> For the complete documentation index, see [llms.txt](https://docs.flowiseai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flowiseai.com/integrations/langchain/vector-stores/postgres.md).
# Postgres
Upsert embedded data and perform similarity search upon query using pgvector on Postgres.
Postgres Node
There are multiple methods to connect to Postgres based on how your instance is set up. Below is an example of a local configuration using a prebuilt Docker image provided by the pgvector team.
Create a file named `docker-compose.yml` with the content below:
```yaml
# Run this command to start the database:
# docker-compose up --build
version: "3"
services:
db:
hostname: 127.0.0.1
image: pgvector/pgvector:pg16
ports:
- 5432:5432
restart: always
environment:
- POSTGRES_DB=api
- POSTGRES_USER=myuser
- POSTGRES_PASSWORD=ChangeMe
volumes:
- ./init.sql:/docker-entrypoint-initdb.d/init.sql
```
`docker compose up` to start the Postgres container.
Create new credential with the configured user and password:
Fill in the node's field with values configured in `docker-compose.yml`. For example:
* Host: **localhost**
* Database: **api**
* Port: **5432**
Voila! You have now successfully setup Postgres Vector ready to be used.
### Troubleshooting
If both Flowise and Postgres are running on Docker, you might see the error: **AggregateError**.
Try to change the Host value from `localhost` to `host.docker.internal`
---
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