chore: consolidate cookbooks/ into an indexed examples/ directory (#5517)
This commit is contained in:
Kartik
@@ -0,0 +1,225 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from typing import List, Dict\n",
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"from mem0 import Memory\n",
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"from datetime import datetime\n",
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"import anthropic\n",
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"\n",
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"# Set up environment variables\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\" # needed for embedding model\n",
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"os.environ[\"ANTHROPIC_API_KEY\"] = \"your_anthropic_api_key\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class SupportChatbot:\n",
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" def __init__(self):\n",
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" # Initialize Mem0 with Anthropic's Claude\n",
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" self.config = {\n",
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" \"llm\": {\n",
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" \"provider\": \"anthropic\",\n",
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" \"config\": {\n",
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" \"model\": \"claude-3-5-sonnet-latest\",\n",
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" \"temperature\": 0.1,\n",
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" \"max_tokens\": 2000,\n",
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" },\n",
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" }\n",
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" }\n",
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" self.client = anthropic.Client(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n",
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" self.memory = Memory.from_config(self.config)\n",
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"\n",
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" # Define support context\n",
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" self.system_context = \"\"\"\n",
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" You are a helpful customer support agent. Use the following guidelines:\n",
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" - Be polite and professional\n",
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" - Show empathy for customer issues\n",
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" - Reference past interactions when relevant\n",
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" - Maintain consistent information across conversations\n",
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" - If you're unsure about something, ask for clarification\n",
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" - Keep track of open issues and follow-ups\n",
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" \"\"\"\n",
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"\n",
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" def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):\n",
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" \"\"\"Store customer interaction in memory.\"\"\"\n",
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" if metadata is None:\n",
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" metadata = {}\n",
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"\n",
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" # Add timestamp to metadata\n",
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" metadata[\"timestamp\"] = datetime.now().isoformat()\n",
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"\n",
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" # Format conversation for storage\n",
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" conversation = [{\"role\": \"user\", \"content\": message}, {\"role\": \"assistant\", \"content\": response}]\n",
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"\n",
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" # Store in Mem0\n",
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" self.memory.add(conversation, user_id=user_id, metadata=metadata)\n",
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"\n",
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" def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:\n",
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" \"\"\"Retrieve relevant past interactions.\"\"\"\n",
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" return self.memory.search(\n",
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" query=query,\n",
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" user_id=user_id,\n",
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" limit=5, # Adjust based on needs\n",
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" )\n",
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"\n",
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" def handle_customer_query(self, user_id: str, query: str) -> str:\n",
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" \"\"\"Process customer query with context from past interactions.\"\"\"\n",
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"\n",
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" # Get relevant past interactions\n",
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" relevant_history = self.get_relevant_history(user_id, query)\n",
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"\n",
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" # Build context from relevant history\n",
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" context = \"Previous relevant interactions:\\n\"\n",
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" for memory in relevant_history:\n",
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" context += f\"Customer: {memory['memory']}\\n\"\n",
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" context += f\"Support: {memory['memory']}\\n\"\n",
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" context += \"---\\n\"\n",
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"\n",
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" # Prepare prompt with context and current query\n",
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" prompt = f\"\"\"\n",
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" {self.system_context}\n",
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"\n",
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" {context}\n",
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"\n",
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" Current customer query: {query}\n",
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"\n",
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" Provide a helpful response that takes into account any relevant past interactions.\n",
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" \"\"\"\n",
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"\n",
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" # Generate response using Claude\n",
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" response = self.client.messages.create(\n",
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" model=\"claude-3-5-sonnet-latest\",\n",
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" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
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" max_tokens=2000,\n",
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" temperature=0.1,\n",
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" )\n",
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"\n",
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" # Store interaction\n",
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" self.store_customer_interaction(\n",
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" user_id=user_id, message=query, response=response, metadata={\"type\": \"support_query\"}\n",
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" )\n",
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"\n",
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" return response.content[0].text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Welcome to Customer Support! Type 'exit' to end the conversation.\n",
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"Customer: Hi, I'm having trouble connecting my new smartwatch to the mobile app. It keeps showing a connection error.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/5x/9kmqjfm947g5yh44m7fjk75r0000gn/T/ipykernel_99777/1076713094.py:55: DeprecationWarning: The current get_all API output format is deprecated. To use the latest format, set `api_version='v1.1'`. The current format will be removed in mem0ai 1.1.0 and later versions.\n",
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" return self.memory.search(\n",
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"/var/folders/5x/9kmqjfm947g5yh44m7fjk75r0000gn/T/ipykernel_99777/1076713094.py:47: DeprecationWarning: The current add API output format is deprecated. To use the latest format, set `api_version='v1.1'`. The current format will be removed in mem0ai 1.1.0 and later versions.\n",
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" self.memory.add(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Support: Hello! Thank you for reaching out about the connection issue with your smartwatch. I understand how frustrating it can be when a new device won't connect properly. I'll be happy to help you resolve this.\n",
