From ba5ef2ae22112bbf7f0756208e9b885cdc66c332 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 26 Mar 2026 17:07:58 +0000 Subject: [PATCH 1/3] feat(mcp): add vector search with semantic knowledge base and event search MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add two new MCP tools powered by local embeddings (all-MiniLM-L6-v2 via @xenova/transformers): - search_knowledge: Semantic search over 103 analysis rules, gather rules, and IME log patterns. Finds relevant rules by meaning, not exact match. - search_events_semantic: Semantic search over enrollment event messages within a session or across recent failed sessions. Matches symptoms by meaning (e.g. "timeout" finds "connection timed out", "waiting exceeded"). Implementation: - vector-store.ts: In-memory vector store with cosine similarity - knowledge-base.ts: Loads rules from disk, computes embeddings at startup - Embeddings computed locally — no external API key required https://claude.ai/code/session_01TBPsvvMBue9Ft7j3CfD4S2 --- .../autopilot-monitor-mcp/package-lock.json | 856 +++++++++++++++++- .../autopilot-monitor-mcp/package.json | 3 +- .../autopilot-monitor-mcp/src/index.ts | 18 +- .../src/knowledge-base.ts | 210 +++++ .../autopilot-monitor-mcp/src/tools.ts | 173 +++- .../autopilot-monitor-mcp/src/vector-store.ts | 61 ++ 6 files changed, 1313 insertions(+), 8 deletions(-) create mode 100644 src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts create mode 100644 src/McpServer/autopilot-monitor-mcp/src/vector-store.ts diff --git a/src/McpServer/autopilot-monitor-mcp/package-lock.json b/src/McpServer/autopilot-monitor-mcp/package-lock.json index b8c831a6..41862f41 100644 --- a/src/McpServer/autopilot-monitor-mcp/package-lock.json 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"sha512-brv7p5WgH0jmQJr1ZDDfKDOSeWWg+OVypG99A/5vYGPqJ6pxiaHLy8nxtFjBA7oMa01ebA9gfh1uMCFqOuXxvA==", + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/feross" + }, + { + "type": "patreon", + "url": "https://www.patreon.com/feross" + }, + { + "type": "consulting", + "url": "https://feross.org/support" + } + ], + "license": "MIT", + "dependencies": { + "decompress-response": "^6.0.0", + "once": "^1.3.1", + "simple-concat": "^1.0.0" + } + }, + "node_modules/simple-swizzle": { + "version": "0.2.4", + "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.4.tgz", + "integrity": "sha512-nAu1WFPQSMNr2Zn9PGSZK9AGn4t/y97lEm+MXTtUDwfP0ksAIX4nO+6ruD9Jwut4C49SB1Ws+fbXsm/yScWOHw==", + "license": "MIT", + "dependencies": { + "is-arrayish": "^0.3.1" + } + }, "node_modules/statuses": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/statuses/-/statuses-2.0.2.tgz", @@ -1638,6 +2396,79 @@ "node": ">= 0.8" } }, + "node_modules/streamx": { + "version": "2.25.0", + "resolved": "https://registry.npmjs.org/streamx/-/streamx-2.25.0.tgz", + "integrity": "sha512-0nQuG6jf1w+wddNEEXCF4nTg3LtufWINB5eFEN+5TNZW7KWJp6x87+JFL43vaAUPyCfH1wID+mNVyW6OHtFamg==", + "license": "MIT", + "dependencies": { + "events-universal": "^1.0.0", + "fast-fifo": "^1.3.2", + "text-decoder": "^1.1.0" + } + }, + "node_modules/string_decoder": { + "version": "1.3.0", + "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz", + "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==", + "license": "MIT", + "dependencies": { + "safe-buffer": "~5.2.0" + } + }, + "node_modules/strip-json-comments": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz", + "integrity": "sha512-4gB8na07fecVVkOI6Rs4e7T6NOTki5EmL7TUduTs6bu3EdnSycntVJ4re8kgZA+wx9IueI2Y11bfbgwtzuE0KQ==", + "license": "MIT", + "engines": { + "node": ">=0.10.0" + } + }, + "node_modules/tar-fs": { + "version": "3.1.2", + "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.1.2.tgz", + "integrity": "sha512-QGxxTxxyleAdyM3kpFs14ymbYmNFrfY+pHj7Z8FgtbZ7w2//VAgLMac7sT6nRpIHjppXO2AwwEOg0bPFVRcmXw==", + "license": "MIT", + "dependencies": { + "pump": "^3.0.0", + "tar-stream": "^3.1.5" + }, + "optionalDependencies": { + "bare-fs": "^4.0.1", + "bare-path": "^3.0.0" + } + }, + "node_modules/tar-stream": { + "version": "3.1.8", + "resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-3.1.8.tgz", + "integrity": "sha512-U6QpVRyCGHva435KoNWy9PRoi2IFYCgtEhq9nmrPPpbRacPs9IH4aJ3gbrFC8dPcXvdSZ4XXfXT5Fshbp2MtlQ==", + "license": "MIT", + "dependencies": { + "b4a": "^1.6.4", + "bare-fs": "^4.5.5", + "fast-fifo": "^1.2.0", + "streamx": "^2.15.0" + } + }, + "node_modules/teex": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/teex/-/teex-1.0.1.tgz", + "integrity": "sha512-eYE6iEI62Ni1H8oIa7KlDU6uQBtqr4Eajni3wX7rpfXD8ysFx8z0+dri+KWEPWpBsxXfxu58x/0jvTVT1ekOSg==", + "license": "MIT", + "dependencies": { + "streamx": "^2.12.5" + } + }, + "node_modules/text-decoder": { + "version": "1.2.7", + "resolved": "https://registry.npmjs.org/text-decoder/-/text-decoder-1.2.7.tgz", + "integrity": "sha512-vlLytXkeP4xvEq2otHeJfSQIRyWxo/oZGEbXrtEEF9Hnmrdly59sUbzZ/QgyWuLYHctCHxFF4tRQZNQ9k60ExQ==", + "license": "Apache-2.0", + "dependencies": { + "b4a": "^1.6.4" + } + }, "node_modules/toidentifier": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.1.tgz", @@ -1673,6 +2504,18 @@ "fsevents": "~2.3.3" } }, + "node_modules/tunnel-agent": { + "version": "0.6.0", + "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", + "integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==", + "license": "Apache-2.0", + "dependencies": { + "safe-buffer": "^5.0.1" + }, + "engines": { + "node": "*" + } + }, "node_modules/type-is": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/type-is/-/type-is-2.0.1.tgz", @@ -1705,7 +2548,6 @@ "version": "6.21.0", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==", - "dev": true, "license": "MIT" }, "node_modules/unpipe": { @@ -1717,6 +2559,12 @@ "node": ">= 0.8" } }, + "node_modules/util-deprecate": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", + "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", + "license": "MIT" + }, "node_modules/vary": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz", diff --git a/src/McpServer/autopilot-monitor-mcp/package.json b/src/McpServer/autopilot-monitor-mcp/package.json index bc6e19b2..ad5beac4 100644 --- a/src/McpServer/autopilot-monitor-mcp/package.json +++ b/src/McpServer/autopilot-monitor-mcp/package.json @@ -1,6 +1,6 @@ { "name": "autopilot-monitor-mcp", - "version": "1.0.0", + "version": "1.1.0", "description": "MCP server for Autopilot-Monitor — enables AI agents to query enrollment sessions, device properties, events and vulnerabilities", "type": "module", "bin": { @@ -14,6 +14,7 @@ "dependencies": { "@anthropic-ai/sdk": "^0.80.0", "@modelcontextprotocol/sdk": "^1.0.0", + "@xenova/transformers": "^2.17.0", "zod": "^3.22.0" }, "devDependencies": { diff --git a/src/McpServer/autopilot-monitor-mcp/src/index.ts b/src/McpServer/autopilot-monitor-mcp/src/index.ts index 84aaa346..05a3faf0 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/index.