# Vector Search

Typesense has the ability to index [embeddings](#what-is-an-embedding) generated by any machine learning model, and then do a nearest-neighbor (KNN) search on this data.

[[toc]]

## Use-cases

Here are some example use-cases you can build, using vector search as the foundation:

1. [Semantic search](../../guide/semantic-search.md)
2. [Recommendations](../../guide/recommendations.md)
3. [Hybrid search](#hybrid-search) (Keyword Search + Semantic Search + Filtering)
4. Visual image search
5. [Integrate with LLMs](https://python.langchain.com/docs/integrations/vectorstores/typesense), to get them to respond to queries using your own dataset (RAG)

You can also combine any of the above with features like filtering, faceting, sorting, grouping, etc to build a user-friendly search experience. 

## What is an embedding?

An embedding for a JSON document is just an array of floating point numbers (eg: `[0.4422, 0.49292, 0.1245, ...]`), that is an alternate numeric representation of the document.

These embeddings are generated by Machine Learning models in such a way that documents that are "similar" to each other (for different definitions of similarity depending on the model used),
have embeddings that are "closer" to each other (cosine similarity).

Here are some common models you can use to generate these document embeddings: 

- Sentence-BERT
- E-5
- CLIP
- OpenAI's Text Embeddings model
- Google's PaLM API
- Google's Vertex API

You can import embeddings generated by these models into Typesense into a special vector field and then do a nearest neighbor search, giving another set of vectors or a document ID as the input, 
and get the documents that are closest (cosine similarity) to your input.

You can also have Typesense generate these embeddings for you, using OpenAI, PaLM API or one of the built-in ML models listed [here](https://huggingface.co/typesense/models/tree/main). 

#### Live Demo

Here is one (of many possible) practical applications of vector search - a "Find Similar" feature in an ecommerce store: [ecommerce-store.typesense.org](https://ecommerce-store.typesense.org/). (Click on `Find Similar` below each product).

#### Read More

Here are two articles that talk about embeddings in more detail:

- [What Are Word and Sentence Embeddings?](https://txt.cohere.ai/sentence-word-embeddings/)
- [Getting Started With Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)

Let's now discuss how to do index and search embeddings in Typesense.

## Index Embeddings

### Option A: Importing externally-generated embeddings into Typesense
 
If you have already generated embeddings using your own models outside Typesense, you can import them into Typesense. 

:::tip
[Here's](https://github.com/typesense/showcase-ecommerce-store/blob/7637d2c4e967419ac8a874c28d3f3e20d79040fa/scripts/vector-generation/main.py) a quick example of how to use the Sentence-BERT model to generate embeddings outside Typesense.
:::

Once your document embeddings are ready, you want to create a collection that contains a `float[]` field
with a `num_dim` property for indexing them. The `num_dim` property specifies the number of 
dimensions (length of the float array) that your embeddings contain.

Let's create a collection called `docs` with a vector field called `vec` that contains just 4 dimensions. 

:::tip
We're creating a vector with 4 dimensions in the examples to keep the code snippets readable. 

Depending on what model you use, real world use will require creating vector fields with at least 256 dimensions to produce good results.
:::


```js
let schema = {
  'name': 'docs',
  'fields': [
    {
      'name': 'title',
      'type': 'string'
    },
    {
      'name': 'points',
      'type': 'int32'
    },
    {
      'name': 'vec',
      'type': 'float[]',
      'num_dim': 4
    }
  ],
  'default_sorting_field': 'points'
}

client.collections().create(schema)
```



```php
$schema = [
  'name'      => 'docs',
  'fields'    => [
    [
      'name'  => 'title',
      'type'  => 'string'
    ],
    [
      'name'  => 'points',
      'type'  => 'int32'
    ],
    [
      'name'  => 'vec',
      'type'  => 'float[]',
      'num_dim'  => 4
    ]
  ],
  'default_sorting_field' => 'points'
];

$client->collections->create($schema);
```



```py
schema = {
  'name': 'docs',
  'fields': [
    {
      'name'  :  'title',
      'type'  :  'string'
    },
    {
      'name'  :  'points',
      'type'  :  'int32'
    },
    {
      'name'     :  'vec',
      'type'     :  'float[]',
      'num_dim'  :  4
    }
  ],
  'default_sorting_field': 'points'
}

client.collections.create(schema)
```



```rb
schema = {
  'name'      => 'places',
  'fields'    => [
    {
      'name'  => 'title',
      'type'  => 'string'
    },
    {
      'name'  => 'points',
      'type'  => 'int32'
    },
    {
      'name'     => 'vec',
      'type'     => 'float[]',
      'num_dim'  => 4
    }
  ],
  'default_sorting_field' => 'points'
}

client.collections.create(schema)
```


```dart
final schema = Schema(
  'docs',
  {
    Field('title', type: Type.string),
    Field('points', type: Type.int32),
    Field('vec', type: Type.float, isMultivalued: true, dimensions: 4),
  },
  defaultSortingField: Field('points', type: Type.int32),
);

await client.collections.create(schema);

