Vera-AI: Persistent Memory Proxy for Ollama

Vera-AI is a transparent proxy for Ollama that adds persistent memory using Qdrant vector storage. It sits between your AI client and Ollama, automatically augmenting conversations with relevant context from previous sessions.

Features

• Persistent Memory: Conversations are stored in Qdrant and retrieved contextually
• Monthly Curation: Daily and monthly cleanup of raw memories
• 4-Layer Context: System prompt + semantic memory + recent context + current messages
• Configurable UID/GID: Match container user to host user for volume permissions
• Timezone Support: Scheduler runs in your local timezone
• Debug Logging: Optional debug logs written to configurable directory

Prerequisites

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

Quick Start

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

# Create environment file from template
cp .env.example .env

# Edit .env with your settings
nano .env

# Create required directories
mkdir -p config prompts logs

# Copy default config (or create your own)
cp config.toml config/

# Build and run
docker compose build
docker compose up -d

# Test
curl http://localhost:11434/

Full Setup Instructions

1. Clone Repository

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

2. Create Environment File

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

# User/Group Configuration (match your host user)
APP_UID=1000
APP_GID=1000

# Timezone Configuration
TZ=America/Chicago

# API Keys (optional)
# OPENROUTER_API_KEY=your_api_key_here

Important: APP_UID and APP_GID must match your host user's UID/GID for volume permissions:

# Get your UID and GID
id -u   # UID
id -g   # GID

# Set in .env
APP_UID=1000  # Replace with your UID
APP_GID=1000  # Replace with your GID

3. Create Required Directories

# Create directories
mkdir -p config prompts logs

# Copy default configuration
cp config.toml config/

# Verify prompts exist (should be in the repo)
ls -la prompts/
# Should show: curator_prompt.md, systemprompt.md

4. Configure Ollama and Qdrant

Edit config/config.toml:

[general]
ollama_host = "http://YOUR_OLLAMA_IP:11434"
qdrant_host = "http://YOUR_QDRANT_IP:6333"
qdrant_collection = "memories"
embedding_model = "snowflake-arctic-embed2"
debug = false

[layers]
semantic_token_budget = 25000
context_token_budget = 22000
semantic_search_turns = 2
semantic_score_threshold = 0.6

[curator]
run_time = "02:00"           # Daily curator time
full_run_time = "03:00"      # Monthly full curator time
full_run_day = 1             # Day of month (1st)
curator_model = "gpt-oss:120b"

5. Build and Run

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

# Run with timezone
docker compose up -d

# Check status
docker ps
docker logs vera-ai --tail 20

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

6. Verify Installation

# Check container is healthy
docker ps --format "table {{.Names}}\t{{.Status}}"
# Expected: vera-ai   Up X minutes (healthy)

# Check timezone
docker exec vera-ai date
# Should show your timezone (e.g., CDT for America/Chicago)

# Check user
docker exec vera-ai id
# Expected: uid=1000(appuser) gid=1000(appgroup)

# Check directories
docker exec vera-ai ls -la /app/prompts/
# Should show: curator_prompt.md, systemprompt.md

docker exec vera-ai ls -la /app/logs/
# Should be writable

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

Configuration

Environment Variables (.env)

Variable	Default	Description
APP_UID	999	User ID for container user (match your host UID)
APP_GID	999	Group ID for container group (match your host GID)
TZ	UTC	Timezone for scheduler
OPENROUTER_API_KEY	-	API key for cloud model routing (optional)
VERA_CONFIG_DIR	/app/config	Configuration directory (optional)
VERA_PROMPTS_DIR	/app/prompts	Prompts directory (optional)
VERA_LOG_DIR	/app/logs	Debug log directory (optional)

Volume Mappings

Host Path	Container Path	Mode	Purpose
./config/config.toml	/app/config/config.toml	ro	Configuration file
./prompts/	/app/prompts/	rw	Curator and system prompts
./logs/	/app/logs/	rw	Debug logs (when debug=true)

Directory Structure

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

Docker Compose

services:
  vera-ai:
    build:
      context: .
      dockerfile: Dockerfile
      args:
        APP_UID: ${APP_UID:-999}
        APP_GID: ${APP_GID:-999}
    image: vera-ai:latest
    container_name: vera-ai
    env_file:
      - .env
    volumes:
      - ./config/config.toml:/app/config/config.toml:ro
      - ./prompts:/app/prompts:rw
      - ./logs:/app/logs:rw
    network_mode: "host"
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:11434/')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

Timezone Configuration

The TZ environment variable sets the container timezone, which affects the scheduler:

# .env file
TZ=America/Chicago

# Scheduler runs at:
# - Daily curator: 02:00 Chicago time
# - Monthly curator: 03:00 Chicago time on 1st

Common timezones:

• UTC - Coordinated Universal Time
• America/New_York - Eastern Time
• America/Chicago - Central Time
• America/Los_Angeles - Pacific Time
• Europe/London - GMT/BST

API Endpoints

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

Manual Curator Trigger

# 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

1. System Prompt: From prompts/systemprompt.md
2. Semantic Memory: Curated Q&A pairs retrieved by relevance
3. Recent Context: Last N conversation turns
4. Current Messages: User/assistant messages from request

Curation Schedule

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

Memory Types

• raw: Unprocessed conversation turns
• curated: Cleaned, summarized Q&A pairs
• test: Test entries (can be ignored)

Troubleshooting

Permission Denied

If you see permission errors on /app/prompts/ or /app/logs/:

# Check your UID/GID
id

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

Timezone Issues

If curator runs at wrong time:

# Check container timezone
docker exec vera-ai date

# Set correct timezone in .env
TZ=America/Chicago

Health Check Failing

# Check container logs
docker logs vera-ai --tail 50

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

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

Container Not Starting

# Check if port is in use
sudo lsof -i :11434

# Check Docker logs
docker compose logs

# Rebuild from scratch
docker compose down
docker compose build --no-cache
docker compose up -d

Development

Building from Source

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

# Install dependencies locally (optional)
pip install -r requirements.txt

# Build Docker image
docker compose build

Running Tests

# Test health endpoint
curl http://localhost:11434/

# Test chat endpoint
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}'

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

License

MIT License - see LICENSE file for details.

Support

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