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/index.ts @@ -1,14 +1,27 @@ +import { resolve, dirname } from 'node:path'; +import { fileURLToPath } from 'node:url'; import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'; import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'; import { registerTools } from './tools.js'; import { registerResources } from './resources.js'; +import { buildKnowledgeBase } from './knowledge-base.js'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +// Resolve the rules directory (relative to project root) +const RULES_DIR = process.env.RULES_DIR ?? resolve(__dirname, '..', '..', '..', '..', 'rules'); const server = new McpServer({ name: 'autopilot-monitor', - version: '1.0.0', + version: '1.1.0', }); -registerTools(server); +// Build the knowledge base (embeddings) before registering tools, +// so the search_knowledge tool has immediate access. +console.error('Initializing vector search knowledge base…'); +const knowledgeBase = await buildKnowledgeBase(RULES_DIR); + +registerTools(server, knowledgeBase); registerResources(server); const transport = new StdioServerTransport(); @@ -16,3 +29,4 @@ await server.connect(transport); console.error('Autopilot-Monitor MCP Server running (stdio transport)'); console.error(`API URL: ${process.env.AUTOPILOT_API_URL ?? 'https://autopilotmonitor-api.azurewebsites.net'}`); +console.error(`Knowledge base: ${knowledgeBase.size} documents indexed from ${RULES_DIR}`); diff --git a/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts new file mode 100644 index 00000000..570c8d25 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts @@ -0,0 +1,210 @@ +/** + * Loads analysis rules, gather rules, and IME log patterns from the rules/ + * directory, computes embeddings via a local transformer model, and populates + * the in-memory vector store for semantic search. + */ + +import { readdir, readFile } from 'node:fs/promises'; +import { join } from 'node:path'; +import { pipeline, type FeatureExtractionPipeline } from '@xenova/transformers'; +import { VectorStore, type VectorDocument } from './vector-store.js'; + +const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2'; + +let embedder: FeatureExtractionPipeline | null = null; + +async function getEmbedder(): Promise { + if (!embedder) { + embedder = await pipeline('feature-extraction', MODEL_NAME, { + quantized: true, + }) as FeatureExtractionPipeline; + } + return embedder; +} + +/** Compute a normalized embedding vector for a text string. */ +export async function embed(text: string): Promise { + const model = await getEmbedder(); + const output = await model(text, { pooling: 'mean', normalize: true }); + return Array.from(output.data as Float32Array); +} + +/** Batch-embed multiple texts (sequential to avoid OOM on large batches). */ +async function embedBatch(texts: string[]): Promise { + const model = await getEmbedder(); + const results: number[][] = []; + // Process in small batches to balance speed and memory + const batchSize = 16; + for (let i = 0; i < texts.length; i += batchSize) { + const batch = texts.slice(i, i + batchSize); + const outputs = await Promise.all( + batch.map(async (t) => { + const out = await model(t, { pooling: 'mean', normalize: true }); + return Array.from(out.data as Float32Array); + }) + ); + results.push(...outputs); + } + return results; +} + +// ────────────────────────────────────────────────────────────── +// Rule / pattern loading helpers +// ────────────────────────────────────────────────────────────── + +interface AnalyzeRule { + ruleId: string; + title: string; + description: string; + severity: string; + category: string; + explanation?: string; + remediation?: Array<{ title: string; steps: string[] }>; + tags?: string[]; +} + +interface GatherRule { + ruleId: string; + title: string; + description: string; + category: string; + collectorType?: string; + target?: string; + trigger?: string; + tags?: string[]; +} + +interface ImeLogPattern { + patternId: string; + category: string; + description: string; + pattern: string; + action?: string; +} + +async function loadJsonFiles(dir: string): Promise { + let entries: string[]; + try { + entries = await readdir(dir); + } catch { + return []; + } + const results: T[] = []; + for (const f of entries) { + if (!f.endsWith('.json')) continue; + try { + const raw = await readFile(join(dir, f), 'utf-8'); + results.push(JSON.parse(raw) as T); + } catch { + // skip malformed files + } + } + return results; +} + +/** Build the searchable text for each document type. */ + +function analyzeRuleText(r: AnalyzeRule): string { + const parts = [ + `[${r.ruleId}] ${r.title}`, + r.description, + r.explanation ?? '', + ]; + if (r.remediation) { + for (const rem of r.remediation) { + parts.push(`Remediation: ${rem.title} — ${rem.steps.join('; ')}`); + } + } + if (r.tags?.length) parts.push(`Tags: ${r.tags.join(', ')}`); + return parts.filter(Boolean).join('\n'); +} + +function gatherRuleText(r: GatherRule): string { + return [ + `[${r.ruleId}] ${r.title}`, + r.description, + r.target ? `Target: ${r.target}` : '', + r.tags?.length ? `Tags: ${r.tags.join(', ')}` : '', + ].filter(Boolean).join('\n'); +} + +function imePatternText(p: ImeLogPattern): string { + return [ + `[${p.patternId}] ${p.description}`, + `Category: ${p.category}`, + p.action ? `Action: ${p.action}` : '', + ].filter(Boolean).join('\n'); +} + +// ────────────────────────────────────────────────────────────── +// Public API +// ────────────────────────────────────────────────────────────── + +export async function buildKnowledgeBase(rulesRoot: string): Promise { + const store = new VectorStore(); + + console.error('Loading knowledge base documents…'); + + const [analyzeRules, gatherRules, imePatterns] = await Promise.all([ + loadJsonFiles(join(rulesRoot, 'analyze')), + loadJsonFiles(join(rulesRoot, 'gather')), + loadJsonFiles(join(rulesRoot, 'ime-log-patterns')), + ]); + + const docs: Array<{ id: string; text: string; metadata: Record }> = []; + + for (const r of analyzeRules) { + docs.push({ + id: r.ruleId, + text: analyzeRuleText(r), + metadata: { + type: 'analyze-rule', + severity: r.severity, + category: r.category, + title: r.title, + tags: r.tags ?? [], + }, + }); + } + + for (const r of gatherRules) { + docs.push({ + id: r.ruleId, + text: gatherRuleText(r), + metadata: { + type: 'gather-rule', + category: r.category, + title: r.title, + tags: r.tags ?? [], + }, + }); + } + + for (const p of imePatterns) { + docs.push({ + id: p.patternId, + text: imePatternText(p), + metadata: { + type: 'ime-log-pattern', + category: p.category, + description: p.description, + action: p.action ?? null, + }, + }); + } + + console.error(`Embedding ${docs.length} documents (model: ${MODEL_NAME})…`); + + const texts = docs.map((d) => d.text); + const embeddings = await embedBatch(texts); + + const vectorDocs: VectorDocument[] = docs.map((d, i) => ({ + ...d, + embedding: embeddings[i], + })); + + store.addMany(vectorDocs); + console.error(`Knowledge base ready — ${store.size} documents indexed.