Integrate self-hosted Supabase with mem0 system
- Configure mem0 to use self-hosted Supabase instead of Qdrant for vector storage - Update docker-compose to connect containers to localai network - Install vecs library for Supabase pgvector integration - Create comprehensive test suite for Supabase + mem0 integration - Update documentation to reflect Supabase configuration - All containers now connected to shared localai network - Successful vector storage and retrieval tests completed 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Docker Config Backup
189
docs/api-reference/introduction.mdx
Normal file
189
docs/api-reference/introduction.mdx
Normal file
@@ -0,0 +1,189 @@
|
||||
---
|
||||
title: 'API Reference'
|
||||
description: 'Complete API documentation for the Mem0 Memory System'
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The Mem0 Memory System provides a comprehensive REST API for memory operations, built on top of the mem0 framework with enhanced local-first capabilities.
|
||||
|
||||
<Note>
|
||||
**Current Status**: Phase 1 Complete - Core infrastructure ready for API development
|
||||
</Note>
|
||||
|
||||
## Base URL
|
||||
|
||||
```
|
||||
http://localhost:8080/v1
|
||||
```
|
||||
|
||||
## Authentication
|
||||
|
||||
All API requests require authentication using API keys:
|
||||
|
||||
```bash
|
||||
curl -H "Authorization: Bearer YOUR_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
http://localhost:8080/v1/memories
|
||||
```
|
||||
|
||||
## Core Endpoints
|
||||
|
||||
### Memory Operations
|
||||
|
||||
| Method | Endpoint | Description |
|
||||
|--------|----------|-------------|
|
||||
| `POST` | `/memories` | Add new memory |
|
||||
| `GET` | `/memories/search` | Search memories |
|
||||
| `GET` | `/memories/{id}` | Get specific memory |
|
||||
| `PUT` | `/memories/{id}` | Update memory |
|
||||
| `DELETE` | `/memories/{id}` | Delete memory |
|
||||
| `GET` | `/memories/user/{user_id}` | Get user memories |
|
||||
|
||||
### Health & Status
|
||||
|
||||
| Method | Endpoint | Description |
|
||||
|--------|----------|-------------|
|
||||
| `GET` | `/health` | System health check |
|
||||
| `GET` | `/status` | Detailed system status |
|
||||
| `GET` | `/metrics` | Performance metrics |
|
||||
|
||||
## Request/Response Format
|
||||
|
||||
### Standard Response Structure
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
// Response data
|
||||
},
|
||||
"message": "Operation completed successfully",
|
||||
"timestamp": "2025-07-30T20:15:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
### Error Response Structure
|
||||
|
||||
```json
|
||||
{
|
||||
"success": false,
|
||||
"error": {
|
||||
"code": "MEMORY_NOT_FOUND",
|
||||
"message": "Memory with ID 'abc123' not found",
|
||||
"details": {}
|
||||
},
|
||||
"timestamp": "2025-07-30T20:15:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
## Memory Object
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "mem_abc123def456",
|
||||
"content": "User loves building AI applications with local models",
|
||||
"user_id": "user_789",
|
||||
"metadata": {
|
||||
"source": "chat",
|
||||
"timestamp": "2025-07-30T20:15:00Z",
|
||||
"entities": ["AI", "applications", "local models"]
|
||||
},
|
||||
"embedding": [0.1, 0.2, 0.3, ...],
|
||||
"relationships": [
|
||||
{
|
||||
"type": "mentions",
|
||||
"entity": "AI applications",
|
||||
"confidence": 0.95
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
The API behavior can be configured through environment variables:
|
||||
|
||||
```bash
|
||||
# API Configuration
|
||||
API_PORT=8080
|
||||
API_HOST=localhost
|
||||
API_KEY=your_secure_api_key
|
||||
|
||||
# Memory Configuration
|
||||
MAX_MEMORY_SIZE=1000000
|
||||
SEARCH_LIMIT=50
|
||||
DEFAULT_USER_ID=default
|
||||
```
|
||||
|
||||
## Rate Limiting
|
||||
|
||||
The API implements rate limiting to ensure fair usage:
|
||||
|
||||
- **Default**: 100 requests per minute per API key
|
||||
- **Burst**: Up to 20 requests in 10 seconds
|
||||
- **Headers**: Rate limit info included in response headers
|
||||
|
||||
```
|
||||
X-RateLimit-Limit: 100
|
||||
X-RateLimit-Remaining: 95
|
||||
X-RateLimit-Reset: 1627849200
|
||||
```
|
||||
|
||||
## Error Codes
|
||||
|
||||
| Code | HTTP Status | Description |
|
||||
|------|-------------|-------------|
|
||||
| `INVALID_REQUEST` | 400 | Malformed request |
|
||||
| `UNAUTHORIZED` | 401 | Invalid or missing API key |
