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Vera-AI

Vera (Latin): True — True AI

Persistent Memory Proxy for Ollama

A transparent proxy that gives your AI conversations lasting memory.

Docker License Gitea

Vera-AI sits between your AI client and Ollama, automatically augmenting conversations with relevant context from previous sessions.

Every conversation is stored in Qdrant vector database and retrieved contextually — giving your AI true memory.

🔄 How It Works

┌─────────────────────────────────────────────────────────────────────────────────┐
│                              REQUEST FLOW                                        │
└─────────────────────────────────────────────────────────────────────────────────┘

    ┌──────────┐         ┌──────────┐         ┌──────────┐         ┌──────────┐
    │  Client  │ ──(1)──▶│ Vera-AI  │ ──(3)──▶│  Ollama  │ ──(5)──▶│ Response │
    │  (You)   │         │  Proxy   │         │   LLM    │         │  to User │
    └──────────┘         └────┬─────┘         └──────────┘         └──────────┘
                              │
                              │ (2) Query semantic memory
                              │
                              ▼
                       ┌──────────┐
                       │ Qdrant   │
                       │ Vector DB│
                       └──────────┘
                              │
                              │ (4) Store conversation turn
                              │
                              ▼
                       ┌──────────┐
                       │ Memory   │
                       │ Storage  │
                       └──────────┘

🌟 Features

Feature Description
🧠 Persistent Memory Conversations stored in Qdrant, retrieved contextually
📅 Monthly Curation Daily + monthly cleanup of raw memories
🔍 4-Layer Context System + semantic + recent + current messages
👤 Configurable UID/GID Match container user to host for permissions
🌍 Timezone Support Scheduler runs in your local timezone
📝 Debug Logging Optional logs written to configurable directory
🐳 Docker Ready One-command build and run

📋 Prerequisites

Requirement Description
Ollama LLM inference server (e.g., http://10.0.0.10:11434)
Qdrant Vector database (e.g., http://10.0.0.22:6333)
Docker Docker and Docker Compose installed
Git For cloning the repository

🐳 Docker Deployment

Option 1: Docker Run (Single Command)

docker run -d \
  --name VeraAI \
  --restart unless-stopped \
  --network host \
  -e APP_UID=1000 \
  -e APP_GID=1000 \
  -e TZ=America/Chicago \
  -e VERA_DEBUG=false \
  -v ./config/config.toml:/app/config/config.toml:ro \
  -v ./prompts:/app/prompts:rw \
  -v ./logs:/app/logs:rw \
  your-username/vera-ai:latest

Option 2: Docker Compose

Create docker-compose.yml:

services:
  vera-ai:
    image: your-username/vera-ai:latest
    container_name: VeraAI
    restart: unless-stopped
    network_mode: host
    environment:
      - APP_UID=1000
      - APP_GID=1000
      - TZ=America/Chicago
      - VERA_DEBUG=false
    volumes:
      - ./config/config.toml:/app/config/config.toml:ro
      - ./prompts:/app/prompts:rw
      - ./logs:/app/logs:rw
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:11434/')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

Run with:

docker compose up -d

Docker Options Explained

Option Description
-d Run detached (background)
--name VeraAI Container name
--restart unless-stopped Auto-start on boot, survive reboots
--network host Use host network (port 11434)
-e APP_UID=1000 User ID (match your host UID)
-e APP_GID=1000 Group ID (match your host GID)
-e TZ=America/Chicago Timezone for scheduler
-e VERA_DEBUG=false Disable debug logging
-v ...:ro Config file (read-only)
-v ...:rw Prompts and logs (read-write)

🚀 Quick Start (From Source)

# 1. Clone
git clone http://10.0.0.61:3000/SpeedyFoxAi/vera-ai-v2.git
cd vera-ai-v2

# 2. Configure
cp .env.example .env
nano .env                    # Set APP_UID, APP_GID, TZ

# 3. Create directories
mkdir -p config prompts logs
cp config.toml config/

# 4. Run
docker compose build
docker compose up -d

# 5. Test
curl http://localhost:11434/
# Expected: {"status":"ok","ollama":"reachable"}

📖 Full Setup Guide

Step 1: Clone Repository

git clone http://10.0.0.61:3000/SpeedyFoxAi/vera-ai-v2.git
cd vera-ai-v2

Step 2: Environment Configuration

Create .env file (or copy from .env.example):

