feat: updates secure proxy plugin to use @oramacloud/client package (#…

…616)

packages/docs/cloud/orama-ai/orama-secure-proxy.md

@@ -137,7 +137,7 @@ const proxy = new OramaProxy({
 })
 
 const chatParams = {
-  model '<YOUR API KEY>',
+  model 'openai/gpt-4',
   messages: [{ role: 'user', content: 'Who is Michael Scott?' }]
 }
 

packages/docs/open-source/plugins/plugin-secure-proxy.md

@@ -28,7 +28,8 @@ import { pluginSecureProxy } from '@orama/plugin-secure-proxy'
 
 const secureProxy = secureProxyPlugin({
   apiKey: 'YOUR API KEY',
-  defaultProperty: 'embeddings'
+  defaultProperty: 'embeddings',
+  model: 'openai/text-embedding-ada-002'
 })
 
 const db = await create({
@@ -41,6 +42,15 @@ const db = await create({
 })
 ```
 
+## Available models
+
+Right now, the Orama Secure Proxy Plugin supports two different models for generating embeddings:
+
+| Model name                     | Provider | Dimensions |
+| ------------------------------ | -------- | ---------- |
+| `orama/gte-small               | Orama    | 384        |
+| `openai/text-embedding-ada-002 | Openai   | 1536       |
+
 ## Running queries
 
 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.

packages/plugin-secure-proxy/README.md

@@ -2,7 +2,7 @@
 
 [![Tests](https://github.com/oramasearch/orama/actions/workflows/turbo.yml/badge.svg)](https://github.com/oramasearch/orama/actions/workflows/turbo.yml)
 
-Orama plugin for generating embeddings securely on the front-end.
+Orama plugin for generating embeddings and performing vector/hybrid search securely on the front-end.
 
 # Usage
 
@@ -18,17 +18,25 @@ const db = await create({
   schema: {
     title: 'string',
     description: 'string',
-    embeddings: 'vector[1536]'
+    embeddings: 'vector[384]'
   },
   plugins: [
     pluginSecureProxy({
       apiKey: '<API-KEY>',
-      defaultProperty: 'embeddings'
+      defaultProperty: 'embeddings',
+      model: 'orama/gte-small'
     })
   ]
 })
 ```
 
+Available models:
+
+- `orama/gte-small`. 384 dimensions.
+- `openai/text-embedding-ada-002`. 1536 dimensions.
+
+Mode models coming soon!
+
 For the full configuration guide of this plugin, please follow the [official plugin documentation](https://docs.oramasearch.com/open-source/plugins/plugin-secure-proxy).
 
 # License

packages/plugin-secure-proxy/package.json

@@ -41,8 +41,8 @@
     "access": "public"
   },
   "devDependencies": {
-    "@types/node": "^20.9.0",
     "@orama/orama": "workspace:*",
+    "@types/node": "^20.9.0",
     "c8": "^7.12.0",
     "sinon": "^17.0.1",
     "tap": "^18.6.1",
@@ -54,5 +54,8 @@
   },
   "tap": {
     "extends": "src/tests/config/tap.yml"
+  },
+  "dependencies": {
+    "@oramacloud/client": "^1.0.4"
   }
 }

packages/plugin-secure-proxy/src/index.ts

@@ -1,60 +1,20 @@
 import type { AnyOrama, SearchParams, TypedDocument, OramaPluginAsync } from '@orama/orama'
-
-type InitResponse = {
-  clientID: string
-  ttl: number
-  csrfToken: string
-}
-
-type EmbeddingsResponse = number[]
+import { OramaProxy, EmbeddingModel } from '@oramacloud/client'
 
 export type SecureProxyPluginOptions = {
   apiKey: string
   defaultProperty: string
+  model: EmbeddingModel
 }
 
-const SECURE_PROXY_ENDPOINT = 'https://secure-proxy.orama.run'
-const INIT_URL = `${SECURE_PROXY_ENDPOINT}/init`
-const SEARCH_URL = `${SECURE_PROXY_ENDPOINT}/query`
-
-function isServer() {
-  return typeof window === 'undefined'
-}
-
-function getReferer() {
-  return isServer() ? 'http://localhost' : window.location.href
-}
-
-async function getCSRFToken(apiKey: string): Promise<InitResponse> {
-  const response = await fetch(`${INIT_URL}?apiKey=${apiKey}`, {
-    headers: {
-      Referer: getReferer()
-    }
-  })
-
-  return response.json()
-}
-
-async function getEmbeddings(apiKey: string, query: string, csrfToken: string): Promise<EmbeddingsResponse> {
-  const body = new URLSearchParams({ query, csrf: csrfToken }).toString()
-
-  const response = await fetch(`${SEARCH_URL}?apiKey=${apiKey}`, {
-    method: 'POST',
-    headers: {
-      'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
-      Referer: getReferer()
-    },
-    body
-  })
-
-  return response.json()
-}
-
-export async function pluginSecureProxy(pluginParams: SecureProxyPluginOptions): OramaPluginAsync {
+export async function pluginSecureProxy(pluginParams: SecureProxyPluginOptions): Promise<OramaPluginAsync> {
   if (!pluginParams.apiKey) throw new Error('Missing "apiKey" parameter for plugin-telemetry')
   if (!pluginParams.defaultProperty) throw new Error('Missing "defaultProperty" parameter for plugin-telemetry')
+  if (!pluginParams.model) throw new Error('Missing "model" parameter for plugin-telemetry')
 
-  const { csrfToken } = await getCSRFToken(pluginParams.apiKey)
+  const proxy = new OramaProxy({
+    api_key: pluginParams.apiKey,
+  })
 
   return {
     name: 'secure-proxy',
@@ -73,11 +33,12 @@ export async function pluginSecureProxy(pluginParams: SecureProxyPluginOptions):
       }
 
       const term = params.term
-      const embeddings = await getEmbeddings(pluginParams.apiKey, term, csrfToken)
+      const embeddings = await proxy.generateEmbeddings(term, pluginParams.model)
 
       if (!params.vector) {
         params.vector = {
-          // @ts-expect-error - vector param is not present in full-text search
+          // eslint-disable-next-line
+          // @ts-ignore
           property: params?.vector?.property ?? pluginParams.defaultProperty,
           value: embeddings
         }

turbo.json

@@ -41,6 +41,10 @@
         "^build"
       ],
       "outputs": ["dist/**"]
-    }    
+    },
+    "@orama/plugin-secure-proxy#build": {
+      "dependsOn": ["@orama/orama#build"],
+      "outputs": ["dist/**"]
+    }
   }
 }