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7689409950
5 changed files with 11 additions and 914 deletions
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@ -30,12 +30,11 @@ class Mem0Manager:
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}
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},
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"embedder": {
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"provider": "ollama",
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"provider": "gemini",
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"config": {
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"model": "hf.co/Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0",
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# "api_key": settings.embedder_api_key,
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"ollama_base_url": "http://host.docker.internal:11434",
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"embedding_dims": 1024
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"model": "models/gemini-embedding-001",
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"api_key": settings.embedder_api_key,
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"embedding_dims": 1536
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}
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},
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"vector_store": {
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@ -46,7 +45,7 @@ class Mem0Manager:
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"password": settings.postgres_password,
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"host": settings.postgres_host,
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"port": settings.postgres_port,
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"embedding_model_dims": 1024
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"embedding_model_dims": 1536
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}
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},
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"graph_store": {
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@ -1,326 +0,0 @@
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# Agent 1: Professional Manager Persona - Daily Driver Testing
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## Persona Overview
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**Name**: Alex Chen
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**Role**: Project Manager at TechFlow Inc
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**Industry**: Fast-growing tech startup
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**Experience**: 5+ years managing technical teams
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## Background Profile
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### Professional Context
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- **Current Responsibilities**: Managing 3 concurrent projects, 8 team members, multiple stakeholders
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- **Key Challenges**: Context switching between projects, tracking decisions and commitments, remembering team member preferences and working styles
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- **Team Structure**: Cross-functional teams with developers, designers, QA engineers, and product managers
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- **Stakeholder Management**: Regular interaction with C-level executives, VPs, and external clients
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### Daily Workflow
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- **Morning**: Review project statuses, check team blockers, prepare for standups
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- **Mid-day**: Attend meetings, make decisions, coordinate between teams
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- **Evening**: Update project documentation, plan next day priorities
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- **Weekly**: Sprint planning, stakeholder updates, retrospectives
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### Pain Points
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1. **Context Switching**: Difficulty maintaining context when jumping between 3+ projects
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2. **Decision Tracking**: Remembering past decisions and the reasoning behind them
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3. **Team Dynamics**: Keeping track of individual team member preferences, skills, and current workload
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4. **Stakeholder Alignment**: Ensuring all stakeholders stay informed and aligned
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5. **Knowledge Silos**: Information scattered across Slack, Jira, Notion, email, and meeting notes
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### Technology Comfort Level
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- **API/Technical Skills**: High - comfortable with REST APIs, JSON, curl commands when necessary
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- **Tool Adoption**: Quick to adopt new productivity tools if they provide clear value
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- **Integration Preference**: Values tools that integrate well with existing tech stack
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- **Efficiency Focus**: Prioritizes speed and reliability over feature richness
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## Testing Mission
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### Primary Objective
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Evaluate the Mem0 interface system as a potential replacement or enhancement for current productivity stack:
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- **Current Tools**: Notion (documentation), Slack (communication), Jira (task tracking), Google Calendar (scheduling)
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- **Success Criteria**: Can it reduce context switching overhead and improve project coordination?
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### Testing Methodology
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Simulate 3 weeks of realistic PM work across multiple projects to stress-test memory persistence, context management, and practical utility.
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## Available API Endpoints
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**Base URL**: `http://localhost:8000`
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### Core Memory Operations
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```bash
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# Memory-enhanced conversations with context
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POST /chat
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{
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"message": "your message",
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"user_id": "alex_chen_pm",
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"context": "optional context",
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"metadata": {"project": "aurora", "type": "meeting_note"}
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}
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# Add memories manually from conversations
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POST /memories
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{
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"messages": [{"role": "user", "content": "text"}],
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"user_id": "alex_chen_pm",
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"metadata": {"project": "zenith", "stakeholder": "ceo"}
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}
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# Search through stored memories
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POST /memories/search
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{
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"query": "search term",
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"user_id": "alex_chen_pm",
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"limit": 10,
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"filters": {"project": "aurora"}
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}
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# Get all user memories
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GET /memories/alex_chen_pm?limit=50
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# Update existing memory
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PUT /memories
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{
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"memory_id": "memory_uuid",
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"content": "updated content"
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}
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# Delete specific memory
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DELETE /memories/{memory_id}
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# Delete all user memories
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DELETE /memories/user/alex_chen_pm
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```
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### Advanced Features
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```bash
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# Get relationship graph between entities
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GET /graph/relationships/alex_chen_pm
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# Get memory change history
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GET /memories/{memory_id}/history
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# Get global application statistics
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GET /stats
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# Get user-specific analytics
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GET /stats/alex_chen_pm
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# Check system health
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GET /health
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# Get current model configuration
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GET /models
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```
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## Testing Scenarios
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### Week 1: Basic Setup & Daily Usage
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#### Day 1-2: Personal and Team Setup
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**Tasks:**
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1. Add personal working preferences and PM style
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2. Store information about all 8 team members (skills, preferences, current projects)
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3. Document current 3 projects (Aurora, Zenith, Nexus) with their status and stakeholders
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4. Test basic memory retrieval for team member information
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**Example Interactions:**
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```bash
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# Store personal PM preferences
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POST /memories
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{
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"messages": [{"role": "user", "content": "I prefer async communication over meetings when possible. I believe in servant leadership and focus on removing blockers for my team. My decision-making style is collaborative but decisive when needed."}],
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"user_id": "alex_chen_pm",
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"metadata": {"type": "personal_preferences"}
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}
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# Store team member information
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POST /memories
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{
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"messages": [{"role": "user", "content": "Sarah is our lead frontend developer on Aurora project. She prefers morning standups, works best with detailed specs, and has expertise in React and TypeScript. She's been advocating for better testing infrastructure."}],
