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docs: Add comprehensive documentation for Routstr Core project

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Co-authored-by: db2002dominic <db2002dominic@gmail.com>
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Cursor Agent
2025-08-25 01:17:40 +00:00
co-authored by db2002dominic
parent 73e701895d
commit d4c1389249
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.env.example
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# NAME = "Your Routstr Proxy Name" # Core Configuration
# DESCRIPTION = "A short Description" UPSTREAM_BASE_URL=https://api.openai.com/v1
UPSTREAM_API_KEY=your-upstream-api-key
ADMIN_PASSWORD=secure-admin-password
# Any openai-compatible api endpoint # Database
UPSTREAM_BASE_URL="https://api.openai.com/v1" DATABASE_URL=sqlite+aiosqlite:///keys.db
UPSTREAM_API_KEY="sk-21212121212121212121212121212121"
# UPSTREAM_PROVIDER_FEE=1 # 1 = no fees, 1.05 = 5% fees
# Lightning address used to receive funds # Node Information
NAME=My Routstr Node
DESCRIPTION=Fast AI API access with Bitcoin payments
NPUB=npub1...
HTTP_URL=https://api.mynode.com
ONION_URL=http://mynode.onion
# When your cashu balance reaches this number of sats, send the funds to RECEIVE_LN_ADDRESS. # Cashu Configuration
# RECEIVE_LN_ADDRESS="user@minibits.cash" CASHU_MINTS=https://mint.minibits.cash/Bitcoin
#MINIMUM_PAYOUT = "100" RECEIVE_LN_ADDRESS=
# If set to true, pricing is loaded from the file specified by MODELS_PATH # Pricing Configuration
# Defaults to "models.json" and falls back to "models.example.json" if missing MODEL_BASED_PRICING=true
# MODEL_BASED_PRICING = "true" COST_PER_REQUEST=1
# MODELS_PATH="models.json" COST_PER_1K_INPUT_TOKENS=0
COST_PER_1K_OUTPUT_TOKENS=0
EXCHANGE_FEE=1.005
UPSTREAM_PROVIDER_FEE=1.05
# Costs in Sats, if MODEL_BASED_PRICING is set to false # Network Configuration
# COST_PER_REQUEST="10" CORS_ORIGINS=*
# COST_PER_1K_INPUT_TOKENS = "0" TOR_PROXY_URL=socks5://127.0.0.1:9050
# COST_PER_1K_OUTPUT_TOKENS = "0"
# EXCHANGE_FEE = "1.005" # 0.5 % currency exchange fee
# password used to log into admin interface # Logging
# ADMIN_PASSWORD="" LOG_LEVEL=INFO
ENABLE_CONSOLE_LOGGING=true
# Public Endpoint # Model Management
# HTTP_URL="https://your.domain.com" MODELS_PATH=models.json
BASE_URL=https://openrouter.ai/api/v1
SOURCE=
# Tor Endpoint (copy from docker logs) # Optional Features
# ONION_URL=".onion" PREPAID_API_KEY=
PREPAID_BALANCE=0
# RELAYS="wss://relay.routstr.com,wss://relay.nostr.band"
# CASHU_MINTS="https://mint.minibits.cash/Bitcoin,https://mint.cubabitcoin.org"
# Development
# DEBUG=TRUE
# LOG_LEVEL=TRACE
Makefile
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@echo "🔥 Running with profiling..." @echo "🔥 Running with profiling..."
$(PYTHON) -m cProfile -o profile.stats -m pytest tests/integration/test_performance_load.py::TestPerformanceBaseline -v $(PYTHON) -m cProfile -o profile.stats -m pytest tests/integration/test_performance_load.py::TestPerformanceBaseline -v
@echo "Profile saved to profile.stats. Use '$(PYTHON) -m pstats profile.stats' to analyze." @echo "Profile saved to profile.stats. Use '$(PYTHON) -m pstats profile.stats' to analyze."
# Documentation
docs-build:
@echo "📚 Building documentation..."
mkdocs build
docs-serve:
@echo "📚 Serving documentation at http://localhost:8001..."
mkdocs serve -a localhost:8001
docs-deploy:
@echo "📚 Deploying documentation to GitHub Pages..."
mkdocs gh-deploy --force
docs-install:
@echo "📚 Installing documentation dependencies..."
pip install -r docs/requirements.txt
build-docs.sh
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#!/bin/bash
# Build script for MkDocs documentation
echo "Building Routstr Core documentation..."
# Check if mkdocs is installed
if ! command -v mkdocs &> /dev/null; then
echo "MkDocs not found. Installing dependencies..."
pip install -r docs/requirements.txt
fi
# Build the documentation
echo "Building docs..."
mkdocs build
# Serve locally for preview (optional)
if [ "$1" = "serve" ]; then
echo "Starting documentation server at http://localhost:8001"
mkdocs serve -a localhost:8001
elif [ "$1" = "deploy" ]; then
echo "Deploying to GitHub Pages..."
mkdocs gh-deploy --force
else
echo "Documentation built successfully in ./site/"
echo "Run './build-docs.sh serve' to preview locally"
echo "Run './build-docs.sh deploy' to deploy to GitHub Pages"
fi
docs/README.md
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# Routstr Core Documentation
This directory contains the comprehensive documentation for Routstr Core, built with MkDocs.