|
||||
| `FORBIDDEN` | 403 | Insufficient permissions |
|
||||
| `MEMORY_NOT_FOUND` | 404 | Memory does not exist |
|
||||
| `RATE_LIMIT_EXCEEDED` | 429 | Too many requests |
|
||||
| `INTERNAL_ERROR` | 500 | Server error |
|
||||
|
||||
## SDK Support
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Python SDK" icon="python">
|
||||
```python
|
||||
from mem0_client import MemoryClient
|
||||
client = MemoryClient(api_key="your_key")
|
||||
```
|
||||
</Card>
|
||||
<Card title="JavaScript SDK" icon="js">
|
||||
```javascript
|
||||
import { MemoryClient } from '@mem0/client';
|
||||
const client = new MemoryClient({ apiKey: 'your_key' });
|
||||
```
|
||||
</Card>
|
||||
<Card title="cURL Examples" icon="terminal">
|
||||
Complete cURL examples for all endpoints
|
||||
</Card>
|
||||
<Card title="Postman Collection" icon="api">
|
||||
Import ready-to-use Postman collection
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Development Status
|
||||
|
||||
<Warning>
|
||||
**In Development**: The API is currently in Phase 2 development. Core infrastructure (Phase 1) is complete and ready for API implementation.
|
||||
</Warning>
|
||||
|
||||
### Completed ✅
|
||||
- Core mem0 integration
|
||||
- Database connections (Neo4j, Qdrant)
|
||||
- LLM provider support (Ollama, OpenAI)
|
||||
- Configuration management
|
||||
|
||||
### In Progress 🚧
|
||||
- REST API endpoints
|
||||
- Authentication system
|
||||
- Rate limiting
|
||||
- Error handling
|
||||
|
||||
### Planned 📋
|
||||
- SDK development
|
||||
- API documentation
|
||||
- Performance optimization
|
||||
- Monitoring and logging
|
||||
76
docs/development.mdx
Normal file
76
docs/development.mdx
Normal file
@@ -0,0 +1,76 @@
|
||||
---
|
||||
title: 'Development Guide'
|
||||
description: 'Complete development environment setup and workflow'
|
||||
---
|
||||
|
||||
## Development Environment
|
||||
|
||||
### Project Structure
|
||||
|
||||
```
|
||||
/home/klas/mem0/
|
||||
├── venv/ # Python virtual environment
|
||||
├── config.py # Configuration management
|
||||
├── test_basic.py # Basic functionality tests
|
||||
├── test_openai.py # OpenAI integration test
|
||||
├── test_all_connections.py # Comprehensive connection tests
|
||||
├── docker-compose.yml # Neo4j & Qdrant containers
|
||||
├── .env # Environment variables
|
||||
└── docs/ # Documentation (Mintlify)
|
||||
```
|
||||
|
||||
### Current Status: Phase 1 Complete ✅
|
||||
|
||||
| Component | Status | Port | Description |
|
||||
|-----------|--------|------|-------------|
|
||||
| Neo4j | ✅ READY | 7474/7687 | Graph memory storage |
|
||||
| Qdrant | ✅ READY | 6333/6334 | Vector memory storage |
|
||||
| Ollama | ✅ READY | 11434 | Local LLM processing |
|
||||
| Mem0 Core | ✅ READY | - | Memory management system v0.1.115 |
|
||||
|
||||
### Development Workflow
|
||||
|
||||
1. **Environment Setup**
|
||||
```bash
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
2. **Start Services**
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
3. **Run Tests**
|
||||
```bash
|
||||
python test_all_connections.py
|
||||
```
|
||||
|
||||
4. **Development**
|
||||
- Edit code and configurations
|
||||
- Test changes with provided test scripts
|
||||
- Document changes in this documentation
|
||||
|
||||
### Next Development Phases
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Phase 2: Core Memory System">
|
||||
- Ollama integration
|
||||
- Basic memory operations
|
||||
- Neo4j graph memory
|
||||
</Card>
|
||||
<Card title="Phase 3: API Development">
|
||||
- REST API endpoints
|
||||
- Authentication layer
|
||||
- Performance optimization
|
||||
</Card>
|
||||
<Card title="Phase 4: MCP Server">
|
||||
- HTTP transport protocol
|
||||
- Claude Code integration
|
||||
- Standardized operations
|
||||
</Card>
|
||||
<Card title="Phase 5: Documentation">
|
||||
- Complete API reference
|
||||
- Deployment guides
|
||||
- Integration examples
|
||||
</Card>
|
||||
</CardGroup>
|
||||
151
docs/essentials/architecture.mdx
Normal file
151
docs/essentials/architecture.mdx
Normal file
@@ -0,0 +1,151 @@
|
||||
---
|
||||
title: 'Architecture Overview'
|
||||
description: 'Understanding the Mem0 Memory System architecture and components'
|
||||
---
|
||||
|
||||
## System Architecture
|
||||
|
||||
The Mem0 Memory System follows a modular, local-first architecture designed for maximum privacy, performance, and control.