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"user_id": "alex_chen_pm",
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"metadata": {"type": "team_member", "person": "sarah", "project": "aurora"}
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}
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```
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#### Day 3-5: Meeting Notes and Decision Tracking
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**Tasks:**
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1. Store meeting notes from sprint planning sessions
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2. Document key decisions and their rationale
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3. Track action items and ownership
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4. Test retrieval of decision history
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**Example Interactions:**
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```bash
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# Store sprint planning meeting
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POST /chat
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{
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"message": "Just finished Aurora sprint planning. We decided to push the integration testing to next sprint due to API instability. Sarah raised concerns about technical debt in the authentication module. Mike will focus on database optimization this sprint. CEO James wants a demo ready by January 30th.",
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"user_id": "alex_chen_pm",
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"metadata": {"type": "meeting_note", "project": "aurora", "meeting": "sprint_planning"}
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}
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# Search for past decisions
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POST /memories/search
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{
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"query": "authentication technical debt",
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"user_id": "alex_chen_pm",
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"limit": 5
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}
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```
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### Week 2: Advanced Workflows
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#### Day 6-8: Multi-Project Coordination
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**Tasks:**
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1. Manage context switching between Aurora, Zenith, and Nexus projects
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2. Track interdependencies between projects
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3. Coordinate shared resources (team members working on multiple projects)
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4. Test memory's ability to maintain project-specific context
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**Example Scenarios:**
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```bash
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# Switch context to Zenith project
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POST /chat
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{
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"message": "Switching to Zenith project status review. What are the current blockers and who's working on performance optimization?",
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"user_id": "alex_chen_pm",
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"context": "zenith_project",
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"metadata": {"project": "zenith", "type": "status_check"}
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}
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# Track resource conflicts
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POST /memories
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{
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"messages": [{"role": "user", "content": "Mike is allocated to both Aurora database work and Zenith performance optimization. This is creating a bottleneck. Need to discuss prioritization with stakeholders."}],
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"user_id": "alex_chen_pm",
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"metadata": {"type": "resource_conflict", "person": "mike", "projects": ["aurora", "zenith"]}
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}
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```
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#### Day 9-10: Stakeholder Management
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**Tasks:**
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1. Track stakeholder preferences and communication styles
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2. Manage stakeholder expectations and updates
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3. Coordinate between technical team and business stakeholders
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4. Test relationship mapping between people and projects
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**Example Interactions:**
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```bash
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# Store stakeholder preferences
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POST /memories
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{
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"messages": [{"role": "user", "content": "CEO James prefers high-level updates focused on business impact. He gets impatient with technical details but wants to understand risks. VP Maria (Engineering) likes detailed technical discussions and data-driven decisions. VP Carlos (Product) focuses on user experience and timeline impact."}],
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"user_id": "alex_chen_pm",
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"metadata": {"type": "stakeholder_preferences"}
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}
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# Check relationship graph
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GET /graph/relationships/alex_chen_pm
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```
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### Week 3: Edge Cases & Integration
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#### Day 11-13: Complex Project Scenarios
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**Tasks:**
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1. Handle crisis situations (critical bugs, deadline changes)
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2. Manage scope changes and their impact across projects
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3. Coordinate emergency response and communication
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4. Test system under high-frequency updates
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**Example Crisis Scenarios:**
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```bash
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# Handle critical bug discovery
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POST /chat
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{
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"message": "CRITICAL: Tom found a security vulnerability in Aurora's user authentication. This affects production and blocks our January 30th demo to the CEO. Need immediate response plan and stakeholder communication.",
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"user_id": "alex_chen_pm",
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"metadata": {"priority": "critical", "type": "incident", "project": "aurora"}
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}
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# Update memory with changed timeline
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PUT /memories
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{
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"memory_id": "demo_timeline_memory_id",
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"content": "CEO demo moved from January 30th to February 15th due to security vulnerability discovery. James agreed to delay after understanding the risk."
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}
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```
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#### Day 14-15: Bulk Operations and Scalability
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**Tasks:**
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1. Test system with large amounts of project data
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2. Simulate quarterly planning with multiple projects
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3. Test search performance across accumulated memories
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4. Evaluate system for long-term use scalability
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## Evaluation Criteria
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### Core Functionality Assessment
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**Memory Quality (Weight: 30%)**
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- Accuracy of information retention
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- Context preservation across sessions
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- Ability to update and evolve memories
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**Relationship Intelligence (Weight: 25%)**
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- Quality of automatic relationship detection
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- Accuracy of people-project-task connections
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- Usefulness of generated relationship graph
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**Search & Retrieval (Weight: 20%)**
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- Relevance of search results
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- Speed of information retrieval
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- Ability to find related information
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**Context Management (Weight: 25%)**
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- Effectiveness in multi-project context switching
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- Maintenance of project-specific context
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- Integration of information across conversations
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### Daily Driver Viability
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**Productivity Impact**
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- Does it reduce time spent searching for information?
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- Does it help with context switching between projects?
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- Does it improve decision tracking and follow-up?
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**Workflow Integration**
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- How well does it fit into existing PM workflows?
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- Can it replace or enhance current tools?
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- What additional tools/integrations would be needed?
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**Team Coordination Enhancement**
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- Does it improve tracking of team member information?
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- Does it help with resource allocation decisions?
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- Does it enhance stakeholder communication?
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**Missing Features for PM Adoption**
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- What essential PM features are absent?
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- What integrations are critical for daily use?
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- What would prevent adoption as primary PM tool?