## Structure
```
docs/
├── index.md # Home page
├── getting-started/ # Quick start guides
│ ├── overview.md # Project overview
│ ├── quickstart.md # Quick setup guide
│ ├── docker.md # Docker deployment
│ └── configuration.md # Configuration options
├── user-guide/ # User documentation
│ ├── introduction.md # User guide intro
│ ├── payment-flow.md # Payment process
│ ├── using-api.md # API usage examples
│ ├── admin-dashboard.md # Admin interface
│ └── models-pricing.md # Pricing details
├── contributing/ # Developer documentation
│ ├── setup.md # Development setup
│ ├── architecture.md # System architecture
│ ├── code-structure.md # Codebase organization
│ ├── testing.md # Testing guide
│ ├── database.md # Database design
│ └── guidelines.md # Contribution guidelines
├── api/ # API reference
│ ├── overview.md # API overview
│ ├── authentication.md # Auth details
│ ├── endpoints.md # Endpoint reference
│ └── errors.md # Error handling
└── advanced/ # Advanced topics
├── tor.md # Tor support
├── nostr.md # Nostr discovery
├── custom-pricing.md # Pricing strategies
└── migrations.md # Database migrations
```
## Building Documentation
### Requirements
Install MkDocs and dependencies:
```bash
pip install -r docs/requirements.txt
```
Or using the Makefile:
```bash
make docs-install
```
### Local Development
Serve documentation locally with live reload:
```bash
mkdocs serve
# Or
make docs-serve
```
Visit http://localhost:8001 to view the documentation.
### Building Static Site
Build the static documentation site:
```bash
mkdocs build
# Or
make docs-build
```
The built site will be in the `site/` directory.
### Deploying to GitHub Pages
Deploy documentation to GitHub Pages:
```bash
mkdocs gh-deploy
# Or
make docs-deploy
```
## Writing Documentation
### Style Guide
- Use clear, concise language
- Include code examples
- Add diagrams where helpful
- Keep sections focused
- Test all code examples
### Markdown Extensions
Available extensions:
- **Admonition** - Notes, warnings, tips
- **Code blocks** - Syntax highlighting
- **Mermaid** - Diagrams and flowcharts
- **Tables** - Data presentation
- **Task lists** - Checklists
### Examples
#### Admonitions
```markdown
!!! note
This is a note.
!!! warning
This is a warning.
!!! tip
This is a tip.
```
#### Mermaid Diagrams
```markdown
```mermaid
graph LR
A[Client] --> B[Routstr]
B --> C[Provider]
```
```
#### Code Blocks
```markdown
```python
def example():
return "Hello, Routstr!"
```
```
## Contributing to Docs
1. Edit markdown files in the appropriate directory
2. Test locally with `mkdocs serve`
3. Submit PR with changes
4. Documentation auto-deploys on merge
## Resources
- [MkDocs Documentation](https://www.mkdocs.org/)
- [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/)
- [Mermaid Diagrams](https://mermaid-js.github.io/mermaid/)
docs/advanced/custom-pricing.md
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# Custom Pricing
This guide covers advanced pricing strategies and customization options for Routstr Core.
## Pricing Models Overview
Routstr supports three pricing models:
1. **Fixed Pricing** - Simple per-request fee
2. **Token-Based Pricing** - Charge per input/output token
3. **Model-Based Pricing** - Dynamic pricing from models.json
## Model-Based Pricing
### Configuration
Enable model-based pricing:
```bash
# .env
MODEL_BASED_PRICING=true
MODELS_PATH=/app/config/models.json
EXCHANGE_FEE=1.005 # 0.5% exchange fee
UPSTREAM_PROVIDER_FEE=1.05 # 5% provider margin
```
### Custom Models File
Create a `models.json` with your pricing:
```json
{
"models": [
{
"id": "gpt-4",
"name": "GPT-4",
"description": "Advanced reasoning model",
"context_length": 8192,
"pricing": {
"prompt": "0.03", // USD per 1K tokens
"completion": "0.06", // USD per 1K tokens
"request": "0.0001", // Fixed per-request fee
"image": "0", // For multimodal models
"web_search": "0.005", // Additional features
"internal_reasoning": "0.01"
},
"supported_features": [
"function_calling",
"vision",
"json_mode"
],
"deprecation_date": null,
"replacement_model": null
},
{
"id": "custom-model",
"name": "Custom Fine-tuned Model",
"pricing": {
"prompt": "0.001",
"completion": "0.002",
"request": "0.00005"
},
"minimum_charge": "0.0001", // Minimum charge per request
"volume_discounts": {
"1000": 0.95, // 5% off after 1K requests
"10000": 0.90, // 10% off after 10K requests
"100000": 0.85 // 15% off after 100K requests
}
}
],
"default_pricing": {
"prompt": "0.002",
"completion": "0.002",
"request": "0"
}
}
```
### Dynamic Price Updates
Automatically fetch prices from providers:
```python
# scripts/update_prices.py
import asyncio
import httpx
import json
async def fetch_openrouter_models():
"""Fetch current model pricing from OpenRouter."""
async with httpx.AsyncClient() as client:
response = await client.get(
"https://openrouter.ai/api/v1/models"
)
return response.json()
async def update_models_json():
"""Update local models.json with latest prices."""
data = await fetch_openrouter_models()
models = []
for model in data['data']:
models.append({
"id": model['id'],
"name": model['name'],
"pricing": {
"prompt": model['pricing']['prompt'],
"completion": model['pricing']['completion'],
"request": model['pricing'].get('request', '0')
},
"context_length": model.get('context_length', 4096)
})
with open('models.json', 'w') as f:
json.dump({"models": models}, f, indent=2)
# Run periodically
if __name__ == "__main__":
asyncio.run(update_models_json())
```
## Token-Based Pricing
### Configuration
Set up token-based pricing:
```bash
# .env
MODEL_BASED_PRICING=false
COST_PER_REQUEST=1 # 1 sat base fee
COST_PER_1K_INPUT_TOKENS=5 # 5 sats per 1K input
COST_PER_1K_OUTPUT_TOKENS=15 # 15 sats per 1K output
```
### Custom Token Counting
Override default token counting:
```python
from tiktoken import encoding_for_model
class CustomTokenCounter:
def __init__(self):
self.encodings = {}
def count_tokens(
self,
text: str,
model: str
) -> int:
"""Custom token counting logic."""