```


```java
CollectionSchema collectionSchema = new CollectionSchema();

collectionschema.name("docs")
                .addFieldsItem(new Field().name("title").type("string"))
                .addFieldsItem(new Field().name("points").type("int32"))
                .addFieldsItem(new Field().name("vec").type("float[]").num_dim(4))
                .defaultSortingField("points");

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```


```bash
curl "http://localhost:8108/collections" \
      -X POST \
      -H "Content-Type: application/json" \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "name": "docs",
            "fields": [
              {"name": "title", "type": "string" },
              {"name": "points", "type": "int32" }, 
              {"name": "vec", "type": "float[]", "num_dim": 4}
            ],
            "default_sorting_field": "points"
          }'
```


Let's now index a document with a vector.


```js
let document = {
  'title': 'Louvre Museuem',
  'points': 1,
  'vec': [0.04, 0.234, 0.113, 0.001]
}

client.collections('docs').documents().create(document)
```



```php
$document = [
  'title'   => 'Louvre Museuem',
  'points'  => 1,
  'vec' => array(0.04, 0.234, 0.113, 0.001)
];

$client->collections['docs']->documents->create($document);
```



```py
document = {
  'title': 'Louvre Museuem',
  'points': 1,
  'vec': [0.04, 0.234, 0.113, 0.001]
}

client.collections['docs'].documents.create(document)
```



```rb
document = {
  'title'    =>   'Louvre Museuem',
  'points'   =>   1,
  'vec' =>  [0.04, 0.234, 0.113, 0.001]
}

client.collections['docs'].documents.create(document)
```


```dart
final document = {
  'title': 'Louvre Museuem',
  'points': 1,
  'vec': [0.04, 0.234, 0.113, 0.001]
};

await client.collection('docs').documents.create(document);

```



```java
HaashMap<String, Object> document = new HashMap<>();
float[] vec =  {0.04, 0.234, 0.113, 0.001}

document.add("title", "Louvre Museuem");
document.add("points", 1);
document.add("vec", vec);

client.collection("docs").documents.create(document);
```



```bash
curl "http://localhost:8108/collections/docs/documents" -X POST \
        -H "Content-Type: application/json" \
        -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
        -d '{"points":1,"title":"Louvre Museuem", "vec": [0.04, 0.234, 0.113, 0.001]}'
```


### Option B: Auto-embedding generation within Typesense

To simplify the process of embedding generation, Typesense can automatically use your JSON data and either OpenAI API, PaLM API or any of the built-in embedding models listed [here](https://huggingface.co/typesense/models/tree/main) to generate & store embeddings. 

When you do a search query on this automatically-generated vector field, your search query will be vectorized using the same model used for the field, which then allows you to do semantic search or combine keyword and semantic search to do hybrid search.

### Creating an auto-embedding field

To create a field that automatically embeds other string or string array fields, you need to set the `embed` property of the field. 

Here's an example:



```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "categories",
      "type": "string[]"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": [
          "product_name",
          "categories"
        ],
        "model_config": {
          "model_name": "ts/e5-small"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "categories",
      "type" => "string[]"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => [
          "product_name",
          "categories"
        ],
        "model_config" => [
          "model_name" => "ts/e5-small"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  

```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "categories",
      "type" : "string[]"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": [
          "product_name",
          "categories"
        ],
        "model_config": {
          "model_name": "ts/e5-small"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


    
```rb

schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "categories",
      "type" => "string[]"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => {
        "from" => [
          "product_name",
          "categories"
        ],
        "model_config" => {
          "model_name" => "ts/e5-small"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("ts/e5-small"))
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```


     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "categories",
            "type": "string[]"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": [
                "product_name",
                "categories"
              ],
              "model_config": {
                "model_name": "ts/e5-small"
              }
            }
          }
        ]
      }'

```
     
In this example the `embedding` vector field will be generated automatically while indexing a document, using the concatenated values of the `product_name` and `categories` fields (separated by spaces).

### Using Built-in Models

These models are officially supported by Typesense and stored in the Typesense Hugging Face repository [here](https://huggingface.co/typesense/models/tree/main).

You can specify them by adding the ```ts``` namespace before the model name. Typesense will automatically download these models and make them available for use when you index documents after creating the collection.



