docs: updates docs

README.md

@@ -12,6 +12,7 @@ If you need more info, help, or want to provide general feedback on Orama, join
 - [Full-Text search](https://docs.orama.com/open-source/usage/search/introduction)
 - [Vector Search](https://docs.orama.com/open-source/usage/search/vector-search)
 - [Hybrid Search](https://docs.orama.com/open-source/usage/search/hybrid-search)
+- [GenAI Chat Sessions](https://docs.orama.com/open-source/usage/answer-engine/introduction)
 - [Search Filters](https://docs.orama.com/open-source/usage/search/filters)
 - [Geosearch](https://docs.orama.com/open-source/usage/search/geosearch)
 - [Facets](https://docs.orama.com/open-source/usage/search/facets)

packages/docs/src/content/docs/open-source/plugins/plugin-embeddings.mdx

@@ -2,6 +2,7 @@
 title: Plugin Embeddings
 description: Generate embeddings for your documents offline and use them for vector search.
 ---
+import { Aside } from '@astrojs/starlight/components';
 import Install from '../../../../components/Install.astro'
 
 To perform vector and hybrid search, you need to convert your text data into embeddings.
@@ -35,6 +36,10 @@ If you're using Orama in Node.js, we recommend using `@tensorflow/tfjs-node`:
 
 ## Usage
 
+<Aside type='caution' title='Search and insert methods are now async!'>
+Since this plugin will need to generate embeddings for your documents, the search and insert methods are now async. Make sure to use `await` when calling them.
+</Aside>
+
 This plugin will generate text embeddings for you at insert and search time, allowing you to perform vector and hybrid searches on your documents.
 
 ```js

packages/docs/src/content/docs/open-source/plugins/plugin-secure-proxy.mdx

@@ -58,19 +58,23 @@ Right now, the Orama Secure Proxy Plugin supports two different models for gener
 
 ## Running queries
 
+<Aside type='caution' title='Search methods are now async!'>
+Since the plugin needs to call the OpenAI API to generate embeddings, the search methods are now async. Make sure to use `await` when calling them.
+</Aside>
+
 By telling on which property to perform search by default (in the example above, `'embeddings'`), the plugin will automatically translate your search term into a vector by calling the OpenAI API for you and setting the result into the `vector.value` property.
 
 This will finally allow you to perform hybrid and vector search with the exact same APIs used for full-text search.
 
 ```js
 import { search } from '@orama/orama'
 
-const resultsHybrid = search(db, {
+const resultsHybrid = await search(db, {
   mode: 'hybrid',
   term: 'Videogame for little kids with a passion about ice cream'
 })
 
