> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vantagewealth.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Docker (Embedding Server)

> Run the embedding server for AI memory using Docker.

## Why Docker?

The RAG pipeline uses **BAAI/bge-small-en-v1.5** to embed financial summaries into vectors. This model runs in a lightweight Docker container so you don't need to install PyTorch on the main server.

***

## Start the embedding server

```bash theme={null}
cd backend/docker
docker compose up -d
```

This starts a FastAPI server on port 8001 that exposes:

```
POST /embed
Body: {"texts": ["Daily summary for April 18..."]}
Response: {"embeddings": [[0.12, -0.34, ...], ...]}
```

***

## Verify it's running

```bash theme={null}
curl -X POST http://localhost:8001/embed \
  -H "Content-Type: application/json" \
  -d '{"texts": ["test"]}'
```

Should return a 384-dimensional float array.

***

## Memory requirements

| Model                  | Memory       |
| ---------------------- | ------------ |
| BAAI/bge-small-en-v1.5 | \~180 MB RAM |
| Docker overhead        | \~100 MB     |
| **Total**              | **\~280 MB** |

A t3.small (2 GB RAM) can comfortably run both the FastAPI backend and the embedding server.

***

## Without Docker

If you can't run Docker, RAG ingestion will fall back to a no-op and the AI advisor will work without memory context. All other features work normally.