```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "brand",
      "type": "string"
    },
    {
      "name": "categories",
      "type": "string[]"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": [
          "brand",
          "categories"
        ],
        "model_config": {
          "model_name": "ts/all-MiniLM-L12-v2"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "brand",
      "type" => "string"
    ],
    [
      "name" => "categories",
      "type" => "string[]"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => [
          "brand",
          "categories"
        ],
        "model_config" => [
          "model_name" => "ts/all-MiniLM-L12-v2"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  


```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "brand",
      "type" : "string"
    },
    {
      "name" : "categories",
      "type" : "string[]"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": [
          "brand",
          "categories"
        ],
        "model_config": {
          "model_name": "ts/all-MiniLM-L12-v2"
        }
      }
    }
  ]
}

client.collections.create(schema)
```



```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "brand",
      "type" => "string"
    },
    {
      "name" => "categories",
      "type" => "string[]"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => {
        "from" => [
          "brand",
          "categories"
        ],
        "model_config" => {
          "model_name" => "ts/all-MiniLM-L12-v2"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("ts/all-MiniLM-L12-v2"))
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```

     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "brand",
            "type": "string"
          },
          {
            "name": "categories",
            "type": "string[]"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": [
                "brand",
                "categories"
              ],
              "model_config": {
                "model_name": "ts/all-MiniLM-L12-v2"
              }
            }
          }
        ]
      }'

```

When you create a collection with the schema above, the `all-MiniLM-L12-v2` model will be downloaded and your documents will be automatically embedded by this model and will be stored in the `embedding` field.

See our [Hugging Face repo](https://huggingface.co/typesense/models/tree/main) for all officially supported models.
If you need support for additional publicly-available models, feel free to convert the model to ONNX format and send a PR to our [Hugging Face models repo](https://huggingface.co/typesense/models/tree/main).

### Using a GPU (optional)

Embedding models are computationally intensive to run. 
So when using one of the [built-in models](#using-built-in-models), you might want to consider running Typesense on a server with a GPU to improve the performance of embedding generation, especially for large datasets.

#### On Typesense Cloud:

For [select RAM / CPU configurations](https://typesense-cloud.helpscoutdocs.com/article/4-gpu-acceleration), you'll find the option to turn on "GPU Acceleration" when provisioning a new cluster or under Cluster Configuration > Modify for Typesense versions `0.25.0` and above.

#### When Self Hosting:

You would have to install the following additional dependencies, after which Typesense will automatically make use of any available Nvidia GPUs:

1. Install CUDA following the instructions on Nvidia's site [here](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html).

    You want to specifically install the following versions of these packages: `cuda=11.8.0-1`, `libcudnn8=8.9.2.26-1+cuda11.8` and `libcudnn8-dev=8.9.2.26-1+cuda11.8` and their dependencies.

2. Install cuDNN following the instructions [here](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html).

    You want to specifically install the `libcudnn8` and `libcudnn8-dev` packages.

3. Add the following to `/etc/profile.d/cuda-path.sh`:

    ```bash
    export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
    export CUDA_HOME=/usr/local/cuda
    ```

4. Install the Typesense GPU dependencies package:

    <div class="manual-highlight">
      <pre class="language-bash"><code># x64
curl -O https://dl.typesense.org/releases/{{ $site.themeConfig.typesenseLatestVersion }}/typesense-gpu-deps-{{ $site.themeConfig.typesenseLatestVersion }}-amd64.deb
sudo apt install ./typesense-gpu-deps-{{ $site.themeConfig.typesenseLatestVersion }}-amd64.deb
<br>
# arm64
curl -O https://dl.typesense.org/releases/{{ $site.themeConfig.typesenseLatestVersion }}/typesense-gpu-deps-{{ $site.themeConfig.typesenseLatestVersion }}-arm64.deb
sudo apt install ./typesense-gpu-deps-{{ $site.themeConfig.typesenseLatestVersion }}-arm64.deb
      </code></pre>
    </div>

### Using OpenAI API

You can also have Typesense send specific fields in your JSON data to OpenAI's API to generate text embeddings. 

You can use any of OpenAI models listed [here](https://platform.openai.com/docs/guides/embeddings/embedding-models).



```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "openai/text-embedding-ada-002",
          "api_key": "your_openai_api_key"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => [
          "product_name"
        ],
        "model_config" => [
          "model_name" => "openai/text-embedding-ada-002",
          "api_key" => "your_openai_api_key"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  


```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "openai/text-embedding-ada-002",
          "api_key": "your_openai_api_key"
        }
      }
    }
  ]
}

client.collections.create(schema)
```



```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => {
        "from" => [
          "product_name"
        ],
        "model_config" => {
          "model_name" => "openai/text-embedding-ada-002",
          "api_key" => "your_openai_api_key"
        }
      }
    }
  ]
}

client.collections.create(schema)

```



```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("openai/text-embedding-ada-002").apiKey("your_openai_api_key")
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```

     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": [
                "product_name"
              ],
              "model_config": {
                "model_name": "openai/text-embedding-ada-002",
                "api_key": "your_openai_api_key"
              }
            }
          }
        ]
      }'

```

When you create the collection above, we will call the OpenAI API to create embeddings from the `product_name` field and store them in the `embedding` field every time you index a document.

You have to provide a valid OpenAI API key in `model_config` to use this feature.

### Using Google PaLM API

This API provided by [Google MakerSuite](https://developers.generativeai.google/products/makersuite) to generate embeddings.



