### Using Environment Variables

Source: https://typesense.org/docs/29.0/api/server-configuration.md

Example of starting the Typesense server using environment variables for configuration.

```bash
TYPESENSE_DATA_DIR=/var/lib/typesense TYPESENSE_API_KEY=AS3das2awQ2 ./typesense-server
```

--------------------------------

### Using a Configuration File (Command Line)

Source: https://typesense.org/docs/29.0/api/server-configuration.md

Example of starting the Typesense server using a configuration file specified via a command-line argument.

```bash
./typesense-server --config=/etc/typesense/typesense-server.ini
```

--------------------------------

### Go Client Installation and Usage

Source: https://typesense.org/docs/29.0/api/api-clients.md

Command to get the Typesense Go client library and the necessary import statements for Go code.

```go
// $ go get github.com/typesense/typesense-go/v3/typesense

import (
  "github.com/typesense/typesense-go/v3/typesense"
  "github.com/typesense/typesense-go/v3/typesense/api"
  "github.com/typesense/typesense-go/v3/typesense/api/pointer"
)
```

--------------------------------

### Go Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using Go.

```go
schema := &api.CollectionSchema{
  Name: "products",
  Fields: []api.Field{
    {
      Name: "product_name",
      Type: "string",
    },
    {
      Name: "categories",
      Type: "string[]",
    },
    {
      Name: "embedding",
      Type: "float[]",
      Embed: &struct {
        From        []string "json:\"from\""
        ModelConfig struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        } "json:\"model_config\""
      }{
        From: []string{"product_name", "categories"},
        ModelConfig: struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        }{
          ModelName: "ts/e5-small",
        },
      },
    }
  }
}

```

--------------------------------

### Configuration File Example (INI)

Source: https://typesense.org/docs/29.0/api/server-configuration.md

Example of the Typesense server configuration file in INI format.

```ini
; /etc/typesense/typesense-server.ini

[server]

api-key = Rhsdhas2asasdasj2
data-dir = /var/lib/typesense
log-dir = /var/log/typesense
api-port = 9090
```

--------------------------------

### Getting all stopwords sets

Source: https://typesense.org/docs/29.0/api/stopwords.md

Example of fetching all available stopword sets.

```shell
curl -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  "http://localhost:8108/stopwords"
```

--------------------------------

### Multi-search for Query Suggestions (Go)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of performing a multi-search to get both query suggestions and actual search results side-by-side.

```go
searchRequests := api.MultiSearchSearchesParameter{
  Searches: []api.MultiSearchCollectionParameters{
    {
      Collection: pointer.Any("product_queries"),
      Q:          pointer.Any("shoe"),
      QueryBy:    pointer.Any("q"),
    },
    {
      Collection: pointer.Any("products"),
      Q:          pointer.Any("shoe"),
      QueryBy:    pointer.Any("product_name"),
    },
  },
}

client.MultiSearch.Perform(context.Background(), &api.MultiSearchParams{}, searchRequests)
```

--------------------------------

### PHP Client Installation

Source: https://typesense.org/docs/29.0/api/api-clients.md

Command to install the Typesense PHP client library using Composer.

```shell
composer require php-http/curl-client typesense/typesense-php
```

--------------------------------

### Ruby Client Installation

Source: https://typesense.org/docs/29.0/api/api-clients.md

Command to install the Typesense Ruby client library using gem.

```shell
gem install typesense
```

--------------------------------

### Go Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a 'no_hits_queries' analytics rule using the Go client.

```go
ruleName := "product_no_hits"
ruleConfiguration := &api.AnalyticsRuleUpsertSchema{
  Type: api.AnalyticsRuleUpsertSchemaTypeNohitsQueries,
  Params: api.AnalyticsRuleParameters{
    Source: api.AnalyticsRuleParametersSource{
      Collections: []string{"products"},
    },
    Destination: api.AnalyticsRuleParametersDestination{
      Collection: "no_hits_queries",
    },
    Limit: pointer.Int(1000),
  },
}

client.Analytics().Rules().Upsert(context.Background(), ruleName, ruleConfiguration)
```

--------------------------------

### Python Client Installation

Source: https://typesense.org/docs/29.0/api/api-clients.md

Command to install the Typesense Python client library using pip.

```shell
pip install typesense
```