# Cache encodings
if model not in self.encodings:
try:
self.encodings[model] = encoding_for_model(model)
except:
# Fallback encoding
self.encodings[model] = encoding_for_model("gpt-3.5-turbo")
encoding = self.encodings[model]
# Special handling for certain content
if text.startswith("```"):
# Code blocks might need special handling
tokens = encoding.encode(text)
return len(tokens) * 1.1 # 10% markup for code
return len(encoding.encode(text))
```
## Advanced Pricing Strategies
### Time-Based Pricing
Implement peak/off-peak pricing:
```python
from datetime import datetime
import pytz
class TimeBased PricingStrategy:
def __init__(self):
self.timezone = pytz.timezone('US/Eastern')
self.peak_hours = [(9, 17)] # 9 AM - 5 PM
self.peak_multiplier = 1.5
self.weekend_discount = 0.8
def get_price_multiplier(self) -> float:
"""Calculate price multiplier based on time."""
now = datetime.now(self.timezone)
# Weekend discount
if now.weekday() >= 5: # Saturday or Sunday
return self.weekend_discount
# Peak hours surcharge
hour = now.hour
for start, end in self.peak_hours:
if start <= hour < end:
return self.peak_multiplier
# Off-peak standard pricing
return 1.0
def apply_to_cost(self, base_cost: int) -> int:
"""Apply time-based pricing to cost."""
multiplier = self.get_price_multiplier()
return int(base_cost * multiplier)
```
### Volume Discounts
Implement tiered pricing based on usage:
```python
class VolumeDiscountStrategy:
def __init__(self):
self.tiers = [
(0, 1.0), # 0-999: Full price
(1000, 0.95), # 1K-9.9K: 5% off
(10000, 0.90), # 10K-99.9K: 10% off
(100000, 0.85), # 100K+: 15% off
]
async def get_discount_rate(
self,
api_key_id: int,
session: AsyncSession
) -> float:
"""Get discount rate based on usage volume."""
# Count requests in current month
start_of_month = datetime.now().replace(
day=1, hour=0, minute=0, second=0
)
result = await session.execute(
select(func.count(Transaction.id))
.where(Transaction.api_key_id == api_key_id)
.where(Transaction.timestamp >= start_of_month)
.where(Transaction.type == TransactionType.USAGE)
)
request_count = result.scalar()
# Find applicable tier
for threshold, rate in reversed(self.tiers):
if request_count >= threshold:
return rate
return 1.0
```
### Model-Specific Surcharges
Add custom fees for specific models:
```python
class ModelSurchargeStrategy:
def __init__(self):
self.surcharges = {
"gpt-4-turbo": 1.1, # 10% premium
"claude-3-opus": 1.15, # 15% premium
"dall-e-3-hd": 1.25, # 25% premium for HD
}
self.discounts = {
"gpt-3.5-turbo": 0.95, # 5% discount
"deprecated-model": 0.8, # 20% discount
}
def get_model_multiplier(self, model: str) -> float:
"""Get price multiplier for model."""
if model in self.surcharges:
return self.surcharges[model]
elif model in self.discounts:
return self.discounts[model]
return 1.0
```
### Geographic Pricing
Adjust pricing based on client location:
```python
import geoip2.database
class GeographicPricingStrategy:
def __init__(self):
self.reader = geoip2.database.Reader('GeoLite2-Country.mmdb')
self.country_multipliers = {
'US': 1.0,
'GB': 1.0,
'DE': 1.0,
'IN': 0.7, # 30% discount
'BR': 0.8, # 20% discount
'NG': 0.6, # 40% discount
}
self.default_multiplier = 0.9
def get_country_multiplier(self, ip_address: str) -> float:
"""Get price multiplier based on country."""
try:
response = self.reader.country(ip_address)
country_code = response.country.iso_code
return self.country_multipliers.get(
country_code,
self.default_multiplier
)
except:
return 1.0 # Default pricing if lookup fails
```
## Cost Calculation Pipeline
### Implementing Custom Calculator
```python
from abc import ABC, abstractmethod
class CostCalculator(ABC):
@abstractmethod
async def calculate(
self,
request_data: dict,
usage_data: dict,
context: dict
) -> CostResult:
pass
class CompositeCostCalculator(CostCalculator):
"""Combine multiple pricing strategies."""
def __init__(self):
self.strategies = [
BaseCostCalculator(),
TimeBasedPricingStrategy(),
VolumeDiscountStrategy(),
ModelSurchargeStrategy(),
GeographicPricingStrategy()
]
async def calculate(
self,
request_data: dict,
usage_data: dict,
context: dict
) -> CostResult:
# Start with base cost
base_cost = await self.strategies[0].calculate(
request_data, usage_data, context
)
# Apply each strategy
final_cost = base_cost.total_msats
breakdown = {"base": base_cost.total_msats}
for strategy in self.strategies[1:]:
multiplier = await strategy.get_multiplier(context)
adjustment = final_cost * (multiplier - 1)
final_cost += adjustment
breakdown[strategy.__class__.__name__] = adjustment
return CostResult(
total_msats=int(final_cost),
breakdown=breakdown
)
```
### Integration with Routstr
```python
# In routstr/payment/cost_calculation.py
async def calculate_request_cost(
model: str,
prompt_tokens: int,
completion_tokens: int,
request_type: str,
context: dict
) -> CostData:
"""Enhanced cost calculation with custom strategies."""
# Use custom calculator if configured
if os.getenv("USE_CUSTOM_PRICING", "false").lower() == "true":
calculator = CompositeCostCalculator()
result = await calculator.calculate(
request_data={
"model": model,
"type": request_type
},
usage_data={
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens
},
context=context
)
return result
# Fall back to standard calculation
return standard_calculate_cost(...)
```
## Monitoring Pricing
### Price Analytics
Track pricing effectiveness:
```python
class PricingAnalytics:
async def analyze_pricing(
self,
start_date: datetime,
end_date: datetime
):
"""Analyze pricing performance."""