```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "google/embedding-gecko-001",
          "api_key": "your_google_api_key"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => [
          "product_name"
        ],
        "model_config" => [
          "model_name" => "google/embedding-gecko-001",
          "api_key" => "your_google_api_key"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  

```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "google/embedding-gecko-001",
          "api_key": "your_google_api_key"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => {
        "from" => [
          "product_name"
        ],
        "model_config" => {
          "model_name" => "google/embedding-gecko-001",
          "api_key" => "your_google_api_key"
        }
      }
    }
  ]
}

client.collections.create(schema)

```

```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("google/embedding-gecko-001").apiKey("your_google_api_key")
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```

     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": [
                "product_name"
              ],
              "model_config": {
                "model_name": "google/embedding-gecko-001",
                "api_key": "your_google_api_key"
              }
            }
          }
        ]
      }'

```


**Note:** The only supported model is `embedding-gecko-001` for now.

### Using GCP Vertex AI API

This API also provided by Google under the Google Cloud Platform (GCP) umbrella. 

You would need the following authentication information to use this method:

- GCP access token (must be valid while creating the field)
- GCP refresh token
- GCP application client ID
- GCP application client secret
- GCP project ID

Please refer to the Vertex AI docs for more information on how to fetch these values.
  


```js
schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "gcp/embedding-gecko-001",
          "access_token": "your_gcp_access_token",
          "refresh_token": "your_gcp_refresh_token",
          "client_id": "your_gcp_app_client_id", 
          "client_secret": "your_gcp_client_secret",
          "project_id": "your_gcp_project_id"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => [
          "product_name"
        ],
        "model_config" => [
          "model_name" => "gcp/embedding-gecko-001",
          "access_token" => "your_gcp_access_token",
          "refresh_token" => "your_gcp_refresh_token",
          "client_id" => "your_gcp_app_client_id", 
          "client_secret" => "your_gcp_client_secret",
          "project_id" => "your_gcp_project_id"
        ]
      }
    ]
  ]
];

$client->collections->create($schema);

```
  

```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": [
          "product_name"
        ],
        "model_config": {
          "model_name": "gcp/embedding-gecko-001",
          "access_token": "your_gcp_access_token",
          "refresh_token": "your_gcp_refresh_token",
          "client_id": "your_gcp_app_client_id", 
          "client_secret": "your_gcp_client_secret",
          "project_id": "your_gcp_project_id"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => {
        "from" => [
          "product_name"
        ],
        "model_config" => {
          "model_name" => "gcp/embedding-gecko-001",
          "access_token" => "your_gcp_access_token",
          "refresh_token" => "your_gcp_refresh_token",
          "client_id" => "your_gcp_app_client_id",
          "client_secret" => "your_gcp_client_secret",
          "project_id" => "your_gcp_project_id"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("gcp/embedding-gecko-001")
                      .accessToken("your_gcp_access_token")
                      .refreshToken("your_gcp_refresh_token")
                      .clientId("your_gcp_app_client_id")
                      .clientSecret("your_gcp_client_secret").projectId("your_gcp_project_id")))
                ));
CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```

     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": [
                "product_name"
              ],
              "model_config": {
                "model_name": "gcp/embedding-gecko-001",
                "access_token": "your_gcp_access_token",
                "refresh_token": "your_gcp_refresh_token",
                "client_id": "your_gcp_app_client_id", 
                "client_secret": "your_gcp_client_secret",
                "project_id": "your_gcp_project_id"
              }
            }
          }
        ]
      }'
```


### Remote Embedding API parameters

You can use any of the following parameters to fine-tune how API calls are made to remote embedding services:

#### During Search

| Parameter                     | Description                                                                                               |  Default  |
|-------------------------------|-----------------------------------------------------------------------------------------------------------|-----------|
| `remote_embedding_timeout_ms` | How long to wait until an API call to a remote embedding service is considered a timeout, during a search |    30s    |
| `remote_embedding_num_tries`  | The number of times to retry an API call to a remote embedding service on failure, during a search        |     2     |



```js
let search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding',
  'prefix'                     : false,
  'remote_embedding_timeout_ms': 5000,
  'remote_embedding_num_tries'   : 3
}

client.collections('products').documents().search(search_parameters)
```