--------------------------------

### Go Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field using an OpenAI-compatible API in Go.

```go
schema := &api.CollectionSchema{
  Name: "products",
  Fields: []api.Field{
    {
      Name: "product_name",
      Type: "string",
    },
    {
      Name: "embedding",
      Type: "float[]",
      Embed: &struct {
        From        []string "json:\"from\""
        ModelConfig struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        } "json:\"model_config\""
      }{
        From: []string{"product_name"},
        ModelConfig: struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""

```

--------------------------------

### JavaScript Client Installation

Source: https://typesense.org/docs/29.0/api/api-clients.md

Instructions for installing the Typesense JavaScript client library for use in a browser environment.

```javascript
// npm install typesense @babel/runtime

// Browser
<script src="dist/typesense.min.js"></script>
```

--------------------------------

### Java Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using Java.

```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);

```

--------------------------------

### Python Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a 'no_hits_queries' analytics rule using the Python client.

```python
let ruleName =  'product_no_hits'
let ruleConfiguration = {
  "type": "nohits_queries",
  "params": {
    "source": {
      "collections": ["products"]
    },
    "destination": {
      "collection": "no_hits_queries"
    },
    "limit": 1000
  }
}

client.analytics.rules.upsert(ruleName, ruleConfiguration)
```

--------------------------------

### Python Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using Python.

```python
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)

```

--------------------------------

### Multi-search for Query Suggestions (Python)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of performing a multi-search to get both query suggestions and actual search results side-by-side.

```python
let searchRequests = {
  'searches': [
    {
      'collection': 'product_queries',
      'q': 'shoe',
      'query_by': 'q'
    },
    {
      'collection': 'products',
      'q': 'shoe',
      'query_by': 'product_name'
    }
  ]
}

client.multi_search.perform(searchRequests, {})
```

--------------------------------

### Multi-search for Query Suggestions (Ruby)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of performing a multi-search to get both query suggestions and actual search results side-by-side.

```rb
search_requests = {
  'searches' => [
    {
      'collection' => 'product_queries',
      'q' => 'shoe',
      'query_by' => 'q'
    },
    {
      'collection' => 'products',
      'q' => 'shoe',
      'query_by' => 'q'
    }
  ]
}

client.multi_search.perform(search_requests, {})
```

--------------------------------

### Ruby Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a 'no_hits_queries' analytics rule using the Ruby client.

```ruby
rule_name = 'product_no_hits'
rule_configuration = {
  'type' => 'nohits_queries',
  'params' => {
    'source' => {
      'collections' => ['products']
    },
    'destination' => {
      'collection' => 'no_hits_queries'
    },
    'limit' => 1000
  }
}

typesense.analytics.rules.upsert(rule_name, rule_configuration)
```

--------------------------------

### Go Schema Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a Typesense collection schema with embedding configuration including indexing and query prefixes in Go.

```go
schema := &api.CollectionSchema{
  Name: "products",
  Fields: []api.Field{
    {Name: "product_name", Type: "string"},
    {Name: "embedding", Type: "float[]",
      Embed: &struct {
        From        []string "json:\"from\""
        ModelConfig struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        } "json:\"model_config\""
      }{
        From: []string{"product_name"},
        ModelConfig: struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        }{
          ModelName:      "e5-base",

```

--------------------------------

### Ruby Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using Ruby.

```ruby
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)

```

--------------------------------

### Ruby Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of setting up a multi-search request with a vector query in Ruby. It shows the structure for specifying the collection, query, vector field, and the `k` parameter for controlling the number of results. The `exclude_fields` parameter is also demonstrated.

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

--------------------------------

### Vector Query Parameters Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

An example of how to use various vector search parameters within the `vector_query` parameter.

```json
{
  "vector_query": "vector_field_name:([], k: 100, alpha: 0.8, distance_threshold:0.30)"
}
```

--------------------------------

### Java Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field using an OpenAI-compatible API in Java.

```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_api_key_as_required_by_the_custom_provider").url("https://your-custom-openai-compatible-api.domain.com")
                ));

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

--------------------------------

### PHP Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using PHP.

```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);

```

--------------------------------

### JavaScript Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a multi-search with a vector query in JavaScript. It demonstrates how to specify the collection, query, vector field, and control the number of results with `k`. It also shows how to exclude the vector field from the response to save bandwidth.

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

--------------------------------

### Ruby Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field using an OpenAI-compatible API in Ruby.