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## Expected Deliverables
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### Comprehensive Report Structure
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1. **Executive Summary**: Overall adoption recommendation with key reasoning
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2. **Functionality Scores**: Detailed ratings for memory, relationships, search, context management
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3. **Workflow Analysis**: How well it supports actual PM work vs. current tools
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4. **Team Coordination Assessment**: Impact on managing team and stakeholder relationships
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5. **Critical Gap Analysis**: Essential missing features preventing full adoption
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6. **Integration Requirements**: What additional tools/features needed for daily driver use
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7. **Scaling Considerations**: Viability for managing larger teams/more complex projects
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### Testing Evidence Required
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- Specific examples of successful memory retrieval
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- Evidence of relationship intelligence quality
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- Examples of effective context switching
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- Documentation of any failures or limitations encountered
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- Quantitative metrics where possible (response times, accuracy rates)
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## Success Metrics
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**Excellent (8-10/10)**: Could replace primary PM tools with minimal additional development
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**Good (6-7/10)**: Strong core capabilities but requires significant additional features
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**Fair (4-5/10)**: Useful for specific PM tasks but not comprehensive enough for daily driver
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**Poor (1-3/10)**: Interesting technology but not practical for PM workflows
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Focus on **realistic daily PM scenarios** rather than technical edge cases. The goal is to determine if this technology can meaningfully improve project management effectiveness and team coordination.
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@ -1,357 +0,0 @@
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# Agent 2: Creative Researcher Persona - Daily Driver Testing
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## Persona Overview
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**Name**: Dr. Sam Rivera
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**Role**: Independent Researcher and Consultant
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**Specialization**: AI Ethics and Cognitive Science
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**Background**: PhD in Cognitive Science, published author, interdisciplinary researcher
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## Background Profile
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### Professional Context
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- **Current Research Areas**: AI ethics, cognitive bias in decision-making, human-AI interaction, algorithmic fairness
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- **Work Style**: 4-5 concurrent research threads, deep interdisciplinary thinking, theory development
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- **Publications**: Peer-reviewed papers in cognitive science and AI ethics journals
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- **Consulting**: Advisory work for tech companies on ethical AI implementation
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### Research Workflow
|
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- **Literature Review**: Continuous reading across psychology, computer science, philosophy, and ethics
|
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- **Idea Development**: Iterative theory building with cross-domain synthesis
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- **Collaboration**: Regular discussions with academics, industry practitioners, and policymakers
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- **Writing**: Academic papers, blog posts, policy recommendations, and book chapters
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### Pain Points
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1. **Information Overwhelm**: Managing vast amounts of research across multiple domains
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2. **Connection Discovery**: Finding unexpected links between disparate research areas
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3. **Source Tracking**: Remembering where specific insights came from and how they evolved
|
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4. **Concept Evolution**: Tracking how theoretical frameworks develop over time
|
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5. **Interdisciplinary Translation**: Bridging concepts between different academic domains
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|
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### Technology Comfort Level
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- **API/Technical Skills**: Moderate-High - comfortable with APIs when they provide research value
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- **Tool Philosophy**: Values flexibility and exploration over rigid structure
|
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- **Current Tools**: Obsidian (knowledge graphs), Zotero (citations), Notion (project management)
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- **Innovation Openness**: Excited to try new tools that enhance creative knowledge work
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|
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## Testing Mission
|
||||
|
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### Primary Objective
|
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Evaluate the Mem0 interface system as a potential enhancement for research knowledge management and creative thinking processes:
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- **Current Challenge**: Information silos across domains, difficulty discovering serendipitous connections
|
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- **Success Criteria**: Can it accelerate theory development and cross-domain insight discovery?
|
||||
|
||||
### Research Domains for Testing
|
||||
1. **AI Ethics**: Algorithmic bias, fairness, transparency, accountability
|
||||
2. **Cognitive Science**: Decision-making, cognitive biases, dual-process theory
|
||||
3. **Philosophy**: Ethics, epistemology, philosophy of mind
|
||||
4. **Technology Policy**: AI governance, regulation, social impact
|
||||
|
||||
## Available API Endpoints
|
||||
|
||||
**Base URL**: `http://localhost:8000`
|
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|
||||
### Core Memory Operations
|
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```bash
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# Memory-enhanced research conversations
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POST /chat
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{
|
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"message": "your research insight or question",
|
||||
"user_id": "sam_rivera_researcher",
|
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"context": "research_domain_context",
|
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"metadata": {"domain": "ai_ethics", "type": "theory_development"}
|
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}
|
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|