  
```php
$search_parameters = [
  'q'                          => 'chair',
  'query_by'                   => 'embedding',
  'prefix'                     => false,
  'remote_embedding_timeout_ms'=> 5000,
  'remote_embedding_num_try'   => 3
];

$client->collections['products']->documents->search($search_parameters);
```


  
  ```py
search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding',
  'prefix'                     : false,
  'remote_embedding_timeout_ms': 5000,
  'remote_embedding_num_try'   : 3
}

client.collections['products'].documents.search(search_parameters)
```


```rb
search_parameters = {
  'q'                          => 'chair',
  'query_by'                   => 'embedding',
  'prefix'                     => false,
  'remote_embedding_timeout_ms'=> 5000,
  'remote_embedding_num_try'   => 3
}

client.collections['products'].documents.search(search_parameters)
```


```java
SearchParameters searchParameters = new SearchParameters();

searchParameters.q("chair")
                .queryBy("embedding")
                .prefix(false)
                .remoteEmbeddingTimeoutMs(5000)
                .remoteEmbeddingNumTries(3);

SearchResult searchResult = client.collections("products").documents().search(searchParameters);
```

    
  ```bash
curl 'http://localhost:8108/multi_search' \
      -X POST \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "searches": [
              {
                "collection": "products",
                "q": "chair",
                "query_by": "embedding",
                "prefix": false,
                "remote_embedding_timeout_ms": 5000,
                "remote_embedding_num_try": 3
              }
            ]
          }'
```




#### During Indexing

| Parameter                     | Description                                                                                                                                                                           | Default |
|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|
| `remote_embedding_batch_size` | Max size of each batch that will be sent to remote APIs while importing multiple documents at once. Using lower amount will lower timeout risk, but increase number of requests made. |200|  



```js
let import_parameters = {
  'remote_embedding_batch_size': 200
}

client.collections('products').documents().import(documents, import_parameters)
```



```php
$import_parameters = [
  'remote_embedding_batch_size' => 200
];

$client->collections['products']->documents->import($documents, $import_parameters);
```


  
  ```py
import_parameters = {
  'remote_embedding_batch_size': 200
}

client.collections['products'].documents.import(documents, import_parameters)
```


  
  ```rb
import_parameters = {
  'remote_embedding_batch_size' => 200
}

client.collections['products'].documents.import(documents, import_parameters)
```


```java
ImportDocumentParameters importDocumentParameters = new ImportDocumentParameters();
importDocumentParameters.remoteEmbeddingBatchSize(200);

client.collections("products").documents().import(documents, importDocumentParameters);
```


```bash
curl 'http://localhost:8108/collections/products/documents/import?remote_embedding_batch_size=200' \
  -X POST \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{"product_name": "chair"}
      {"product_name": "table"}'
```



### Using your own models

You can also use your own models to generate embeddings from within Typesense. They must be in the ONNX file format.

Create a directory under `<data_dir>/models` and store your ONNX model file, vocab file, and a JSON for model config there.

***Note:*** Your model file MUST be named as `model.onnx` and the config file MUST be named as `config.json`.

#### Model config file

This file will contain information about the type of model you want to use. 

The JSON file must contain `model_type` (type of the model; we support `bert` and `xlm_roberta` at the moment) and `vocab_file_name` keys.

**Directory Structure:**

```
<data_dir>/models/test_model/model.onnx
<data_dir>/models/test_model/vocab.txt
<data_dir>/models/test_model/config.json
```

**Contents of `config.json`:**
```
{
    "model_type": "bert",
    "vocab_file_name": "vocab.txt"
}
```

Create an embedding field using the directory name as `model_name` in `model_config`.
   


```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": ["product_name"],
        "model_config": {
          "model_name": "test_model"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => ["product_name"],
        "model_config" => [
          "model_name" => "test_model"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  

```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": ["product_name"],
        "model_config": {
          "model_name": "test_model"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => ["product_name"],
        "model_config" => [
          "model_name" => "test_model"
        ]
      }
    }
  ]
}

client.collections.create(schema)

```

```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("test_model")
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);
```
     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": ["product_name"],
              "model_config": {
                "model_name": "test_model"
              }
            }
          }
        ]
      }'

```


#### Optional Model Parameters

These are optional model parameters, which may be required to use with your custom models.

##### Indexing prefix and query prefix

Some models may require a prefix to know if texts are queries or they are actual texts to query on (you can check `intfloat/e5-small`, for example).