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
A[AI Applications] --> B[MCP Server - Port 8765]
|
||||
B --> C[Memory API - Port 8080]
|
||||
C --> D[Mem0 Core v0.1.115]
|
||||
D --> E[Vector Store - Qdrant]
|
||||
D --> F[Graph Store - Neo4j]
|
||||
D --> G[LLM Provider]
|
||||
G --> H[Ollama - Port 11434]
|
||||
G --> I[OpenAI/Remote APIs]
|
||||
E --> J[Qdrant - Port 6333]
|
||||
F --> K[Neo4j - Port 7687]
|
||||
```
|
||||
|
||||
## Core Components
|
||||
|
||||
### Memory Layer (Mem0 Core)
|
||||
- **Version**: 0.1.115
|
||||
- **Purpose**: Central memory management and coordination
|
||||
- **Features**: Memory operations, provider abstraction, configuration management
|
||||
|
||||
### Vector Storage (Qdrant)
|
||||
- **Port**: 6333 (REST), 6334 (gRPC)
|
||||
- **Purpose**: High-performance vector search and similarity matching
|
||||
- **Features**: Collections management, semantic search, embeddings storage
|
||||
|
||||
### Graph Storage (Neo4j)
|
||||
- **Port**: 7474 (HTTP), 7687 (Bolt)
|
||||
- **Version**: 5.23.0
|
||||
- **Purpose**: Entity relationships and contextual memory connections
|
||||
- **Features**: Knowledge graph, relationship mapping, graph queries
|
||||
|
||||
### LLM Providers
|
||||
|
||||
#### Ollama (Local)
|
||||
- **Port**: 11434
|
||||
- **Models Available**: 21+ including Llama, Qwen, embeddings
|
||||
- **Benefits**: Privacy, cost control, offline operation
|
||||
|
||||
#### OpenAI (Remote)
|
||||
- **API**: External service
|
||||
- **Models**: GPT-4, embeddings
|
||||
- **Benefits**: State-of-the-art performance, reliability
|
||||
|
||||
## Data Flow
|
||||
|
||||
### Memory Addition
|
||||
1. **Input**: User messages or content
|
||||
2. **Processing**: LLM extracts facts and relationships
|
||||
3. **Storage**:
|
||||
- Facts stored as vectors in Qdrant
|
||||
- Relationships stored as graph in Neo4j
|
||||
4. **Indexing**: Content indexed for fast retrieval
|
||||
|
||||
### Memory Retrieval
|
||||
1. **Query**: Semantic search query
|
||||
2. **Vector Search**: Qdrant finds similar memories
|
||||
3. **Graph Traversal**: Neo4j provides contextual relationships
|
||||
4. **Ranking**: Combined scoring and relevance
|
||||
5. **Response**: Structured memory results
|
||||
|
||||
## Configuration Architecture
|
||||
|
||||
### Environment Management
|
||||
```bash
|
||||
# Core Services
|
||||
NEO4J_URI=bolt://localhost:7687
|
||||
QDRANT_URL=http://localhost:6333
|
||||
OLLAMA_BASE_URL=http://localhost:11434
|
||||
|
||||
# Provider Selection
|
||||
LLM_PROVIDER=ollama # or openai
|
||||
VECTOR_STORE=qdrant
|
||||
GRAPH_STORE=neo4j
|
||||
```
|
||||
|
||||
### Provider Abstraction
|
||||
The system supports multiple providers through a unified interface:
|
||||
|
||||
- **LLM Providers**: OpenAI, Ollama, Anthropic, etc.