# Average cost per request by model
model_costs = await self.get_average_costs_by_model(
start_date, end_date
)
# Revenue by pricing strategy
strategy_revenue = await self.get_revenue_by_strategy(
start_date, end_date
)
# Price elasticity
elasticity = await self.calculate_price_elasticity()
return {
"model_costs": model_costs,
"strategy_revenue": strategy_revenue,
"price_elasticity": elasticity,
"recommendations": self.generate_recommendations(
model_costs, elasticity
)
}
```
### A/B Testing Prices
Test different pricing strategies:
```python
class PricingExperiment:
def __init__(self):
self.experiments = {
"exp_001": {
"name": "10% discount test",
"group_a": {"multiplier": 1.0},
"group_b": {"multiplier": 0.9},
"allocation": 0.5 # 50/50 split
}
}
def assign_group(self, api_key_id: int) -> str:
"""Assign API key to experiment group."""
# Consistent assignment based on key ID
import hashlib
hash_value = int(hashlib.md5(
str(api_key_id).encode()
).hexdigest()[:8], 16)
return "group_b" if (hash_value % 100) < 50 else "group_a"
def get_experiment_multiplier(
self,
api_key_id: int,
experiment_id: str
) -> float:
"""Get price multiplier for experiment."""
experiment = self.experiments.get(experiment_id)
if not experiment:
return 1.0
group = self.assign_group(api_key_id)
return experiment[group]["multiplier"]
```
## Configuration Examples
### Enterprise Pricing
```json
{
"models": [
{
"id": "gpt-4-enterprise",
"name": "GPT-4 Enterprise",
"pricing": {
"prompt": "0.02",
"completion": "0.04"
},
"minimum_commitment": "1000", // $1000/month minimum
"sla": {
"uptime": "99.9%",
"support_response": "1 hour",
"dedicated_capacity": true
},
"volume_discounts": {
"10000": 0.95,
"100000": 0.90,
"1000000": 0.85
}
}
],
"enterprise_features": {
"priority_queue": true,
"custom_models": true,
"audit_logs": true,
"sso": true
}
}
```
### Budget-Friendly Options
```json
{
"models": [
{
"id": "gpt-3.5-turbo-budget",
"name": "GPT-3.5 Turbo Budget",
"pricing": {
"prompt": "0.0005",
"completion": "0.001"
},
"restrictions": {
"max_tokens_per_request": 1000,
"requests_per_minute": 10,
"peak_hours_blocked": true
}
}
],
"prepaid_packages": [
{
"name": "Starter Pack",
"price_usd": 10,
"tokens_included": 10000000,
"expires_days": 30
}
]
}
```
## Troubleshooting
### Price Calculation Issues
```python
# Debug pricing
async def debug_price_calculation(
model: str,
tokens: dict,
api_key_id: int
):
"""Debug price calculation step by step."""
print(f"Model: {model}")
print(f"Tokens: {tokens}")
# Base price
base_price = get_model_price(model)
print(f"Base price: {base_price}")
# Token cost
token_cost = calculate_token_cost(base_price, tokens)
print(f"Token cost: {token_cost}")
# Strategies
strategies = get_active_strategies()
for strategy in strategies:
multiplier = await strategy.get_multiplier(api_key_id)
print(f"{strategy.name}: {multiplier}x")
# Final cost
final_cost = apply_all_strategies(token_cost, api_key_id)
print(f"Final cost: {final_cost} msats")
return final_cost
```
### Common Issues
1. **Prices Not Updating**
- Check `MODELS_PATH` is correct
- Verify file permissions
- Check background task logs
2. **Wrong Currency Conversion**
- Verify BTC/USD rate source
- Check `EXCHANGE_FEE` setting
- Monitor rate update frequency
3. **Discounts Not Applied**
- Verify strategy configuration
- Check API key metadata
- Review transaction history
## Best Practices
1. **Transparent Pricing**
- Publish pricing clearly
- Show cost breakdowns
- Notify of price changes
2. **Fair Pricing**
- Regular competitive analysis
- Consider user feedback
- Offer budget options
3. **Performance**
- Cache price calculations
- Optimize database queries
- Monitor calculation time
## Next Steps
- [Migrations](migrations.md) - Database migration guide
- [API Endpoints](../api/endpoints.md) - Pricing endpoints
- [Monitoring](../user-guide/admin-dashboard.md) - Track pricing metrics
docs/advanced/migrations.md
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# Database Migrations
This guide covers database schema management using Alembic migrations in Routstr Core.
## Overview
Routstr uses Alembic for database migrations with these features:
- **Automatic migrations** on startup
- **Version control** for schema changes
- **Rollback capability** for safety
- **Support for multiple databases** (SQLite, PostgreSQL)
## Automatic Migrations
### Startup Behavior
Migrations run automatically when Routstr starts:
```python
# In routstr/core/main.py
@asynccontextmanager
async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
logger.info("Running database migrations")
run_migrations() # Automatic migration
await init_db() # Initialize connection pool
# ... rest of startup
```
This ensures:
- ✅ Database is always up-to-date
- ✅ No manual migration steps in production
- ✅ Zero-downtime deployments
- ✅ Backwards compatibility
### Migration Safety
Migrations are designed to be safe:
- Idempotent (can run multiple times)
- Non-destructive by default
- Tested before release
- Reversible when possible
## Creating Migrations
### Auto-generating from Models
After modifying SQLModel classes:
```bash
# Generate migration from model changes
make db-migrate
# You'll be prompted for a description
Enter migration message: Add user preferences table
# Review generated file
cat migrations/versions/xxxx_add_user_preferences_table.py
```
### Manual Migrations
For complex changes, create manually:
```bash
# Create empty migration
alembic revision -m "Complex data transformation"
# Edit the generated file
vim migrations/versions/xxxx_complex_data_transformation.py
```
### Migration Template
```python
"""Add user preferences table
Revision ID: a1b2c3d4e5f6
Revises: f6e5d4c3b2a1
Create Date: 2024-01-15 10:30:00.123456
"""
from alembic import op
import sqlalchemy as sa
import sqlmodel
# revision identifiers
revision = 'a1b2c3d4e5f6'
down_revision = 'f6e5d4c3b2a1'
branch_labels = None
depends_on = None
def upgrade() -> None:
"""Apply migration."""