If you set this property in `model_config`, the given indexing prefix will be added to the text that will be used to create embeddings when you index a document and `query_prefix` to the actual query before creating embeddings of it.Example:



```js
let schema = {
  "name": "products",
  "fields": [
    {
      "name": "product_name",
      "type": "string"
    },
    {
      "name": "embedding",
      "type": "float[]",
      "embed": {
        "from": ["product_name"],
        "model_config": {
          "model_name": "e5-base",
          "indexing_prefix": "passage:",
          "query_prefix": "query:"
        }
      }
    }
  ]
};

client.collections('products').create(schema);

```


```php
$schema = [
  "name" => "products",
  "fields" => [
    [
      "name" => "product_name",
      "type" => "string"
    ],
    [
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => ["product_name"],
        "model_config" => [
          "model_name" => "e5-base",
          "indexing_prefix" => "passage:",
          "query_prefix" => "query:"
        ]
      ]
    ]
  ]
];

$client->collections->create($schema);

```
  

```py
schema = {
  "name": "products",
  "fields": [
    {
      "name" : "product_name",
      "type" : "string"
    },
    {
      "name" : "embedding",
      "type" : "float[]",
      "embed": {
        "from": ["product_name"],
        "model_config": {
          "model_name": "e5-base",
          "indexing_prefix": "passage:",
          "query_prefix": "query:"
        }
      }
    }
  ]
}

client.collections.create(schema)
```


```rb
schema = {
  "name" => "products",
  "fields" => [
    {
      "name" => "product_name",
      "type" => "string"
    },
    {
      "name" => "embedding",
      "type" => "float[]",
      "embed" => [
        "from" => ["product_name"],
        "model_config" => [
          "model_name" => "e5-base",
          "indexing_prefix" => "passage:",
          "query_prefix" => "query:"
        ]
    }
  ]
}

client.collections.create(schema)
```


```java
CollectionSchema collectionSchema = new CollectionSchema();
ArrayList<String> embedFrom = new ArrayList<>();
embedFrom.add("product_name");
embedFrom.add("categories");

collectionschema.name("products")
                .addFieldsItem(new Field().name("product_name").type(FieldTypes.STRING))
                .addFieldsItem(new Field().name("categories").type(FieldTypes.STRING_ARRAY))
                .addFieldsItem(new Field().name("embedding").type(FieldTypes.FLOAT_ARRAY).embed(
                  new FieldEmbed().from(embedFrom).modelConfig(new FieldEmbedModelConfig().modelName("e5-base").indexingPrefix("passage:").queryPrefix("query:")
                ));

CollectionResponse collectionResponse = client.collections().create(collectionSchema);

```

     
```bash
curl 'http://localhost:8108/collections' \
  -X POST \
  -H 'Content-Type: application/json' \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "name": "products",
        "fields": [
          {
            "name": "product_name",
            "type": "string"
          },
          {
            "name": "embedding",
            "type": "float[]",
            "embed": {
              "from": ["product_name"],
              "model_config": {
                "model_name": "e5-base",
                "indexing_prefix": "passage:",
                "query_prefix": "query:"
              }
            }
          }
        ]
      }'

```


For this example, when you index a document:
```
{
   "product_name": "ABCD"
}

```
The text used to generate embeddings for the `embedding` field will be `passage: ABCD` instead of `ABCD`. And when you query, if your query is `EFGH`, it will be embedded as `query: EFGH` instead of `EFGH`.

## Nearest-neighbor vector search

Once you've indexed your embeddings in a vector field, you can now search for documents that are "closest" to a given query vector. 

To control the number of documents that are returned, you can either use the `per_page` pagination parameter or the `k` parameter within the vector query.


```js
let searchRequests = {
  'searches': [
    {
      'collection': 'docs',
      'q': '*',
      'vector_query' : 'vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)'
    }
  ]
}
let commonSearchParams = {}
client.multiSearch.perform(searchRequests, commonSearchParams)
```



```php
$searchRequests = [
  'searches' => [
    [
      'collection' => 'docs',
      'q' => '*',
      'vector_query' => 'vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)'
    ]
  ]
];

// Search parameters that are common to all searches go here
$commonSearchParams =  [];
$client->multiSearch->perform($searchRequests, $commonSearchParams);
```



```py
search_requests = {
  'searches': [
    {
      'collection': 'docs',
      'q' : '*',
      'vector_query': 'vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)'
    }
  ]
}

# Search parameters that are common to all searches go here
common_search_params =  {}
client.multi_search.perform(search_requests, common_search_params)
```


```rb
search_requests = {
  'searches': [
    {
      'collection' => 'docs',
      'q' => '*',
      'vector_query' => 'vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)'
    }
  ]
}

# Search parameters that are common to all searches go here
common_search_params =  {}
client.multi_search.perform(search_requests, common_search_params)
```


```dart
final searchRequests = {
  'searches': [
    {
      'collection': 'docs',
      'q': '*',
      'vector_query': 'vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)',
    }
  ]
};

// Search parameters that are common to all searches go here
final commonSearchParams =  {};

await client.multiSearch.perform(searchRequests, queryParams: commonSearchParams);