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# Add research insights and literature notes
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POST /memories
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{
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"messages": [{"role": "user", "content": "research content"}],
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"user_id": "sam_rivera_researcher",
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"metadata": {"paper": "author_2024", "domain": "cognitive_science", "concept": "availability_heuristic"}
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}
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# Search across research knowledge base
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POST /memories/search
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{
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||||
"query": "cognitive bias artificial intelligence",
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||||
"user_id": "sam_rivera_researcher",
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||||
"limit": 15,
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||||
"filters": {"domain": "ai_ethics"}
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||||
}
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||||
# Get all research memories
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||||
GET /memories/sam_rivera_researcher?limit=100
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||||
|
||||
# Update evolving research concepts
|
||||
PUT /memories
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{
|
||||
"memory_id": "theory_memory_id",
|
||||
"content": "updated theoretical framework with new evidence"
|
||||
}
|
||||
|
||||
# Remove outdated research notes
|
||||
DELETE /memories/{memory_id}
|
||||
|
||||
# Clean slate for new research direction
|
||||
DELETE /memories/user/sam_rivera_researcher
|
||||
```
|
||||
|
||||
### Advanced Features
|
||||
```bash
|
||||
# Explore concept relationship networks
|
||||
GET /graph/relationships/sam_rivera_researcher
|
||||
|
||||
# Track concept evolution over time
|
||||
GET /memories/{memory_id}/history
|
||||
|
||||
# Research productivity analytics
|
||||
GET /stats/sam_rivera_researcher
|
||||
|
||||
# System health for reliable research work
|
||||
GET /health
|
||||
```
|
||||
|
||||
## Testing Scenarios
|
||||
|
||||
### Week 1: Research Foundation Building
|
||||
|
||||
#### Day 1-2: Core Research Areas Setup
|
||||
**Tasks:**
|
||||
1. Establish primary research interests and current theoretical frameworks
|
||||
2. Add key papers and researchers from each domain
|
||||
3. Store ongoing research questions and hypotheses
|
||||
4. Test basic concept retrieval and organization
|
||||
|
||||
**Example Research Setup:**
|
||||
```bash
|
||||
# Store primary research focus
|
||||
POST /memories
|
||||
{
|
||||
"messages": [{"role": "user", "content": "My research focuses on the intersection of cognitive biases and AI decision-making systems. I'm particularly interested in how confirmation bias and availability heuristic manifest in training data selection and algorithmic outputs. Current hypothesis: AI systems amplify human cognitive biases through data selection and optimization processes."}],
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "research_focus", "domains": ["ai_ethics", "cognitive_science"]}
|
||||
}
|
||||
|
||||
# Add influential paper
|
||||
POST /memories
|
||||
{
|
||||
"messages": [{"role": "user", "content": "Kahneman & Tversky (1974) 'Judgment under Uncertainty: Heuristics and Biases' - foundational work on availability heuristic. People judge probability by ease of recall. Connection to AI: training datasets may reflect availability bias in what data is easily accessible vs. representative."}],
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "literature", "authors": "kahneman_tversky", "year": "1974", "concept": "availability_heuristic"}
|
||||
}
|
||||
```
|
||||
|
||||
#### Day 3-5: Literature Integration and Theory Building
|
||||
**Tasks:**
|
||||
1. Add diverse sources from psychology, AI research, and philosophy
|
||||
2. Explore connections between different theoretical frameworks
|
||||
3. Develop initial cross-domain hypotheses
|
||||
4. Test memory's ability to synthesize information
|
||||
|
||||
**Example Theory Development:**
|
||||
```bash
|
||||
# Cross-domain insight development
|
||||
POST /chat
|
||||
{
|
||||
"message": "Building on dual-process theory from psychology and recent work on AI explainability, I'm thinking that System 1 (fast, intuitive) vs System 2 (slow, deliberate) thinking might map onto different AI decision-making architectures. How might this framework help us understand AI bias?",
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "theory_building", "concepts": ["dual_process_theory", "ai_explainability"]}
|
||||
}
|
||||
|
||||
# Search for related concepts
|
||||
POST /memories/search
|
||||
{
|
||||
"query": "dual process theory artificial intelligence decision making",
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"limit": 10
|
||||
}
|
||||
```
|
||||
|
||||
### Week 2: Cross-Domain Exploration
|
||||
|
||||
#### Day 6-8: Interdisciplinary Connection Discovery
|
||||
**Tasks:**
|
||||
1. Explore unexpected connections between AI ethics and cognitive science
|
||||
2. Map relationships between philosophical ethics and technical AI implementation
|
||||
3. Investigate policy implications of cognitive bias research
|
||||
4. Test system's ability to reveal serendipitous insights
|
||||
|
||||
**Example Interdisciplinary Work:**
|
||||
```bash
|
||||
# Philosophy-technology bridge
|
||||
POST /memories
|
||||
{
|
||||
"messages": [{"role": "user", "content": "Rawls' veil of ignorance (1971) as potential framework for fair AI system design. If algorithm designers didn't know their position in society, what fairness constraints would they choose? This could address systemic bias in AI systems by encouraging impartial design principles."}],
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "theory_synthesis", "domains": ["philosophy", "ai_ethics"], "concept": "veil_of_ignorance"}
|
||||
}
|
||||
|
||||
# Explore relationship networks
|
||||
GET /graph/relationships/sam_rivera_researcher
|
||||
```
|
||||
|
||||
#### Day 9-10: Methodology and Framework Development
|
||||
**Tasks:**
|
||||
1. Develop research methodologies that cross disciplinary boundaries
|
||||
2. Create evaluation frameworks for AI ethics research
|
||||
3. Design experimental approaches for testing cognitive bias in AI
|
||||
4. Test memory system's support for methodological thinking
|
||||
|
||||
**Example Methodology Development:**
|
||||
```bash
|
||||
# Research methodology synthesis
|
||||
POST /chat
|
||||
{
|
||||
"message": "Developing a mixed-methods approach to study cognitive bias in AI systems: (1) Computational analysis of training data for bias patterns (2) Behavioral experiments with AI system outputs (3) Qualitative interviews with AI developers about decision-making processes. How do these methods complement each other?",
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "methodology", "approaches": ["computational", "behavioral", "qualitative"]}
|
||||
}
|
||||
```
|
||||
|
||||
### Week 3: Advanced Knowledge Work
|
||||
|
||||
#### Day 11-13: Concept Evolution and Refinement
|
||||
**Tasks:**
|
||||
1. Track how theoretical frameworks evolve with new evidence
|
||||
2. Refine hypotheses based on accumulated research
|
||||
3. Test memory system's ability to handle conceptual change
|
||||
4. Evaluate support for iterative theory development
|
||||
|
||||
**Example Concept Evolution:**
|
||||
```bash
|
||||
# Update evolving theory
|
||||
PUT /memories
|
||||
{
|
||||
"memory_id": "ai_bias_framework_memory_id",
|
||||
"content": "UPDATED FRAMEWORK: AI bias manifests through three pathways: (1) Historical bias in training data (2) Representation bias in data selection (3) Evaluation bias in metric choice. New evidence from Barocas & Selbst (2016) suggests interaction effects between these pathways that amplify bias beyond individual contributions."
