Fix LLM config and Doc update for anthropic (#1983)
This commit is contained in:
Dev Khant
261
cookbooks/customer-support-chatbot.ipynb
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261
cookbooks/customer-support-chatbot.ipynb
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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,\n",
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" user_id: str,\n",
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" message: str,\n",
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" response: str,\n",
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" 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 = [\n",
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" {\"role\": \"user\", \"content\": message},\n",
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" {\"role\": \"assistant\", \"content\": response}\n",
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" ]\n",
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"\n",
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" # Store in Mem0\n",
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" self.memory.add(\n",
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" conversation,\n",
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" user_id=user_id,\n",
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" metadata=metadata\n",
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" )\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,\n",
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" message=query,\n",
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" response=response,\n",
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" 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_91762/3000273332.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_91762/3000273332.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 and if it's the latest version\n",
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"\n",
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"In the meantime, here are some general troubleshooting steps you can try:\n",
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"1. Ensure Bluetooth is enabled on your phone\n",
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"2. Restart both your smartwatch and phone\n",
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"3. Make sure your smartwatch is sufficiently charged\n",
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"4. Check if your phone's operating system is up to date\n",
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"\n",
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"Once you provide those additional details, I can give you more specific guidance to resolve the connection error. Would you mind sharing that information? \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 even after trying the previous troubleshooting steps. I understand how frustrating this must be for you. Let's try some additional steps to resolve this persistent problem.\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 on the screen?\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 couple of additional troubleshooting steps we can try:\n",
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"\n",
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"1. Completely reset the Bluetooth connection:\n",
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" - Turn off Bluetooth on your phone\n",
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" - Restart both your phone and smartwatch\n",
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" - Turn Bluetooth back on and attempt to pair again\n",
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"\n",
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"2. Check if your smartwatch firmware is up to date\n",
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"\n",
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"Would you be able to try these steps and let me know the results? I'll stay with you until we resolve this issue. \n",
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"\n",
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"\n",
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"Customer: \n"
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]
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},
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{
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"ename": "BadRequestError",
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"evalue": "Error code: 400 - {'error': {'message': \"'$.input' is invalid. Please check the API reference: https://platform.openai.com/docs/api-reference.\", 'type': 'invalid_request_error', 'param': None, 'code': None}}",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mBadRequestError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Handle the query and print the response\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mchatbot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_customer_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43muser_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSupport:\u001b[39m\u001b[38;5;124m\"\u001b[39m, response, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
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"Cell \u001b[0;32mIn[2], line 65\u001b[0m, in \u001b[0;36mSupportChatbot.handle_customer_query\u001b[0;34m(self, user_id, query)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Process customer query with context from past interactions.\"\"\"\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Get relevant past interactions\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m relevant_history \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_relevant_history\u001b[49m\u001b[43m(\u001b[49m\u001b[43muser_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# Build context from relevant history\u001b[39;00m\n\u001b[1;32m 68\u001b[0m context \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrevious relevant interactions:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
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"Cell \u001b[0;32mIn[2], line 55\u001b[0m, in \u001b[0;36mSupportChatbot.get_relevant_history\u001b[0;34m(self, user_id, query)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_relevant_history\u001b[39m(\u001b[38;5;28mself\u001b[39m, user_id: \u001b[38;5;28mstr\u001b[39m, query: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Dict]:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Retrieve relevant past interactions.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmemory\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Adjust based on needs\u001b[39;49;00m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/mem0/memory/main.py:404\u001b[0m, in \u001b[0;36mMemory.search\u001b[0;34m(self, query, user_id, agent_id, run_id, limit, filters)\u001b[0m\n\u001b[1;32m 396\u001b[0m future_graph_entities \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 397\u001b[0m executor\u001b[38;5;241m.\u001b[39msubmit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39msearch, query, filters, limit)\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv1.1\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menable_graph\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 400\u001b[0m )\n\u001b[1;32m 402\u001b[0m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mwait([future_memories, future_graph_entities] \u001b[38;5;28;01mif\u001b[39;00m future_graph_entities \u001b[38;5;28;01melse\u001b[39;00m [future_memories])\n\u001b[0;32m--> 404\u001b[0m original_memories \u001b[38;5;241m=\u001b[39m \u001b[43mfuture_memories\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m graph_entities \u001b[38;5;241m=\u001b[39m future_graph_entities\u001b[38;5;241m.\u001b[39mresult() \u001b[38;5;28;01mif\u001b[39;00m future_graph_entities \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv1.1\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
