Docker Setup

Sigmie ships a docker-compose.yml that runs Elasticsearch (or OpenSearch) together with local embedding and reranking services. The Infinity-based services let you build semantic search without paid APIs in development.

#Start everything

docker-compose up -d

The first start downloads models and takes 5–10 minutes. After that, restarts are quick.

Note: Elasticsearch and OpenSearch both bind port 9200. Start one or the other, not both.

#Services

Service	Port	Model	Purpose
elasticsearch	9200	—	Elasticsearch 9.1.3, security disabled
opensearch	9200	—	OpenSearch 3.0 with default admin auth
embeddings	7997	BAAI/bge-small-en-v1.5 (384-dim)	Text embeddings
reranker	7998	cross-encoder/ms-marco-MiniLM-L-6-v2	Result reranking
image-embeddings	7996	TinyCLIP ViT-8M-16	Image/text embeddings
llm	7999	Ollama (app-side only)	Optional; Sigmie has no LLM client

#Start only what you need

Minimal: keyword search only
docker-compose up -d elasticsearch
 
Add semantic search
docker-compose up -d elasticsearch embeddings
 
Semantic search + reranking
docker-compose up -d elasticsearch embeddings reranker
 
Image search
docker-compose up -d elasticsearch image-embeddings

#Connect Sigmie to the local services

Register the local embeddings service with Sigmie:

use Sigmie\AI\APIs\InfinityEmbeddingsApi;
 
$sigmie->registerApi('embeddings', new InfinityEmbeddingsApi(
    baseUrl: 'http://localhost:7997',
    model: 'BAAI/bge-small-en-v1.5',
));

Register the reranker:

use Sigmie\AI\APIs\InfinityRerankApi;
 
$sigmie->registerApi('my-rerank', new InfinityRerankApi(
    baseUrl: 'http://localhost:7998',
    model: 'cross-encoder/ms-marco-MiniLM-L-6-v2',
));
 
$response = $sigmie->newSearch('docs')
    ->properties($props)
    ->queryString('return policy')
    ->get();
 
$reranked = $response->rerank('my-rerank', ['content']);

See Semantic Search for using these in mappings, and Retrieval and Agents for combining them with generation.

#Connect to Elasticsearch

use Sigmie\Sigmie;
 
$sigmie = Sigmie::create(hosts: ['127.0.0.1:9200']);

#Connect to OpenSearch

use Sigmie\Sigmie;
use Sigmie\Enums\SearchEngineType;
 
$sigmie = Sigmie::create(
    hosts: ['https://localhost:9200'],
    engine: SearchEngineType::OpenSearch,
    config: [
        'auth' => ['admin', 'MyStrongPass123!@#'],
        'verify' => false,
    ]
);

#Environment variables

Copy .env.example to .env to customize service URLs:

LOCAL_EMBEDDING_URL=http://localhost:7997
LOCAL_RERANK_URL=http://localhost:7998
LOCAL_CLIP_URL=http://localhost:7996

For cloud API keys:

OPENAI_API_KEY=sk-...
VOYAGE_API_KEY=pa-...
COHERE_API_KEY=...
MIXEDBREAD_API_KEY=...

Sigmie itself doesn’t read these — your application registers the API clients via registerApi().

#Health checks

docker-compose ps                                  all services
curl http://localhost:7997/health                  embeddings
curl http://localhost:7998/health                  reranker
curl http://localhost:7996/health                  image embeddings
curl http://localhost:9200/_cluster/health         Elasticsearch
curl -u admin:MyStrongPass123!@# -k \
    https://localhost:9200/_cluster/health         OpenSearch

#Data persistence

All data lives in ./data/:

./data/
├── embeddings/         # downloaded model
├── reranker/           # downloaded model
├── image-embeddings/   # downloaded model
├── llm/                # Ollama models (if used)
├── elasticsearch/      # indices and documents
└── opensearch/         # indices and documents

Reset everything:

docker-compose down -v
rm -rf ./data/

Warning: This deletes all indices, documents, and downloaded models.

#Logs

docker-compose logs -f                all services, follow
docker-compose logs embeddings        one service
docker-compose logs elasticsearch

#Resource budget

Service	RAM	Disk
Embeddings	1–2 GB	~500 MB
Reranker	1–2 GB	~400 MB
Image embeddings	1–2 GB	~300 MB
Elasticsearch	2–4 GB	varies
OpenSearch	2–4 GB	varies

For Elasticsearch + embeddings + reranker, give Docker at least 8 GB.

#Troubleshooting

Port 9200 already in use. Stop the engine you’re not using:

docker-compose stop elasticsearch
docker-compose up -d opensearch

Embeddings service won’t start. Check the logs — the first start downloads the model:

docker-compose logs embeddings

Wait for “Model loaded successfully.”

Out of memory. Allocate more RAM in Docker Desktop preferences.