|
||||
}
|
||||
|
||||
# Track conceptual development
|
||||
GET /memories/ai_bias_framework_memory_id/history
|
||||
```
|
||||
|
||||
#### Day 14-15: Writing and Synthesis Support
|
||||
**Tasks:**
|
||||
1. Use system to support academic writing process
|
||||
2. Test ability to organize complex arguments across papers
|
||||
3. Evaluate citation and source tracking capabilities
|
||||
4. Assess support for collaborative research discussions
|
||||
|
||||
**Example Writing Support:**
|
||||
```bash
|
||||
# Organize paper argument structure
|
||||
POST /chat
|
||||
{
|
||||
"message": "I'm writing a paper on 'Cognitive Bias Amplification in AI Systems.' Need to organize my argument: (1) Establish cognitive bias foundation from psychology (2) Show how AI training processes can amplify these biases (3) Propose technical and policy interventions (4) Discuss implications for AI governance. What key citations and evidence support each section?",
|
||||
"user_id": "sam_rivera_researcher",
|
||||
"metadata": {"type": "writing_support", "paper": "bias_amplification_2024"}
|
||||
}
|
||||
```
|
||||
|
||||
## Evaluation Criteria
|
||||
|
||||
### Core Research Functionality Assessment
|
||||
|
||||
**Knowledge Organization (Weight: 25%)**
|
||||
- Ability to organize complex, interconnected research concepts
|
||||
- Support for hierarchical and networked knowledge structures
|
||||
- Flexibility in categorization and tagging systems
|
||||
|
||||
**Discovery & Serendipity (Weight: 30%)**
|
||||
- Quality of unexpected connection identification
|
||||
- Support for cross-domain insight generation
|
||||
- Effectiveness in revealing hidden research patterns
|
||||
|
||||
**Concept Evolution Support (Weight: 20%)**
|
||||
- Tracking theoretical framework development over time
|
||||
- Support for iterative refinement of ideas
|
||||
- Memory versioning for concept history
|
||||
|
||||
**Research Workflow Enhancement (Weight: 25%)**
|
||||
- Integration with literature review processes
|
||||
- Support for hypothesis development and testing
|
||||
- Enhancement of writing and synthesis activities
|
||||
|
||||
### Creative Knowledge Work Assessment
|
||||
|
||||
**Theory Development**
|
||||
- Does it accelerate theoretical framework creation?
|
||||
- Can it help identify gaps in current theories?
|
||||
- Does it support hypothesis generation and refinement?
|
||||
|
||||
**Literature Integration**
|
||||
- How well does it organize diverse sources?
|
||||
- Can it identify key papers and influential authors?
|
||||
- Does it reveal citation networks and influence patterns?
|
||||
|
||||
**Cross-Domain Synthesis**
|
||||
- Can it bridge concepts between different fields?
|
||||
- Does it identify unexpected interdisciplinary connections?
|
||||
- How well does it support translation between domains?
|
||||
|
||||
**Research Productivity**
|
||||
- Does it reduce time spent searching for information?
|
||||
- Does it accelerate idea development processes?
|
||||
- Can it enhance collaborative research discussions?
|
||||
|
||||
## Specific Research Use Cases to Test
|
||||
|
||||
### Use Case 1: Literature Review
|
||||
**Scenario**: Conducting comprehensive review of AI fairness literature across computer science, law, and philosophy
|
||||
**Test**: Add 20+ papers from different domains and evaluate cross-referencing and synthesis capabilities
|
||||
|
||||
### Use Case 2: Hypothesis Development
|
||||
**Scenario**: Developing new theoretical framework connecting cognitive biases to AI system behaviors
|
||||
**Test**: Iteratively build theory through conversations and memory updates, track evolution
|
||||
|
||||
### Use Case 3: Interdisciplinary Bridge Building
|
||||
**Scenario**: Connecting philosophical ethics concepts to technical AI implementation challenges
|
||||
**Test**: Store concepts from both domains and evaluate connection discovery quality
|
||||
|
||||
### Use Case 4: Collaborative Research
|
||||
**Scenario**: Preparing for research discussions with colleagues from different disciplines
|
||||
**Test**: Use system to organize talking points and anticipate cross-domain questions
|
||||
|
||||
### Use Case 5: Writing Support
|
||||
**Scenario**: Organizing complex multi-paper argument for academic publication
|
||||
**Test**: Use memory system to structure arguments and track supporting evidence
|
||||
|
||||
## Expected Deliverables
|
||||
|
||||
### Research Impact Assessment
|
||||
1. **Knowledge Organization**: How effectively does it organize research across domains?
|
||||
2. **Discovery Enhancement**: Does it reveal connections that wouldn't be found otherwise?
|
||||
3. **Theory Development**: How well does it support iterative theoretical framework building?
|
||||
4. **Research Acceleration**: Does it meaningfully speed up research processes?
|
||||
5. **Creative Thinking**: Does it enhance or inhibit creative research thinking?
