Doclea MCP

import { z } from "zod"; import type { SQLiteDatabase } from "@/database/sqlite"; import type { EmbeddingClient } from "@/embeddings/provider"; import { MemoryTypeSchema, type SearchResult } from "@/types"; import type { VectorStore } from "@/vectors/interface"; export const SearchMemoryInputSchema = z.object({ query: z.string().describe("Natural language search query"), type: MemoryTypeSchema.optional().describe("Filter by memory type"), tags: z.array(z.string()).optional().describe("Filter by tags (any match)"), minImportance: z .number() .min(0) .max(1) .optional() .describe("Minimum importance threshold"), relatedFiles: z .array(z.string()) .optional() .describe("Filter by related files"), limit: z .number() .min(1) .max(50) .default(10) .describe("Maximum results to return"), }); export type SearchMemoryInput = z.infer<typeof SearchMemoryInputSchema>; export async function searchMemory( input: SearchMemoryInput, db: SQLiteDatabase, vectors: VectorStore, embeddings: EmbeddingClient, ): Promise<SearchResult[]> { // Generate query embedding const queryVector = await embeddings.embed(input.query); // Search vectors with filters const vectorResults = await vectors.search( queryVector, { type: input.type, tags: input.tags, minImportance: input.minImportance, relatedFiles: input.relatedFiles, }, input.limit, ); if (vectorResults.length === 0) { return []; } // Fetch full memories from SQLite const memoryIds = vectorResults.map((r) => r.memoryId); const memories = db.getMemoriesByIds(memoryIds); // Map scores to memories const scoreMap = new Map(vectorResults.map((r) => [r.memoryId, r.score])); return memories .map((memory) => ({ memory, score: scoreMap.get(memory.id) ?? 0, })) .sort((a, b) => b.score - a.score); }