Meilisearch v1.6 release notes - Meilisearch Documentation

New Features

Automated embeddings generation for vector search

Meilisearch can now automatically generate embeddings using OpenAI, HuggingFace, or your own pre-computed vectors. Configure embedders in your index settings:

curl \
 -X PATCH 'http://localhost:7700/indexes/movies/settings' \
 -H 'Content-Type: application/json' \
 --data-binary << 'EOF'
{
 "embedders": {
 "default": {
 "source": "openAi",
 "apiKey": "<your-OpenAI-API-key>",
 "model": "text-embedding-ada-002",
 "documentTemplate": "A movie titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
 },
 "image": {
 "source": "userProvided",
 "dimensions": 512
 },
 "translation": {
 "source": "huggingFace",
 "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
 "documentTemplate": "A movie titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
 }
 }
}
EOF

The documentTemplate field uses Liquid format to define what content gets embedded. The model parameter specifies which OpenAI or HuggingFace model to use.

Hybrid search

Combine keyword and semantic search in a single query using the new hybrid parameter:

curl \
 -X POST 'http://localhost:7700/indexes/movies/search' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "q": "Plumbers and dinosaurs",
 "hybrid": {
 "semanticRatio": 0.9,
 "embedder": "default"
 }
 }'

The semanticRatio controls the balance between semantic and keyword search (0 = pure keyword search, 1 = pure semantic search, default = 0.5).

Task queue webhook

Receive notifications when Meilisearch finishes processing tasks by configuring a webhook:

./meilisearch \
 --task-webhook-url=https://example.com/example-webhook?foo=bar&number=8 \
 --task-webhook-authorization-header=Bearer aSampleAPISearchKey

You can also set these via MEILI_TASK_WEBHOOK_URL and MEILI_TASK_WEBHOOK_AUTHORIZATION_HEADER environment variables or in your configuration file.

Experimental: Limit batched tasks

Control how many tasks Meilisearch batches together to improve system stability:

./meilisearch --experimental-max-number-of-batched-tasks 100

Configure via MEILI_EXPERIMENTAL_MAX_NUMBER_OF_BATCHED_TASKS environment variable or in your configuration file.

Improvements

Indexing performance

Meilisearch v1.6 significantly improves indexing speed by storing less internal data and only re-indexing the specific fields you update. On a 2.5GB e-commerce dataset, initial document addition is over 50% faster. Partial document updates show 50-75% performance improvements depending on your dataset and indexing patterns.

Disk space reduction

Database disk usage is now 40-50% smaller on typical datasets due to reduced internal data storage. Database growth is also more stable with new document additions.

Proximity ranking rule precision

Control the accuracy and performance of proximity-based ranking:

curl \
 -X PATCH 'http://localhost:7700/indexes/books/settings/proximity-precision' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "proximityPrecision": "byAttribute"
 }'

Choose between byWord (exact distance, default) or byAttribute (faster but less precise, checks only if words appear in the same field).

Other

Vector search breaking changes

If you’ve used vector search in v1.3.0 through v1.5.0, update your implementation:

When using both q and vector parameters together, you must now include the hybrid parameter
Vectors must be JSON objects instead of arrays:

{
  "_vectors": {
    "image2text": [
      0,
      0.1,
      0.2
    ]
  }
}

Define a model in your embedder settings (previously optional for user-provided embeddings):

{
  "embedders": {
    "default": {
      "source": "userProvided",
      "dimensions": 512
    }
  }
}

Find more information on GitHub

​New Features

​Automated embeddings generation for vector search

​Hybrid search

​Task queue webhook

​Experimental: Limit batched tasks

​Improvements

​Indexing performance

​Disk space reduction

​Proximity ranking rule precision

​Other

​Vector search breaking changes