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Add full config.toml documentation and examples

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Vera-AI
2026-03-26 13:17:44 -05:00
parent 4ff7b7b03b
commit b24f00c2e1
2 changed files with 192 additions and 78 deletions
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94
DOCKERHUB.md
View File

@@ -56,9 +56,9 @@ docker run -d \
-e APP_GID=1000 \
-e TZ=America/Chicago \
-e VERA_DEBUG=false \
-v /path/to/config/config.toml:/app/config/config.toml:ro \
-v /path/to/prompts:/app/prompts:rw \
-v /path/to/logs:/app/logs:rw \
-v ./config/config.toml:/app/config/config.toml:ro \
-v ./prompts:/app/prompts:rw \
-v ./logs:/app/logs:rw \
your-username/vera-ai:latest
```
@@ -82,9 +82,15 @@ services:
- ./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
```
Then run:
Run with:
```bash
docker compose up -d
@@ -92,16 +98,6 @@ docker compose up -d
---
## 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 installed |
---
## Configuration
### Environment Variables
@@ -119,22 +115,45 @@ Create `config/config.toml`:
```toml
[general]
ollama_host = "http://YOUR_OLLAMA_IP:11434"
qdrant_host = "http://YOUR_QDRANT_IP:6333"
# Ollama server URL
ollama_host = "http://10.0.0.10:11434"
# Qdrant vector database URL
qdrant_host = "http://10.0.0.22:6333"
# Collection name for memories
qdrant_collection = "memories"
# Embedding model for semantic search
embedding_model = "snowflake-arctic-embed2"
# Enable debug logging (set to true for verbose logs)
debug = false
[layers]
# Token budget for semantic memory layer
semantic_token_budget = 25000
# Token budget for recent context layer
context_token_budget = 22000
# Number of recent turns to include in semantic search
semantic_search_turns = 2
# Minimum similarity score for semantic search (0.0-1.0)
semantic_score_threshold = 0.6
[curator]
# Time for daily curation (HH:MM format)
run_time = "02:00"
# Time for monthly full curation (HH:MM format)
full_run_time = "03:00"
# Day of month for full curation (1-28)
full_run_day = 1
# Model to use for curation
curator_model = "gpt-oss:120b"
```
@@ -142,8 +161,47 @@ curator_model = "gpt-oss:120b"
Create `prompts/` directory with:
- `curator_prompt.md` - Prompt for memory curation
- `systemprompt.md` - System context for Vera
**`prompts/curator_prompt.md`** - Prompt for memory curation:
```markdown
You are a memory curator. Your job is to summarize conversation turns
into concise Q&A pairs that will be stored for future reference.
Extract the key information and create clear, searchable entries.
```
**`prompts/systemprompt.md`** - System context for Vera:
```markdown
You are Vera, an AI with persistent memory. You remember all previous
conversations with this user and can reference them contextually.
```
---
## 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 ...config.toml:ro` | Config file (read-only) |
| `-v ...prompts:rw` | Prompts directory (read-write) |
| `-v ...logs:rw` | Logs directory (read-write) |
---
## 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 installed |
---

176
README.md
View File

@@ -146,6 +146,111 @@ docker compose up -d
---
## ⚙️ Configuration
### 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 |
### config.toml
Create `config/config.toml` with all settings:
```toml
[general]
# ═══════════════════════════════════════════════════════════════
# General Settings
# ═══════════════════════════════════════════════════════════════
# Ollama server URL
ollama_host = "http://10.0.0.10:11434"
# Qdrant vector database URL
qdrant_host = "http://10.0.0.22:6333"
# Collection name for memories
qdrant_collection = "memories"
# Embedding model for semantic search
embedding_model = "snowflake-arctic-embed2"
# Enable debug logging (set to true for verbose logs)
debug = false
[layers]
# ═══════════════════════════════════════════════════════════════
# Context Layer Settings
# ═══════════════════════════════════════════════════════════════
# Token budget for semantic memory layer
# Controls how much curated memory can be included
semantic_token_budget = 25000
# Token budget for recent context layer
# Controls how much recent conversation can be included
context_token_budget = 22000
# Number of recent turns to include in semantic search
# Higher = more context, but slower
semantic_search_turns = 2
# Minimum similarity score for semantic search (0.0-1.0)
# Higher = more relevant results, but fewer matches
semantic_score_threshold = 0.6
[curator]
# ═══════════════════════════════════════════════════════════════
# Curation Settings
# ═══════════════════════════════════════════════════════════════
# Time for daily curation (HH:MM format, 24-hour)
# Processes raw memories from last 24h
run_time = "02:00"
# Time for monthly full curation (HH:MM format, 24-hour)
# Processes ALL raw memories
full_run_time = "03:00"
# Day of month for full curation (1-28)
full_run_day = 1
# Model to use for curation
# Should be a capable model for summarization
curator_model = "gpt-oss:120b"
```
### prompts/ Directory
Create `prompts/` directory with:
**`prompts/curator_prompt.md`** - Prompt for memory curation:
```markdown
You are a memory curator. Your job is to summarize conversation turns
into concise Q&A pairs that will be stored for future reference.
Extract the key information and create clear, searchable entries.
Focus on facts, decisions, and important context.
```
**`prompts/systemprompt.md`** - System context for Vera:
```markdown
You are Vera, an AI with persistent memory. You remember all previous
conversations with this user and can reference them contextually.
Use the provided context to give informed, personalized responses.
```
---
## 🚀 Quick Start (From Source)
```bash
@@ -157,15 +262,20 @@ cd vera-ai-v2
cp .env.example .env
nano .env # Set APP_UID, APP_GID, TZ
# 3. Create directories
# 3. Create directories and config
mkdir -p config prompts logs
cp config.toml config/
nano config/config.toml # Set ollama_host, qdrant_host
# 4. Run
# 4. Create prompts
nano prompts/curator_prompt.md
nano prompts/systemprompt.md
# 5. Run
docker compose build
docker compose up -d
# 5. Test
# 6. Test
curl http://localhost:11434/
# Expected: {"status":"ok","ollama":"reachable"}
```
@@ -183,25 +293,18 @@ cd vera-ai-v2
### Step 2: Environment Configuration
Create `.env` file (or copy from `.env.example`):
Create `.env` file:
```bash
# 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
@@ -220,39 +323,7 @@ ls -la prompts/
### Step 4: Configure Services
Edit `config/config.toml`:
```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"
```
Edit `config/config.toml` (see full example above)
### Step 5: Build and Run
@@ -299,22 +370,7 @@ curl -X POST http://localhost:11434/api/chat \
---
## ⚙️ 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
## 📁 Volume Mappings
| Host Path | Container Path | Mode | Purpose |
|-----------|----------------|------|---------|