|
||||
|
||||
### Comparative Analysis
|
||||
**vs. Obsidian**: How does relationship discovery compare to manual linking?
|
||||
**vs. Zotero**: How does literature organization compare to traditional citation management?
|
||||
**vs. Notion**: How does flexible knowledge organization compare to structured databases?
|
||||
**vs. Roam Research**: How does bi-directional linking compare to AI-powered connections?
|
||||
|
||||
### Missing Capabilities for Research
|
||||
- Citation management and academic database integration
|
||||
- Visual knowledge graph manipulation and exploration
|
||||
- Collaboration features for research team coordination
|
||||
- Export capabilities for academic writing workflows
|
||||
- Integration with existing research tools and databases
|
||||
|
||||
### Unique Value Identification
|
||||
- What research capabilities does this system provide that no other tool offers?
|
||||
- How might AI-powered knowledge organization transform research workflows?
|
||||
- What new research methodologies might become possible?
|
||||
|
||||
## Success Metrics
|
||||
|
||||
**Revolutionary (9-10/10)**: Fundamentally changes how research is conducted
|
||||
**Excellent (7-8/10)**: Significant enhancement to research workflows with minor gaps
|
||||
**Good (5-6/10)**: Useful for specific research tasks but not comprehensive
|
||||
**Fair (3-4/10)**: Interesting capabilities but not practical for daily research use
|
||||
**Poor (1-2/10)**: Limited research value despite technological sophistication
|
||||
|
||||
**Focus Areas**: Prioritize creative knowledge work enhancement over technical metrics. The goal is to determine if this technology can meaningfully advance research thinking and discovery processes.
|
||||
|
||||
## Research Ethics Note
|
||||
All testing will use realistic but non-sensitive research concepts. No proprietary research ideas or unpublished work will be stored in the test system. Focus on publicly available knowledge synthesis and theoretical framework development.
|
||||
|
|
@ -1,219 +0,0 @@
|
|||
# Benchmark 1: Daily Driver Evaluation - Baseline Test
|
||||
|
||||
**Date**: 2025-08-10
|
||||
**System Version**: Mem0 Interface v1.0.0
|
||||
**Test Type**: Blind Black Box Testing
|
||||
**Duration**: 3-week simulated usage per agent
|
||||
|
||||
## Model Configuration
|
||||
|
||||
**Current Model Setup** (from .env file):
|
||||
```bash
|
||||
LOG_LEVEL=INFO
|
||||
CORS_ORIGINS=http://localhost:3000
|
||||
|
||||
# Model Configuration
|
||||
DEFAULT_MODEL=claude-sonnet-4
|
||||
EXTRACTION_MODEL=claude-sonnet-4
|
||||
FAST_MODEL=o4-mini
|
||||
ANALYTICAL_MODEL=gemini-2.5-pro
|
||||
REASONING_MODEL=claude-sonnet-4
|
||||
EXPERT_MODEL=o3
|
||||
```
|
||||
|
||||
**LLM Endpoint**: Custom OpenAI-compatible endpoint (veronica.pratikn.com/v1)
|
||||
**Embedding Model**: Google Gemini (models/gemini-embedding-001)
|
||||
**Vector Database**: PostgreSQL with pgvector
|
||||
**Graph Database**: Neo4j 5.18-community
|
||||
|
||||
## Testing Agents
|
||||
|
||||
### Agent 1: Professional Manager (Alex Chen)
|
||||
|
||||
**Role & Background:**
|
||||
- Project Manager at TechFlow Inc (fast-growing tech startup)
|
||||
- Manages 3 concurrent projects, 8 team members, multiple stakeholders
|
||||
- 5+ years experience in technical team management
|
||||
- High technical comfort level, efficiency-focused
|
||||
|
||||
**Test Scope:**
|
||||
- **Primary Focus**: Task tracking, meeting notes, deadline management, team coordination
|
||||
- **Projects Tested**: Aurora (main product), Zenith (performance optimization), Nexus (new feature development)
|
||||
- **Team Members**: 8 simulated team members with distinct roles and preferences
|
||||
- **Stakeholders**: CEO James, VP Maria (Engineering), VP Carlos (Product)
|
||||
- **User ID**: `alex_chen_pm`
|
||||
|
||||
**Testing Methodology:**
|
||||
- Week 1: Basic setup, team information storage, daily PM tasks
|
||||
- Week 2: Multi-project coordination, stakeholder management, context switching
|
||||
- Week 3: Crisis scenarios, bulk operations, scalability testing
|
||||
|
||||
### Agent 2: Creative Researcher (Dr. Sam Rivera)
|
||||
|
||||
**Role & Background:**
|
||||
- Independent researcher and consultant in AI Ethics and Cognitive Science
|
||||
- PhD in Cognitive Science, published author, interdisciplinary researcher
|
||||
- Works on 4-5 concurrent research threads simultaneously
|
||||
- Moderate-high technical comfort, values exploration over rigid structure
|
||||
|
||||
**Test Scope:**
|
||||
- **Primary Focus**: Research note organization, idea development, concept mapping, source tracking
|
||||
- **Research Domains**: AI ethics, cognitive science, philosophy, technology policy
|
||||
- **Key Concepts**: Cognitive bias, algorithmic fairness, dual-process theory, ethical AI
|
||||
- **Literature**: 20+ academic papers across multiple disciplines
|
||||
- **User ID**: `sam_rivera_researcher`
|
||||
|
||||
**Testing Methodology:**
|
||||
- Week 1: Research foundation building, literature integration, theory development
|
||||
- Week 2: Cross-domain exploration, interdisciplinary connections, methodology development
|
||||
- Week 3: Concept evolution tracking, writing support, collaborative research simulation
|
||||
|
||||
## Test Results Analysis
|
||||
|
||||
### Agent 1 (Professional Manager) Results
|
||||
|
||||
#### Core Functionality Scores
|
||||
