Dr. QuantMaster MCP Server

import { getCollection, isVectorSearchAvailable } from "./client.js"; import { COLLECTIONS } from "./collections.js"; export async function searchKnowledgeBase(query, category = "all", nResults = 5) { // If ChromaDB is not available, return empty results with a note if (!isVectorSearchAvailable()) { return [{ content: "Vector search is currently unavailable. The tool is operating without RAG support. To enable, start ChromaDB server: chroma run --path ./chroma-data", metadata: { source: "system", type: "notice" }, distance: 0 }]; } const collectionsToSearch = category === "all" ? Object.values(COLLECTIONS).map((c) => c.name) : Object.values(COLLECTIONS) .filter((c) => c.metadata.category === category) .map((c) => c.name); const results = []; for (const collectionName of collectionsToSearch) { try { const collection = await getCollection(collectionName); if (!collection) continue; const queryResult = await collection.query({ queryTexts: [query], nResults: nResults, }); if (queryResult.documents[0]) { results.push(...queryResult.documents[0].map((doc, i) => ({ content: doc || "", metadata: (queryResult.metadatas[0]?.[i] || {}), distance: queryResult.distances?.[0]?.[i], }))); } } catch (error) { // Silently skip failed collections } } // Sort by relevance (lower distance = more relevant) results.sort((a, b) => (a.distance || 0) - (b.distance || 0)); return results.slice(0, nResults); } export async function addDocument(collectionName, id, content, metadata) { if (!isVectorSearchAvailable()) return; const collection = await getCollection(collectionName); if (!collection) return; await collection.add({ ids: [id], documents: [content], metadatas: [metadata], }); } export async function addDocuments(collectionName, ids, contents, metadatas) { if (!isVectorSearchAvailable()) return; const collection = await getCollection(collectionName); if (!collection) return; await collection.add({ ids, documents: contents, metadatas, }); } //# sourceMappingURL=embeddings.js.map