Prem AI langchain integration (migrating PR from org account to personal account)

docs/docs/integrations/chat/premai.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Prem\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ChatPrem\n",
+    "\n",
+    ">[PremAI](https://app.premai.io) is an unified platform that let's you build powerful production-ready GenAI powered applications with least effort, so that you can focus more on user experience and overall growth. \n",
+    "\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with `ChatPremAI`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and setup\n",
+    "\n",
+    "We start by installing langchain and premai-sdk. You can type the following command to install:\n",
+    "\n",
+    "```bash\n",
+    "pip install premai langchain\n",
+    "```\n",
+    "\n",
+    "Before proceeding further, please make sure that you have made an account on Prem and already started a project. If not, then here's how you can start for free:\n",
+    "\n",
+    "1. Sign in to [PremAI](https://app.premai.io/accounts/login/), if you are coming for the first time and create your API key [here](https://app.premai.io/api_keys/).\n",
+    "\n",
+    "2. Go to [app.premai.io](https://app.premai.io) and this will take you to the project's dashboard. \n",
+    "\n",
+    "3. Create a project and this will generate a project-id (written as ID). This ID will help you to interact with your deployed application. \n",
+    "\n",
+    "4. Head over to LaunchPad (the one with 🚀 icon). And there deploy your model of choice. Your default model will be `gpt-4`. You can also set and fix different generation paramters (like: max-tokens, temperature etc) and also pre-set your system prompt. \n",
+    "\n",
+    "Congratulations on creating your first deployed application on Prem 🎉 Now we can use langchain to interact with our application. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_community.chat_models import ChatPremAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup ChatPrem instance in LangChain \n",
+    "\n",
+    "Once we imported our required modules, let's setup our client. For now let's assume that our `project_id` is 8. But make sure you use your project-id, otherwise it will throw error.\n",
+    "\n",
+    "In order to use langchain with prem, you do not need to pass any model name or set any parameters with our chat-client. All of those will use the default model name and paramters of the LaunchPad model. \n",
+    "\n",
+    "`NOTE:` If you change the `model_name` or any other parameter like `temperature` while setting the client, it will override existing default configurations. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "if os.environ.get(\"PREMAI_API_KEY\") is None:\n",
+    "    os.environ[\"PREMAI_API_KEY\"] = getpass.getpass(\"PremAI API Key:\")\n",
+    "\n",
+    "chat = ChatPremAI(project_id=8)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calling the Model\n",
+    "\n",
+    "Now you are all set. We can now start with interacting with our application. `ChatPrem` supports two methods `invoke` (which is same as `generate`) and `stream`. \n",
+    "\n",
+    "The first one will give us a static result. Where as the second one will stream tokens one by one. Here's how you can generate chat like completions. \n",
+    "\n",
+    "### Generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Ahoy there, matey! I be a virtual assistant here to assist ye with all yer needs. What can I be helpin' ye with today?\")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "human_message = HumanMessage(content=\"Who are you?\")\n",
+    "\n",
+    "chat.invoke([human_message])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above looks interesting right? I set my default lanchpad system-prompt as: `Always sound like a pirate` You can also, override the default system prompt if you need to. Here's how you can do it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='I am a friendly assistant here to help you with any questions or tasks you may have. How can I assist you today?')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system_message = SystemMessage(content=\"You are a friendly assistant.\")\n",
+    "human_message = HumanMessage(content=\"Who are you?\")\n",
+    "\n",
+    "chat.invoke([system_message, human_message])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also change generation parameters while calling the model. Here's how you can do that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='I am a friendly assistant here to help you with any questions or tasks you may have. Feel free')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke(\n",
+    "    [system_message, human_message],\n",
+    "    temperature = 0.7, max_tokens = 20, top_p = 0.95\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Important notes:\n",
+    "\n",
+    "Before proceeding further, please note that the current version of ChatPrem does not support parameters: [n](https://platform.openai.com/docs/api-reference/chat/create#chat-create-n) and [stop](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop) are not supported. \n",
+    "\n",
+    "We will provide support for those two above parameters in sooner versions. \n",
+    "\n",
+    "### Streaming\n",
+    "\n",
+    "And finally, here's how you do token streaming for dynamic chat like applications. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ahoy there! I be doin' well, me hearty! And yerself? Arrr!"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "for chunk in chat.stream(\"hello how are you\"):\n",
+    "    sys.stdout.write(chunk.content)\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to above, if you want to override the system-prompt and the generation parameters, here's how you can do it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello! I'm here to help you. How can I assist you today?"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "for chunk in chat.stream(\n",
+    "    \"hello how are you\",\n",
+    "    system_prompt = \"You are an helpful assistant\", temperature = 0.7, max_tokens = 20\n",
+    "):\n",
+    "    sys.stdout.write(chunk.content)\n",
+    "    sys.stdout.flush()"
+   ]
+  }
+ ],
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+}