An fast in-memory VectorDB in rust.
- 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.
- Open Source: MIT Licensed, free forever.
- Clone the repository:
git clone https://github.com/KevKibe/memvectordb.git
- Build dependencies:
make build
- Start the server
make run
- Server runs on http://localhost:8000
- Pull the Docker image:
- On x86_64 (Intel/AMD) systems:
docker pull kevkibe/memvectordb
- On ARM-based systems (e.g., M1, M2, M3):
docker pull --platform linux/amd64 kevkibe/memvectordb
- Run the Docker container:
- On x86_64 (Intel/AMD) systems:
docker run -p 8000:8000 kevkibe/memvectordb
- On ARM-based systems (e.g., M1, M2, M3):
docker run -p 8000:8000 --platform linux/amd64 kevkibe/memvectordb
- Server runs on http://localhost:8000
MemVectorDB Python client: Docs
- All tests done with 100000 requests on a Macbook Air M1.
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
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
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
Summary:
Success rate: 100.00%
Total: 2.8909 secs
Slowest: 0.0307 secs
Fastest: 0.0001 secs
Average: 0.0014 secs
Requests/sec: 34591.4733
Total data: 7.72 MiB
Size/request: 80 B
Size/sec: 2.67 MiB