```ruby
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_api_key_as_required_by_the_custom_provider",
          "url" => "https://your-custom-openai-compatible-api.domain.com"
        }
      }
    }
  ]
}

client.collections.create(schema)
```

--------------------------------

### cURL Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a 'no_hits_queries' analytics rule using cURL.

```bash
curl -k "http://localhost:8108/analytics/rules" \
      -X POST \
      -H "Content-Type: application/json" \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
      -d '{ "name": "product_no_hits", "type": "nohits_queries", "params": { "source": { "collections": ["products"] }, "destination": { "collection": "no_hits_queries" }, "limit": 1000 } }'
```

--------------------------------

### Python Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field using an OpenAI-compatible API in Python.

```python
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_api_key_as_required_by_the_custom_provider",
          "url": "https://your-custom-openai-compatible-api.domain.com"
        }
      }
    }
  ]
}

client.collections.create(schema)
```

--------------------------------

### Go Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense Go client.

```go
searchParameters := &api.SearchCollectionParams{
    Q:       pointer.Any("chair"),
    QueryBy: pointer.Any("embedding"),
}

client.Collection("products").Documents().Search(context.Background(), searchParameters)
```

--------------------------------

### PHP Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field using an OpenAI-compatible API in PHP.

```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_api_key_as_required_by_the_custom_provider",
          "url" => "https://your-custom-openai-compatible-api.domain.com"
        ]
      ]
    ]
  ]
];

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

--------------------------------

### Python Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a multi-search with a vector query in Python. This snippet illustrates how to define the search parameters, including the collection, query, vector field, and the `k` parameter. It also demonstrates excluding the vector field from the results.

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

common_search_params =  {}
client.multi_search.perform(search_requests, common_search_params)
```

--------------------------------

### Go

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a multi-search with a vector query in Go.

```go
searchRequests := api.MultiSearchSearchesParameter{
  Searches: []api.MultiSearchCollectionParameters{
    {
      Collection:    pointer.Any("docs"),
      Q:             pointer.Any("*"),
      VectorQuery:   pointer.Any("embedding:([0.96826, 0.94, 0.39557, 0.306488], k:100)"), // <=== Be sure to replace `embedding` with the name of the field that stores your embeddings.
      ExcludeFields: pointer.Any("embedding"),                                             // <=== Don't return the raw floating point numbers in the vector field in the search API response, to save on network bandwidth.
    },
  },
}

commonSearchParams := &api.MultiSearchParams{}
client.MultiSearch.Perform(context.Background(), commonSearchParams, searchRequests)
```

--------------------------------

### JavaScript Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a 'no_hits_queries' analytics rule using the JavaScript client.

```javascript
let ruleName =  'product_no_hits'
let ruleConfiguration = {
  "type": "nohits_queries",
  "params": {
    "source": {
      "collections": ["products"]
    },
    "destination": {
      "collection": "no_hits_queries"
    },
    "limit": 1000
  }
}

client.analytics.rules().upsert(ruleName, ruleConfiguration)
```

--------------------------------

### Python Schema Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a Typesense collection schema with embedding configuration including indexing and query prefixes in Python.

```python
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)
```

--------------------------------

### Ruby Schema Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a Typesense collection schema with embedding configuration including indexing and query prefixes in Ruby.

```ruby
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 Schema Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a Typesense collection schema with embedding configuration including indexing and query prefixes in Java.

```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);
```

--------------------------------

### PHP Schema Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a Typesense collection schema with embedding configuration including indexing and query prefixes in PHP.

```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);
```

--------------------------------

### PHP Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a multi-search with a vector query in PHP. It shows how to structure the search request, including the collection, query, vector field, and the `k` parameter for result count. It also includes excluding the vector field from the response.

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

$commonSearchParams =  [];
$client->multiSearch->perform($searchRequests, $commonSearchParams);
```

--------------------------------

### cURL Command

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example cURL command to create a collection.

```bash
curl 'http://localhost:8108/collections' \
  -X POST \
```

--------------------------------

### Sample Response for Scoped Search Key

Source: https://typesense.org/docs/29.0/api/api-keys.md

Example of a generated scoped API key.