-const resultsVector = search(db, {
+const resultsVector = await search(db, {
   mode: 'vector',
   term: 'Videogame for little kids with a passion about ice cream'
 })
@@ -81,9 +85,9 @@ const resultsVector = search(db, {
 If you have a more complex schema with multiple vector properties, you can always override the vector property to perform search on by using the default `vector` property:
 
 ```js
-const resultsVector = search(db, {
+const resultsVector = await search(db, {
   mode: 'vector',
-  term: 'Videogame for little kids with a passion about ice cream'
+  term: 'Videogame for little kids with a passion about ice cream',
   vector: {
     property: 'myAlternativeProperty'
   }

packages/orama/README.md

@@ -9,8 +9,10 @@ If you need more info, help, or want to provide general feedback on Orama, join
 
 # Highlighted features
 
+- [Full-Text search](https://docs.orama.com/open-source/usage/search/introduction)
 - [Vector Search](https://docs.orama.com/open-source/usage/search/vector-search)
 - [Hybrid Search](https://docs.orama.com/open-source/usage/search/hybrid-search)
+- [GenAI Chat Sessions](https://docs.orama.com/open-source/usage/answer-engine/introduction)
 - [Search Filters](https://docs.orama.com/open-source/usage/search/filters)
 - [Geosearch](https://docs.orama.com/open-source/usage/search/geosearch)
 - [Facets](https://docs.orama.com/open-source/usage/search/facets)
@@ -129,20 +131,150 @@ Orama currently supports 10 different data types:
 | `enum[]`         | An array of enums.                                                          | `['comedy', 'action', 'romance']`                                           |
 | `vector[<size>]` | A vector of numbers to perform vector search on.                            | `[0.403, 0.192, 0.830]`                                                     |
 
+# Vector and Hybrid Search Support
+
+Orama supports both vector and hybrid search by just setting `mode: 'vector'` when performing search.
+
+To perform this kind of search, you'll need to provide [text embeddings](https://en.wikipedia.org/wiki/Word_embedding) at search time:
+
+```js
+import { create, insertMultiple, search } from '@orama/orama'
+
+const db = create({
+  schema: {
+    title: 'string',
+    embedding: 'vector[5]'', // we are using a 5-dimensional vector.
+  },
+});
+
+insertMultiple(db, [
+  { title: 'The Prestige', embedding: [0.938293, 0.284951, 0.348264, 0.948276, 0.56472] },
+  { title: 'Barbie', embedding: [0.192839, 0.028471, 0.284738, 0.937463, 0.092827] },
+  { title: 'Oppenheimer', embedding: [0.827391, 0.927381, 0.001982, 0.983821, 0.294841] },
+])
+
+const results = search(db, {
+  // Search mode. Can be 'vector', 'hybrid', or 'fulltext'
+  mode: 'vector',
+  vector: {
+    // The vector (text embedding) to use for search
+    value: [0.938292, 0.284961, 0.248264, 0.748276, 0.26472],
+    // The schema property where Orama should compare embeddings
+    property: 'embedding',
+  },
+  // Minimum similarity to determine a match. Defaults to `0.8`
+  similarity: 0.85,
+  // Defaults to `false`. Setting to 'true' will return the embeddings in the response (which can be very large).
+  includeVectors: true,
+})
+```
+
+Have trouble generating embeddings for vector and hybrid search? Try our `@orama/plugin-embeddings` plugin!
+
+```js
+import { create } from '@orama/orama'
+import { pluginEmbeddings } from '@orama/plugin-embeddings'
+import '@tensorflow/tfjs-node' // Or any other appropriate TensorflowJS backend, like @tensorflow/tfjs-backend-webgl
+
+const plugin = await pluginEmbeddings({
+  embeddings: {
+    // Schema property used to store generated embeddings
+    defaultProperty: 'embeddings',
+    onInsert: {
+      // Generate embeddings at insert-time
+      generate: true,
+      // properties to use for generating embeddings at insert time.
+      // Will be concatenated to generate a unique embedding.
+      properties: ['description'],
+      verbose: true,
+    }
+  }
+})
+
+const db = create({
+  schema: {
+    description: 'string',
+    // Orama generates 512-dimensions vectors.
+    // When using @orama/plugin-embeddings, set the property where you want to store embeddings as `vector[512]`.
+    embeddings: 'vector[512]'
+  },
+  plugins: [plugin]
+})
+
+// Orama will generate and store embeddings at insert-time!
+await insert(db, { description: 'Classroom Headphones Bulk 5 Pack, Student On Ear Color Varieties' })
+await insert(db, { description: 'Kids Wired Headphones for School Students K-12' })
+await insert(db, { description: 'Kids Headphones Bulk 5-Pack for K-12 School' })
+await insert(db, { description: 'Bose QuietComfort Bluetooth Headphones' })
+
+// Orama will also generate and use embeddings at search time when search mode is set to "vector" or "hybrid"!
+const searchResults = await search(db, {
+  term: 'Headphones for 12th grade students',
+  mode: 'vector'
+})
+```
+
+Want to use OpenAI embedding models? Use our [Secure Proxy](https://docs.orama.com/open-source/plugins/plugin-secure-proxy) plugin to call OpenAI from the client-side securely.
+
+# RAG and Chat Experiences with Orama
+
+Since `v3.0.0`, Orama allows you to create your own ChatGPT/Perplexity/SearchGPT-like experience. You will need to call the OpenAI APIs, so we strongly recommend using the [Secure Proxy Plugin](https://docs.orama.com/open-source/plugins/plugin-secure-proxy) to do that securely from your client side. It's free!
+
+```js
+import { create, insert } from '@orama/orama'
+import { pluginSecureProxy } from '@orama/plugin-secure-proxy'
+
+const secureProxy = await pluginSecureProxy({
+  apiKey: 'my-api-key',
+  defaultProperty: 'embeddings',
+  models: {
+    // The chat model to use to generate the chat answer
+    chat: 'openai/gpt-4o-mini'
+  }
+})
+
+const db = create({
+  schema: {
+    name: 'string'
+  },
+  plugins: [secureProxy]
+})
+
+insert(db, { name: 'John Doe' })
+insert(db, { name: 'Jane Doe' })
+
+const session = new AnswerSession(db, {
+  // Customize the prompt for the system
+  systemPrompt: 'You will get a name as context, please provide a greeting message',
+  events: {
+    // Log all state changes. Useful to reactively update a UI on a new message chunk, sources, etc.
+    onStateChange: console.log,
+  }
+})
+
+const response = await session.ask({
+  term: 'john'
+})
+
+console.log(response) // Hello, John Doe! How are you doing?
+```
+
+Read the complete documentation [here](https://docs.orama.com/open-source/usage/answer-engine/introduction).
+
 # Official Docs
 
 Read the complete documentation at [https://docs.orama.com/open-source](https://docs.orama.com/open-source).
 
 # Official Orama Plugins
 
 - [Plugin Embeddings](https://docs.orama.com/open-source/plugins/plugin-embeddings)
+- [Plugin Secure Proxy](https://docs.orama.com/open-source/plugins/plugin-secure-proxy)
 - [Plugin Analytics](https://docs.orama.com/open-source/plugins/plugin-analytics)
 - [Plugin Data Persistence](https://docs.orama.com/open-source/plugins/plugin-data-persistence)
 - [Plugin QPS](https://docs.orama.com/open-source/plugins/plugin-qps)
 - [Plugin PT15](https://docs.orama.com/open-source/plugins/plugin-pt15)
 - [Plugin Vitepress](https://docs.orama.com/open-source/plugins/plugin-vitepress)
 - [Plugin Docusaurus](https://docs.orama.com/open-source/plugins/plugin-docusaurus)
-- [Plugin Secure Proxy](https://docs.orama.com/open-source/plugins/plugin-secure-proxy)
 - [Plugin Astro](https://docs.orama.com/open-source/plugins/plugin-astro)
 - [Plugin Nextra](https://docs.orama.com/open-source/plugins/plugin-nextra)