`); + + return store; +} diff --git a/src/McpServer/autopilot-monitor-mcp/src/tools.ts b/src/McpServer/autopilot-monitor-mcp/src/tools.ts index d04a084b..1018e83b 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/tools.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/tools.ts @@ -1,8 +1,10 @@ import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'; import { z } from 'zod'; import { apiFetch, buildQuery } from './client.js'; +import type { VectorStore } from './vector-store.js'; +import { embed } from './knowledge-base.js'; -export function registerTools(server: McpServer): void { +export function registerTools(server: McpServer, knowledgeBase?: VectorStore): void { // Tool 1: search_sessions server.tool( 'search_sessions', @@ -167,4 +169,173 @@ export function registerTools(server: McpServer): void { return { content: [{ type: 'text' as const, text: JSON.stringify(data, null, 2) }] }; } ); + + // Tool 8: search_events_semantic (vector search over live events) + server.tool( + 'search_events_semantic', + 'Semantic search over enrollment event messages within a session or across recent failed sessions. ' + + 'Unlike exact filters, this finds events by MEANING — e.g. "network timeout" also matches "connection timed out", "request failed after waiting". ' + + 'Use this when you need to find events that match a symptom description rather than an exact event type. ' + + 'Provide a sessionId to search within one session, or omit it to search across recent failed sessions.', + { + query: z.string().describe('Natural language description of what to find (e.g. "app download stuck", "certificate error", "disk space low")'), + sessionId: z.string().optional().describe('Search within a specific session. If omitted, searches across recent failed sessions.'), + tenantId: z.string().optional().describe('Tenant ID (global-scoped key only)'), + topK: z.number().min(1).max(30).optional().default(10).describe('Number of matching events to return (1-30, default 10)'), + minScore: z.number().min(0).max(1).optional().default(0.35) + .describe('Minimum similarity score (0-1, default 0.35)'), + }, + async ({ query, sessionId, tenantId, topK, minScore }) => { + // Fetch events — either from a specific session or from recent failed sessions + type EventEntry = { eventType?: string; severity?: string; source?: string; message?: string; timestamp?: string; phase?: string; data?: Record }; + let events: EventEntry[] = []; + let sessionIds: string[] = []; + + if (sessionId) { + // Single session mode + const q = buildQuery({ tenantId } as Record); + const data = await apiFetch(`/api/sessions/${sessionId}/events${q}`) as { events?: EventEntry[] }; + events = data?.events ?? []; + sessionIds = [sessionId]; + } else { + // Cross-session mode: fetch recent failed sessions, then their events + const searchQ = buildQuery({ status: 'Failed', tenantId, limit: 5 } as Record); + const sessions = await apiFetch(`/api/sessions/search${searchQ}`) as { + sessions?: Array<{ sessionId?: string }>; + }; + const ids = (sessions?.sessions ?? []).map((s) => s.sessionId).filter(Boolean) as string[]; + sessionIds = ids; + const q = buildQuery({ tenantId } as Record); + const allEvents = await Promise.all( + ids.map(async (sid) => { + try { + const d = await apiFetch(`/api/sessions/${sid}/events${q}`) as { events?: EventEntry[] }; + return (d?.events ?? []).map((e) => ({ ...e, _sessionId: sid })); + } catch { return []; } + }) + ); + events = allEvents.flat(); + } + + // Filter to events that have a meaningful message + const candidates = events.filter((e) => e.message && e.message.length > 5); + + if (candidates.length === 0) { + return { + content: [{ + type: 'text' as const, + text: JSON.stringify({ query, resultCount: 0, results: [], note: 'No events with messages found.' }), + }], + }; + } + + // Build text representations and compute embeddings + const texts = candidates.map((e) => { + const parts = [e.message]; + if (e.eventType) parts.push(`Event: ${e.eventType}`); + if (e.severity) parts.push(`Severity: ${e.severity}`); + if (e.source) parts.push(`Source: ${e.source}`); + return parts.join(' | '); + }); + + const queryEmbedding = await embed(query); + + // Embed events in batches + const eventEmbeddings = await Promise.all(texts.map((t) => embed(t))); + + // Score and rank + const scored = candidates.map((e, i) => { + let dot = 0, normA = 0, normB = 0; + for (let j = 0; j < queryEmbedding.length; j++) { + dot += queryEmbedding[j] * eventEmbeddings[i][j]; + normA += queryEmbedding[j] * queryEmbedding[j]; + normB += eventEmbeddings[i][j] * eventEmbeddings[i][j]; + } + const denom = Math.sqrt(normA) * Math.sqrt(normB); + const score = denom === 0 ? 0 : dot / denom; + return { event: e, score, text: texts[i] }; + }); + + const results = scored + .filter((r) => r.score >= minScore) + .sort((a, b) => b.score - a.score) + .slice(0, topK) + .map((r) => ({ + score: Math.round(r.score * 1000) / 1000, + sessionId: (r.event as Record)._sessionId ?? sessionId, + eventType: r.event.eventType, + severity: r.event.severity, + source: r.event.source, + phase: r.event.phase, + timestamp: r.event.timestamp, + message: r.event.message, + })); + + return { + content: [{ + type: 'text' as const, + text: JSON.stringify({ + query, + sessionsSearched: sessionIds, + eventsAnalyzed: candidates.length, + resultCount: results.length, + results, + }, null, 2), + }], + }; + } + ); + + // Tool 9: search_knowledge (vector search over rules) + server.tool( + 'search_knowledge', + 'Semantic search over the Autopilot Monitor knowledge base: analysis rules, gather rules, and IME log patterns. ' + + 'Use natural language queries like "app install timeout", "BitLocker issues", "detection script failure". ' + + 'Returns the most relevant rules and patterns ranked by semantic similarity. ' + + 'Great for finding remediation steps, understanding error patterns, or discovering relevant diagnostic rules.', + { + query: z.string().describe('Natural language search query (e.g. "app download timeout", "TPM not ready", "ESP stuck")'), + topK: z.number().min(1).max(20).optional().default(5).describe('Number of results to return (1-20, default 5)'), + type: z.enum(['all', 'analyze-rule', 'gather-rule', 'ime-log-pattern']).optional().default('all') + .describe('Filter by document type. Default: search all types.'), + minScore: z.number().min(0).max(1).optional().default(0.3) + .describe('Minimum similarity score threshold (0-1, default 0.3). Lower = more results, higher = stricter matching.'), + }, + async ({ query, topK, type, minScore }) => { + if (!knowledgeBase || knowledgeBase.size === 0) { + return { + content: [{ + type: 'text' as const, + text: JSON.stringify({ error: 'Knowledge base not initialized. The server may still be loading embeddings.' }), + }], + }; + } + + const queryEmbedding = await embed(query); + let results = knowledgeBase.search(queryEmbedding, type === 'all' ? topK * 3 : topK, minScore); + + // Filter by type if specified + if (type !