```text
OW9DYWZGS1Q1RGdSbmo0S1QrOWxhbk9PL2kxbTU1eXA3bCthdmE5eXJKRT1STjIzeyJmaWx0ZXJfYnkiOiJjb21wYW55X2lkOjEyNCIsImV4cGlyZXNfYXQiOjE5MDYwNTQxMDZ9
```

--------------------------------

### Hybrid Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

This example demonstrates how to perform a hybrid search by querying both a regular field ('product_name') and an embedding field ('embedding'). Typesense combines keyword and semantic search results using Rank Fusion.

```javascript
let search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'product_name,embedding',
  'sort_by'                   :  '_text_match:desc',
}

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

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

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

```python
search_parameters = {
  'q'                          : 'chair',
  'query_by'                   : 'product_name,embedding',
  'sort_by'                   :  '_text_match:desc',
}

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

```ruby
search_parameters = {
  'q'                          => 'chair',
  'query_by'                   => 'product_name,embedding',
  'sort_by'                    => '_text_match:desc',
}

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

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

searchParameters.q("chair")
                .queryBy("product_name,embedding,")
                .sortBy("_text_match:desc");

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

```go
searchParameters := &api.SearchCollectionParams{
    Q:       pointer.String("chair"),
    QueryBy: pointer.String("product_name,embedding"),
    SortBy:  pointer.String("_text_match:desc"),
}

client.Collection("products").Documents().Search(context.Background(), searchParameters)
```

--------------------------------

### Index a document with a vector

Source: https://typesense.org/docs/29.0/api/vector-search.md

This example demonstrates how to index a document that includes a vector embedding.

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

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

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

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

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

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

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

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

--------------------------------

### Get a specific stopwords set

Source: https://typesense.org/docs/29.0/api/stopwords.md

Example of retrieving stopwords associated with a specific stopword set named 'countries'.

```shell
curl -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
  "http://localhost:8108/stopwords/countries" 
```

--------------------------------

### Multi-search for Query Suggestions (cURL)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of performing a multi-search to get both query suggestions and actual search results side-by-side.

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

--------------------------------

### Java Client Setup

Source: https://typesense.org/docs/29.0/api/api-clients.md

Instructions for setting up the Typesense Java client library by downloading the JAR file and importing necessary classes.

```java
// Download the JAR file from the releases section in the typesense-java repository.
// (https://github.com/typesense/typesense-java/releases)
// And the import it them to your project

import org.typesense.api.*;
import org.typesense.models.*;
import org.typesense.resources.*;
```

--------------------------------

### Query for Similar Documents

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of how to find documents similar to a specific document by its ID.

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

# Be sure to replace `embedding` with the name of the field that stores your embeddings.
```

--------------------------------

### List all aliases

Source: https://typesense.org/docs/29.0/api/collection-alias.html

Example of listing all collection aliases using Go.

```go
client.Aliases().Retrieve(context.Background())
```

--------------------------------

### Multi-search for Query Suggestions (JavaScript)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of performing a multi-search to get both query suggestions and actual search results side-by-side.

```js
let searchRequests = {
  'searches': [
    {
      'collection': 'product_queries',
      'q': 'shoe',
      'query_by': 'q'
    },
    {
      'collection': 'products',
      'q': 'shoe',
      'query_by': 'product_name'
    }
  ]
}

client.multiSearch.perform(searchRequests, {})
```

--------------------------------

### Go Import Parameters

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of setting remote embedding batch size during document import in Go.

```go
importParameters := &api.ImportDocumentsParams{
  RemoteEmbeddingBatchSize: pointer.Int(200),
}

client.Collection("products").Documents().Import(context.Background(), documents, importParameters)
```

--------------------------------

### List all aliases

Source: https://typesense.org/docs/29.0/api/collection-alias.html

Example of listing all collection aliases using Shell.

```shell
curl -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
     "http://localhost:8108/aliases"
```

--------------------------------

### List all Keys

Source: https://typesense.org/docs/29.0/api/api-keys.md

Examples of how to list all API keys using different programming languages and cURL.

```js
client.keys().retrieve()
```

```php
$client->keys->retrieve();
```

```python
client.keys.retrieve()
```

```ruby
client.keys.retrieve
```

```dart
await client.keys.retrieve();
```

```java
ApiKeysResponse apiKeysResponse = client.keys().retrieve();
```

```go
client.Keys().Retrieve(context.Background())
```

```swift
let (apiKeys, response) = try await client.keys().retrieve()
```

```bash
curl 'http://localhost:8108/keys' \
    -X GET \
    -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}"
```

--------------------------------

### Command Line Arguments

Source: https://typesense.org/docs/29.0/api/server-configuration.md

Command line arguments can be passed to the server as --parameter=value.