|
||||
- **Vector Stores**: Qdrant, Pinecone, Weaviate, etc.
|
||||
- **Graph Stores**: Neo4j, Amazon Neptune, etc.
|
||||
|
||||
## Security Architecture
|
||||
|
||||
### Local-First Design
|
||||
- All data stored locally
|
||||
- No external dependencies required
|
||||
- Full control over data processing
|
||||
|
||||
### Authentication Layers
|
||||
- API key management
|
||||
- Rate limiting
|
||||
- Access control per user/application
|
||||
|
||||
### Network Security
|
||||
- Services bound to localhost by default
|
||||
- Configurable network policies
|
||||
- TLS support for remote connections
|
||||
|
||||
## Scalability Considerations
|
||||
|
||||
### Horizontal Scaling
|
||||
- Qdrant cluster support
|
||||
- Neo4j clustering capabilities
|
||||
- Load balancing for API layer
|
||||
|
||||
### Performance Optimization
|
||||
- Vector search optimization
|
||||
- Graph query optimization
|
||||
- Caching strategies
|
||||
- Connection pooling
|
||||
|
||||
## Deployment Patterns
|
||||
|
||||
### Development
|
||||
- Docker Compose for local services
|
||||
- Python virtual environment
|
||||
- File-based configuration
|
||||
|
||||
### Production
|
||||
- Container orchestration
|
||||
- Service mesh integration
|
||||
- Monitoring and logging
|
||||
- Backup and recovery
|
||||
|
||||
## Integration Points
|
||||
|
||||
### MCP Protocol
|
||||
- Standardized AI tool integration
|
||||
- Claude Code compatibility
|
||||
- Protocol-based communication
|
||||
|
||||
### API Layer
|
||||
- RESTful endpoints
|
||||
- OpenAPI specification
|
||||
- SDK support multiple languages
|
||||
|
||||
### Webhook Support
|
||||
- Event-driven updates
|
||||
- Real-time notifications
|
||||
- Integration with external systems
|
||||
117
docs/introduction.mdx
Normal file
117
docs/introduction.mdx
Normal file
@@ -0,0 +1,117 @@
|
||||
---
|
||||
title: Introduction
|
||||
description: 'Welcome to the Mem0 Memory System - A comprehensive memory layer for AI agents'
|
||||
---
|
||||
|
||||
<img
|
||||
className="block dark:hidden"
|
||||
src="/images/hero-light.svg"
|
||||
alt="Hero Light"
|
||||
/>
|
||||
<img
|
||||
className="hidden dark:block"
|
||||
src="/images/hero-dark.svg"
|
||||
alt="Hero Dark"
|
||||
/>
|
||||
|
||||
## What is Mem0 Memory System?
|
||||
|
||||
The Mem0 Memory System is a comprehensive, self-hosted memory layer designed for AI agents and applications. Built on top of the open-source mem0 framework, it provides persistent, intelligent memory capabilities that enhance AI interactions through contextual understanding and knowledge retention.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Local-First Architecture"
|
||||
icon="server"
|
||||
href="/essentials/architecture"
|
||||
>
|
||||
Complete local deployment with Ollama, Neo4j, and Supabase for maximum privacy and control
|
||||
</Card>
|
||||
<Card
|
||||
title="Multi-Provider Support"
|
||||
icon="plug"
|
||||
href="/llm/configuration"
|
||||
>
|
||||
Seamlessly switch between OpenAI, Ollama, and other LLM providers
|
||||
</Card>
|
||||
<Card
|
||||
title="Graph Memory"
|
||||
icon="project-diagram"
|
||||
href="/database/neo4j"
|
||||
>
|
||||
Advanced relationship mapping with Neo4j for contextual memory connections
|
||||
</Card>
|
||||
<Card
|
||||
title="MCP Integration"
|
||||
icon="link"
|
||||
href="/guides/mcp-integration"
|
||||
>
|
||||
Model Context Protocol server for Claude Code and other AI tools
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Key Features
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Vector Memory Storage">
|
||||
High-performance vector search using Supabase with pgvector for semantic memory retrieval and similarity matching.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Graph Relationships">
|
||||
Neo4j-powered knowledge graph for complex entity relationships and contextual memory connections.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local LLM Support">
|
||||
Full Ollama integration with 20+ local models including Llama, Qwen, and specialized embedding models.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="API-First Design">
|
||||
RESTful API with comprehensive memory operations, authentication, and rate limiting.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Self-Hosted Privacy">
|
||||
Complete local deployment ensuring your data never leaves your infrastructure.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
The system consists of several key components working together:
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
A[AI Applications] --> B[MCP Server]
|
||||
B --> C[Memory API]
|
||||
C --> D[Mem0 Core]
|
||||
D --> E[Vector Store - Supabase]
|
||||
D --> F[Graph Store - Neo4j]
|
||||
D --> G[LLM Provider]
|
||||
G --> H[Ollama Local]
|
||||
G --> I[OpenAI/Remote]