# User/Group Configuration
# IMPORTANT: Match these to your host user for volume permissions

APP_UID=1000    # Run: id -u  to get your UID
APP_GID=1000    # Run: id -g  to get your GID

# Timezone Configuration
# Affects curator schedule (daily at 02:00, monthly on 1st at 03:00)

TZ=America/Chicago

# Debug Logging
VERA_DEBUG=false

# Optional: Cloud Model Routing
# OPENROUTER_API_KEY=your_api_key_here

Step 3: Directory Structure

# Create required directories
mkdir -p config prompts logs

# Copy default configuration
cp config.toml config/

# Verify prompts exist
ls -la prompts/
# Should show: curator_prompt.md, systemprompt.md

Step 4: Configure Services

Edit config/config.toml:

[general]
# Your Ollama server
ollama_host = "http://10.0.0.10:11434"

# Your Qdrant server  
qdrant_host = "http://10.0.0.22:6333"
qdrant_collection = "memories"

# Embedding model for semantic search
embedding_model = "snowflake-arctic-embed2"
debug = false

[layers]
# Token budgets for context layers
semantic_token_budget = 25000
context_token_budget = 22000
semantic_search_turns = 2
semantic_score_threshold = 0.6

[curator]
# Daily curator: processes recent 24h
run_time = "02:00"

# Monthly curator: processes ALL raw memories
full_run_time = "03:00"
full_run_day = 1    # Day of month (1st)

# Model for curation
curator_model = "gpt-oss:120b"

Step 5: Build and Run

# Build with your UID/GID
APP_UID=$(id -u) APP_GID=$(id -g) docker compose build

# Start container
docker compose up -d

# Check status
docker ps
docker logs VeraAI --tail 20

Step 6: Verify Installation

# Health check
curl http://localhost:11434/
# Expected: {"status":"ok","ollama":"reachable"}

# Container status
docker ps --format "table {{.Names}}\t{{.Status}}"
# Expected: VeraAI   Up X minutes (healthy)

# Timezone
docker exec VeraAI date
# Should show your timezone (e.g., CDT for America/Chicago)

# User permissions
docker exec VeraAI id
# Expected: uid=1000(appuser) gid=1000(appgroup)

# Directories
docker exec VeraAI ls -la /app/prompts/
# Should show: curator_prompt.md, systemprompt.md

# Test chat
curl -X POST http://localhost:11434/api/chat \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5:397b-cloud","messages":[{"role":"user","content":"hello"}],"stream":false}'

⚙️ Configuration Reference

Environment Variables

Variable Default Description
APP_UID 999 Container user ID (match host)
APP_GID 999 Container group ID (match host)
TZ UTC Container timezone
VERA_DEBUG false Enable debug logging
OPENROUTER_API_KEY - Cloud model routing key
VERA_CONFIG_DIR /app/config Config directory
VERA_PROMPTS_DIR /app/prompts Prompts directory
VERA_LOG_DIR /app/logs Debug logs directory

Volume Mappings

Host Path Container Path Mode Purpose
./config/config.toml /app/config/config.toml ro Configuration
./prompts/ /app/prompts/ rw Curator prompts
./logs/ /app/logs/ rw Debug logs

Directory Structure

vera-ai-v2/
├── config/
│   └── config.toml        # Main configuration
├── prompts/
│   ├── curator_prompt.md  # Memory curation prompt
│   └── systemprompt.md    # System context
├── logs/                  # Debug logs (when debug=true)
├── app/
│   ├── main.py            # FastAPI application
│   ├── config.py          # Configuration loader
│   ├── curator.py         # Memory curation
│   ├── proxy_handler.py   # Chat handling
│   ├── qdrant_service.py  # Vector operations
│   ├── singleton.py       # QdrantService singleton
│   └── utils.py           # Utilities
├── static/                # Legacy symlinks
├── .env.example           # Environment template
├── docker-compose.yml     # Docker Compose
├── Dockerfile             # Container definition
├── requirements.txt       # Python dependencies
└── README.md              # This file

