Semantic Search with Jina Embeddings

Jina AI provides a range of embedding models with strong multilingual support and competitive pricing. This guide shows you how to configure Meilisearch with Jina embeddings using the REST embedder.

Requirements

A Meilisearch project
A Jina AI account with an API key

Available models

Model	Dimensions	Notes
`jina-embeddings-v4`	128, 256, 512, 1,024, or 2,048	Multimodal (text, images, PDFs), 32K context, multilingual
`jina-embeddings-v5-text-small`	1,024	Text-only, balanced quality and speed
`jina-embeddings-v5-text-nano`	768	Smallest and fastest v5 model
`jina-embeddings-v3`	1,024	Previous generation, well-tested
`jina-colbert-v2`	128	Multi-vector model for fine-grained matching

For new projects, jina-embeddings-v4 is the recommended choice with its multimodal support and flexible dimensions. If you only need text embeddings, the v5-text models offer a lighter alternative. See the Jina models page for details.

Configure the embedder

Standard embedding models

Use this configuration for jina-embeddings-v5-text-small, jina-embeddings-v5-text-nano, or jina-embeddings-v3:

{
  "jina": {
    "source": "rest",
    "apiKey": "<JINA_API_KEY>",
    "dimensions": 1024,
    "documentTemplate": "A product named '{{doc.name}}': {{doc.description}}",
    "url": "https://api.jina.ai/v1/embeddings",
    "request": {
      "model": "jina-embeddings-v5-text-small",
      "input": ["{{text}}", "{{..}}"]
    },
    "response": {
      "data": [
        { "embedding": "{{embedding}}" },
        "{{..}}"
      ]
    }
  }
}

Adjust model and dimensions to match the model you choose (1024 for v5-text-small and v3, 768 for v5-text-nano).

ColBERT multi-vector model

jina-colbert-v2 uses a different API endpoint and response format:

{
  "jina-colbert": {
    "source": "rest",
    "apiKey": "<JINA_API_KEY>",
    "dimensions": 128,
    "documentTemplate": "A product named '{{doc.name}}': {{doc.description}}",
    "url": "https://api.jina.ai/v1/multi-vector",
    "request": {
      "model": "jina-colbert-v2",
      "input_type": "document",
      "embedding_type": "float",
      "input": ["{{text}}", "{{..}}"]
    },
    "response": {
      "data": [
        { "embeddings": ["{{embedding}}"] },
        "{{..}}"
      ]
    }
  }
}

Send the configuration

curl \
  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_NAME/settings' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MEILISEARCH_KEY' \
  --data-binary '{
    "embedders": {
      "jina": {
        "source": "rest",
        "apiKey": "<JINA_API_KEY>",
        "dimensions": 1024,
        "documentTemplate": "A product named '\''{{doc.name}}'\'': {{doc.description}}",
        "url": "https://api.jina.ai/v1/embeddings",
        "request": {
          "model": "jina-embeddings-v5-text-small",
          "input": ["{{text}}", "{{..}}"]
        },
        "response": {
          "data": [
            { "embedding": "{{embedding}}" },
            "{{..}}"
          ]
        }
      }
    }
  }'

Replace <JINA_API_KEY> with your actual Jina API key. Meilisearch handles batching and rate limiting automatically. Monitor the tasks queue to track indexing progress.

Test the search

{
  "q": "comfortable shoes for walking",
  "hybrid": {
    "semanticRatio": 0.5,
    "embedder": "jina"
  }
}

Next steps

Document template best practices to optimize which fields are embedded
Custom hybrid ranking to tune the balance between keyword and semantic results
Embedder settings reference for all configuration options

Capabilities

Full-text search

Hybrid and semantic search

Geo search

Conversational search

Multi-search

Filtering, sorting, and faceting

Personalization

Analytics

Security and tenant tokens

Search rules

Platform

Indexing

Requirements

Available models

Configure the embedder

Standard embedding models

ColBERT multi-vector model

Send the configuration

Test the search

Next steps

​Requirements

​Available models

​Configure the embedder

​Standard embedding models

​ColBERT multi-vector model

​Send the configuration

​Test the search

​Next steps

Requirements

Available models

Configure the embedder

Standard embedding models

ColBERT multi-vector model

Send the configuration

Test the search

Next steps