feat():Multishot_prompting_using_history

week2/community-contributions/day3_Multishot_prompting_via_historical_conversation.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f4e0dbbb-2b3f-4c4b-8b25-642648cfe72c",
+   "metadata": {},
+   "source": [
+    "# Multishot Prompting via learning from Historical Conversation\n",
+    "Learning from historical conversations (Which could be stored in databases) allows the model to cache information and utilize in particular conversation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c71c5ba7-d30f-4b78-abde-4ff465196256",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8304702a-8a8d-40de-96ee-3ae911949952",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "# Print the key prefixes to help with any debugging\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set\")\n",
+    "    \n",
+    "if anthropic_api_key:\n",
+    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
+    "else:\n",
+    "    print(\"Anthropic API Key not set\")\n",
+    "\n",
+    "if google_api_key:\n",
+    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"Google API Key not set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ef47f00-e0fe-45cf-a4da-f60b47fadc98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()\n",
+    "MODEL = 'gpt-4o-mini'\n",
+    "\n",
+    "system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
+    "the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
+    "For example, if the customer says 'I'm looking to buy a hat', \\\n",
+    "you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
+    "Encourage the customer to buy hats if they are unsure what to get.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78c29e44-c121-4af9-b70f-1b5559040829",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "archievedConversation = [{\"role\": \"user\", \"content\": \"Customer A: Hi, I am looking to buy a belt.\"},\n",
+    "                        {\"role\": \"assistant\", \"content\": \"I am sorry but we do not sell belts in this store; but you can find them in our second store.\\\n",
+    "                        Do you want me to tell you the address of that store?\"}\n",
+    "                        ,{\"role\": \"user\", \"content\": \"Customer A: Yes please tell me the location.\"},\n",
+    "                        {\"role\": \"assistant\", \"content\": \"Please walk straight from this store and then take a right, the second store is 3 streets after next to a burger joint.\" }]\n",
+    "\n",
+    "def chat(message, history):\n",
+    "\n",
+    "    if 'belt' in message:\n",
+    "        messages = [{\"role\": \"system\", \"content\": system_message}] + archievedConversation + history + [{\"role\": \"user\", \"content\": message}]\n",
+    "    else:\n",
+    "        messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
+    "\n",
+    "    stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
+    "\n",
+    "    response = \"\"\n",
+    "    for chunk in stream:\n",
+    "        response += chunk.choices[0].delta.content or ''\n",
+    "        yield response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e48d30f8-f040-4c01-bb4f-47562bba5fa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr.ChatInterface(fn=chat, type=\"messages\").launch(inbrowser=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}