== 'all') { + results = results.filter((r) => r.metadata.type === type); + } + + results = results.slice(0, topK); + + const formatted = results.map((r) => ({ + id: r.id, + score: Math.round(r.score * 1000) / 1000, + type: r.metadata.type, + title: r.metadata.title ?? r.metadata.description ?? r.id, + content: r.text, + metadata: r.metadata, + })); + + return { + content: [{ + type: 'text' as const, + text: JSON.stringify({ query, resultCount: formatted.length, results: formatted }, null, 2), + }], + }; + } + ); } diff --git a/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts b/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts new file mode 100644 index 00000000..c3828562 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts @@ -0,0 +1,61 @@ +/** + * Lightweight in-memory vector store using cosine similarity. + * Each document is stored alongside its embedding vector. + */ + +export interface VectorDocument { + id: string; + text: string; + metadata: Record; + embedding: number[]; +} + +export interface SearchResult { + id: string; + text: string; + metadata: Record; + score: number; +} + +function cosineSimilarity(a: number[], b: number[]): number { + let dot = 0; + let normA = 0; + let normB = 0; + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + const denom = Math.sqrt(normA) * Math.sqrt(normB); + return denom === 0 ? 0 : dot / denom; +} + +export class VectorStore { + private documents: VectorDocument[] = []; + + add(doc: VectorDocument): void { + this.documents.push(doc); + } + + addMany(docs: VectorDocument[]): void { + this.documents.push(...docs); + } + + search(queryEmbedding: number[], topK = 5, minScore = 0.3): SearchResult[] { + const scored = this.documents.map((doc) => ({ + id: doc.id, + text: doc.text, + metadata: doc.metadata, + score: cosineSimilarity(queryEmbedding, doc.embedding), + })); + + return scored + .filter((r) => r.score >= minScore) + .sort((a, b) => b.score - a.score) + .slice(0, topK); + } + + get size(): number { + return this.documents.length; + } +} From a219affd91152491198378fbb3479c380aced04d Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 26 Mar 2026 17:30:21 +0000 Subject: [PATCH 2/3] refactor(mcp): abstract search behind SearchProvider interface MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Introduce a pluggable search architecture with two backends: - VectorSearchProvider: Sentence-transformer embeddings (all-MiniLM-L6-v2) for true semantic search. Best quality, requires model download. - FuseSearchProvider: Fuse.js fuzzy text matching. Zero model download, instant startup, lightweight fallback. Auto-detection: if @xenova/transformers is available, uses vector; otherwise falls back to fuse. Override via SEARCH_BACKEND=vector|fuse env var. Architecture: SearchProvider (interface) ← tools.ts programs against this ├── VectorSearchProvider ← embeddings + cosine similarity └── FuseSearchProvider ← fuse.js weighted fuzzy match createSearchProvider() ← factory with auto-detect Also refactored knowledge-base.ts to be provider-agnostic (just loads documents) and removed the old monolithic vector-store.ts. https://claude.ai/code/session_01TBPsvvMBue9Ft7j3CfD4S2 --- .../autopilot-monitor-mcp/package-lock.json | 14 ++- .../autopilot-monitor-mcp/package.json | 3 +- .../src/fuse-search-provider.ts | 57 +++++++++++ .../autopilot-monitor-mcp/src/index.ts | 21 ++-- .../src/knowledge-base.ts | 87 ++++------------- .../src/search-factory.ts | 46 +++++++++ .../src/search-provider.ts | 54 +++++++++++ .../autopilot-monitor-mcp/src/tools.ts | 95 +++++++++--------- .../src/vector-search-provider.ts | 96 +++++++++++++++++++ .../autopilot-monitor-mcp/src/vector-store.ts | 61 ------------ 10 files changed, 340 insertions(+), 194 deletions(-) create mode 100644 src/McpServer/autopilot-monitor-mcp/src/fuse-search-provider.ts create mode 100644 src/McpServer/autopilot-monitor-mcp/src/search-factory.ts create mode 100644 src/McpServer/autopilot-monitor-mcp/src/search-provider.ts create mode 100644 src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts delete mode 100644 src/McpServer/autopilot-monitor-mcp/src/vector-store.ts diff --git a/src/McpServer/autopilot-monitor-mcp/package-lock.json b/src/McpServer/autopilot-monitor-mcp/package-lock.json index 41862f41..75853ce0 100644 --- a/src/McpServer/autopilot-monitor-mcp/package-lock.json +++ b/src/McpServer/autopilot-monitor-mcp/package-lock.json @@ -1,16 +1,17 @@ { "name": "autopilot-monitor-mcp", - "version": "1.1.0", + "version": "1.2.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "autopilot-monitor-mcp", - "version": "1.1.0", + "version": "1.2.0", "dependencies": { "@anthropic-ai/sdk": "^0.80.0", "@modelcontextprotocol/sdk": "^1.0.0", "@xenova/transformers": "^2.17.0", + "fuse.js": "^7.1.0", "zod": "^3.22.0" }, "bin": { @@ -1420,6 +1421,15 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/fuse.js": { + "version": "7.1.0", + "resolved": "https://registry.npmjs.org/fuse.js/-/fuse.js-7.1.0.tgz", + "integrity": "sha512-trLf4SzuuUxfusZADLINj+dE8clK1frKdmqiJNb1Es75fmI5oY6X2mxLVUciLLjxqw/xr72Dhy+lER6dGd02FQ==", + "license": "Apache-2.0", + "engines": { + "node": ">=10" + } + }, "node_modules/get-intrinsic": { "version": "1.3.0", "resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz", diff --git a/src/McpServer/autopilot-monitor-mcp/package.json b/src/McpServer/autopilot-monitor-mcp/package.json index ad5beac4..ba60345f 100644 --- a/src/McpServer/autopilot-monitor-mcp/package.json +++ b/src/McpServer/autopilot-monitor-mcp/package.json @@ -1,6 +1,6 @@ { "name": "autopilot-monitor-mcp", - "version": "1.1.0", + "version": "1.2.0", "description": "MCP server for Autopilot-Monitor — enables AI agents to query enrollment sessions, device properties, events and vulnerabilities", "type": "module", "bin": { @@ -15,6 +15,7 @@ "@anthropic-ai/sdk": "^0.80.0", "@modelcontextprotocol/sdk": "^1.0.0", "@xenova/transformers": "^2.17.0", + "fuse.js": "^7.1.0", "zod": "^3.22.0" }, "devDependencies": { diff --git a/src/McpServer/autopilot-monitor-mcp/src/fuse-search-provider.ts b/src/McpServer/autopilot-monitor-mcp/src/fuse-search-provider.ts new file mode 100644 index 00000000..01b1b320 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/fuse-search-provider.ts @@ -0,0 +1,57 @@ +/** + * SearchProvider backed by Fuse.js — lightweight fuzzy text search. + * + * Pros: Zero model download, instant startup, tiny memory footprint. + * Cons: Token/substring matching only — no true semantic understanding. + * "timeout" will NOT match "waiting exceeded" (use vector for that). + */ + +import Fuse from 'fuse.js'; +import type { SearchDocument, SearchOptions, SearchProvider, SearchResult } from './search-provider.js'; + +export class FuseSearchProvider implements SearchProvider { + readonly name = 'fuse'; + private documents: SearchDocument[] = []; + private fuse: Fuse | null = null; + + get size(): number { + return this.documents.length; + } + + async index(docs: SearchDocument[]): Promise { + this.documents.push(...docs); + this.fuse = new Fuse(this.documents, { + keys: [ + { name: 'text', weight: 0.7 }, + { name: 'id', weight: 0.15 }, + { name: 'metadata.title', weight: 0.1 }, + { name: 'metadata.category', weight: 0.05 }, + ], + includeScore: true, + threshold: 0.5, + ignoreLocation: true, + useExtendedSearch: true, + }); + } + + async search(query: string, options: SearchOptions = {}): Promise { + const { topK = 5, minScore = 0.3 } = options; + + if (!this.fuse || this.documents.length === 0) { + return []; + } + + const fuseResults = this.fuse.search(query, { limit: topK * 2 }); + + return fuseResults + .map((r) => ({ + id: r.item.id, + text: r.item.text, + metadata: r.item.metadata, + // Fuse score is 0 = perfect, 1 = no match. Invert to 0..1 where 1 = best. + score: 1 - (r.score ?? 