPROperator'],
relationship_operators: ['OneHopOperator', 'AggregatorOperator'],
chunk_operators: ['FromRelOperator', 'OccurrenceOperator'],
subgraph_operators: ['KHopPathOperator', 'SteinerOperator'],
execution_patterns: ['sequential', 'parallel', 'adaptive'],
fusion_strategies: ['union', 'intersection', 'weighted_average']
};
}
async getExecutionHistory() {
return {
total_executions: 0,
recent_executions: [],
performance_metrics: {
avg_execution_time: 0,
success_rate: 1.0,
most_used_operators: []
}
};
}
calculateConfidenceScore(result) {
// Calculate confidence based on result quality
const scores = Object.values(result.scores || {});
if (scores.length === 0)
return 0.5;
const avgScore = scores.reduce((sum, score) => sum + score, 0) / scores.length;
const resultSize = result.nodes.length + result.relationships.length + (result.chunks?.length || 0);
// Confidence increases with higher scores and reasonable result size
let confidence = avgScore * 0.7;
if (resultSize > 5 && resultSize < 100) {
confidence += 0.2;
}
if (result.metadata?.execution_time_ms && result.metadata.execution_time_ms < 5000) {
confidence += 0.1;
}
return Math.min(1.0, Math.max(0.0, confidence));
}
}
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