Add full config.toml documentation and examples
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Vera-AI
176
README.md
176
README.md
@@ -146,6 +146,111 @@ docker compose up -d
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---
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## ⚙️ Configuration
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### Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `APP_UID` | `999` | Container user ID (match host) |
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| `APP_GID` | `999` | Container group ID (match host) |
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| `TZ` | `UTC` | Container timezone |
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| `VERA_DEBUG` | `false` | Enable debug logging |
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| `OPENROUTER_API_KEY` | - | Cloud model routing key |
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| `VERA_CONFIG_DIR` | `/app/config` | Config directory |
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| `VERA_PROMPTS_DIR` | `/app/prompts` | Prompts directory |
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| `VERA_LOG_DIR` | `/app/logs` | Debug logs directory |
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### config.toml
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Create `config/config.toml` with all settings:
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```toml
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[general]
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# ═══════════════════════════════════════════════════════════════
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# General Settings
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# ═══════════════════════════════════════════════════════════════
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# Ollama server URL
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ollama_host = "http://10.0.0.10:11434"
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# Qdrant vector database URL
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qdrant_host = "http://10.0.0.22:6333"
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# Collection name for memories
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qdrant_collection = "memories"
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# Embedding model for semantic search
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embedding_model = "snowflake-arctic-embed2"
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# Enable debug logging (set to true for verbose logs)
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debug = false
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[layers]
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# ═══════════════════════════════════════════════════════════════
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# Context Layer Settings
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# ═══════════════════════════════════════════════════════════════
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# Token budget for semantic memory layer
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# Controls how much curated memory can be included
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semantic_token_budget = 25000
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# Token budget for recent context layer
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# Controls how much recent conversation can be included
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context_token_budget = 22000
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# Number of recent turns to include in semantic search
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# Higher = more context, but slower
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semantic_search_turns = 2
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# Minimum similarity score for semantic search (0.0-1.0)
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# Higher = more relevant results, but fewer matches
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semantic_score_threshold = 0.6
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[curator]
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# ═══════════════════════════════════════════════════════════════
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# Curation Settings
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# ═══════════════════════════════════════════════════════════════
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# Time for daily curation (HH:MM format, 24-hour)
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# Processes raw memories from last 24h
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run_time = "02:00"
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# Time for monthly full curation (HH:MM format, 24-hour)
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# Processes ALL raw memories
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full_run_time = "03:00"
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# Day of month for full curation (1-28)
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full_run_day = 1
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# Model to use for curation
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# Should be a capable model for summarization
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curator_model = "gpt-oss:120b"
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```
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### prompts/ Directory
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Create `prompts/` directory with:
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**`prompts/curator_prompt.md`** - Prompt for memory curation:
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```markdown
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You are a memory curator. Your job is to summarize conversation turns
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into concise Q&A pairs that will be stored for future reference.
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Extract the key information and create clear, searchable entries.
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Focus on facts, decisions, and important context.
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```
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**`prompts/systemprompt.md`** - System context for Vera:
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```markdown
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You are Vera, an AI with persistent memory. You remember all previous
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conversations with this user and can reference them contextually.
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Use the provided context to give informed, personalized responses.
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```
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---
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## 🚀 Quick Start (From Source)
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```bash
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@@ -157,15 +262,20 @@ cd vera-ai-v2
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cp .env.example .env
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nano .env # Set APP_UID, APP_GID, TZ
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# 3. Create directories
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# 3. Create directories and config
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mkdir -p config prompts logs
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cp config.toml config/
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nano config/config.toml # Set ollama_host, qdrant_host
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# 4. Run
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# 4. Create prompts
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nano prompts/curator_prompt.md
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nano prompts/systemprompt.md
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# 5. Run
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docker compose build
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docker compose up -d
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# 5. Test
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# 6. Test
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curl http://localhost:11434/
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# Expected: {"status":"ok","ollama":"reachable"}
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```
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@@ -183,25 +293,18 @@ cd vera-ai-v2
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### Step 2: Environment Configuration
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Create `.env` file (or copy from `.env.example`):
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Create `.env` file:
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```bash
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# User/Group Configuration
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# IMPORTANT: Match these to your host user for volume permissions
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APP_UID=1000 # Run: id -u to get your UID
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APP_GID=1000 # Run: id -g to get your GID
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# Timezone Configuration
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# Affects curator schedule (daily at 02:00, monthly on 1st at 03:00)
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TZ=America/Chicago
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# Debug Logging
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VERA_DEBUG=false
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# Optional: Cloud Model Routing
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# OPENROUTER_API_KEY=your_api_key_here
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```
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### Step 3: Directory Structure
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@@ -220,39 +323,7 @@ ls -la prompts/
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### Step 4: Configure Services
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Edit `config/config.toml`:
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```toml
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[general]
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# Your Ollama server
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ollama_host = "http://10.0.0.10:11434"
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# Your Qdrant server
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qdrant_host = "http://10.0.0.22:6333"
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qdrant_collection = "memories"
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# Embedding model for semantic search
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embedding_model = "snowflake-arctic-embed2"
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debug = false
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[layers]
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# Token budgets for context layers
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semantic_token_budget = 25000
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context_token_budget = 22000
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semantic_search_turns = 2
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semantic_score_threshold = 0.6
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[curator]
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# Daily curator: processes recent 24h
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run_time = "02:00"
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# Monthly curator: processes ALL raw memories
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full_run_time = "03:00"
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full_run_day = 1 # Day of month (1st)
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# Model for curation
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curator_model = "gpt-oss:120b"
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```
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Edit `config/config.toml` (see full example above)
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### Step 5: Build and Run
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@@ -299,22 +370,7 @@ curl -X POST http://localhost:11434/api/chat \
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---
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## ⚙️ Configuration Reference
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### Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `APP_UID` | `999` | Container user ID (match host) |
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| `APP_GID` | `999` | Container group ID (match host) |
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| `TZ` | `UTC` | Container timezone |
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| `VERA_DEBUG` | `false` | Enable debug logging |
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| `OPENROUTER_API_KEY` | - | Cloud model routing key |
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| `VERA_CONFIG_DIR` | `/app/config` | Config directory |
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| `VERA_PROMPTS_DIR` | `/app/prompts` | Prompts directory |
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| `VERA_LOG_DIR` | `/app/logs` | Debug logs directory |
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### Volume Mappings
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## 📁 Volume Mappings
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| Host Path | Container Path | Mode | Purpose |
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|-----------|----------------|------|---------|
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