# Create new table
op.create_table(
'userpreferences',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('api_key_id', sa.Integer(), nullable=False),
sa.Column('theme', sa.String(), nullable=True),
sa.Column('notifications_enabled', sa.Boolean(), default=True),
sa.Column('created_at', sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint('id'),
sa.ForeignKeyConstraint(['api_key_id'], ['apikey.id'], )
)
# Create index
op.create_index(
'ix_userpreferences_api_key_id',
'userpreferences',
['api_key_id']
)
def downgrade() -> None:
"""Revert migration."""
op.drop_index('ix_userpreferences_api_key_id', table_name='userpreferences')
op.drop_table('userpreferences')
```
## Common Migration Patterns
### Adding Columns
Add column with default value:
```python
def upgrade():
# Add nullable column first
op.add_column(
'apikey',
sa.Column('last_rotation', sa.DateTime(), nullable=True)
)
# Populate existing rows
connection = op.get_bind()
connection.execute(
"UPDATE apikey SET last_rotation = created_at WHERE last_rotation IS NULL"
)
# Make non-nullable if needed
op.alter_column('apikey', 'last_rotation', nullable=False)
```
### Renaming Columns
Safe column rename:
```python
def upgrade():
# SQLite doesn't support ALTER COLUMN, so we need a workaround
with op.batch_alter_table('apikey') as batch_op:
batch_op.alter_column('old_name', new_column_name='new_name')
```
### Adding Indexes
Performance-improving indexes:
```python
def upgrade():
# Single column index
op.create_index(
'ix_transaction_timestamp',
'transaction',
['timestamp']
)
# Composite index
op.create_index(
'ix_transaction_key_time',
'transaction',
['api_key_id', 'timestamp']
)
# Partial index (PostgreSQL only)
op.create_index(
'ix_apikey_active',
'apikey',
['balance'],
postgresql_where='balance > 0'
)
```
### Data Migrations
Transform existing data:
```python
def upgrade():
# Add new column
op.add_column(
'apikey',
sa.Column('key_type', sa.String(), nullable=True)
)
# Migrate data
connection = op.get_bind()
result = connection.execute('SELECT id, metadata FROM apikey')
for row in result:
key_type = 'premium' if row.metadata.get('premium') else 'standard'
connection.execute(
f"UPDATE apikey SET key_type = '{key_type}' WHERE id = {row.id}"
)
# Make column non-nullable
op.alter_column('apikey', 'key_type', nullable=False)
```
### Enum Types
Add enum column:
```python
from enum import Enum
class KeyStatus(str, Enum):
ACTIVE = "active"
SUSPENDED = "suspended"
EXPIRED = "expired"
def upgrade():
# Create enum type (PostgreSQL)
key_status_enum = sa.Enum(KeyStatus, name='keystatus')
key_status_enum.create(op.get_bind(), checkfirst=True)
# Add column
op.add_column(
'apikey',
sa.Column(
'status',
key_status_enum,
nullable=False,
server_default='active'
)
)
```
## Database-Specific Considerations
### SQLite Limitations
SQLite has limitations requiring workarounds:
```python
def upgrade():
# SQLite doesn't support ALTER COLUMN directly
# Use batch_alter_table for compatibility
with op.batch_alter_table('apikey') as batch_op:
batch_op.alter_column(
'balance',
type_=sa.BigInteger(), # Change from Integer
existing_type=sa.Integer()
)
```
### PostgreSQL Features
Leverage PostgreSQL-specific features:
```python
def upgrade():
# Use JSONB for better performance
op.add_column(
'apikey',
sa.Column('metadata', sa.JSON().with_variant(
sa.dialects.postgresql.JSONB(), 'postgresql'
))
)
# Add GIN index for JSONB queries
op.create_index(
'ix_apikey_metadata',
'apikey',
['metadata'],
postgresql_using='gin'
)
# Add check constraint
op.create_check_constraint(
'ck_apikey_balance_positive',
'apikey',
'balance >= 0'
)
```
## Migration Commands
### Running Migrations
```bash
# Apply all pending migrations
make db-upgrade
# Upgrade to specific revision
alembic upgrade a1b2c3d4e5f6
# Upgrade one revision
alembic upgrade +1
```
### Checking Status
```bash
# Show current revision
make db-current
# Output: a1b2c3d4e5f6 (head)
# Show migration history
make db-history
# Output:
# a1b2c3d4e5f6 -> b2c3d4e5f6a7 (head), Add user preferences
# f6e5d4c3b2a1 -> a1b2c3d4e5f6, Add indexes
# e5d4c3b2a1f6 -> f6e5d4c3b2a1, Initial schema
```
### Rolling Back
```bash
# Rollback one migration
make db-downgrade
# Rollback to specific revision
alembic downgrade f6e5d4c3b2a1
# Rollback all (dangerous!)
alembic downgrade base
```
## Testing Migrations
### Unit Testing
Test migrations in isolation:
```python
import pytest
from alembic import command
from alembic.config import Config
from sqlalchemy import create_engine, inspect
def test_migration_add_user_preferences():
"""Test user preferences migration."""
# Create test database
engine = create_engine("sqlite:///:memory:")
# Run migrations up to previous version
alembic_cfg = Config("alembic.ini")
alembic_cfg.set_main_option("sqlalchemy.url", str(engine.url))
command.upgrade(alembic_cfg, "f6e5d4c3b2a1")
# Verify state before migration
inspector = inspect(engine)
tables = inspector.get_table_names()
assert "userpreferences" not in tables
# Run target migration
command.upgrade(alembic_cfg, "a1b2c3d4e5f6")
# Verify state after migration
inspector = inspect(engine)
tables = inspector.get_table_names()
assert "userpreferences" in tables
# Check columns
columns = {col['name'] for col in inspector.get_columns('userpreferences')}
assert columns == {'id', 'api_key_id', 'theme', 'notifications_enabled', 'created_at'}
# Test downgrade
command.downgrade(alembic_cfg, "f6e5d4c3b2a1")
inspector = inspect(engine)
tables = inspector.get_table_names()
assert "userpreferences" not in tables
```
### Integration Testing
Test with real data:
```python
async def test_migration_with_data():
"""Test migration preserves existing data."""
