Skip to main content
Glama

Convex MCP server

Official
by get-convex
memory_index.rs1.53 kB
use std::collections::BTreeMap; use common::types::{ Timestamp, WriteTimestamp, }; use criterion::{ black_box, criterion_group, criterion_main, Criterion, }; use rand::Rng; use value::InternalId; use vector::{ CompiledVectorSearch, MemoryVectorIndex, QdrantDocument, }; pub fn criterion_benchmark(c: &mut Criterion) { let mut rng = rand::rng(); let n = 20000; let d = 1536; let k = 1024; let ts = Timestamp::must(1); let mut index = MemoryVectorIndex::new(WriteTimestamp::Committed(ts)); let mut next_id = 1u128; for _ in 0..n { let id = InternalId(next_id.to_le_bytes()); next_id += 1; let document = QdrantDocument { internal_id: id, vector: (0..d) .map(|_| rng.random()) .collect::<Vec<_>>() .try_into() .unwrap(), filter_fields: BTreeMap::new(), }; index .update(id, WriteTimestamp::Committed(ts), None, Some(document)) .unwrap(); } println!("size: {}", index.size()); let search = CompiledVectorSearch { vector: (0..d) .map(|_| rng.random()) .collect::<Vec<_>>() .try_into() .unwrap(), limit: k, filter_conditions: BTreeMap::new(), }; c.bench_function("query", |b| b.iter(|| index.query(ts, black_box(&search)))); } criterion_group!(benches, criterion_benchmark); criterion_main!(benches);

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/get-convex/convex-backend'

If you have feedback or need assistance with the MCP directory API, please join our Discord server