Home
Softono

Memvectordb

Open source Rust
GitHub
31
Stars
2
Forks
8
Issues
1
Watchers
1 year
Last Commit
Vector Databases

 About Memvectordb

⚡️Lightning fast in-memory VectorDB written in rust🦀

Platforms

Web Self-hosted

Languages

Rust

Links

Source Code

Need Help Installing Memvectordb?

We provide expert installation service for this software. Our team will install, configure, and secure Memvectordb on your server. plans start at just $30.

Hire an Expert

Alternatives to Memvectordb

View All

Helix Db

HelixDB is an OLTP graph-vector database built in Rust.

4.7K

Pgvecto.rs

Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.

2.2K

SeekStorm

SeekStorm: vector & lexical search - in-process library & multi-tenancy server, in Rust.

1.9K

Oasysdb

In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching.

376

Victor

Web-optimized vector database (written in Rust).

261

Ahnlich

Suite of tools containing an in-memory vector datastore and AI proxy

192
Published by
kevkibe
Visit View Profile

Memvectordb

View on GitHub

MemVectorDB

An fast in-memory VectorDB in rust.

⚡️ Features

  • Fast: MemVectorDB stores vectors in-memory, ensuring fast insertion and retrieval operations.
  • Vertical Scalability: With vectors stored in-memory, MemVectorDB can scale vertically based on available system resources.
  • Metadata Support: Supports metadata storage, beneficial for RAG (Retrieval Augmented Generation) applications and pipelines.
  • Option for Persistence: Supports full restoration of data from logs.
  • Open Source: MIT Licensed, free forever.

🚀 Usage

1. Clone the repository:

git clone https://github.com/KevKibe/memvectordb.git

2. Build dependencies:

make build

3. Start the DB.

make run

4. Restore the DB from logs.

make run-restore

🐳 Using Docker

1. Pull the Docker image:

  • On x86_64 (Intel/AMD) systems:
docker pull kevkibe/memvectordb:v2.1.0
  • On ARM-based systems (e.g., M1, M2, M3):
docker pull --platform linux/amd64 kevkibe/memvectordb:v2.1.0

2. Run the Docker container:

  • On x86_64 (Intel/AMD) systems:
docker run -it --rm \
    -p 8000:8000 \
    -v /var/memvectordb:/memvectordb \
    kevkibe/memvectordb:v2.1.0
  • On ARM-based systems (e.g., M1, M2, M3):
docker run -it --rm \
    --platform linux/amd64 \
    -p 8000:8000 \
    -v /var/memvectordb:/memvectordb \
    kevkibe/memvectordb:v2.1.0

3. Run the Docker container with DB restoration:

  • On x86_64 (Intel/AMD) systems:
docker run -it --rm \
    -p 8000:8000 \
    -v /var/memvectordb:/memvectordb \
    -e RESTORE_DB=true \
    kevkibe/memvectordb:v2.1.0
  • On ARM-based systems (e.g., M1, M2, M3):
docker run -it --rm \
    --platform linux/amd64 \
    -p 8000:8000 \
    -v /var/memvectordb:/memvectordb \
    -e RESTORE_DB=true \
    kevkibe/memvectordb:v2.1.0

4. DB runs on http://localhost:8000

MemVectorDB Python client: Docs

Load Tests

  • All tests done with 100000 requests on a Macbook Air M1.

POST

Summary:
  Success rate: 100.00%
  Total:        1.9317 secs
  Slowest:      0.0363 secs
  Fastest:      0.0000 secs
  Average:      0.0010 secs
  Requests/sec: 51766.9796

  Total data:   5.15 MiB
  Size/request: 54 B
  Size/sec:     2.67 MiB

GET

Summary:
  Success rate: 100.00%
  Total:        1.0847 secs
  Slowest:      0.0081 secs
  Fastest:      0.0000 secs
  Average:      0.0005 secs
  Requests/sec: 92191.6443

  Total data:   4.58 MiB
  Size/request: 48 B
  Size/sec:     4.22 MiB

DELETE

Summary:
  Success rate: 100.00%
  Total:        1.0714 secs
  Slowest:      0.0168 secs
  Fastest:      0.0000 secs
  Average:      0.0005 secs
  Requests/sec: 93339.6446

  Total data:   5.05 MiB
  Size/request: 52 B
  Size/sec:     4.72 MiB

PUT

Summary:
  Success rate: 100.00%
  Total:        2.7216 secs
  Slowest:      0.0395 secs
  Fastest:      0.0001 secs
  Average:      0.0014 secs
  Requests/sec: 36743.3094

  Total data:   7.06 MiB
  Size/request: 74 B
  Size/sec:     2.59 MiB