```


```java
HashMap<String,String > search1 = new HashMap<>();
search1.put("collection","docs");
search1.put("q","*");
search1.put("vector_query", "vec:([0.96826, 0.94, 0.39557, 0.306488], k:100)");

List<HashMap<String, String>> searches = new ArrayList<>();
searches.add(search1);

HashMap<String, List<HashMap<String ,String>>> searchRequests = new HashMap<>();
searchRequests.put("searches", searches);

HashMap<String,String> commonSearchParams = new HashMap<>();
commonSearchParams.put("query_by","name");

client.multiSearch.perform(searchRequests, commonSearchParams);
```


```bash
curl 'http://localhost:8108/multi_search' \
      -X POST \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "searches": [
              {
                "collection": "docs",
                "q": "*",
                "vector_query": "vec:([0.96826,0.94,0.39557,0.306488], k:100)"
              }
            ]
          }'
```


NOTE: If both `per_page` and `k` parameters are provided, the larger value is used.

:::tip
Since vector search queries tend to be large because of the large dimension of the query vector, we are
using the `multi_searc`h end-point that sends the search parameters as a POST request body.
:::

Every matching hit in the response will contain a `vector_distance` field that indicates how "close" the document's
vector value is to the query vector. Typesense uses the cosine similarity, so this distance will be a value between 
`0` and `2`.

- If the document's vector perfectly matches the query vector, the distance will be `0`
- If the document's vector is extremely different from the query vector, then the distance will be `2`.

The hits are automatically sorted in ascending order of the `vector_distance`, i.e. best matching documents appear first.

**Sample Response**


```json
{
  "facet_counts": [],
  "found": 1,
  "hits": [
    {
      "document": {
        "id": "0",
        "vec": [
          0.04, 0.234, 0.113, 0.001
        ]
      },
      "highlight": {
        "full": {},
        "snippet": {}
      },
      "highlights": [],
      "vector_distance": 0.19744956493377686
    }
  ],
  "out_of": 1,
  "page": 1,
  "request_params": {
    "collection_name": "docs",
    "per_page": 10,
    "q": "*"
  },
  "search_cutoff": false,
  "search_time_ms": 0
}
```


## Querying for similar documents

If you have a particular document `id` and want to find documents that are "similar" to this document, you can do a vector query that references this `id` directly.

```shell
curl 'http://localhost:8108/multi_search' \
  -X POST \
  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  -d '{
        "searches": [
          {
            "collection": "docs",
            "q": "*",
            "vector_query": "vec:([], id: foobar)"
          }
        ]
      }'
```

By specifying an empty query vector `[]` and passing an `id` parameter, this query 
would return all documents whose `vec` value is closest to the `foobar` document's `vec` value.

:::tip
The `foobar` document itself will not be returned in the results.
:::

## Semantic Search

When using [auto-embedding](#option-b-auto-embedding-generation-within-typesense), you can directly set `query_by` to the auto-embedding field to do a semantic search on this field.

Typesense will use the same embedding model that was used to generate the auto-embedding field to generate vectors for the `q` parameter and then do a nearest neighbor search internally.



```js
let search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding',
}

client.collections('products').documents().search(search_parameters)
```



```php
$search_parameters = [
  'q'                          => 'chair',
  'query_by'                   => 'embedding',
];

$client->collections['products']->documents->search($search_parameters);
```



  ```py
search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding',
}

client.collections['products'].documents.search(search_parameters)
```



  ```rb
search_parameters = {
  'q'                          => 'chair',
  'query_by'                   => 'embedding',
}

client.collections['products'].documents.search(search_parameters)
```


  
  ```java 

SearchParameters searchParameters = new SearchParameters();

searchParameters.q("chair")
                .queryBy("embedding");

SearchResult searchResult = client.collections("products").documents().search(searchParameters);
```


  ```bash
curl 'http://localhost:8108/multi_search' \
      -X POST \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "searches": [
              {
                "collection": "products",
                "q": "chair",
                "query_by": "embedding"
              }
            ]
          }'      
```



This will automatically embed the `chair` query with the same model used for the `embedding` field and will perform a nearest neighbor vector search.

## Hybrid Search

When using [auto-embedding](#option-b-auto-embedding-generation-within-typesense), you can set `query_by` to a list of both regular fields and auto-embedding fields, to do a hybrid search on multiple fields.

