Outline Wiki MCP Server

/** * Brain Module * * Smart Wiki brain that combines Vector DB and LLM for intelligent operations */ import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters'; import { EmbeddingService } from './embeddings.js'; import { VectorStore } from './vector-store.js'; import { LlmProcessor } from './processor.js'; import { CHUNKING, SEARCH, ERROR_MESSAGES } from './constants.js'; import type { WikiDocument, VectorRecord, SearchResult, SmartConfig, IBrain, IEmbeddingService, ILlmProcessor, IVectorStore, } from './types.js'; export type { WikiDocument, VectorRecord, SearchResult, SmartConfig, IBrain }; export interface BrainDependencies { embeddings: IEmbeddingService; store: IVectorStore; processor: ILlmProcessor; } export class Brain implements IBrain { private readonly embeddings: IEmbeddingService; private readonly store: IVectorStore; private readonly processor: ILlmProcessor; private readonly enabled: boolean; private readonly chunkSize: number; private readonly chunkOverlap: number; private initialized: boolean = false; constructor(config: SmartConfig = {}, deps?: BrainDependencies) { const apiKey = config.openaiApiKey || process.env.OPENAI_API_KEY; const enabledEnv = process.env.ENABLE_SMART_FEATURES === 'true'; this.enabled = config.enabled ?? (enabledEnv && !!apiKey); this.chunkSize = config.chunking?.chunkSize ?? CHUNKING.CHUNK_SIZE; this.chunkOverlap = config.chunking?.chunkOverlap ?? CHUNKING.CHUNK_OVERLAP; // Use injected dependencies or create default implementations if (deps) { this.embeddings = deps.embeddings; this.store = deps.store; this.processor = deps.processor; } else { this.embeddings = new EmbeddingService(apiKey, config.embedding); this.store = new VectorStore(config.vectorStore, this.embeddings.getDimensions()); this.processor = new LlmProcessor(apiKey, config.llm); } } isEnabled(): boolean { return this.enabled; } private checkEnabled(): void { if (!this.enabled) { throw new Error(ERROR_MESSAGES.SMART_FEATURES_DISABLED); } } private async ensureInitialized(): Promise<void> { if (!this.initialized) { await this.store.init(); this.initialized = true; } } /** * Sync documents to vector store */ async syncDocuments(docs: WikiDocument[]): Promise<{ chunks: number; documents: number }> { this.checkEnabled(); await this.ensureInitialized(); const splitter = new RecursiveCharacterTextSplitter({ chunkSize: this.chunkSize, chunkOverlap: this.chunkOverlap, }); const records: VectorRecord[] = []; let processedDocs = 0; for (const doc of docs) { if (!doc.text || doc.text.trim().length === 0) continue; const chunks = await splitter.createDocuments([doc.text]); for (let i = 0; i < chunks.length; i++) { const chunk = chunks[i]; const vector = await this.embeddings.getEmbedding(chunk.pageContent); records.push({ id: `${doc.id}-chunk-${i}`, vector, text: chunk.pageContent, title: doc.title, url: doc.url || `https://app.getoutline.com/doc/${doc.id}`, }); } processedDocs++; } if (records.length > 0) { await this.store.save(records); } return { chunks: records.length, documents: processedDocs }; } /** * Search for relevant documents */ async search(query: string, limit: number = SEARCH.DEFAULT_LIMIT): Promise<SearchResult[]> { this.checkEnabled(); await this.ensureInitialized(); const queryVector = await this.embeddings.getEmbedding(query); return this.store.search(queryVector, limit); } /** * Ask a question and get an answer based on wiki content */ async ask(question: string): Promise<{ answer: string; sources: SearchResult[] }> { this.checkEnabled(); await this.ensureInitialized(); // Search for relevant content const results = await this.search(question, SEARCH.DEFAULT_LIMIT); if (results.length === 0) { return { answer: ERROR_MESSAGES.NO_RELEVANT_DOCUMENTS, sources: [], }; } // Build context from search results const context = results .map((r) => `[Document: ${r.title}]\n[URL: ${r.url}]\n${r.text}`) .join('\n\n---\n\n'); // Generate answer const answer = await this.processor.answerFromContext(question, context); return { answer, sources: results }; } /** * Summarize a document */ async summarize(text: string, language?: string): Promise<string> { this.checkEnabled(); return this.processor.summarize(text, language); } /** * Suggest tags for a document */ async suggestTags(text: string): Promise<string[]> { this.checkEnabled(); return this.processor.suggestTags(text); } /** * Generate Mermaid diagram from description */ async generateDiagram(description: string): Promise<string> { this.checkEnabled(); return this.processor.generateMermaid(description); } /** * Get statistics */ async getStats(): Promise<{ enabled: boolean; chunks: number }> { if (!this.enabled) { return { enabled: false, chunks: 0 }; } await this.ensureInitialized(); const count = await this.store.count(); return { enabled: true, chunks: count }; } /** * Clear all stored data */ async clear(): Promise<void> { this.checkEnabled(); await this.ensureInitialized(); await this.store.clear(); } } /** * Factory function to create a Brain instance */ export function createBrain(config?: SmartConfig, deps?: BrainDependencies): Brain { return new Brain(config, deps); } // Re-export services for external use export { EmbeddingService } from './embeddings.js'; export { VectorStore } from './vector-store.js'; export { LlmProcessor } from './processor.js'; export * from './constants.js';