🌍 Timezone Configuration

The TZ variable sets the container timezone for the scheduler:

# Common timezones
TZ=UTC                  # Coordinated Universal Time
TZ=America/New_York     # Eastern Time
TZ=America/Chicago      # Central Time
TZ=America/Los_Angeles  # Pacific Time
TZ=Europe/London        # GMT/BST

Curation Schedule:

Schedule Time What Frequency
Daily 02:00 Recent 24h Every day
Monthly 03:00 on 1st ALL raw memories 1st of month

🔌 API Endpoints

Endpoint Method Description
/ GET Health check
/api/chat POST Chat completion (with memory)
/api/tags GET List available models
/api/generate POST Generate completion
/curator/run POST Trigger curator manually

Manual Curation

# Daily curation (recent 24h)
curl -X POST http://localhost:11434/curator/run

# Full curation (all raw memories)
curl -X POST "http://localhost:11434/curator/run?full=true"

🧠 Memory System

4-Layer Context Build

┌─────────────────────────────────────────────────────────────┐
│ Layer 1: System Prompt                                      │
│   • From prompts/systemprompt.md                            │
│   • Preserved unchanged, passed through                     │
├─────────────────────────────────────────────────────────────┤
│ Layer 2: Semantic Memory                                    │
│   • Query Qdrant with user question                         │
│   • Retrieve curated Q&A pairs by relevance                 │
│   • Limited by semantic_token_budget                        │
├─────────────────────────────────────────────────────────────┤
│ Layer 3: Recent Context                                     │
│   • Last N conversation turns from Qdrant                   │
│   • Chronological order, recent memories first              │
│   • Limited by context_token_budget                         │
├─────────────────────────────────────────────────────────────┤
│ Layer 4: Current Messages                                   │
│   • User message from current request                       │
│   • Passed through unchanged                                │
└─────────────────────────────────────────────────────────────┘

Memory Types

Type Description Retention
raw Unprocessed conversation turns Until curation
curated Cleaned Q&A pairs Permanent
test Test entries Can be ignored

Curation Process

  1. Daily (02:00): Processes raw memories from last 24h into curated Q&A pairs
  2. Monthly (03:00 on 1st): Processes ALL remaining raw memories for full cleanup

🔧 Troubleshooting

Permission Denied

# Check your UID/GID
id

# Rebuild with correct values
APP_UID=$(id -u) APP_GID=$(id -g) docker compose build --no-cache
docker compose up -d

Wrong Timezone

# Check container time
docker exec VeraAI date

# Fix in .env
TZ=America/Chicago

Health Check Failing

# Check logs
docker logs VeraAI --tail 50

# Test Ollama connectivity
docker exec VeraAI python -c "
import urllib.request
print(urllib.request.urlopen('http://YOUR_OLLAMA_IP:11434/').read())
"

# Test Qdrant connectivity
docker exec VeraAI python -c "
import urllib.request
print(urllib.request.urlopen('http://YOUR_QDRANT_IP:6333/').read())
"

Port Already in Use

# Check what's using port 11434
sudo lsof -i :11434

# Stop conflicting service or change port in config

🛠️ Development

Build from Source

git clone http://10.0.0.61:3000/SpeedyFoxAi/vera-ai-v2.git
cd vera-ai-v2
pip install -r requirements.txt
docker compose build

Run Tests

# Health check
curl http://localhost:11434/

# Non-streaming chat
curl -X POST http://localhost:11434/api/chat \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5:397b-cloud","messages":[{"role":"user","content":"test"}],"stream":false}'

# Trigger curation
curl -X POST http://localhost:11434/curator/run

📄 License

MIT License - see LICENSE file for details.

🤝 Support

Resource Link
Repository http://10.0.0.61:3000/SpeedyFoxAi/vera-ai-v2
Issues http://10.0.0.61:3000/SpeedyFoxAi/vera-ai-v2/issues

Vera-AI — True AI Memory

Brought to you by SpeedyFoxAi

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