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"\n",
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"To better assist you, could you please provide me with:\n",
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"1. The model of your smartwatch\n",
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"2. The type of phone you're using (iOS or Android)\n",
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"3. Whether you've already installed the companion app on your phone\n",
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"4. If you've tried pairing the devices before\n",
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"\n",
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"These details will help me provide you with the most accurate troubleshooting steps. In the meantime, here are some general tips that might help:\n",
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"- Make sure Bluetooth is enabled on your phone\n",
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"- Keep your smartwatch and phone within close range (within 3 feet) during pairing\n",
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"- Ensure both devices have sufficient battery power\n",
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"- Check if your phone's operating system meets the minimum requirements for the smartwatch\n",
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"\n",
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"Please provide the requested information, and I'll guide you through the specific steps to resolve the connection error.\n",
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"\n",
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"Is there anything else you'd like to share about the issue? \n",
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"\n",
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"\n",
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"Customer: The connection issue is still happening even after trying the steps you suggested.\n",
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"Support: I apologize that you're still experiencing connection issues with your smartwatch. I understand how frustrating it must be to have this problem persist even after trying the initial troubleshooting steps. Let's try some additional solutions to resolve this.\n",
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"\n",
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"Before we proceed, could you please confirm:\n",
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"1. Which specific steps you've already attempted?\n",
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"2. Are you seeing any particular error message?\n",
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"3. What model of smartwatch and phone are you using?\n",
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"\n",
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"This information will help me provide more targeted solutions and avoid suggesting steps you've already tried. In the meantime, here are a few advanced troubleshooting steps we can consider:\n",
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"\n",
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"1. Completely resetting the Bluetooth connection\n",
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"2. Checking for any software updates for both the watch and phone\n",
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"3. Testing the connection with a different mobile device to isolate the issue\n",
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"\n",
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"Would you be able to provide those details so I can better assist you? I'll make sure to document this ongoing issue to help track its resolution. \n",
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"\n",
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"\n",
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"Customer: exit\n",
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"Thank you for using our support service. Goodbye!\n"
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]
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}
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],
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"source": [
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"chatbot = SupportChatbot()\n",
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"user_id = \"customer_bot\"\n",
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"print(\"Welcome to Customer Support! Type 'exit' to end the conversation.\")\n",
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"\n",
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"while True:\n",
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" # Get user input\n",
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" query = input()\n",
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" print(\"Customer:\", query)\n",
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"\n",
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" # Check if user wants to exit\n",
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" if query.lower() == \"exit\":\n",
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" print(\"Thank you for using our support service. Goodbye!\")\n",
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" break\n",
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"\n",
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" # Handle the query and print the response\n",
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" response = chatbot.handle_customer_query(user_id, query)\n",
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" print(\"Support:\", response, \"\\n\\n\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@@ -0,0 +1,172 @@
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# Copyright (c) 2023 - 2024, Owners of https://github.com/autogen-ai
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#
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# SPDX-License-Identifier: Apache-2.0
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#
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# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
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# SPDX-License-Identifier: MIT
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# forked from autogen.agentchat.contrib.capabilities.teachability.Teachability
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from typing import Dict, Optional, Union
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from autogen.agentchat.assistant_agent import ConversableAgent
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from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
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from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
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from termcolor import colored
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from mem0 import Memory
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class Mem0Teachability(AgentCapability):
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def __init__(
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self,
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verbosity: Optional[int] = 0,
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reset_db: Optional[bool] = False,
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recall_threshold: Optional[float] = 1.5,
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max_num_retrievals: Optional[int] = 10,
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llm_config: Optional[Union[Dict, bool]] = None,
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agent_id: Optional[str] = None,
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memory_client: Optional[Memory] = None,
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):
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self.verbosity = verbosity
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self.recall_threshold = recall_threshold
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self.max_num_retrievals = max_num_retrievals
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self.llm_config = llm_config
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self.analyzer = None
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self.teachable_agent = None
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self.agent_id = agent_id
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self.memory = memory_client if memory_client else Memory()
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if reset_db:
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self.memory.reset()
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def add_to_agent(self, agent: ConversableAgent):
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self.teachable_agent = agent
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agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message)
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if self.llm_config is None:
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self.llm_config = agent.llm_config
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assert self.llm_config, "Teachability requires a valid llm_config."