Typesense will do a keyword search on all the regular fields, and a semantic search on the auto-embedding field and combine the results into a ranked set of results using Rank Fusion: 

```
K = rank of document in keyword search
S = rank of document in semantic search

rank_fusion_score = 0.7 * K + 0.3 * S
```



```js
let search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding,product_name',
}

client.collections('products').documents().search(search_parameters)
```



  ```php
$search_parameters = [
  'q'                          => 'chair',
  'query_by'                   => 'embedding,product_name',
];

$client->collections['products']->documents->search($search_parameters);
```



  ```py
search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding,product_name',
}

client.collections['products'].documents.search(search_parameters)
```



  ```rb
search_parameters = {
  'q'                          => 'chair',
  'query_by'                   => 'embedding,product_name',
}

client.collections['products'].documents.search(search_parameters)
```


  
  ```java
SearchParameters searchParameters = new SearchParameters();

searchParameters.q("chair")
                .queryBy("embedding,product_name");

SearchResult searchResult = client.collections("products").documents().search(searchParameters);
```


```bash
curl 'http://localhost:8108/multi_search' \
    -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
    -X POST \
    -d '{
          "searches": [
            {
              "collection": "products",
              "query_by": "embedding,product_name",
              "q": "chair",
              "exclude_fields": "embedding"
            }
          ]
        }'
```



You can also do a hybrid search when using your own embedding field, by combining the `q` parameter with the `vector_query` parameter.

Typesense will do a keyword search using the `q` parameter, and a nearest neighbor search using the `vector_query` field and combine the results into a ranked set of results using Rank Fusion (see above).

### Distance Threshold

You can also set a maximum vector distance threshold for results of semantic search and hybrid search. You should set `distance_threshold` in `vector_query` parameter for this.



```js
let search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding,product_name',
  'vector_query'               : 'embedding:([], distance_threshold:0.30)'
}

client.collections('products').documents().search(search_parameters)
```



  ```php
$search_parameters = [
  'q'                          => 'chair',
  'query_by'                   => 'embedding,product_name',
  'vector_query'               => 'embedding:([], distance_threshold:0.30)'
];

$client->collections['products']->documents->search($search_parameters);
```



  ```py
search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'embedding,product_name',
  'vector_query'               : 'embedding:([], distance_threshold:0.30)'
}

client.collections['products'].documents.search(search_parameters)
```



  ```rb
search_parameters = {
  'q'                          => 'chair',
  'query_by'                   => 'embedding,product_name',
  'vector_query'               => 'embedding:([], distance_threshold:0.30)'
}

client.collections['products'].documents.search(search_parameters)
```



```bash
curl 'http://localhost:8108/multi_search' \
      -X POST \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "searches": [
              {
                "collection": "products",
                "q": "chair",
                "query_by": "embedding,product_name",
                "vector_query": "embedding:([], distance_threshold:0.30)"
              }
            ]
          }'
```



### Hybrid Sorting

When doing a hybrid search (especially with a `q` parameter and an explicit `vector_query` parameter), you can sort by a combination of vector distance and also other numeric parameters using the special sort keyword `_vector_distance`  in `sort_by`.

Here's an example:

```json
{
  ...
  "sort_by": "popularity_score:desc,_vector_distance:asc"
  ...
}
```

## Brute-force searching

By default, Typesense uses the built-in HNSW index to do approximate nearest neighbor vector searches. This scales 
well for large datasets. However, if you wish to bypass the HNSW index and do a flat / brute-force ranking of 
vectors, you can do that via the `flat_search_cutoff` parameter.

For example, if you wish to do brute-force vector search when a given query matches fewer than 20 documents, sending 
`flat_search_cutoff=20` will bypass the HNSW index when the number of results found is less than 20.

Here's an example where we are filtering on the `category` field and asking the vector search to use direct 
flat searching if the number of results produced by the filtering operation is less than 20 results.

```shell
curl 'http://localhost:8108/multi_search' \
      -X POST \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{
            "searches": [
              {
                "collection": "docs",
                "q": "*",
                "filter_by": "category:shoes",
                "vector_query": "vec:([0.96826, 0.94, 0.39557, 0.306488], k:100, flat_search_cutoff: 20)"
              }
            ]
          }'
```

## UI Examples

- [Here's](https://hn-comments-search.typesense.org) a demo that shows you how to implement Hybrid Search (Semantic Search + Keyword Search + Filtering + Faceting) using Typesense's built-in embedding generation mechanism.

- [Here's](https://ecommerce-store.typesense.org/) a demo that shows you how to implement a "Find Similar" feature using Vector Search in an ecommerce store.
  
  Click on "Find Similar" below each product tile for notes on how to implement this.

- [Here's](https://github.com/typesense/typesense-instantsearch-semantic-search-demo) a demo that shows you how to implement Semantic Search, using an external embeddings API and Vector Search.