```bash
--parameter=value
```

--------------------------------

### JavaScript Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a schema with an auto-embedding field using JavaScript.

```javascript
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);

```

--------------------------------

### Go Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Schema definition and collection creation using a built-in embedding model in Go.

```go
schema := &api.CollectionSchema{
  Name: "products",
  Fields: []api.Field{
    {
      Name: "brand",
      Type: "string",
    },
    {
      Name: "categories",
      Type: "string[]",
    },
    {
      Name: "embedding",
      Type: "float[]",
      Embed: &struct {
        From        []string "json:\"from\""
        ModelConfig struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""
          RefreshToken   *string "json:\"refresh_token,omitempty\""
          Url            *string "json:\"url,omitempty\""
        } "json:\"model_config\""
      }{
        From: []string{"brand", "categories"},
        ModelConfig: struct {
          AccessToken    *string "json:\"access_token,omitempty\""
          ApiKey         *string "json:\"api_key,omitempty\""
          ClientId       *string "json:\"client_id,omitempty\""
          ClientSecret   *string "json:\"client_secret,omitempty\""
          DocumentTask   *string "json:\"document_task,omitempty\""
          IndexingPrefix *string "json:\"indexing_prefix,omitempty\""
          ModelName      string  "json:\"model_name\""
          ProjectId      *string "json:\"project_id,omitempty\""
          QueryPrefix    *string "json:\"query_prefix,omitempty\""
          QueryTask      *string "json:\"query_task,omitempty\""

```

--------------------------------

### Dart Client Installation and Usage

Source: https://typesense.org/docs/29.0/api/api-clients.md

Instructions for adding the Typesense Dart client library to a Dart or Flutter project and how to import it.

```dart
// With Dart:
//  $ dart pub add typesense

// With Flutter:
//  $ flutter pub add typesense

// This will add a line like this to your package's pubspec.yaml:
// dependencies:
//   typesense: ^0.3.0

// Now in your Dart code, you can use:

import 'package:typesense/typesense.js';
```

--------------------------------

### Cloning a collection schema

Source: https://typesense.org/docs/29.0/api/collections.html

This example shows how to clone an existing collection's schema, including overrides and synonyms, but not the documents themselves.

```bash
curl -k "http://localhost:8108/collections?src_name=existing_coll" -X POST -H "Content-Type: application/json" \
      -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" -d '{
        "name": "new_coll"
      }'
```

--------------------------------

### Bash Example for Sending Click Events

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Sends a click event to Typesense to be aggregated by a counter rule.

```bash
curl "http://localhost:8108/analytics/events" -X POST \
     -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
     -d '{
            "type": "click",
            "name": "products_click_event",
            "data": {
                  "doc_id": "1024",
                  "user_id": "111112"
            }
        }'
```

--------------------------------

### Create a stemming dictionary

Source: https://typesense.org/docs/29.0/api/stemming.md

First, create a JSONL file with your word mappings.

```json
{"word": "people", "root": "person"}
{"word": "children", "root": "child"}
{"word": "geese", "root": "goose"}
```

--------------------------------

### Sample Response for API Key Creation

Source: https://typesense.org/docs/29.0/api/api-keys.md

This is a sample JSON response when an API key is created.

```json
{
  "actions": [
    "*"
  ],
  "collections": [
    "*"
  ],
  "description": "Admin key.",
  "expires_at": 64723363199,
  "id": 1,
  "value": "k8pX5hD0793d8YQC5aD1aEPd7VleSuGP"
}
```

--------------------------------

### Java Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Schema definition and collection creation using a built-in embedding model in Java.

```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);
```

--------------------------------

### Create Collection for No Hits Queries (Go)

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of creating a collection to log no-hits search queries using Go.

```go
schema := &api.CollectionSchema{
  Name: "no_hits_queries",
  Fields: []api.Field{
    {Name: "q", Type: "string"},
    {Name: "count", Type: "int32"},
  }}

client.Collections().Create(context.Background(), schema)
```

--------------------------------

### Go Example for Creating a Counter Rule

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Defines a 'counter' analytics rule to increment a field when a click event occurs.