1), + })) + .filter((r) => r.score >= minScore) + .slice(0, topK); + } +} diff --git a/src/McpServer/autopilot-monitor-mcp/src/index.ts b/src/McpServer/autopilot-monitor-mcp/src/index.ts index 05a3faf0..b1aa0a26 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/index.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/index.ts @@ -4,22 +4,27 @@ import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'; import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'; import { registerTools } from './tools.js'; import { registerResources } from './resources.js'; -import { buildKnowledgeBase } from './knowledge-base.js'; +import { loadKnowledgeDocs } from './knowledge-base.js'; +import { createSearchProvider } from './search-factory.js'; const __dirname = dirname(fileURLToPath(import.meta.url)); -// Resolve the rules directory (relative to project root) const RULES_DIR = process.env.RULES_DIR ?? resolve(__dirname, '..', '..', '..', '..', 'rules'); const server = new McpServer({ name: 'autopilot-monitor', - version: '1.1.0', + version: '1.2.0', }); -// Build the knowledge base (embeddings) before registering tools, -// so the search_knowledge tool has immediate access. -console.error('Initializing vector search knowledge base…'); -const knowledgeBase = await buildKnowledgeBase(RULES_DIR); +// Initialize the knowledge base search provider +console.error('Loading knowledge base documents…'); +const docs = await loadKnowledgeDocs(RULES_DIR); + +console.error(`Initializing search provider (${docs.length} documents)…`); +const knowledgeBase = await createSearchProvider(); +await knowledgeBase.index(docs); + +console.error(`Search provider ready: ${knowledgeBase.name} — ${knowledgeBase.size} documents indexed.`); registerTools(server, knowledgeBase); registerResources(server); @@ -29,4 +34,4 @@ await server.connect(transport); console.error('Autopilot-Monitor MCP Server running (stdio transport)'); console.error(`API URL: ${process.env.AUTOPILOT_API_URL ?? 'https://autopilotmonitor-api.azurewebsites.net'}`); -console.error(`Knowledge base: ${knowledgeBase.size} documents indexed from ${RULES_DIR}`); +console.error(`Search backend: ${knowledgeBase.name} | Documents: ${knowledgeBase.size} | Rules dir: ${RULES_DIR}`); diff --git a/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts index 570c8d25..dd159d4c 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts @@ -1,56 +1,16 @@ /** * Loads analysis rules, gather rules, and IME log patterns from the rules/ - * directory, computes embeddings via a local transformer model, and populates - * the in-memory vector store for semantic search. + * directory and indexes them into a SearchProvider for semantic or fuzzy search. + * + * This module is backend-agnostic — it only deals with document loading and + * text preparation. The actual search strategy is determined by the provider. */ import { readdir, readFile } from 'node:fs/promises'; import { join } from 'node:path'; -import { pipeline, type FeatureExtractionPipeline } from '@xenova/transformers'; -import { VectorStore, type VectorDocument } from './vector-store.js'; +import type { SearchDocument } from './search-provider.js'; -const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2'; - -let embedder: FeatureExtractionPipeline | null = null; - -async function getEmbedder(): Promise { - if (!embedder) { - embedder = await pipeline('feature-extraction', MODEL_NAME, { - quantized: true, - }) as FeatureExtractionPipeline; - } - return embedder; -} - -/** Compute a normalized embedding vector for a text string. */ -export async function embed(text: string): Promise { - const model = await getEmbedder(); - const output = await model(text, { pooling: 'mean', normalize: true }); - return Array.from(output.data as Float32Array); -} - -/** Batch-embed multiple texts (sequential to avoid OOM on large batches). */ -async function embedBatch(texts: string[]): Promise { - const model = await getEmbedder(); - const results: number[][] = []; - // Process in small batches to balance speed and memory - const batchSize = 16; - for (let i = 0; i < texts.length; i += batchSize) { - const batch = texts.slice(i, i + batchSize); - const outputs = await Promise.all( - batch.map(async (t) => { - const out = await model(t, { pooling: 'mean', normalize: true }); - return Array.from(out.data as Float32Array); - }) - ); - results.push(...outputs); - } - return results; -} - -// ────────────────────────────────────────────────────────────── -// Rule / pattern loading helpers -// ────────────────────────────────────────────────────────────── +// ── Rule / pattern types ───────────────────────────────────── interface AnalyzeRule { ruleId: string; @@ -82,6 +42,8 @@ interface ImeLogPattern { action?: string; } +// ── File helpers ───────────────────────────────────────────── + async function loadJsonFiles(dir: string): Promise { let entries: string[]; try { @@ -102,7 +64,7 @@ async function loadJsonFiles(dir: string): Promise { return results; } -/** Build the searchable text for each document type. */ +// ── Text builders ──────────────────────────────────────────── function analyzeRuleText(r: AnalyzeRule): string { const parts = [ @@ -136,22 +98,20 @@ function imePatternText(p: ImeLogPattern): string { ].filter(Boolean).join('\n'); } -// ────────────────────────────────────────────────────────────── -// Public API -// ────────────────────────────────────────────────────────────── - -export async function buildKnowledgeBase(rulesRoot: string): Promise { - const store = new VectorStore(); - - console.error('Loading knowledge base documents…'); +// ── Public API ─────────────────────────────────────────────── +/** + * Load all rule/pattern documents from the rules directory. + * Returns SearchDocument[] ready to be fed into any SearchProvider. + */ +export async function loadKnowledgeDocs(rulesRoot: string): Promise { const [analyzeRules, gatherRules, imePatterns] = await Promise.all([ loadJsonFiles(join(rulesRoot, 'analyze')), loadJsonFiles(join(rulesRoot, 'gather')), loadJsonFiles(join(rulesRoot, 'ime-log-patterns')), ]); - const docs: Array<{ id: string; text: string; metadata: Record }> = []; + const docs: SearchDocument[] = []; for (const r of analyzeRules) { docs.push({ @@ -193,18 +153,5 @@ export async function buildKnowledgeBase(rulesRoot: string): Promise d.text); - const embeddings = await embedBatch(texts); - - const vectorDocs: VectorDocument[] = docs.map((d, i) => ({ - ...d, - embedding: embeddings[i], - })); - - store.addMany(vectorDocs); - console.error(`Knowledge base ready — ${store.size} documents indexed.