- **Memory Intelligence**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Relationship Mapping**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Context Management**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Knowledge Synthesis**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
|
||||
**Overall Core Engine Quality**: **5/5** ⭐⭐⭐⭐⭐
|
||||
|
||||
#### Key Achievements
|
||||
1. **Multi-Project Context Management**: Successfully maintained context across 3 concurrent projects (Aurora, Zenith, Nexus)
|
||||
2. **Stakeholder Relationship Tracking**: Mapped complex relationships between CEO James, VP Maria, VP Carlos, and 8 team members
|
||||
3. **Automatic Relationship Generation**: Created 50+ meaningful relationships from minimal conversation inputs
|
||||
4. **Dynamic Information Updates**: Successfully updated CEO demo date (Jan 30 → Feb 15) with automatic cascade effect recognition
|
||||
5. **Resource Conflict Detection**: Identified Mike's dual allocation (Aurora DB + Zenith performance) as high-risk scenario
|
||||
6. **Decision Impact Analysis**: Connected Tom's security vulnerability discovery to all production deployment delays
|
||||
|
||||
#### Notable Evidence of Intelligence
|
||||
- **Context Switching Excellence**: Seamlessly moved between project contexts while maintaining relevant information
|
||||
- **Team Dynamics Understanding**: Tracked Sarah's promotion and expanded responsibilities across multiple projects
|
||||
- **Stakeholder Preference Learning**: Remembered that CEO James prefers business impact over technical details
|
||||
- **Timeline Integration**: Connected demo timing with quarterly review scheduling automatically
|
||||
|
||||
#### Workflow Integration Assessment
|
||||
- **Current PM Tool Replacement Potential**: High for knowledge management, medium for task execution
|
||||
- **Productivity Impact**: Significant reduction in context switching overhead
|
||||
- **Team Coordination Enhancement**: Excellent for tracking team member preferences and capabilities
|
||||
- **Decision History Tracking**: Superior to current tools for maintaining decision context and rationale
|
||||
|
||||
### Agent 2 (Creative Researcher) Results
|
||||
|
||||
#### Core Functionality Scores
|
||||
- **Knowledge Organization**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Discovery Potential**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Memory Architecture**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
- **Research Enhancement**: ⭐⭐⭐⭐⭐ (5/5)
|
||||
|
||||
**Overall Core Engine Quality**: **4.8/5** ⭐⭐⭐⭐⭐
|
||||
|
||||
#### Key Achievements
|
||||
1. **Sophisticated Memory Architecture**: Demonstrated user-specific isolation with comprehensive analytics tracking
|
||||
2. **Cross-Domain Synthesis Capability**: Showed potential for connecting psychology, AI, and philosophy concepts
|
||||
3. **Research Productivity Analytics**: Tracked usage patterns and knowledge growth metrics effectively
|
||||
4. **Memory Evolution Support**: Supported iterative theory development with versioning capabilities
|
||||
5. **Semantic Search Excellence**: Context-aware information organization beyond simple keyword matching
|
||||
|
||||
#### Research-Specific Capabilities
|
||||
- **Literature Integration**: Organized diverse sources with automatic relationship detection
|
||||
- **Theory Development Support**: Memory-enhanced conversations for framework building
|
||||
- **Concept Evolution Tracking**: Historical versioning for idea development over time
|
||||
- **Interdisciplinary Bridge Building**: Potential for unexpected connection discovery across domains
|
||||
|
||||
#### Research Workflow Assessment
|
||||
- **vs. Obsidian**: Superior AI-powered connection discovery vs. manual linking
|
||||
- **vs. Zotero**: Enhanced semantic organization beyond traditional citation management
|
||||
- **vs. Notion**: More flexible knowledge organization with AI-enhanced relationships
|
||||
- **vs. Roam Research**: AI-powered bi-directional connections vs. manual relationship creation
|
||||
|
||||
## System Performance Analysis
|
||||
|
||||
### Resource Constraints Encountered
|
||||
Both agents experienced:
|
||||
- **429 RESOURCE_EXHAUSTED errors**: Limited write operations during peak testing
|
||||
- **Quota limitations**: Restricted full functionality evaluation
|
||||
- **API availability**: Some operations succeeded while others failed due to resource limits
|
||||
|
||||
### Successful Operations
|
||||
- **Read operations**: Fully functional (memory retrieval, stats, relationship graphs)
|
||||
- **Health checks**: Consistent system status monitoring
|
||||
- **Analytics**: Comprehensive usage pattern tracking
|
||||
- **Search functionality**: Semantic search worked reliably when resources available
|
||||
|
||||
### Technical Architecture Strengths
|
||||
- **Graph-based knowledge organization**: Sophisticated entity/relationship separation
|
||||
- **User-specific analytics**: Comprehensive usage intelligence and progress tracking
|
||||
- **API-first design**: Enables unlimited wrapper development possibilities
|
||||
- **Memory versioning**: Tracks knowledge evolution over time effectively
|
||||
|
||||
## Competitive Analysis
|
||||
|
||||
### Unique Capabilities (Cannot Be Easily Replicated)
|
||||
1. **Memory-Enhanced Conversations**: Active context retrieval during discussions - unique in market
|
||||
2. **Automatic Relationship Inference**: Expert-level domain understanding for connection generation
|
||||
3. **Cross-Domain Synthesis**: AI-native intelligence for interdisciplinary insight discovery
|
||||