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"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
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||||
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/mem0/memory/main.py:423\u001b[0m, in \u001b[0;36mMemory._search_vector_store\u001b[0;34m(self, query, filters, limit)\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_search_vector_store\u001b[39m(\u001b[38;5;28mself\u001b[39m, query, filters, limit):\n\u001b[0;32m--> 423\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membedding_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 424\u001b[0m memories \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvector_store\u001b[38;5;241m.\u001b[39msearch(query\u001b[38;5;241m=\u001b[39membeddings, limit\u001b[38;5;241m=\u001b[39mlimit, filters\u001b[38;5;241m=\u001b[39mfilters)\n\u001b[1;32m 426\u001b[0m excluded_keys \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 427\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser_id\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 428\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magent_id\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupdated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 434\u001b[0m }\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/mem0/embeddings/openai.py:32\u001b[0m, in \u001b[0;36mOpenAIEmbedding.embed\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03mGet the embedding for the given text using OpenAI.\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;124;03m list: The embedding vector.\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 31\u001b[0m text \u001b[38;5;241m=\u001b[39m text\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39membedding\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/openai/resources/embeddings.py:124\u001b[0m, in \u001b[0;36mEmbeddings.create\u001b[0;34m(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m 118\u001b[0m embedding\u001b[38;5;241m.\u001b[39membedding \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer( \u001b[38;5;66;03m# type: ignore[no-untyped-call]\u001b[39;00m\n\u001b[1;32m 119\u001b[0m base64\u001b[38;5;241m.\u001b[39mb64decode(data), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 120\u001b[0m )\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n\u001b[0;32m--> 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 125\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/embeddings\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmbeddingCreateParams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_parser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCreateEmbeddingResponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/openai/_base_client.py:1270\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m 1256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m 1257\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1258\u001b[0m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1265\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1266\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m 1267\u001b[0m opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m 1268\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m 1269\u001b[0m )\n\u001b[0;32m-> 1270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/openai/_base_client.py:947\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 945\u001b[0m retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 948\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 949\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 950\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 951\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 952\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 953\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/mem0/.venv/lib/python3.12/site-packages/openai/_base_client.py:1051\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m 1048\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 1050\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1051\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m 1054\u001b[0m cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m 1055\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1059\u001b[0m retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[1;32m 1060\u001b[0m )\n",
|
||||
"\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"'$.input' is invalid. Please check the API reference: https://platform.openai.com/docs/api-reference.\", 'type': 'invalid_request_error', 'param': None, 'code': None}}"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chatbot = SupportChatbot()\n",
|
||||
"user_id = \"customer_bot\"\n",
|
||||
"print(\"Welcome to Customer Support! Type 'exit' to end the conversation.\")\n",
|
||||
"\n",
|
||||
"while True:\n",
|
||||
" # Get user input\n",
|
||||
" query = input()\n",
|
||||
" print(\"Customer:\", query)\n",
|
||||
" \n",
|
||||
" # Check if user wants to exit\n",
|
||||
" if query.lower() == 'exit':\n",
|
||||
" print(\"Thank you for using our support service. Goodbye!\")\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
" # Handle the query and print the response\n",
|
||||
" response = chatbot.handle_customer_query(user_id, query)\n",
|
||||
" print(\"Support:\", response, \"\\n\\n\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"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": "3.12.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -13,7 +13,7 @@ config = {
|
||||
"llm": {
|
||||
"provider": "anthropic",
|
||||
"config": {
|
||||
"model": "claude-3-opus-20240229",
|
||||
"model": "claude-3-5-sonnet-latest",
|
||||
"temperature": 0.1,
|
||||
"max_tokens": 2000,
|
||||
}
|
||||
|
||||
@@ -2,6 +2,10 @@
|
||||
title: Overview
|
||||
---
|
||||
|
||||
<Note type="info">
|
||||
🎉 Exciting news! Mem0 now supports all the latest Claude models.
|
||||
</Note>
|
||||
|
||||
[Mem0](https://mem0.dev/wd) (pronounced "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences and traits and continuously updates over time, making it ideal for applications like customer support chatbots and AI assistants.
|
||||
|
||||
Mem0 offers two powerful ways to leverage our technology: our [managed platform](/platform/overview) and our [open source solution](/open-source/quickstart).
|
||||
|
||||
@@ -13,6 +13,7 @@ class LlmConfig(BaseModel):
|
||||
if provider in (
|
||||
"openai",
|
||||
"ollama",
|
||||
"anthropic",
|
||||
"groq",
|
||||
"together",
|
||||
"aws_bedrock",
|
||||
|
||||
Reference in New Issue
Block a user