|
||||
```
|
||||
|
||||
## Current Status: Phase 1 Complete ✅
|
||||
|
||||
<Note>
|
||||
**Foundation Ready**: All core infrastructure components are operational and tested.
|
||||
</Note>
|
||||
|
||||
| Component | Status | Description |
|
||||
|-----------|--------|-------------|
|
||||
| **Neo4j** | ✅ Ready | Graph database running on localhost:7474 |
|
||||
| **Supabase** | ✅ Ready | Self-hosted database with pgvector on localhost:8000 |
|
||||
| **Ollama** | ✅ Ready | 21+ local models available on localhost:11434 |
|
||||
| **Mem0 Core** | ✅ Ready | Memory management system v0.1.115 |
|
||||
|
||||
## Getting Started
|
||||
|
||||
<CardGroup cols={1}>
|
||||
<Card
|
||||
title="Quick Start Guide"
|
||||
icon="rocket"
|
||||
href="/quickstart"
|
||||
>
|
||||
Get your memory system running in under 5 minutes
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
Ready to enhance your AI applications with persistent, intelligent memory? Let's get started!
|
||||
118
docs/mint.json
Normal file
118
docs/mint.json
Normal file
@@ -0,0 +1,118 @@
|
||||
{
|
||||
"name": "Mem0 Memory System",
|
||||
"logo": {
|
||||
"dark": "/logo/dark.svg",
|
||||
"light": "/logo/light.svg"
|
||||
},
|
||||
"favicon": "/favicon.svg",
|
||||
"colors": {
|
||||
"primary": "#0D9488",
|
||||
"light": "#07C983",
|
||||
"dark": "#0D9488",
|
||||
"anchors": {
|
||||
"from": "#0D9488",
|
||||
"to": "#07C983"
|
||||
}
|
||||
},
|
||||
"topbarLinks": [
|
||||
{
|
||||
"name": "Support",
|
||||
"url": "mailto:support@klas.chat"
|
||||
}
|
||||
],
|
||||
"topbarCtaButton": {
|
||||
"name": "Dashboard",
|
||||
"url": "https://n8n.klas.chat"
|
||||
},
|
||||
"tabs": [
|
||||
{
|
||||
"name": "API Reference",
|
||||
"url": "api-reference"
|
||||
},
|
||||
{
|
||||
"name": "Guides",
|
||||
"url": "guides"
|
||||
}
|
||||
],
|
||||
"anchors": [
|
||||
{
|
||||
"name": "Documentation",
|
||||
"icon": "book-open-cover",
|
||||
"url": "https://docs.klas.chat"
|
||||
},
|
||||
{
|
||||
"name": "Community",
|
||||
"icon": "slack",
|
||||
"url": "https://matrix.klas.chat"
|
||||
},
|
||||
{
|
||||
"name": "Blog",
|
||||
"icon": "newspaper",
|
||||
"url": "https://klas.chat"
|
||||
}
|
||||
],
|
||||
"navigation": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"quickstart",
|
||||
"development"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Core Concepts",
|
||||
"pages": [
|
||||
"essentials/architecture",
|
||||
"essentials/memory-types",
|
||||
"essentials/configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Database Integration",
|
||||
"pages": [
|
||||
"database/neo4j",
|
||||
"database/qdrant",
|
||||
"database/supabase"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "LLM Providers",
|
||||
"pages": [
|
||||
"llm/ollama",
|
||||
"llm/openai",
|
||||
"llm/configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "API Documentation",
|
||||
"pages": [
|
||||
"api-reference/introduction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Memory Operations",
|
||||
"pages": [
|
||||
"api-reference/add-memory",
|
||||
"api-reference/search-memory",
|
||||
"api-reference/get-memory",
|
||||
"api-reference/update-memory",
|
||||
"api-reference/delete-memory"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
"guides/getting-started",
|
||||
"guides/local-development",
|
||||
"guides/production-deployment",
|
||||
"guides/mcp-integration"
|
||||
]
|
||||
}
|
||||
],
|
||||
"footerSocials": {
|
||||
"website": "https://klas.chat",
|
||||
"github": "https://github.com/klas",
|
||||
"linkedin": "https://www.linkedin.com/in/klasmachacek"
|
||||
}
|
||||
}
|
||||
1
docs/mintlify.pid
Normal file
1
docs/mintlify.pid
Normal file
@@ -0,0 +1 @@
|
||||
3080755
|
||||
@@ -1,421 +1,31 @@
|
||||
---
|
||||
title: Quickstart
|
||||
icon: "bolt"
|
||||
iconType: "solid"
|
||||
title: 'Quickstart'
|
||||
description: 'Get your Mem0 Memory System running in under 5 minutes'
|
||||
---
|
||||
|
||||
