docs: improves docs

oramasearch · Jul 24, 2024 · 77ba63c · 77ba63c
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diff --git a/packages/docs/src/content/docs/cloud/answer-engine/creating-an-answer-session.mdx b/packages/docs/src/content/docs/cloud/answer-engine/creating-an-answer-session.mdx
@@ -938,7 +938,6 @@ The main difference between `userContext` and `userData` is that the `userData`
 
 Of course, Orama will remember the `userData` in subsequent answer requests, but will tend to forget it after a while. So, if that data is important to you, you should either provide it in every answer request, or use the `userContext` instead.
 
-
 ## Aborting Answers Generation
 
 In any moment in time (when a user cancels an answer request, for example) you can abort an answer by using the `.abortAnswer` function:
@@ -1007,7 +1006,63 @@ This will trigger the `onAnswerAborted` event, which will simply return a `true`
   </TabItem>
 </Tabs>
 
-## Getting all the Messages
+## Getting related queries
+
+When creating answer sessions, you may want to provide your users with a list of related queries that they can use to get more information about a specific topic. You can do this by using the `related` parameter:
+
+When asking for `"question"` (default) related queries, Orama will return an array of related queries in a question format. For example, if the user asks for: "What is Orama?", Orama will return an array of related queries like: `["How does Orama work?", "Why is Orama the best?", "How do I run vector search?"]`.
+
+If the format is `"query"`, Orama will return an array of related queries in a query format. For example, if the user asks for: "What is Orama?", Orama will return an array of related queries like: `["How Orama works", "Why Orama is the best", "Vector search with Orama"]`.
+
+<Tabs syncKey="sdk">
+  <TabItem label="JavaScript" icon="seti:javascript">
+    ```javascript copy
+    const answerSession = orama.createAnswerSession({
+      events: {
+        onRelatedQueries: (relatedQueries) => {
+          console.log(relatedQueries);
+          // ["How Orama works", "Why Orama is the best", "Vector search with Orama"]
+        },
+      }
+    });
+
+    await answerSession.ask({
+      term: "What is Orama?",
+      related: {
+        howMany: 3, // How many related queries you want to get. Maximum is 5.
+        format: 'query'
+      }
+    });
+    ```
+  </TabItem>
+  <TabItem label="Swift" icon="seti:swift">
+    ```swift copy
+        ```swift copy
+      let answer = try await answerSession.ask(params: AnswerParams.AskParams(
+        query: "What is Orama?",
+        userData: "The user is a beginner in programming",
+        related: AnswerParams<MyDoc>.RelatedQueries(howMany: 3, format: "query")
+      ))
+    ```
+  </TabItem>
+  <TabItem label="Kotlin" icon="seti:kotlin">
+    ```kotlin copy
+    print("coming soon")
+    ```
+  </TabItem>
+  <TabItem label="Python" icon="seti:python">
+    ```python copy
+    print("coming soon")
+    ```
+  </TabItem>
+  <TabItem label="PHP" icon="seti:php">
+    ```php copy
+    // coming soon
+    ```
+  </TabItem>
+</Tabs>
+
+## Getting all the messages
 
 At any point in time, you can retrieve all the messages by calling the `.getMessages` function:
 
@@ -1089,7 +1144,7 @@ Sometimes, the user may want to regenerate the last answer. You can do this by c
 
     // Regenerate the answer
     // "stream" is true by default, but you can set it to false if you want to get the entire answer at once
-    let answer = try await answerSession.regenerateLast(params: AnswerParams.RegenerateParams(stream: false))
+    let answer = try await answerSession.regenerateLast(stream: false)
 
     // Check the messages
     let messages = answerSession.getMessages()

diff --git a/packages/docs/src/content/docs/cloud/performing-search/full-text-search.mdx b/packages/docs/src/content/docs/cloud/performing-search/full-text-search.mdx
@@ -46,7 +46,7 @@ The client exposes a simple `search` method that can be used to query the index:
   </TabItem>
   <TabItem label="Swift" icon="seti:swift">
     ```swift copy
-    import OramaCloud
+    import OramaCloudClient
 
     struct MyDoc: Codable {
       let title: String
@@ -267,42 +267,18 @@ With the Orama Faceted Search API, users can filter their search results by vari
 
 Given the following Orama schema:
 
-<Tabs syncKey="sdk">
-  <TabItem label="JavaScript" icon="seti:javascript">
-    ```json
-    {
-      title: "string",
-      description: "string",
-      categories: {
-        primary: "string",
-        secondary: "string",
-      },
-      rating: "number",
-      isFavorite: "boolean",
-    }
-    ```
-  </TabItem>
-  <TabItem label="Swift" icon="seti:swift">
-    ```swift copy
-    print("coming soon")
-    ```
-  </TabItem>
-  <TabItem label="Kotlin" icon="seti:kotlin">
-    ```kotlin copy
-    print("coming soon")
-    ```
-  </TabItem>
-  <TabItem label="Python" icon="seti:python">
-    ```python copy
-    print("coming soon")
-    ```
-  </TabItem>
-  <TabItem label="PHP" icon="seti:php">
-    ```php copy
-    // coming soon
-    ```
-  </TabItem>
-</Tabs>
+```json title=schema.json
+{
+  "title": "string",
+  "description": "string",
+  "categories": {
+    "primary": "string",
+    "secondary": "string",
+  },
+  "rating": "number",
+  "isFavorite": "boolean",
+}
+```
 
 Orama will be able to generate facets at search-time based on the schema. To do so, we need to specify the `facets` property in the `search` configuration:
 
@@ -311,6 +287,7 @@ Orama will be able to generate facets at search-time based on the schema. To do
     ```js
     const results = await client.search({
       term: "Movie about cars and racing",
+      mode: "hybrid",
       properties: ["description"],
       facets: {
         "categories.primary": {
@@ -338,7 +315,34 @@ Orama will be able to generate facets at search-time based on the schema. To do
   </TabItem>
   <TabItem label="Swift" icon="seti:swift">
     ```swift copy
-    print("coming soon")
+    import OramaCloudClient
+
+    struct MyDoc: Codable {
+      let title: String
+      let description: String
+    }
+
+    let clientParams = OramaClientParams(endpoint: "<Your Orama Cloud Endpoint>", apiKey: "<Your Orama Cloud API Key>")
+    let client = OramaClient(params: clientParams)
+
+    let searchParams = ClientSearchParams.builder(
+        term: "Movie about cars and racing",
+        mode: .hybrid 
+      )
+      .properties(["description"]) // optional
+      .facets([
+        "category.primary": .string(limit: 3, order: .desc),
+        "category.secondary": .string(limit: 2, order: .desc),
+        "rating": .number(ranges: [
+          ClientSearchParams.Facet.NumberRange(from: 0, to: 3),
+          ClientSearchParams.Facet.NumberRange(from: 3, to: 7),
+          ClientSearchParams.Facet.NumberRange(from: 7, to: 10),
+        ]),
+        "isFavorite": .boolean(isTrue: true, isFalse: true),
+      ])
+      .build()
+
+    let searchResults: SearchResults<MyDoc> = try await client.search(query: searchParams)
     ```
   </TabItem>
   <TabItem label="Kotlin" icon="seti:kotlin">

diff --git a/packages/docs/src/content/docs/cloud/performing-search/hybrid-search.mdx b/packages/docs/src/content/docs/cloud/performing-search/hybrid-search.mdx
@@ -52,7 +52,7 @@ Once you have at least one index containing vectors, you can perform hybrid sear
   </TabItem>
   <TabItem label="Swift" icon="seti:swift">
     ```swift copy
-    import OramaCloud
+    import OramaCloudClient
 
     struct MyDoc: Encodable & Decodable {
       let title: String

diff --git a/packages/docs/src/content/docs/cloud/performing-search/vector-search.mdx b/packages/docs/src/content/docs/cloud/performing-search/vector-search.mdx
@@ -48,7 +48,7 @@ Once you have at least one index containing vectors, you can perform vector sear
   </TabItem>
   <TabItem label="Swift" icon="seti:swift">
     ```swift copy
-    import OramaCloud
+    import OramaCloudClient
 
     struct MyDoc: Encodable & Decodable {
       let title: String