```go
ruleName := "product_clicks"
ruleConfiguration := &api.AnalyticsRuleUpsertSchema{
  Type: "counter",
  Params: api.AnalyticsRuleParameters{
    Source: api.AnalyticsRuleParametersSource{
      Collections: []string{"products"},
      Events: &[]struct {
        Name   string  "json:\"name\""
        Type   string  "json:\"type\""
        Weight float32 "json:\"weight\""
      }{
        {Type: "click", Weight: 1, Name: "products_click_event"},
      },
    },
    Destination: api.AnalyticsRuleParametersDestination{
      Collection:   "products",
      CounterField: pointer.Any("popularity"),
    },
  },
}

client.Analytics().Rules().Upsert(context.Background(), ruleName, ruleConfiguration)
```

--------------------------------

### Java Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense Java client.

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

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

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

--------------------------------

### Ruby Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense Ruby client.

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

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

--------------------------------

### Python Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Schema definition and collection creation using a built-in embedding model in Python.

```python
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)
```

--------------------------------

### List all synonyms

Source: https://typesense.org/docs/29.0/api/synonyms.md

Examples of how to list all synonyms associated with a collection using various programming languages.

```javascript
client.collections('products').synonyms().retrieve()
```

```php
$client->collections['products']->synonyms->retrieve();
```

```python
client.collections['products'].synonyms.retrieve()
```

```ruby
client.collections['products'].synonyms.retrieve
```

```dart
await client.collection('products').synonyms.retrieve();
```

```java
SearchSynonymsResponse searchSynonymsResponse =  client.collections("products").synonyms().retrieve();
```

```go
client.Collection("products").Synonyms().Retrieve(context.Background())
```

```swift
let (searchSynonyms, response) = try await client.collection(name: "products").synonyms().retrieve()
```

```bash
curl -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
"http://localhost:8108/collections/products/synonyms"
```

--------------------------------

### Python Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense Python client.

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

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

--------------------------------

### Sample Response for Stemming Dictionary Import

Source: https://typesense.org/docs/29.0/api/stemming.md

This is a sample response after importing a stemming dictionary.

```json
{
  "id": "irregular-plurals",
  "words": [
    {"root": "person", "word": "people"},
    {"root": "child", "word": "children"},
    {"root": "goose", "word": "geese"}
  ]
}
```

--------------------------------

### PHP Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense PHP client.

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

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

--------------------------------

### JavaScript Semantic Search Example

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of performing a semantic search using the Typesense JavaScript client.

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

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

--------------------------------

### Aggregated Data Example

Source: https://typesense.org/docs/29.0/api/analytics-query-suggestions.md

Example of the aggregated data structure when using meta fields in search.

```json
{
  "facet_counts": [],
  "found": 1,
  "hits": [
    {
      "document": {
        "count": 1,
        "analytics_tag": "oxford",
        "filter_by": "points:>100",
        "id": "9677940243608161791",
        "q": "iron"
      },
      "highlight": {},
      "highlights": []
    }
  ],
  "out_of": 1,
  "page": 1,
  "request_params": {
    "collection_name": "top_queries",
    "first_q": "*",
    "per_page": 10,
    "q": "*"
  },
  "search_cutoff": false,
  "search_time_ms": 0
}
```

--------------------------------

### Create Collection with GCP Vertex AI Embedding (Go)

Source: https://typesense.org/docs/29.0/api/vector-search.md

Example of creating a collection with an embedding field configured to use GCP Vertex AI in Go.

```go
schema := &api.CollectionSchema{
  Name: "products",
  Fields: []api.Field{
    {Name: "product_name", Type: "string"},
    {Name: "embedding", Type: "float[]",
      Embed: &struct {
        From        []string "json:\"from\""
        ModelConfig struct {

```

--------------------------------

### Upload a stemming dictionary

Source: https://typesense.org/docs/29.0/api/stemming.md

Then upload it using the stemming dictionary API.

```bash
curl "http://localhost:8108/stemming/dictionaries/import?id=irregular-plurals" \
-X POST \
-H "Content-Type: application/json" \
-H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
--data-binary @dictionary.jsonl
```

--------------------------------

### Field Schema Example

Source: https://typesense.org/docs/29.0/api/collections.html

Example of how to define a field with name, type, facet, and index properties.

```json
{
  "name": "title",
  "type": "string",
  "facet": false,
  "index": true
}
```