diff --git a/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb b/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb
index 63ba8931a60..ed91304e36e 100644
--- a/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb
+++ b/docs/extras/guides/privacy/presidio_data_anonymization/multi_language.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
        "'Me llamo Sofía'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -93,16 +93,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Bridget Kirk soy Sally Knight'"
+       "'Kari Lopez soy Mary Walker'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,15 +157,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Me llamo Michelle Smith\n",
-      "Yo soy Rachel Wright\n"
+      "Me llamo Christopher Smith\n",
+      "Yo soy Joseph Jenkins\n"
      ]
     }
    ],
@@ -190,14 +190,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "My name is Ronnie Ayala\n"
+      "My name is Shawna Bennett\n"
      ]
     }
    ],
@@ -205,6 +205,218 @@
     "print(anonymizer.anonymize(\"My name is John\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage with other frameworks\n",
+    "\n",
+    "### Language detection\n",
+    "\n",
+    "One of the drawbacks of the presented approach is that we have to pass the **language** of the input text directly. However, there is a remedy for that - *language detection* libraries.\n",
+    "\n",
+    "We recommend using one of the following frameworks:\n",
+    "- fasttext (recommended)\n",
+    "- langdetect\n",
+    "\n",
+    "From our exprience *fasttext* performs a bit better, but you should verify it on your use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install necessary packages\n",
+    "# ! pip install fasttext langdetect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### langdetect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langdetect\n",
+    "from langchain.schema import runnable\n",
+    "\n",
+    "def detect_language(text: str) -> dict:\n",
+    "    language = langdetect.detect(text)\n",
+    "    print(language)\n",
+    "    return {\"text\": text, \"language\": language}\n",
+    "\n",
+    "\n",
+    "chain = (\n",
+    "    runnable.RunnableLambda(detect_language)\n",
+    "    | (lambda x: anonymizer.anonymize(x[\"text\"], language=x[\"language\"]))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "es\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Me llamo Michael Perez III'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Me llamo Sofía\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "en\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Ronald Bennett'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"My name is John Doe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### fasttext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You need to download the fasttext model first from https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import fasttext\n",
+    "\n",
+    "model = fasttext.load_model(\"lid.176.ftz\")\n",
+    "def detect_language(text: str) -> dict:\n",
+    "    language = model.predict(text)[0][0].replace('__label__', '')\n",
+    "    print(language)\n",
+    "    return {\"text\": text, \"language\": language}\n",
+    "\n",
+    "chain = (\n",
+    "    runnable.RunnableLambda(detect_language)\n",
+    "    | (lambda x: anonymizer.anonymize(x[\"text\"], language=x[\"language\"]))\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "es\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Yo soy Angela Werner'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Yo soy Sofía\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "en\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Carlos Newton'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"My name is John Doe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This way you only need to initialize the model with the engines corresponding to the relevant languages, but using the tool is fully automated."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -485,15 +697,6 @@
    "source": [
     "In many cases, even the larger models from spaCy will not be sufficient - there are already other, more complex and better methods of detecting named entities, based on transformers. You can read more about this [here](https://microsoft.github.io/presidio/analyzer/nlp_engines/transformers/)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Future works\n",
-    "\n",
-    "- **automatic language detection** - instead of passing the language as a parameter in `anonymizer.anonymize`, we could detect the language/s beforehand and then use the corresponding NER model."
-   ]
   }
  ],
  "metadata": {
@@ -512,7 +715,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,