<Snippet file="async-memory-add.mdx" />
|
||||
|
||||
|
||||
Mem0 offers two powerful ways to leverage our technology: [our managed platform](#mem0-platform-managed-solution) and [our open source solution](#mem0-open-source).
|
||||
|
||||
Check out our [Playground](https://mem0.dev/pd-pg) to see Mem0 in action.
|
||||
## Prerequisites
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Mem0 Platform (Managed Solution)" icon="chart-simple" href="#mem0-platform-managed-solution">
|
||||
Better, faster, fully managed, and hassle free solution.
|
||||
<Card title="Docker & Docker Compose" icon="docker">
|
||||
Required for Neo4j and Qdrant containers
|
||||
</Card>
|
||||
<Card title="Mem0 Open Source" icon="code-branch" href="#mem0-open-source">
|
||||
Self hosted, fully customizable, and open source.
|
||||
<Card title="Python 3.10+" icon="python">
|
||||
For the mem0 core system and API
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Installation
|
||||
|
||||
## Mem0 Platform (Managed Solution)
|
||||
### Step 1: Start Database Services
|
||||
|
||||
Our fully managed platform provides a hassle-free way to integrate Mem0's capabilities into your AI agents and assistants. Sign up for Mem0 platform [here](https://mem0.dev/pd).
|
||||
|
||||
The Mem0 SDK supports both Python and JavaScript, with full [TypeScript](/platform/quickstart/#4-11-working-with-mem0-in-typescript) support as well.
|
||||
|
||||
Follow the steps below to get started with Mem0 Platform:
|
||||
|
||||
1. [Install Mem0](#1-install-mem0)
|
||||
2. [Add Memories](#2-add-memories)
|
||||
3. [Retrieve Memories](#3-retrieve-memories)
|
||||
|
||||
### 1. Install Mem0
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Install package">
|
||||
<CodeGroup>
|
||||
```bash pip
|
||||
pip install mem0ai
|
||||
```bash
|
||||
docker compose up -d neo4j qdrant
|
||||
```
|
||||
|
||||
```bash npm
|
||||
npm install mem0ai
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
<Accordion title="Get API Key">
|
||||
### Step 2: Test Your Installation
|
||||
|
||||
1. Sign in to [Mem0 Platform](https://mem0.dev/pd-api)
|
||||
2. Copy your API Key from the dashboard
|
||||
|
||||
|
||||
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
### 2. Add Memories
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Instantiate client">
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import MemoryClient
|
||||
|
||||
os.environ["MEM0_API_KEY"] = "your-api-key"
|
||||
|
||||
client = MemoryClient()
|
||||
```bash
|
||||
python test_all_connections.py
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
import MemoryClient from 'mem0ai';
|
||||
const client = new MemoryClient({ apiKey: 'your-api-key' });
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
<Accordion title="Add memories">
|
||||
<CodeGroup>
|
||||
|
||||
```python Python
|
||||
messages = [
|
||||
{"role": "user", "content": "Thinking of making a sandwich. What do you recommend?"},
|
||||
{"role": "assistant", "content": "How about adding some cheese for extra flavor?"},
|
||||
{"role": "user", "content": "Actually, I don't like cheese."},
|
||||
{"role": "assistant", "content": "I'll remember that you don't like cheese for future recommendations."}
|
||||
]
|
||||
client.add(messages, user_id="alex")
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
const messages = [
|
||||
{"role": "user", "content": "Thinking of making a sandwich. What do you recommend?"},
|
||||
{"role": "assistant", "content": "How about adding some cheese for extra flavor?"},
|
||||
{"role": "user", "content": "Actually, I don't like cheese."},
|
||||
{"role": "assistant", "content": "I'll remember that you don't like cheese for future recommendations."}
|
||||
];
|
||||
client.add(messages, { user_id: "alex" })
|
||||
.then(response => console.log(response))
|
||||
.catch(error => console.error(error));
|
||||
```
|
||||
|
||||
```bash cURL
|
||||
curl -X POST "https://api.mem0.ai/v1/memories/" \
