Maxime Killinger
4ea4132653
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- Multi-stage Dockerfile for optimized image size (676MB vs 2GB) - Support for PostgreSQL 15, 16, 17, 18 - TimescaleDB 2.24.0, VectorChord 1.0.0, pgvector 0.8.1 - Auto-creation of extensions on first startup - CI/CD with tests for all versions - OCI labels and healthcheck included
91 lines
2.5 KiB
Markdown
91 lines
2.5 KiB
Markdown
# TimescaleDB + VectorChord + pgvector
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[](https://gitea.killinger.fr/maxime.killinger/postgres-ts-vectors/actions)
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Docker image combining **PostgreSQL** with three powerful extensions:
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- **[TimescaleDB](https://www.timescale.com/)** - Time-series database extension
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- **[VectorChord](https://vectorchord.ai/)** - High-performance vector similarity search
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- **[pgvector](https://github.com/pgvector/pgvector)** - Open-source vector similarity search
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## Supported PostgreSQL Versions
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| Tag | PostgreSQL | TimescaleDB | VectorChord | pgvector |
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|-----|------------|-------------|-------------|----------|
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| `pg15` | 15 | 2.24.0 | 1.0.0 | 0.8.1 |
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| `pg16` | 16 | 2.24.0 | 1.0.0 | 0.8.1 |
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| `pg17` | 17 | 2.24.0 | 1.0.0 | 0.8.1 |
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| `pg18`, `latest` | 18 | 2.24.0 | 1.0.0 | 0.8.1 |
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## Quick Start
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```bash
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docker run -d \
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--name my-postgres \
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-e POSTGRES_PASSWORD=mysecretpassword \
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-p 5432:5432 \
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gitea.killinger.fr/maxime.killinger/postgres-ts-vectors:latest
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```
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All extensions are **automatically created** on first startup.
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## Environment Variables
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `POSTGRES_PASSWORD` | PostgreSQL password (required) | - |
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| `POSTGRES_USER` | PostgreSQL user | `postgres` |
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| `POSTGRES_DB` | Default database | `postgres` |
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## Extensions Usage
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### TimescaleDB
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```sql
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-- Create a hypertable
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CREATE TABLE conditions (
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time TIMESTAMPTZ NOT NULL,
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device_id INTEGER,
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temperature DOUBLE PRECISION
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);
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SELECT create_hypertable('conditions', 'time');
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```
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### VectorChord / pgvector
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```sql
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-- Create a table with vector embeddings
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CREATE TABLE documents (
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id SERIAL PRIMARY KEY,
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content TEXT,
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embedding vector(1536)
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);
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-- Create an index for fast similarity search
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CREATE INDEX ON documents USING vchordrq (embedding vector_l2_ops);
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-- Query similar documents
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SELECT * FROM documents
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ORDER BY embedding <-> '[0.1, 0.2, ...]'
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LIMIT 10;
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```
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## Building Locally
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```bash
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# Build for PG18
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docker build --build-arg PG_VERSION=18 -t my-image:pg18 .
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# Build for PG16
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docker build --build-arg PG_VERSION=16 -t my-image:pg16 .
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```
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## License
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This project is licensed under the Apache 2.0 License.
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- TimescaleDB: [Timescale License](https://github.com/timescale/timescaledb/blob/main/LICENSE)
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- VectorChord: [Apache 2.0](https://github.com/tensorchord/VectorChord/blob/main/LICENSE)
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- pgvector: [PostgreSQL License](https://github.com/pgvector/pgvector/blob/master/LICENSE)
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