# Setup test database with data
async with test_engine.begin() as conn:
# Insert test data
await conn.execute(
"INSERT INTO apikey (key_hash, balance) VALUES ('test', 1000)"
)
# Run migration
run_migrations()
# Verify data integrity
async with test_engine.connect() as conn:
result = await conn.execute("SELECT * FROM apikey WHERE key_hash = 'test'")
row = result.first()
assert row.balance == 1000
assert row.key_type == 'standard' # New column with default
```
## Production Deployment
### Zero-Downtime Migrations
Strategy for seamless updates:
1. **Make migrations backwards compatible**
```python
# Good: Add nullable column
op.add_column('apikey', sa.Column('new_field', sa.String(), nullable=True))
# Bad: Drop column immediately
# op.drop_column('apikey', 'old_field')
```
2. **Deploy in phases**
```bash
# Phase 1: Deploy code that works with both schemas
# Phase 2: Run migration
# Phase 3: Deploy code that requires new schema
# Phase 4: Clean up deprecated columns
```
3. **Use feature flags**
```python
if feature_enabled('use_new_schema'):
# Use new column
query = select(APIKey.new_field)
else:
# Use old column
query = select(APIKey.old_field)
```
### Migration Monitoring
Track migration execution:
```python
# Add to migration
def upgrade():
start_time = time.time()
logger.info(f"Starting migration {revision}")
try:
# Migration logic here
op.create_table(...)
duration = time.time() - start_time
logger.info(f"Migration {revision} completed in {duration:.2f}s")
except Exception as e:
logger.error(f"Migration {revision} failed: {e}")
raise
```
### Backup Before Migration
Always backup before major changes:
```bash
#!/bin/bash
# backup_before_migration.sh
# Backup database
if [[ "$DATABASE_URL" == *"sqlite"* ]]; then
cp database.db "backup_$(date +%Y%m%d_%H%M%S).db"
else
pg_dump $DATABASE_URL > "backup_$(date +%Y%m%d_%H%M%S).sql"
fi
# Run migration
alembic upgrade head
# Verify
alembic current
```
## Troubleshooting
### Common Issues
**Migration Conflicts**
```bash
# Multiple heads detected
alembic heads
# a1b2c3d4e5f6 (head)
# b2c3d4e5f6a7 (head)
# Merge heads
alembic merge -m "Merge migrations" a1b2c3 b2c3d4
```
**Failed Migration**
```python
# Add rollback logic
def upgrade():
try:
op.create_table(...)
except Exception as e:
# Clean up partial changes
op.drop_table('partial_table', checkfirst=True)
raise
def downgrade():
# Ensure clean rollback
op.drop_table('new_table', checkfirst=True)
```
**Lock Timeout**
```python
# Add timeout handling
def upgrade():
connection = op.get_bind()
# Set timeout (PostgreSQL)
connection.execute("SET lock_timeout = '10s'")
try:
op.add_column(...)
except OperationalError as e:
if 'lock timeout' in str(e):
logger.error("Migration failed due to lock timeout")
raise
```
### Recovery Procedures
If migration fails in production:
1. **Check current state**
```bash
alembic current
alembic history
```
2. **Manual rollback if needed**
```sql
-- Check migration table
SELECT * FROM alembic_version;
-- Force version if necessary
UPDATE alembic_version SET version_num = 'previous_version';
```
3. **Fix and retry**
```bash
# Fix migration file
vim migrations/versions/problematic_migration.py
# Retry
alembic upgrade head
```
## Best Practices
### Migration Guidelines
1. **Keep migrations small and focused**
- One logical change per migration
- Easier to review and rollback
2. **Test migrations thoroughly**
- Test upgrade and downgrade
- Test with production-like data
- Test database-specific features
3. **Document breaking changes**
```python
"""BREAKING: Change balance column type
This migration requires application update.
Deploy order:
1. Update application to handle both int and bigint
2. Run this migration
3. Update application to use only bigint
"""
```
4. **Make migrations idempotent**
```python
def upgrade():
# Check if column exists
inspector = inspect(op.get_bind())
columns = [col['name'] for col in inspector.get_columns('apikey')]
if 'new_column' not in columns:
op.add_column(
'apikey',
sa.Column('new_column', sa.String())
)
```
### Performance Considerations
1. **Add indexes concurrently (PostgreSQL)**
```python
def upgrade():
# Create index without locking table
op.create_index(
'ix_large_table_column',
'large_table',
['column'],
postgresql_concurrently=True
)
```
2. **Batch large updates**
```python
def upgrade():
connection = op.get_bind()
# Process in batches
batch_size = 1000
offset = 0
while True:
result = connection.execute(
f"UPDATE apikey SET processed = true "
f"WHERE id IN (SELECT id FROM apikey WHERE processed = false LIMIT {batch_size})"
)
if result.rowcount == 0:
break
offset += batch_size
time.sleep(0.1) # Prevent overload
```
## Next Steps
- [Database Guide](../contributing/database.md) - Database design details
- [Testing Guide](../contributing/testing.md) - Testing migrations
- [Deployment](../getting-started/docker.md) - Production deployment
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# Nostr Discovery
Routstr Core integrates with Nostr (Notes and Other Stuff Transmitted by Relays) for decentralized provider discovery. This enables users to find Routstr nodes without relying on centralized directories.
## Overview
Nostr integration provides:
- **Decentralized Discovery**: Find providers through relay network
- **Cryptographic Identity**: Providers identified by public keys
- **Real-time Updates**: Live provider status and pricing
- **Censorship Resistance**: No central point of control
## How It Works
```mermaid
graph LR
A[Routstr Node] --> B[Nostr Relay]
B --> C[Nostr Relay]
B --> D[Nostr Relay]
E[User Client] --> B
E --> C
E --> D
B --> F[Provider List]
C --> F
D --> F
```
Providers announce themselves by publishing signed events to Nostr relays. Clients can query these relays to discover available providers.