`); - - return store; + return docs; } diff --git a/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts b/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts new file mode 100644 index 00000000..9c794a72 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts @@ -0,0 +1,46 @@ +/** + * Factory for creating SearchProvider instances. + * + * Selection logic: + * 1. Explicit env var SEARCH_BACKEND=vector|fuse → use that + * 2. Auto-detect: try to load @xenova/transformers + * - available → vector + * - missing → fuse (graceful fallback) + * + * This allows running the MCP server without the ~23 MB model download + * by setting SEARCH_BACKEND=fuse or simply not installing @xenova/transformers. + */ + +import type { SearchBackend, SearchProvider } from './search-provider.js'; + +async function isTransformersAvailable(): Promise { + try { + await import('@xenova/transformers'); + return true; + } catch { + return false; + } +} + +export async function resolveBackend(): Promise { + const explicit = process.env.SEARCH_BACKEND?.toLowerCase(); + if (explicit === 'vector' || explicit === 'fuse') return explicit; + + return (await isTransformersAvailable()) ? 'vector' : 'fuse'; +} + +/** + * Create a new SearchProvider instance. + * Pass a backend explicitly, or let it auto-detect. + */ +export async function createSearchProvider(backend?: SearchBackend): Promise { + const resolved = backend ?? await resolveBackend(); + + if (resolved === 'vector') { + const { VectorSearchProvider } = await import('./vector-search-provider.js'); + return new VectorSearchProvider(); + } + + const { FuseSearchProvider } = await import('./fuse-search-provider.js'); + return new FuseSearchProvider(); +} diff --git a/src/McpServer/autopilot-monitor-mcp/src/search-provider.ts b/src/McpServer/autopilot-monitor-mcp/src/search-provider.ts new file mode 100644 index 00000000..6300bc70 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/search-provider.ts @@ -0,0 +1,54 @@ +/** + * Abstract search provider interface. + * + * Implementations can use vector embeddings, fuzzy text matching, or any other + * ranking strategy. The MCP tools program against this interface so the backend + * is swappable without touching tool registration code. + */ + +// ── Shared types ───────────────────────────────────────────── + +export interface SearchDocument { + id: string; + text: string; + metadata: Record; +} + +export interface SearchResult { + id: string; + text: string; + metadata: Record; + /** Relevance score — always normalized to 0..1 regardless of backend. */ + score: number; +} + +export interface SearchOptions { + topK?: number; + minScore?: number; +} + +// ── Provider contract ──────────────────────────────────────── + +export interface SearchProvider { + /** Human-readable backend name (e.g. "vector/all-MiniLM-L6-v2", "fuse"). */ + readonly name: string; + + /** Number of currently indexed documents. */ + readonly size: number; + + /** + * Index a batch of documents. Can be called multiple times to add more docs. + * Implementation may precompute embeddings, build a Fuse index, etc. + */ + index(docs: SearchDocument[]): Promise; + + /** + * Search indexed documents by a natural-language query string. + * Returns results sorted by descending relevance. + */ + search(query: string, options?: SearchOptions): Promise; +} + +// ── Provider identifiers ───────────────────────────────────── + +export type SearchBackend = 'vector' | 'fuse'; diff --git a/src/McpServer/autopilot-monitor-mcp/src/tools.ts b/src/McpServer/autopilot-monitor-mcp/src/tools.ts index 1018e83b..09c1b9e4 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/tools.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/tools.ts @@ -1,10 +1,10 @@ import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'; import { z } from 'zod'; import { apiFetch, buildQuery } from './client.js'; -import type { VectorStore } from './vector-store.js'; -import { embed } from './knowledge-base.js'; +import type { SearchProvider } from './search-provider.js'; +import { createSearchProvider } from './search-factory.js'; -export function registerTools(server: McpServer, knowledgeBase?: VectorStore): void { +export function registerTools(server: McpServer, knowledgeBase?: SearchProvider): void { // Tool 1: search_sessions server.tool( 'search_sessions', @@ -40,7 +40,6 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v async (args) => { const { deviceProperties, ...rest } = args; const queryParams: Record = { ...rest }; - // Convert deviceProperties record into prop.* query parameters if (deviceProperties) { for (const [key, value] of Object.entries(deviceProperties)) { queryParams[`prop.${key}`] = value; @@ -107,7 +106,6 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v events?: Array<{ eventType?: string; severity?: string; source?: string }>; count?: number; }; - // Client-side filter since backend doesn't support these filters on the events endpoint if (data?.events && (filters.eventType || filters.severity || filters.source)) { data.events = data.events.filter((e) => { if (filters.eventType && e.eventType !== filters.eventType) return false; @@ -170,10 +168,10 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v } ); - // Tool 8: search_events_semantic (vector search over live events) + // Tool 8: search_events_semantic server.tool( 'search_events_semantic', - 'Semantic search over enrollment event messages within a session or across recent failed sessions. ' + + 'Semantic/fuzzy search over enrollment event messages within a session or across recent failed sessions. ' + 'Unlike exact filters, this finds events by MEANING — e.g. "network timeout" also matches "connection timed out", "request failed after waiting". ' + 'Use this when you need to find events that match a symptom description rather than an exact event type. ' + 'Provide a sessionId to search within one session, or omit it to search across recent failed sessions.', @@ -186,19 +184,16 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v .describe('Minimum similarity score (0-1, default 0.35)'), }, async ({ query, sessionId, tenantId, topK, minScore }) => { - // Fetch events — either from a specific session or from recent failed sessions type EventEntry = { eventType?: string; severity?: string; source?: string; message?: string; timestamp?: string; phase?: string; data?: Record }; let events: EventEntry[] = []; let sessionIds: string[] = []; if (sessionId) { - // Single session mode const q = buildQuery({ tenantId } as