Clarifai : Added Clarifai as LLM and embedding model provider. (#1311)

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

notebooks/clarifai.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cookbook for using Clarifai LLM and Embedders with Embedchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step-1: Install embedchain-clarifai package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install embedchain[clarifai]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step-2: Set Clarifai PAT as env variable.\n",
+    "Sign-up to [Clarifai](https://clarifai.com/signup?utm_source=clarifai_home&utm_medium=direct&) platform and you can obtain `CLARIFAI_PAT` by following this [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/).\n",
+    "\n",
+    "optionally you can also pass `api_key` in config of llm/embedder class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from embedchain import App\n",
+    "\n",
+    "os.environ[\"CLARIFAI_PAT\"]=\"xxx\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step-3 Create embedchain app using clarifai LLM and embedder and define your config.\n",
+    "\n",
+    "Browse through Clarifai community page to get the URL of different [LLM](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) and [embedding](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22input_fields%22%2C%22value%22%3A%5B%22text%22%5D%7D%2C%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) models available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use model_kwargs to pass all model specific parameters for inference.\n",
+    "app = App.from_config(config={\n",
+    "    \"llm\": {\n",
+    "        \"provider\": \"clarifai\",\n",
+    "        \"config\": {\n",
+    "            \"model\": \"https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct\",\n",
+    "            \"model_kwargs\": {\n",
+    "            \"temperature\": 0.5,\n",
+    "            \"max_tokens\": 1000\n",
+    "            }\n",
+    "        }\n",
+    "    },\n",
+    "    \"embedder\": {\n",
+    "        \"provider\": \"clarifai\",\n",
+    "        \"config\": {\n",
+    "            \"model\": \"https://clarifai.com/openai/embed/models/text-embedding-ada\",\n",
+    "        }\n",
+    "}\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step-4: Add data sources to your app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "app.add(\"https://www.forbes.com/profile/elon-musk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step-5: All set. Now start asking questions related to your data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "while(True):\n",
+    "    question = input(\"Enter question: \")\n",
+    "    if question in ['q', 'exit', 'quit']:\n",
+    "        break\n",
+    "    answer = app.query(question)\n",
+    "    print(answer)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "v1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}