## Provider Configuration
### Setting Up Nostr Identity
1. **Generate Nostr Keys**
```bash
# Using nostril or similar tool
nostril --generate-keypair
# Output:
# Private key (nsec): nsec1abc...
# Public key (npub): npub1xyz...
```
2. **Configure Environment**
```bash
# .env
NPUB=npub1xyz... # Your public key
NSEC=nsec1abc... # Your private key (keep secret!)
NAME=Lightning AI Gateway
DESCRIPTION=Fast and reliable AI API with Bitcoin payments
HTTP_URL=https://api.lightning-ai.com
ONION_URL=http://lightningai.onion
```
### Publishing to Nostr
Routstr automatically publishes provider information to configured relays:
```python
# Published event structure (NIP-89)
{
"kind": 31990, # Application handler event
"pubkey": "your_public_key",
"content": {
"name": "Lightning AI Gateway",
"description": "Fast and reliable AI API",
"endpoints": {
"http": "https://api.lightning-ai.com",
"onion": "http://lightningai.onion"
},
"models": ["gpt-3.5-turbo", "gpt-4", "claude-3"],
"pricing": {
"gpt-3.5-turbo": {
"prompt_sats_per_1k": 3,
"completion_sats_per_1k": 4
}
},
"cashu_mints": [
"https://mint.minibits.cash/Bitcoin"
]
},
"tags": [
["d", "routstr"],
["t", "ai-api"],
["t", "bitcoin"],
["p", "payment-proxy"]
]
}
```
### Relay Configuration
Configure which relays to publish to:
```python
# Default relays
DEFAULT_RELAYS = [
"wss://relay.damus.io",
"wss://relay.nostr.band",
"wss://nostr.mom",
"wss://nos.lol"
]
# Custom relay configuration
NOSTR_RELAYS=wss://relay1.com,wss://relay2.com
```
## Client Discovery
### Using the Discovery Endpoint
Find providers through the API:
```bash
GET /v1/providers
Response:
{
"providers": [
{
"name": "Lightning AI Gateway",
"npub": "npub1xyz...",
"description": "Fast and reliable AI API",
"endpoints": {
"http": "https://api.lightning-ai.com",
"onion": "http://lightningai.onion"
},
"models": ["gpt-3.5-turbo", "gpt-4"],
"pricing": {
"gpt-3.5-turbo": {
"prompt_sats_per_1k": 3,
"completion_sats_per_1k": 4
}
},
"last_seen": "2024-01-01T12:00:00Z",
"reliability_score": 0.99
}
]
}
```
### Direct Nostr Queries
Query Nostr relays directly:
```python
import json
import websocket
def discover_providers(relay_url: str):
"""Discover Routstr providers from Nostr relay."""
ws = websocket.create_connection(relay_url)
# Subscribe to provider events
subscription = {
"kinds": [31990],
"tags": {
"d": ["routstr"]
}
}
ws.send(json.dumps(["REQ", "sub1", subscription]))
providers = []
while True:
response = json.loads(ws.recv())
if response[0] == "EVENT":
event = response[2]
providers.append(parse_provider_event(event))
elif response[0] == "EOSE": # End of stored events
break
ws.close()
return providers
```
### JavaScript/TypeScript
```typescript
import { SimplePool } from 'nostr-tools';
async function discoverProviders(): Promise<Provider[]> {
const pool = new SimplePool();
const relays = [
'wss://relay.damus.io',
'wss://relay.nostr.band'
];
const filter = {
kinds: [31990],
'#d': ['routstr']
};
const events = await pool.list(relays, [filter]);
return events.map(event => ({
name: event.content.name,
npub: nip19.npubEncode(event.pubkey),
url: event.content.endpoints.http,
models: event.content.models,
pricing: event.content.pricing
}));
}
```
## Provider Ranking
### Reliability Scoring
Providers are ranked based on:
```python
class ProviderScore:
def calculate(self, provider: Provider) -> float:
score = 1.0
# Uptime (based on recent checks)
uptime_ratio = provider.successful_pings / provider.total_pings
score *= uptime_ratio
# Response time
if provider.avg_response_time < 500: # ms
score *= 1.0
elif provider.avg_response_time < 1000:
score *= 0.9
else:
score *= 0.7
# Model availability
model_score = len(provider.models) / 10 # Max 10 models
score *= min(1.0, 0.5 + model_score * 0.5)
# Price competitiveness
if provider.is_cheapest_for_any_model():
score *= 1.1
return min(1.0, score)
```
### Provider Selection
Choose optimal provider:
```python
def select_provider(
providers: list[Provider],
model: str,
requirements: dict
) -> Provider:
"""Select best provider for requirements."""
# Filter by model availability
candidates = [p for p in providers if model in p.models]
# Filter by requirements
if requirements.get('tor_required'):
candidates = [p for p in candidates if p.onion_url]
if requirements.get('max_price_per_1k'):
max_price = requirements['max_price_per_1k']
candidates = [
p for p in candidates
if p.pricing[model]['prompt_sats_per_1k'] <= max_price
]
# Sort by score
candidates.sort(key=lambda p: p.reliability_score, reverse=True)
return candidates[0] if candidates else None
```
## Publishing Updates
### Automatic Updates
Routstr publishes updates when:
- Node starts up
- Configuration changes
- Models are added/removed
- Pricing updates
### Manual Publishing
Force publish current state:
```python
async def publish_provider_info():
"""Manually publish provider information."""
event = create_provider_event(
name=os.getenv("NAME"),
description=os.getenv("DESCRIPTION"),
models=get_available_models(),
pricing=get_current_pricing()
)
await publish_to_relays(event, NOSTR_RELAYS)
```
### Event Lifecycle
```python
# Publish every 6 hours
@periodic_task(hours=6)
async def update_nostr_presence():
"""Keep provider information fresh."""
try:
await publish_provider_info()
logger.info("Updated Nostr presence")
except Exception as e:
logger.error(f"Failed to update Nostr: {e}")
# Delete on shutdown
async def remove_nostr_presence():
"""Remove provider from discovery."""
deletion_event = create_deletion_event()
await publish_to_relays(deletion_event, NOSTR_RELAYS)
```
## Security Considerations
### Key Management
1. **Secure Storage**
```python
# Never log private keys
SENSITIVE_VARS = ['NSEC', 'ADMIN_PASSWORD']
def sanitize_env(env_dict: dict) -> dict:
return {
k: '***' if k in SENSITIVE_VARS else v
for k, v in env_dict.items()
}
```
2. **Key Rotation**
```bash
# Generate new keys
nostril --generate-keypair
# Update configuration
# Publish transition event
# Update all references
```
### Event Validation
Verify provider events:
```python
def validate_provider_event(event: dict) -> bool:
"""Validate provider announcement."""
