> ## 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.
# Hybrid and semantic search
> Combine full-text keyword search with AI-powered semantic search to deliver results that match both exact terms and meaning.
Hybrid search combines two search strategies: [full-text search](/capabilities/full_text_search/overview) (matching keywords) and semantic search (matching meaning). This gives users the best of both worlds, returning results that are both textually and conceptually relevant.
## Embedding models, not LLMs
Semantic search in Meilisearch relies on **embedding models**, not large language models (LLMs). This is an important distinction:
* **Embedding models** convert text into numerical vectors that capture meaning. They are small, fast, and inexpensive to run.
* **LLMs** (like GPT-4 or Claude) generate text and reason about it. They are much larger, slower, and more expensive.
Meilisearch uses embedding models for hybrid and semantic search, making it orders of magnitude cheaper and faster than LLM-based approaches. For conversational AI features that do use LLMs, see [conversational search](/capabilities/conversational_search/overview).
## How it works
When you configure an embedder, Meilisearch automatically generates vector embeddings for every document in your index. You don't need to compute or manage embeddings yourself.
```mermaid theme={null}
flowchart LR
A[Documents] --> B[Meilisearch]
B -->|auto-embeds| C[Vector index]
B -->|indexes| D[Keyword index]
E[Search query] --> B
B --> F[Merge & rank results]
```
At search time, Meilisearch runs both keyword and semantic search in parallel, then merges the results using a smart scoring system.
### Automatic embedding generation
Meilisearch handles the entire embedding pipeline for you:
* **Batching**: documents are grouped and sent to the embedding provider in optimized batches, minimizing API calls and maximizing throughput
* **Caching**: embeddings are stored and only regenerated when document content changes, so re-indexing unchanged documents costs nothing. Note that changing your embedder configuration (switching model, provider, or document template) triggers a full re-embedding of all documents, which may incur significant API costs for large indexes
* **Rate limit handling**: Meilisearch automatically retries when providers return rate limit errors, with no configuration needed
* **Document templates**: you control exactly which fields are embedded using [Liquid templates](/capabilities/hybrid_search/advanced/document_template_best_practices), so the embedding captures the most relevant parts of each document
Updating embedder settings may trigger a full reindex. When you partially update an index's embedder settings (for example, changing `model`, `source`, `documentTemplate`, `dimensions`, or `pooling`), Meilisearch may reindex all documents and regenerate their embeddings. For large indexes this can take a long time and, with paid providers, incur significant API costs.
[`distribution`](/capabilities/hybrid_search/advanced/tune_distribution) is a notable exception: changing it does not trigger a reindex.
### Smart result ranking
When you perform a hybrid search, Meilisearch does not simply concatenate keyword and semantic results. It uses a scoring system that automatically determines, for each query, whether full-text or semantic results are more relevant:
* A precise query like `"iPhone 15 Pro Max 256GB"` will naturally favor keyword matches, because the exact terms appear in matching documents
* A descriptive query like `"lightweight laptop for travel"` will favor semantic matches, because the meaning matters more than the exact words
* Ambiguous queries get a balanced mix of both strategies
You can influence this balance with the [`semanticRatio`](/capabilities/hybrid_search/advanced/custom_hybrid_ranking) parameter, but the default (`0.5`) works well for most use cases because Meilisearch's scoring handles the blending intelligently.
## When to use hybrid search
| Scenario | Best approach |
| ----------------------------------------------- | ---------------- |
| User searches for a product name or SKU | Full-text search |
| User describes a problem in natural language | Semantic search |
| Ecommerce product search with varied vocabulary | Hybrid search |
| Documentation search with technical terms | Hybrid search |
| FAQ or support knowledge base | Hybrid search |
## Supported embedder providers
Meilisearch supports a wide range of embedding providers. Some have native integrations, while others are available through the flexible [REST embedder](/capabilities/hybrid_search/how_to/configure_rest_embedder) that works with any API.
### Native integrations
| Provider | Source | Guide |
| ------------------- | ------------- | ------------------------------------------------------------------------------------------ |
| OpenAI | `openAi` | [Configure OpenAI](/capabilities/hybrid_search/how_to/configure_openai_embedder) |
| HuggingFace (local) | `huggingFace` | [Configure HuggingFace](/capabilities/hybrid_search/how_to/configure_huggingface_embedder) |
### Available via REST embedder
| Provider | Guide |
| ------------------------------- | ------------------------------------------------------------------------------------- |
| Cohere | [Configure Cohere](/capabilities/hybrid_search/how_to/configure_cohere_embedder) |
| Mistral | [Configure Mistral](/capabilities/hybrid_search/providers/mistral) |
| Google Gemini | [Configure Gemini](/capabilities/hybrid_search/providers/gemini) |
| Cloudflare Workers AI | [Configure Cloudflare](/capabilities/hybrid_search/providers/cloudflare) |
| Voyage AI | [Configure Voyage](/capabilities/hybrid_search/providers/voyage) |
| AWS Bedrock | [Configure Bedrock](/capabilities/hybrid_search/providers/bedrock) |
| HuggingFace Inference Endpoints | [Configure HF Inference](/capabilities/hybrid_search/providers/huggingface) |
| Jina | [Configure Jina](/capabilities/hybrid_search/providers/jina) |
| Any REST API | [Configure REST embedder](/capabilities/hybrid_search/how_to/configure_rest_embedder) |
### User-provided embeddings
If you pre-compute embeddings externally (for example, for images or audio content), you can supply them directly. See [search with user-provided embeddings](/capabilities/hybrid_search/how_to/search_with_user_provided_embeddings).
## Embedder field compatibility
Different embedder sources accept different configuration fields. Setting an invalid field for a given source returns an error on settings update.
| Field | `openAi` | `huggingFace` | `ollama` | `rest` | `userProvided` |
| ----------------------------------------------- | -------- | ----------------------------- | -------- | ---------------- | -------------- |
| `url` / `apiKey` | optional | invalid | optional | required (`url`) | invalid |
| `model` | optional | optional | optional | invalid | invalid |
| `documentTemplate` / `documentTemplateMaxBytes` | optional | optional | optional | optional | invalid |
| `dimensions` | optional | optional | optional | optional | **mandatory** |
| `pooling` | invalid | optional (default `useModel`) | invalid | invalid | invalid |
| `distribution` | optional | optional | optional | optional | optional |
| `binaryQuantized` | optional | optional | optional | optional | optional |
For `composite` embedders, these rules apply to each sub-embedder with additional constraints. See [composite embedders](/capabilities/hybrid_search/advanced/composite_embedders#sub-embedder-constraints).
## Next steps
Configure an embedder and perform your first semantic search
Compare providers and pick the right one for your use case
Control which document fields are used for embedding generation
Tune semanticRatio to balance keyword and semantic results