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self.analyzer = TextAnalyzerAgent(llm_config=self.llm_config)
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agent.update_system_message(
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agent.system_message
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+ "\nYou've been given the special ability to remember user teachings from prior conversations."
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)
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def process_last_received_message(self, text: Union[Dict, str]):
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expanded_text = text
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if self.memory.get_all(filters={"agent_id": self.agent_id}):
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expanded_text = self._consider_memo_retrieval(text)
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self._consider_memo_storage(text)
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return expanded_text
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def _consider_memo_storage(self, comment: Union[Dict, str]):
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response = self._analyze(
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comment,
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"Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
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)
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if "yes" in response.lower():
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advice = self._analyze(
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comment,
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"Briefly copy any advice from the TEXT that may be useful for a similar but different task in the future. But if no advice is present, just respond with 'none'.",
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)
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if "none" not in advice.lower():
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task = self._analyze(
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comment,
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"Briefly copy just the task from the TEXT, then stop. Don't solve it, and don't include any advice.",
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)
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general_task = self._analyze(
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task,
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"Summarize very briefly, in general terms, the type of task described in the TEXT. Leave out details that might not appear in a similar problem.",
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)
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if self.verbosity >= 1:
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print(colored("\nREMEMBER THIS TASK-ADVICE PAIR", "light_yellow"))
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self.memory.add(
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[{"role": "user", "content": f"Task: {general_task}\nAdvice: {advice}"}], agent_id=self.agent_id
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)
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response = self._analyze(
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comment,
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"Does the TEXT contain information that could be committed to memory? Answer with just one word, yes or no.",
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)
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if "yes" in response.lower():
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question = self._analyze(
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comment,
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"Imagine that the user forgot this information in the TEXT. How would they ask you for this information? Include no other text in your response.",
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)
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answer = self._analyze(
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comment, "Copy the information from the TEXT that should be committed to memory. Add no explanation."
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)
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if self.verbosity >= 1:
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print(colored("\nREMEMBER THIS QUESTION-ANSWER PAIR", "light_yellow"))
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self.memory.add(
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[{"role": "user", "content": f"Question: {question}\nAnswer: {answer}"}], agent_id=self.agent_id
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)
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def _consider_memo_retrieval(self, comment: Union[Dict, str]):
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if self.verbosity >= 1:
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print(colored("\nLOOK FOR RELEVANT MEMOS, AS QUESTION-ANSWER PAIRS", "light_yellow"))
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memo_list = self._retrieve_relevant_memos(comment)
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response = self._analyze(
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comment,
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"Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
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)
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if "yes" in response.lower():
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if self.verbosity >= 1:
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print(colored("\nLOOK FOR RELEVANT MEMOS, AS TASK-ADVICE PAIRS", "light_yellow"))
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task = self._analyze(
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comment, "Copy just the task from the TEXT, then stop. Don't solve it, and don't include any advice."
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)
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general_task = self._analyze(
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task,
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"Summarize very briefly, in general terms, the type of task described in the TEXT. Leave out details that might not appear in a similar problem.",
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)
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memo_list.extend(self._retrieve_relevant_memos(general_task))
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memo_list = list(set(memo_list))
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return comment + self._concatenate_memo_texts(memo_list)
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def _retrieve_relevant_memos(self, input_text: str) -> list:
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search_results = self.memory.search(input_text, filters={"agent_id": self.agent_id}, top_k=self.max_num_retrievals)
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memo_list = [result["memory"] for result in search_results if result["score"] <= self.recall_threshold]
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if self.verbosity >= 1 and not memo_list:
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print(colored("\nTHE CLOSEST MEMO IS BEYOND THE THRESHOLD:", "light_yellow"))
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if search_results["results"]:
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print(search_results["results"][0])
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print()
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return memo_list
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def _concatenate_memo_texts(self, memo_list: list) -> str:
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memo_texts = ""
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if memo_list:
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info = "\n# Memories that might help\n"
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for memo in memo_list:
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info += f"- {memo}\n"
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if self.verbosity >= 1:
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print(colored(f"\nMEMOS APPENDED TO LAST MESSAGE...\n{info}\n", "light_yellow"))
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memo_texts += "\n" + info
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return memo_texts
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def _analyze(self, text_to_analyze: Union[Dict, str], analysis_instructions: Union[Dict, str]):
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self.analyzer.reset()
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self.teachable_agent.send(
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recipient=self.analyzer, message=text_to_analyze, request_reply=False, silent=(self.verbosity < 2)
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)
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self.teachable_agent.send(
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recipient=self.analyzer, message=analysis_instructions, request_reply=True, silent=(self.verbosity < 2)
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)
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return self.teachable_agent.last_message(self.analyzer)["content"]
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