|
||||
-H "Authorization: Token your-api-key" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"messages": [
|
||||
{"role": "user", "content": "I live in San Francisco. Thinking of making a sandwich. What do you recommend?"},
|
||||
{"role": "assistant", "content": "How about adding some cheese for extra flavor?"},
|
||||
{"role": "user", "content": "Actually, I don't like cheese."},
|
||||
{"role": "assistant", "content": "I'll remember that you don't like cheese for future recommendations."}
|
||||
],
|
||||
"user_id": "alex"
|
||||
}'
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "24e466b5-e1c6-4bde-8a92-f09a327ffa60",
|
||||
"memory": "Does not like cheese",
|
||||
"event": "ADD"
|
||||
},
|
||||
{
|
||||
"id": "e8d78459-fadd-4c5a-bece-abb8c3dc7ed7",
|
||||
"memory": "Lives in San Francisco",
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
### 3. Retrieve Memories
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Search for relevant memories">
|
||||
<CodeGroup>
|
||||
|
||||
```python Python
|
||||
# Example showing location and preference-aware recommendations
|
||||
query = "I'm craving some pizza. Any recommendations?"
|
||||
filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
}
|
||||
client.search(query, version="v2", filters=filters)
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
const query = "I'm craving some pizza. Any recommendations?";
|
||||
const filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
};
|
||||
client.search(query, { version: "v2", filters })
|
||||
.then(results => console.log(results))
|
||||
.catch(error => console.error(error));
|
||||
```
|
||||
|
||||
```bash cURL
|
||||
curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
|
||||
-H "Authorization: Token your-api-key" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "I'm craving some pizza. Any recommendations?",
|
||||
"filters": {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "7f165f7e-b411-4afe-b7e5-35789b72c4a5",
|
||||
"memory": "Does not like cheese",
|
||||
"user_id": "alex",
|
||||
"metadata": null,
|
||||
"created_at": "2024-07-20T01:30:36.275141-07:00",
|
||||
"updated_at": "2024-07-20T01:30:36.275172-07:00",
|
||||
"score": 0.92
|
||||
},
|
||||
{
|
||||
"id": "8f165f7e-b411-4afe-b7e5-35789b72c4b6",
|
||||
"memory": "Lives in San Francisco",
|
||||
"user_id": "alex",
|
||||
"metadata": null,
|
||||
"created_at": "2024-07-20T01:30:36.275141-07:00",
|
||||
"updated_at": "2024-07-20T01:30:36.275172-07:00",
|
||||
"score": 0.85
|
||||
}
|
||||
]
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
</Accordion>
|
||||
<Accordion title="Get all memories of a user">
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python Python
|
||||
filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
all_memories = client.get_all(version="v2", filters=filters, page=1, page_size=50)
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
const filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
client.getAll({ version: "v2", filters, page: 1, page_size: 50 })
|
||||
.then(memories => console.log(memories))
|
||||
.catch(error => console.error(error));
|
||||
```
|
||||
|
||||
```bash cURL
|
||||
curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
|
||||
-H "Authorization: Token your-api-key" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"filters": {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alice"
|
||||
}
|
||||
]
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "7f165f7e-b411-4afe-b7e5-35789b72c4a5",
|
||||
"memory": "Does not like cheese",
|
||||
"user_id": "alex",
|
||||
"metadata": null,
|
||||
"created_at": "2024-07-20T01:30:36.275141-07:00",
|
||||
"updated_at": "2024-07-20T01:30:36.275172-07:00",
|
||||
"score": 0.92
|
||||
},
|
||||
{
|
||||
"id": "8f165f7e-b411-4afe-b7e5-35789b72c4b6",
|
||||
"memory": "Lives in San Francisco",
|
||||
"user_id": "alex",
|
||||
"metadata": null,
|
||||