Record); const data = await apiFetch(`/api/sessions/${sessionId}/events${q}`) as { events?: EventEntry[] }; events = data?.events ?? []; sessionIds = [sessionId]; } else { - // Cross-session mode: fetch recent failed sessions, then their events const searchQ = buildQuery({ status: 'Failed', tenantId, limit: 5 } as Record); const sessions = await apiFetch(`/api/sessions/search${searchQ}`) as { sessions?: Array<{ sessionId?: string }>; @@ -217,7 +212,6 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v events = allEvents.flat(); } - // Filter to events that have a meaningful message const candidates = events.filter((e) => e.message && e.message.length > 5); if (candidates.length === 0) { @@ -229,53 +223,45 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v }; } - // Build text representations and compute embeddings - const texts = candidates.map((e) => { + // Build searchable documents from events + const docs = candidates.map((e, i) => { const parts = [e.message]; if (e.eventType) parts.push(`Event: ${e.eventType}`); if (e.severity) parts.push(`Severity: ${e.severity}`); if (e.source) parts.push(`Source: ${e.source}`); - return parts.join(' | '); + return { + id: `event-${i}`, + text: parts.join(' | '), + metadata: { index: i } as Record, + }; }); - const queryEmbedding = await embed(query); + // Create a temporary search provider for this batch of events + const provider = await createSearchProvider(); + await provider.index(docs); + const searchResults = await provider.search(query, { topK, minScore }); - // Embed events in batches - const eventEmbeddings = await Promise.all(texts.map((t) => embed(t))); - - // Score and rank - const scored = candidates.map((e, i) => { - let dot = 0, normA = 0, normB = 0; - for (let j = 0; j < queryEmbedding.length; j++) { - dot += queryEmbedding[j] * eventEmbeddings[i][j]; - normA += queryEmbedding[j] * queryEmbedding[j]; - normB += eventEmbeddings[i][j] * eventEmbeddings[i][j]; - } - const denom = Math.sqrt(normA) * Math.sqrt(normB); - const score = denom === 0 ? 0 : dot / denom; - return { event: e, score, text: texts[i] }; - }); - - const results = scored - .filter((r) => r.score >= minScore) - .sort((a, b) => b.score - a.score) - .slice(0, topK) - .map((r) => ({ + const results = searchResults.map((r) => { + const idx = r.metadata.index as number; + const e = candidates[idx]; + return { score: Math.round(r.score * 1000) / 1000, - sessionId: (r.event as Record)._sessionId ?? sessionId, - eventType: r.event.eventType, - severity: r.event.severity, - source: r.event.source, - phase: r.event.phase, - timestamp: r.event.timestamp, - message: r.event.message, - })); + sessionId: (e as Record)._sessionId ?? sessionId, + eventType: e.eventType, + severity: e.severity, + source: e.source, + phase: e.phase, + timestamp: e.timestamp, + message: e.message, + }; + }); return { content: [{ type: 'text' as const, text: JSON.stringify({ query, + searchBackend: provider.name, sessionsSearched: sessionIds, eventsAnalyzed: candidates.length, resultCount: results.length, @@ -286,12 +272,12 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v } ); - // Tool 9: search_knowledge (vector search over rules) + // Tool 9: search_knowledge server.tool( 'search_knowledge', - 'Semantic search over the Autopilot Monitor knowledge base: analysis rules, gather rules, and IME log patterns. ' + + 'Semantic/fuzzy search over the Autopilot Monitor knowledge base: analysis rules, gather rules, and IME log patterns. ' + 'Use natural language queries like "app install timeout", "BitLocker issues", "detection script failure". ' + - 'Returns the most relevant rules and patterns ranked by semantic similarity. ' + + 'Returns the most relevant rules and patterns ranked by similarity. ' + 'Great for finding remediation steps, understanding error patterns, or discovering relevant diagnostic rules.', { query: z.string().describe('Natural language search query (e.g. "app download timeout", "TPM not ready", "ESP stuck")'), @@ -306,15 +292,15 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v return { content: [{ type: 'text' as const, - text: JSON.stringify({ error: 'Knowledge base not initialized. The server may still be loading embeddings.' }), + text: JSON.stringify({ error: 'Knowledge base not initialized. The server may still be loading.' }), }], }; } - const queryEmbedding = await embed(query); - let results = knowledgeBase.search(queryEmbedding, type === 'all' ? topK * 3 : topK, minScore); + // Over-fetch when filtering by type, then trim + const fetchK = type === 'all' ? topK : topK * 3; + let results = await knowledgeBase.search(query, { topK: fetchK, minScore }); - // Filter by type if specified if (type !== 'all') { results = results.filter((r) => r.metadata.type === type); } @@ -333,7 +319,12 @@ export function registerTools(server: McpServer, knowledgeBase?: VectorStore): v return { content: [{ type: 'text' as const, - text: JSON.stringify({ query, resultCount: formatted.length, results: formatted }, null, 2), + text: JSON.stringify({ + query, + searchBackend: knowledgeBase.name, + resultCount: formatted.length, + results: formatted, + }, null, 2), }], }; } diff --git a/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts b/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts new file mode 100644 index 00000000..1a1feb79 --- /dev/null +++ b/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts @@ -0,0 +1,96 @@ +/** + * SearchProvider backed by sentence-transformer embeddings + cosine similarity. + * Uses @xenova/transformers with the all-MiniLM-L6-v2 model (quantized, ~23 MB). + * + * Pros: True semantic understanding — "timeout" matches "waiting exceeded". + * Cons: First-run downloads the model; higher memory & CPU at index time. + */ + +import { pipeline, type FeatureExtractionPipeline } from '@xenova/transformers'; +import type { SearchDocument, SearchOptions, SearchProvider, SearchResult } from './search-provider.js'; + +const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2'; + +// ── Shared singleton embedder ──────────────────────────────── + +let embedder: FeatureExtractionPipeline | null = null; + +async function getEmbedder(): Promise { + if (!embedder) { + embedder = await pipeline('feature-extraction', MODEL_NAME, { + quantized: true, + }) as FeatureExtractionPipeline; + } + return embedder; +} + +/** Compute a normalized embedding for a single text. */ +export async function embed(text: string): Promise { + const model = await getEmbedder(); + const output = await model(text, { pooling: 'mean', normalize: true }); + return Array.from(output.data as Float32Array); +} + +// ── Helpers ────────────────────────────────────────────────── + +function cosineSimilarity(a: number[], b: number[]): number { + let dot = 0; + let normA = 0; + let normB = 0; + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + const denom = Math.sqrt(normA) * Math.sqrt(normB); + return denom === 0 ? 