added ingestion in notebook

Author: mogith-pn

clarifai_llm_retriever_example.ipynb

@@ -46,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Initialize clarifai app id, user id and PAT.\n",
+    "#### Initialize clarifai app id, user id and PAT.\n",
     "\n",
     "You can browse the portal to obtain [MODEL URL](https://clarifai.com/explore/models) for different models in clarifai community."
    ]
@@ -68,7 +68,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Initialize LLM class"
+    "### Data ingestion into clarifai vectordatabase\n",
+    "\n",
+    "To use clarifai as retriever all you have to do is ingest the documents into clarifai app that serves as your vectordatabase to retrieve similar documents.\n",
+    "To simplify the ingestion, we are utilising the clarifaivectordatabase integration for ingestion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#run this block to ingest the documents into clarifai app as chunks.\n",
+    "# if you encounter any issue, make sure to run `pip install langchain`\n",
+    "\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.vectorstores import Clarifai \n",
+    "\n",
+    "loader = TextLoader(\"YOUR_TEXT_FILE_PATH\") #replace with your file path\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "clarifai_vector_db = Clarifai.from_documents(\n",
+    "    user_id=USER_ID,\n",
+    "    app_id=APP_ID,\n",
+    "    documents=docs,\n",
+    "    pat=PAT\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Initialize LLM class"
    ]
   },
   {