"created_at": "2024-07-20T01:30:36.275141-07:00",
|
||||
"updated_at": "2024-07-20T01:30:36.275172-07:00",
|
||||
"score": 0.85
|
||||
}
|
||||
]
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Card title="Mem0 Platform" icon="chart-simple" href="/platform/overview">
|
||||
Learn more about Mem0 platform
|
||||
</Card>
|
||||
|
||||
## Mem0 Open Source
|
||||
|
||||
Our open-source version is available for those who prefer full control and customization. You can self-host Mem0 on your infrastructure and integrate it with your AI agents and assistants. Checkout our [GitHub repository](https://mem0.dev/gd)
|
||||
|
||||
Follow the steps below to get started with Mem0 Open Source:
|
||||
|
||||
1. [Install Mem0 Open Source](#1-install-mem0-open-source)
|
||||
2. [Add Memories](#2-add-memories-open-source)
|
||||
3. [Retrieve Memories](#3-retrieve-memories-open-source)
|
||||
|
||||
### 1. Install Mem0 Open Source
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Install package">
|
||||
<CodeGroup>
|
||||
```bash pip
|
||||
pip install mem0ai
|
||||
```
|
||||
|
||||
```bash npm
|
||||
npm install mem0ai
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
### 2. Add Memories <a name="2-add-memories-open-source"></a>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Instantiate client">
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
m = Memory()
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
const memory = new Memory();
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
<Accordion title="Add memories">
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
# For a user
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "I like to drink coffee in the morning and go for a walk"
|
||||
}
|
||||
]
|
||||
result = m.add(messages, user_id="alice", metadata={"category": "preferences"})
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
const messages = [
|
||||
{
|
||||
role: "user",
|
||||
content: "I like to drink coffee in the morning and go for a walk"
|
||||
}
|
||||
];
|
||||
const result = memory.add(messages, { userId: "alice", metadata: { category: "preferences" } });
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "3dc6f65f-fb3f-4e91-89a8-ed1a22f8898a",
|
||||
"data": {"memory": "Likes to drink coffee in the morning"},
|
||||
"event": "ADD"
|
||||
},
|
||||
{
|
||||
"id": "f1673706-e3d6-4f12-a767-0384c7697d53",
|
||||
"data": {"memory": "Likes to go for a walk"},
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
### 3. Retrieve Memories <a name="3-retrieve-memories-open-source"></a>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Search for relevant memories">
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "3dc6f65f-fb3f-4e91-89a8-ed1a22f8898a",
|
||||
"memory": "Likes to drink coffee in the morning",
|
||||
"user_id": "alice",
|
||||
"metadata": {"category": "preferences"},
|
||||
"categories": ["user_preferences", "food"],
|
||||
"immutable": false,
|
||||
"created_at": "2025-02-24T20:11:39.010261-08:00",
|
||||
"updated_at": "2025-02-24T20:11:39.010274-08:00",
|
||||
"score": 0.5915589089130715
|
||||
},
|
||||
{
|
||||
"id": "e8d78459-fadd-4c5a-bece-abb8c3dc7ed7",
|
||||
"memory": "Likes to go for a walk",
|
||||
"user_id": "alice",
|
||||
"metadata": {"category": "preferences"},
|
||||
"categories": ["hobby", "food"],
|
||||
"immutable": false,
|
||||
"created_at": "2025-02-24T11:47:52.893038-08:00",
|
||||
"updated_at": "2025-02-24T11:47:52.893048-08:00",
|
||||
"score": 0.43263634637810866
|
||||
}
|
||||
]
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Mem0 OSS Python SDK" icon="python" href="/open-source/python-quickstart">
|
||||
Learn more about Mem0 OSS Python SDK
|
||||
</Card>
|
||||
<Card title="Mem0 OSS Node.js SDK" icon="node" href="/open-source/node-quickstart">
|
||||
Learn more about Mem0 OSS Node.js SDK
|
||||
</Card>
|
||||
</CardGroup>
|
||||
You should see all systems passing.
|
||||
Reference in New Issue
Block a user