0 : dot / denom; +} + +// ── Provider implementation ────────────────────────────────── + +interface StoredDocument extends SearchDocument { + embedding: number[]; +} + +export class VectorSearchProvider implements SearchProvider { + readonly name = `vector/${MODEL_NAME}`; + private documents: StoredDocument[] = []; + + get size(): number { + return this.documents.length; + } + + async index(docs: SearchDocument[]): Promise { + const model = await getEmbedder(); + const batchSize = 16; + for (let i = 0; i < docs.length; i += batchSize) { + const batch = docs.slice(i, i + batchSize); + const embeddings = await Promise.all( + batch.map(async (d) => { + const out = await model(d.text, { pooling: 'mean', normalize: true }); + return Array.from(out.data as Float32Array); + }) + ); + for (let j = 0; j < batch.length; j++) { + this.documents.push({ ...batch[j], embedding: embeddings[j] }); + } + } + } + + async search(query: string, options: SearchOptions = {}): Promise { + const { topK = 5, minScore = 0.3 } = options; + const queryEmbedding = await embed(query); + + const scored = this.documents.map((doc) => ({ + id: doc.id, + text: doc.text, + metadata: doc.metadata, + score: cosineSimilarity(queryEmbedding, doc.embedding), + })); + + return scored + .filter((r) => r.score >= minScore) + .sort((a, b) => b.score - a.score) + .slice(0, topK); + } +} diff --git a/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts b/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts deleted file mode 100644 index c3828562..00000000 --- a/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts +++ /dev/null @@ -1,61 +0,0 @@ -/** - * Lightweight in-memory vector store using cosine similarity. - * Each document is stored alongside its embedding vector. - */ - -export interface VectorDocument { - id: string; - text: string; - metadata: Record; - embedding: number[]; -} - -export interface SearchResult { - id: string; - text: string; - metadata: Record; - score: number; -} - -function cosineSimilarity(a: number[], b: number[]): number { - let dot = 0; - let normA = 0; - let normB = 0; - for (let i = 0; i < a.length; i++) { - dot += a[i] * b[i]; - normA += a[i] * a[i]; - normB += b[i] * b[i]; - } - const denom = Math.sqrt(normA) * Math.sqrt(normB); - return denom === 0 ? 0 : dot / denom; -} - -export class VectorStore { - private documents: VectorDocument[] = []; - - add(doc: VectorDocument): void { - this.documents.push(doc); - } - - addMany(docs: VectorDocument[]): void { - this.documents.push(...docs); - } - - search(queryEmbedding: number[], topK = 5, minScore = 0.3): SearchResult[] { - const scored = this.documents.map((doc) => ({ - id: doc.id, - text: doc.text, - metadata: doc.metadata, - score: cosineSimilarity(queryEmbedding, doc.embedding), - })); - - return scored - .filter((r) => r.score >= minScore) - .sort((a, b) => b.score - a.score) - .slice(0, topK); - } - - get size(): number { - return this.documents.length; - } -} From 73592068d8550af76528cbf62cb4d0096350376c Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 27 Mar 2026 00:11:53 +0000 Subject: [PATCH 3/3] chore(mcp): migrate from @xenova/transformers to @huggingface/transformers MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the deprecated @xenova/transformers v2 with the actively maintained @huggingface/transformers v3. Migration changes: - Import path: @xenova/transformers → @huggingface/transformers - Quantization: quantized: true → dtype: 'q8' (v3 API) - Type workaround: dynamic import for pipeline() to avoid TS2590 from the more complex v3 overload signatures Model (Xenova/all-MiniLM-L6-v2) and output format unchanged. https://claude.ai/code/session_01TBPsvvMBue9Ft7j3CfD4S2 --- .../autopilot-monitor-mcp/package-lock.json | 1458 +++++++++-------- .../autopilot-monitor-mcp/package.json | 2 +- .../src/search-factory.ts | 6 +- .../src/vector-search-provider.ts | 12 +- 4 files changed, 807 insertions(+), 671 deletions(-) diff --git a/src/McpServer/autopilot-monitor-mcp/package-lock.json b/src/McpServer/autopilot-monitor-mcp/package-lock.json index 75853ce0..bda691cc 100644 --- a/src/McpServer/autopilot-monitor-mcp/package-lock.json +++ b/src/McpServer/autopilot-monitor-mcp/package-lock.json @@ -9,8 +9,8 @@ "version": "1.2.0", "dependencies": { "@anthropic-ai/sdk": "^0.80.0", + 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"@xenova/transformers": "^2.17.0", "fuse.js": "^7.1.0", "zod": "^3.22.0" }, diff --git a/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts b/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts index 9c794a72..e76b5e76 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/search-factory.ts @@ -3,19 +3,19 @@ * * Selection logic: * 1. Explicit env var SEARCH_BACKEND=vector|fuse → use that - * 2. Auto-detect: try to load @xenova/transformers + * 2. Auto-detect: try to load @huggingface/transformers * - available → vector * - missing → fuse (graceful fallback) * * This allows running the MCP server without the ~23 MB model download - * by setting SEARCH_BACKEND=fuse or simply not installing @xenova/transformers. + * by setting SEARCH_BACKEND=fuse or simply not installing @huggingface/transformers. */ import type { SearchBackend, SearchProvider } from './search-provider.js'; async function isTransformersAvailable(): Promise { try { - await import('@xenova/transformers'); + await import('@huggingface/transformers'); return true; } catch { return false; diff --git a/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts b/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts index 1a1feb79..aad6d3aa 100644 --- a/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts +++ b/src/McpServer/autopilot-monitor-mcp/src/vector-search-provider.ts @@ -1,12 +1,12 @@ /** * SearchProvider backed by sentence-transformer embeddings + cosine similarity. - * Uses @xenova/transformers with the all-MiniLM-L6-v2 model (quantized, ~23 MB). + * Uses @huggingface/transformers with the all-MiniLM-L6-v2 model (quantized, ~23 MB). * * Pros: True semantic understanding — "timeout" matches "waiting exceeded". * Cons: First-run downloads the model; higher memory & CPU at index time. */ -import { pipeline, type FeatureExtractionPipeline } from '@xenova/transformers'; +import type { FeatureExtractionPipeline } from '@huggingface/transformers'; import type { SearchDocument, SearchOptions, SearchProvider, SearchResult } from './search-provider.js'; const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2'; @@ -17,9 +17,11 @@ let embedder: FeatureExtractionPipeline | null = null; async function getEmbedder(): Promise { if (!embedder) { - embedder = await pipeline('feature-extraction', MODEL_NAME, { - quantized: true, - }) as FeatureExtractionPipeline; + // Dynamic import + cast to avoid TS2590 from the overloaded pipeline() signature + const { pipeline } = await import('@huggingface/transformers'); + embedder = (await (pipeline as Function)('feature-extraction', MODEL_NAME, { + dtype: 'q8', + })) as FeatureExtractionPipeline; } return embedder; }