https://github.com/oaklight/vectorsearch
Dockerized vector database based on pgvector, pgvectorscale and pg_search
https://github.com/oaklight/vectorsearch
bm25 pgsearch pgvector postgres semantic-search vector-database vector-search
Last synced: 7 months ago
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Dockerized vector database based on pgvector, pgvectorscale and pg_search
- Host: GitHub
- URL: https://github.com/oaklight/vectorsearch
- Owner: Oaklight
- Created: 2024-12-14T02:27:04.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-03-18T18:58:54.000Z (7 months ago)
- Last Synced: 2025-03-18T19:43:40.994Z (7 months ago)
- Topics: bm25, pgsearch, pgvector, postgres, semantic-search, vector-database, vector-search
- Language: Makefile
- Homepage: https://oaklight.github.io/vectorsearch/
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vectorsearch Docker Image
## About
Initially, I targeted the `paradedb/paradedb:0.12.0-pg17` image for my task, but it became clear that we only required its `pg_search` and `pgvector` extensions, with the rest being unnecessary for our use case.
Additionally, an older release of paradedb included `pgvectorscale` from Timescale for its advanced algorithms. However, this was removed in the latest release. I decided to explore using it, which brings us to the current setup.
## Build
I started with the Postgres 17 base image featuring `pgvector` (by time of writing, the latest version is `pgvector/pgvector:0.8.0-pg17` ).
Subsequently, I added `pg_search` and `pgvectorscale` as dependencies.
The `bootstrap.sh` script includes SQL commands to create these extensions for every potential database, ensuring a comprehensive setup.
## Usage
1. I have published a docker image for vectorsearch at [dockerhub](https://hub.docker.com/r/oaklight/vectorsearch)
2. To build from the Dockerfile: `make build`. For more details regarding the build process, please refer to the [makefile details](./README_makefile.md).
To run the container:
```bash
docker run --name vectorsearch -p <5432>:5432 -v <$pwd/pgdata>:/var/lib/postgresql/data/ -e POSTGRES_USER=myuser -e POSTGRES_PASSWORD=mypassword -e POSTGRES_DB=mydatabase oaklight/vectorsearch
```
or use `docker compose` :
```bash
docker compose up -d
```
## Docker Compose Sample Configuration
For easier management of the container, a sample `docker-compose.yaml` is provided as `compose.yaml.sample` . You can use this file as a starting point for configuring and running the service with Docker Compose.
Here is the content of `compose.yaml.sample` :
```yaml
services:
paradedb:
image: oaklight/vectorsearch:latest-pg17
container_name: vectorsearch
environment:
POSTGRES_USER: myuser
POSTGRES_PASSWORD: mypassword
POSTGRES_DB: mydatabase
ports:
- 5432:5432
volumes:
- $pwd/pgdata:/var/lib/postgresql/data/
```
### How to Use the Sample Configuration
1. Copy `compose.yaml.sample` to `compose.yaml`:
```bash
cp compose.yaml.sample compose.yaml
```
2. Modify `compose.yaml` as needed to match your environment and configuration preferences.
3. Run the following command to start the service using Docker Compose:
```bash
docker compose up -d
```
This will start the `vectorsearch` container with the configurations specified in `compose.yaml` .
## Note
The image is based on the official postgres image, so you can use the same commands to interact with the database.
For a detailed history of changes, see the [CHANGELOG](CHANGELOG.md).
For example, you could use `psql` to connect to the database:
```bash
psql -h -U -d
```
You can also use `pgAdmin` to connect to the database.