4. **Context Persistence Quality**: Nuanced understanding that persists across sessions
|
||||
5. **Dynamic Knowledge Evolution**: Real-time relationship updates based on new information
|
||||
|
||||
### Competitive Positioning
|
||||
**vs. Traditional Tools:**
|
||||
- **Notion/Obsidian**: Static linking vs. AI-powered relationship discovery
|
||||
- **Slack/Teams**: No memory persistence vs. comprehensive context retention
|
||||
- **Jira/Asana**: Task-focused vs. knowledge-relationship focused
|
||||
- **Research Tools**: Manual organization vs. AI-enhanced connection discovery
|
||||
|
||||
## Critical Insights
|
||||
|
||||
### Core Engine Strengths
|
||||
- **Memory Quality**: Both agents rated memory persistence and accuracy as exceptional
|
||||
- **Relationship Intelligence**: Automatic relationship generation exceeded expectations
|
||||
- **Context Management**: Superior handling of complex, multi-threaded conversations
|
||||
- **Knowledge Synthesis**: Demonstrated ability to combine information meaningfully
|
||||
|
||||
### Interface vs. Engine Quality Gap
|
||||
- **Core Engine**: 5/5 rating from both agents for underlying AI capabilities
|
||||
- **Interface Usability**: 2/5 rating due to API-only access limitations
|
||||
- **Gap Assessment**: UI/UX development needed, but core technology is exceptional
|
||||
|
||||
### Daily Driver Readiness
|
||||
**Current State**: Not ready for mainstream adoption due to interface limitations
|
||||
**Core Technology**: Ready for production use with proper frontend development
|
||||
**Competitive Moat**: Strong - core AI capabilities provide significant differentiation
|
||||
|
||||
## Recommendations for Future Benchmarks
|
||||
|
||||
### Model Comparison Framework
|
||||
1. **Consistent Agent Personas**: Use identical agent1.md and agent2.md prompts
|
||||
2. **Standardized Test Scenarios**: Same project names, team members, research concepts
|
||||
3. **Quantitative Metrics**: Track memory accuracy, relationship quality, response relevance
|
||||
4. **Resource Environment**: Ensure consistent system resources across model tests
|
||||
|
||||
### Key Metrics to Track
|
||||
- **Memory Persistence Quality**: Information retention accuracy across sessions
|
||||
- **Relationship Inference Accuracy**: Quality of automatically generated connections
|
||||
- **Context Switching Effectiveness**: Multi-thread conversation management
|
||||
- **Search Relevance**: Semantic search result quality and ranking
|
||||
- **Response Time Performance**: API response speed under different model configurations
|
||||
|
||||
### Model Variations to Test
|
||||
1. **Different LLM Endpoints**: Compare custom endpoint vs. OpenAI, Anthropic, Google
|
||||
2. **Model Size Variations**: Test different parameter sizes for memory processing
|
||||
3. **Embedding Model Alternatives**: Compare Google Gemini vs. OpenAI vs. local models
|
||||
4. **Model Combination Strategies**: Test different model allocations for different operations
|
||||
|
||||
## Conclusion
|
||||
|
||||
**Baseline Benchmark Summary:**
|
||||
- **Core Engine Quality**: Exceptional (4.9/5 average across both agents)
|
||||
- **Memory Intelligence**: Industry-leading capabilities for knowledge work
|
||||
- **Relationship Discovery**: Breakthrough technology for automatic connection identification
|
||||
- **Daily Driver Potential**: High with proper interface development
|
||||
|
||||
**Key Finding**: The Mem0 interface demonstrates **exceptional core AI capabilities** that both agents rated as revolutionary for their respective workflows. The underlying memory intelligence, relationship inference, and context management capabilities represent a significant technological breakthrough.
|
||||
|
||||
**Future Benchmark Value**: This baseline establishes the high-quality standard for core memory functionality. Future model comparisons should maintain this level of memory intelligence while potentially improving response speed, resource efficiency, or specialized domain knowledge.
|
||||
|
||||
**Competitive Position**: The core engine provides a strong competitive moat through AI-native capabilities that traditional tools cannot replicate. Interface development is the primary barrier to market adoption, not underlying technology quality.
|
||||
|
|
@ -7,8 +7,8 @@ services:
|
|||
POSTGRES_DB: ${POSTGRES_DB:-mem0_db}
|
||||
POSTGRES_USER: ${POSTGRES_USER:-mem0_user}
|
||||
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-mem0_password}
|
||||
expose:
|
||||
- "5432"
|
||||
ports:
|
||||
- "5433:5432"
|
||||
volumes:
|
||||
- postgres_data:/var/lib/postgresql/data
|
||||
- ./config/postgres-init.sql:/docker-entrypoint-initdb.d/init.sql
|
||||
|
|
@ -32,9 +32,9 @@ services:
|
|||
NEO4J_ACCEPT_LICENSE_AGREEMENT: yes
|
||||
NEO4J_dbms_security_procedures_unrestricted: apoc.*
|
||||
NEO4J_dbms_security_procedures_allowlist: apoc.*
|
||||
expose:
|
||||
- "7474" # HTTP - Internal only
|
||||
- "7687" # Bolt - Internal only
|
||||
ports:
|
||||
- "7474:7474" # HTTP
|
||||
- "7687:7687" # Bolt
|
||||
volumes:
|
||||
- neo4j_data:/data
|
||||
- neo4j_logs:/logs
|
||||
|
|
@ -55,7 +55,7 @@ services:
|
|||
container_name: mem0-backend
|
||||
environment:
|
||||
OPENAI_API_KEY: ${OPENAI_COMPAT_API_KEY}
|
||||
OPENAI_BASE_URL: ${OPENAI_COMPAT_BASE_URL}
|
||||
OPENAI_BASE_URL: https://veronica.pratikn.com/v1
|
||||
EMBEDDER_API_KEY: ${EMBEDDER_API_KEY:-AIzaSyA_}
|
||||
POSTGRES_HOST: postgres
|
||||
POSTGRES_PORT: 5432
|
||||
|
|
|
|||
Loading…
Reference in a new issue