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

# Meilisearch v1.6 release notes

> Released January 15, 2024

## 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:

```bash theme={null}
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:

```bash theme={null}
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:

```bash theme={null}
./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:

```bash theme={null}
./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:

```bash theme={null}
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:

```json theme={null}
{
  "_vectors": {
    "image2text": [
      0,
      0.1,
      0.2
    ]
  }
}
```

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

```json theme={null}
{
  "embedders": {
    "default": {
      "source": "userProvided",
      "dimensions": 512
    }
  }
}
```

[Find more information on GitHub](https://github.com/meilisearch/meilisearch/releases/tag/v1.6.0)


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