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docs: override install md file (#675)
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MaheshtheDev
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apps/docs/install.md
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apps/docs/install.md
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You are integrating Supermemory into my application. Supermemory provides user memory, semantic search, and automatic knowledge extraction for AI applications.
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<Note>
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You can always reference the documentation by using the **SearchSupermemoryDocs MCP** or running a web search tool for content on **supermemory.ai/docs**.
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</Note>
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## STEP 1: ASK ME THESE QUESTIONS
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1. What are you building?
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- Personal chatbot/assistant
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- Team knowledge base
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- Customer support bot
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- Document Q&A
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- Other
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2. How do you want to integrate?
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- Vercel AI SDK (@supermemory/tools)
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- OpenAI plugins
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- Direct SDK (supermemory npm/pip)
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- Direct API calls
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3. Data model?
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- Individual users only → containerTag: userId
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- Organizations only → containerTag: orgId
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- Both users AND orgs → ask for strategy
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4. Do you want USER PROFILES?
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User profiles are automatically-maintained facts about users (what they like, what they're working on, preferences).
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- Yes (RECOMMENDED) → Use client.profile() to get context
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- No → Just use search
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5. How should I retrieve context?
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- OPTION A: One call with search included → profile({ containerTag, q: userMessage })
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- OPTION B: Separate calls → profile() for facts, search() for memories
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## STEP 2: INSTALL
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```bash
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# Get API key: https://console.supermemory.ai
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npm install supermemory # or: pip install supermemory
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# For Vercel AI SDK: npm install @supermemory/tools
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export SUPERMEMORY_API_KEY="sm_..."
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```
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## STEP 3: CONFIGURE SETTINGS (DO THIS FIRST)
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```typescript
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// PATCH https://api.supermemory.ai/v3/settings
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fetch('https://api.supermemory.ai/v3/settings', {
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method: 'PATCH',
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headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
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body: JSON.stringify({
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shouldLLMFilter: true,
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filterPrompt: `This is a [your app description]. containerTag is [userId/orgId]. We store [what data].`
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})
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})
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```
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## STEP 4: CONTAINER TAG STRATEGY
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Based on their data model answer:
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**USER-ONLY APP:**
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```typescript
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containerTag: userId
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```
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**ORG-ONLY APP:**
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```typescript
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containerTag: orgId // Org members share memories
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```
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**BOTH (ask which):**
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- Option A: `containerTag: \`\${userId}-\${orgId}\``
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- Option B: `containerTag: orgId, metadata: { userId }`
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- Option C: `containerTag: userId, metadata: { orgId }`
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## STEP 5: INTEGRATION CODE
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Based on their integration choice:
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### VERCEL AI SDK
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```typescript
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import { streamText } from 'ai'
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import { anthropic } from '@ai-sdk/anthropic'
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import { supermemoryTools } from '@supermemory/tools/ai-sdk'
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// Option 1: Agent tools (recommended for agentic flows)
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const result = await streamText({
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model: anthropic('claude-3-5-sonnet-20241022'),
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prompt: userMessage,
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tools: supermemoryTools(process.env.SUPERMEMORY_API_KEY, {
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containerTags: [userId]
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})
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})
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// Agent gets searchMemories, addMemory, fetchMemory tools
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// Option 2: Profile middleware (automatic context injection)
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import { withSupermemory } from '@supermemory/tools/ai-sdk'
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const modelWithMemory = withSupermemory(anthropic('claude-3-5-sonnet-20241022'), userId)
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const result = await generateText({
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model: modelWithMemory,
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messages: [{ role: 'user', content: userMessage }]
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})
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// Profile is automatically injected into context
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```
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### DIRECT SDK (WITH PROFILES)
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```typescript
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import Supermemory from 'supermemory'
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const client = new Supermemory()
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// Before each LLM call:
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const { profile, searchResults } = await client.profile({
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containerTag: userId,
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q: userMessage // Include this if they chose OPTION A (one call)
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// Omit if they chose OPTION B (separate calls)
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})
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// Build context
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const context = `
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Static facts: ${profile.static.join('\n')}
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Recent context: ${profile.dynamic.join('\n')}
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${searchResults ? `Memories: ${searchResults.results.map(r => r.content).join('\n')}` : ''}
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`
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// Send to LLM
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const messages = [
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{ role: 'system', content: `User context:\n${context}` },
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{ role: 'user', content: userMessage }
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]
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// After LLM responds:
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await client.memories.add({
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content: `user: ${userMessage}\nassistant: ${response}`,
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containerTag: userId
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})
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```
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### DIRECT SDK (NO PROFILES)
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```typescript
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import Supermemory from 'supermemory'
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const client = new Supermemory()
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// Search for relevant memories
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const results = await client.search({
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q: userMessage,
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containerTag: userId,
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searchMode: 'hybrid', // Searches memories + document chunks
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limit: 5
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})
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// Build context
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const context = results.results.map(r => r.content).join('\n')
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// Send to LLM with context
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const messages = [
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{ role: 'system', content: `Relevant context:\n${context}` },
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{ role: 'user', content: userMessage }
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]
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// Store the conversation
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await client.memories.add({
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content: `user: ${userMessage}\nassistant: ${response}`,
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containerTag: userId
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})
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```
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### PYTHON VERSION
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```python
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from supermemory import Supermemory
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client = Supermemory()
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# With profiles (if they want it)
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profile_data = client.profile(
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container_tag=user_id,
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q=user_message # Include if OPTION A, omit if OPTION B
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)
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context = f"""
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Static: {chr(10).join(profile_data.profile.static)}
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Dynamic: {chr(10).join(profile_data.profile.dynamic)}
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"""
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# Store conversation
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client.add(content=f"user: {user_message}\\nassistant: {response}", container_tag=user_id)
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```
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### DIRECT API
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```bash
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# Add memory
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curl -X POST https://api.supermemory.ai/v3/documents \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"content": "conversation", "containerTag": "userId"}'
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# Get profile
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curl -X POST https://api.supermemory.ai/v4/profile \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"containerTag": "userId", "q": "search query"}'
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# Search
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curl -X POST https://api.supermemory.ai/v4/search \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"q": "query", "containerTag": "userId", "searchMode": "hybrid"}'
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```
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## STEP 6: FILE UPLOADS (if they need it)
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```typescript
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// Files are automatically extracted (PDFs, images with OCR, videos with transcription)
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const formData = new FormData()
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formData.append('file', fileBlob)
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formData.append('containerTag', userId)
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await fetch('https://api.supermemory.ai/v3/documents/file', {
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method: 'POST',
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headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
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body: formData
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})
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// Processing is async - check status before assuming searchable
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// GET /v3/documents/{documentId}
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```
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## STEP 7: SEARCH MODES
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```typescript
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// HYBRID (recommended) - searches memories + document chunks
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searchMode: 'hybrid'
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// MEMORIES ONLY - just extracted memories, no original text
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searchMode: 'memories'
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```
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## STEP 8: METADATA FILTERS (if they need secondary filtering)
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```typescript
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await client.search({
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q: query,
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containerTag: userId,
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filters: {
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AND: [
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{ key: 'type', value: 'conversation', type: 'string_equal' },
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{ key: 'timestamp', value: '2024', type: 'string_contains' }
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]
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}
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})
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```
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## KEY POINTS:
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1. Configure settings FIRST with filterPrompt
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2. User profiles = automatic facts about users (profile.static + profile.dynamic)
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3. profile({ containerTag, q }) combines profile + search in ONE call
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4. Search modes: 'hybrid' (recommended) or 'memories'
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5. File extraction is automatic - no config needed
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6. Store conversations after each interaction
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7. containerTag should match what you put in filterPrompt
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## TESTING:
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```bash
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# 1. Configure settings
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curl -X PATCH https://api.supermemory.ai/v3/settings \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"shouldLLMFilter": true, "filterPrompt": "..."}'
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# 2. Add test memory
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curl -X POST https://api.supermemory.ai/v3/documents \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"content": "Test", "containerTag": "test_user"}'
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# 3. Get profile
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curl -X POST https://api.supermemory.ai/v4/profile \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"containerTag": "test_user"}'
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```
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## NOW:
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1. Ask me the 5 questions above
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2. Generate complete working code based on my answers
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3. Include installation, settings config, and full integration
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**DOCS:** https://supermemory.ai/docs
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