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update quickstart
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Dhravya Shah
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apps/docs/user-profiles/examples.mdx
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apps/docs/user-profiles/examples.mdx
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---
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title: "Profile Examples"
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description: "Complete code examples for integrating user profiles"
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sidebarTitle: "Examples"
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icon: "laptop-code"
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---
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## Building a Personalized Prompt
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The most common use case: inject profile data into your LLM's system prompt.
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<CodeGroup>
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```typescript TypeScript
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async function handleChatMessage(userId: string, message: string) {
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// Get user profile
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const profileResponse = await fetch('https://api.supermemory.ai/v4/profile', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({ containerTag: userId })
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});
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const { profile } = await profileResponse.json();
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// Build personalized system prompt
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const systemPrompt = `You are assisting a user with the following context:
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ABOUT THE USER:
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${profile.static?.join('\n') || 'No profile information yet.'}
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CURRENT CONTEXT:
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${profile.dynamic?.join('\n') || 'No recent activity.'}
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Provide responses personalized to their expertise level and preferences.`;
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// Send to your LLM
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const response = await llm.chat({
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messages: [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: message }
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]
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});
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return response;
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}
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```
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```python Python
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import requests
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import os
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async def handle_chat_message(user_id: str, message: str):
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# Get user profile
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response = requests.post(
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'https://api.supermemory.ai/v4/profile',
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headers={
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'Authorization': f'Bearer {os.getenv("SUPERMEMORY_API_KEY")}',
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'Content-Type': 'application/json'
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},
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json={'containerTag': user_id}
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)
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profile = response.json()['profile']
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# Build personalized system prompt
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static_facts = '\n'.join(profile.get('static', ['No profile information yet.']))
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dynamic_context = '\n'.join(profile.get('dynamic', ['No recent activity.']))
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system_prompt = f"""You are assisting a user with the following context:
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ABOUT THE USER:
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{static_facts}
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CURRENT CONTEXT:
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{dynamic_context}
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Provide responses personalized to their expertise level and preferences."""
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# Send to your LLM
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llm_response = await llm.chat(
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": message}
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]
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)
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return llm_response
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```
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</CodeGroup>
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## Full Context Mode
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Combine profile data with query-specific search for comprehensive context:
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<CodeGroup>
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```typescript TypeScript
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async function getFullContext(userId: string, userQuery: string) {
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// Single call gets both profile and search results
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const response = await fetch('https://api.supermemory.ai/v4/profile', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({
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containerTag: userId,
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q: userQuery // Include the user's query
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})
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});
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const data = await response.json();
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return {
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// Static background about the user
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userBackground: data.profile.static,
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// Current activities and context
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currentContext: data.profile.dynamic,
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// Query-specific memories
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relevantMemories: data.searchResults?.results || []
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};
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}
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// Usage
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const context = await getFullContext('user_123', 'deployment error last week');
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const systemPrompt = `
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User Background:
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${context.userBackground.join('\n')}
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Current Context:
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${context.currentContext.join('\n')}
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Relevant Information:
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${context.relevantMemories.map(m => m.content).join('\n')}
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`;
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```
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```python Python
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async def get_full_context(user_id: str, user_query: str):
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# Single call gets both profile and search results
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response = requests.post(
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'https://api.supermemory.ai/v4/profile',
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headers={
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'Authorization': f'Bearer {os.getenv("SUPERMEMORY_API_KEY")}',
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'Content-Type': 'application/json'
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},
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json={
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'containerTag': user_id,
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'q': user_query # Include the user's query
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}
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)
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data = response.json()
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return {
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'user_background': data['profile'].get('static', []),
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'current_context': data['profile'].get('dynamic', []),
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'relevant_memories': data.get('searchResults', {}).get('results', [])
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}
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# Usage
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context = await get_full_context('user_123', 'deployment error last week')
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system_prompt = f"""
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User Background:
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{chr(10).join(context['user_background'])}
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Current Context:
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{chr(10).join(context['current_context'])}
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Relevant Information:
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{chr(10).join(m['content'] for m in context['relevant_memories'])}
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"""
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```
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</CodeGroup>
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## Separate Profile and Search
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For more control, you can call profile and search endpoints separately:
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```typescript TypeScript
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async function advancedContext(userId: string, query: string) {
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// Parallel requests for profile and search
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const [profileRes, searchRes] = await Promise.all([
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fetch('https://api.supermemory.ai/v4/profile', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({ containerTag: userId })
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}),
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fetch('https://api.supermemory.ai/v3/search', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({
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q: query,
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containerTag: userId,
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limit: 5
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})
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})
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]);
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const profile = await profileRes.json();
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const search = await searchRes.json();
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return { profile: profile.profile, searchResults: search.results };
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}
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```
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## Express.js Middleware
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Add profile context to all authenticated requests:
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```typescript TypeScript
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import express from 'express';
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// Middleware to fetch user profile
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async function withUserProfile(req, res, next) {
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if (!req.user?.id) {
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return next();
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}
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try {
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const response = await fetch('https://api.supermemory.ai/v4/profile', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({ containerTag: req.user.id })
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});
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req.userProfile = await response.json();
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} catch (error) {
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console.error('Failed to fetch profile:', error);
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req.userProfile = null;
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}
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next();
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}
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const app = express();
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// Apply to all routes
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app.use(withUserProfile);
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app.post('/chat', async (req, res) => {
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const { message } = req.body;
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// Profile is automatically available
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const profile = req.userProfile?.profile;
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// Use in your LLM call...
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});
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```
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## Next.js API Route
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```typescript TypeScript
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// app/api/chat/route.ts
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import { NextRequest, NextResponse } from 'next/server';
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export async function POST(req: NextRequest) {
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const { userId, message } = await req.json();
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// Fetch profile
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const profileRes = await fetch('https://api.supermemory.ai/v4/profile', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${process.env.SUPERMEMORY_API_KEY}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({ containerTag: userId })
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});
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const { profile } = await profileRes.json();
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// Build context and call your LLM...
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const response = await generateResponse(message, profile);
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return NextResponse.json({ response });
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}
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```
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## AI SDK Integration
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For the cleanest integration, use the Supermemory AI SDK middleware:
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```typescript TypeScript
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import { generateText } from "ai"
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import { withSupermemory } from "@supermemory/tools/ai-sdk"
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import { openai } from "@ai-sdk/openai"
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// One line setup - profiles automatically injected
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const model = withSupermemory(openai("gpt-4"), "user-123")
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const result = await generateText({
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model,
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messages: [{ role: "user", content: "Help me with my current project" }]
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})
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// Model automatically has access to user's profile!
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```
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<Card title="AI SDK User Profiles" icon="triangle" href="/ai-sdk/user-profiles">
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Learn more about automatic profile injection with the AI SDK
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</Card>
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