# Check signature
if not verify_signature(event):
return False
# Check required fields
required = ['name', 'endpoints', 'models', 'pricing']
content = json.loads(event['content'])
if not all(field in content for field in required):
return False
# Verify endpoints are reachable
if not await check_endpoints(content['endpoints']):
return False
return True
```
### Relay Security
Choose relays carefully:
```python
TRUSTED_RELAYS = {
'wss://relay.damus.io': {
'operator': 'Damus',
'reputation': 'high',
'filters_spam': True
},
'wss://relay.nostr.band': {
'operator': 'Nostr.Band',
'reputation': 'high',
'paid_tier': True
}
}
```
## Advanced Features
### Multi-Relay Broadcasting
Ensure wide distribution:
```python
async def broadcast_to_relays(event: dict, relays: list[str]):
"""Broadcast event to multiple relays."""
tasks = []
for relay in relays:
task = asyncio.create_task(
publish_to_relay(event, relay)
)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
successful = sum(1 for r in results if not isinstance(r, Exception))
logger.info(f"Published to {successful}/{len(relays)} relays")
```
### Provider Metadata
Extended metadata in events:
```json
{
"kind": 31990,
"content": {
"name": "Lightning AI",
"description": "Enterprise AI API",
"metadata": {
"established": "2024-01-01",
"total_requests": 1000000,
"average_response_ms": 250,
"supported_features": [
"streaming",
"function_calling",
"vision",
"embeddings"
],
"certifications": ["SOC2", "GDPR"],
"contact": {
"nostr": "npub1contact...",
"email": "support@lightning-ai.com"
}
}
}
}
```
### Discovery Filters
Advanced filtering options:
```python
# Find providers with specific features
GET /v1/providers?features=streaming,vision&max_price=5&min_reliability=0.95
# Response includes filtered results
{
"providers": [...],
"filters_applied": {
"features": ["streaming", "vision"],
"max_price_sats_per_1k": 5,
"min_reliability": 0.95
},
"total_providers": 50,
"matching_providers": 12
}
```
## Monitoring
### Discovery Metrics
Track discovery performance:
```python
class DiscoveryMetrics:
def __init__(self):
self.relay_health = {}
self.provider_count = 0
self.query_latency = []
async def check_relay_health(self, relay_url: str):
"""Monitor relay connectivity."""
start = time.time()
try:
await connect_to_relay(relay_url)
latency = time.time() - start
self.relay_health[relay_url] = {
'status': 'healthy',
'latency_ms': latency * 1000
}
except Exception as e:
self.relay_health[relay_url] = {
'status': 'unhealthy',
'error': str(e)
}
```
### Provider Monitoring
```python
@periodic_task(minutes=5)
async def monitor_providers():
"""Check provider health."""
providers = await discover_providers()
for provider in providers:
try:
# Test endpoint
response = await test_provider_endpoint(provider.http_url)
# Update metrics
await update_provider_metrics(
provider.npub,
success=response.status_code == 200,
response_time=response.elapsed
)
except Exception as e:
logger.warning(f"Provider {provider.name} check failed: {e}")
```
## Troubleshooting
### No Providers Found
```python
# Debug discovery issues
async def debug_discovery():
"""Diagnose discovery problems."""
issues = []
# Check relay connectivity
for relay in NOSTR_RELAYS:
if not await can_connect_to_relay(relay):
issues.append(f"Cannot connect to {relay}")
# Check event publishing
if not await verify_own_events_visible():
issues.append("Own events not visible on relays")
# Check filters
if len(await get_all_provider_events()) == 0:
issues.append("No provider events on any relay")
return issues
```
### Relay Connection Issues
```bash
# Test relay connection
wscat -c wss://relay.damus.io
# Send subscription
["REQ","test",{"kinds":[31990],"#d":["routstr"]}]
```
## Best Practices
### For Providers
1. **Consistent Identity**
- Use same npub across services
- Maintain profile metadata
- Verify identity on multiple platforms
2. **Regular Updates**
- Publish status every few hours
- Update pricing promptly
- Remove stale information
3. **Relay Diversity**
- Publish to 5+ relays
- Include regional relays
- Monitor relay health
### For Clients
1. **Verify Providers**
- Check multiple relays
- Verify endpoints work
- Monitor reliability over time
2. **Cache Discovery**
- Cache provider list
- Refresh periodically
- Handle stale data gracefully
3. **Fallback Options**
- Keep backup providers
- Handle discovery failures
- Support manual configuration
## Next Steps
- [Tor Support](tor.md) - Anonymous provider access
- [Custom Pricing](custom-pricing.md) - Dynamic pricing strategies
- [API Reference](../api/endpoints.md) - Discovery API details
docs/advanced/tor.md
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# Tor Support
Routstr Core includes built-in support for Tor hidden services, enabling anonymous access to your API and enhanced privacy for users.
## Overview
Tor support provides:
- **Anonymous Access**: Hidden service (.onion) address
- **Enhanced Privacy**: No IP address logging
- **Censorship Resistance**: Accessible from restricted networks
- **Optional Usage**: Regular HTTP/HTTPS access remains available
## Docker Setup
### Using Docker Compose
The included `compose.yml` automatically sets up Tor:
```yaml
version: '3.8'
services:
routstr:
build: .
environment:
- TOR_PROXY_URL=socks5://tor:9050
ports:
- 8000:8000
tor:
image: ghcr.io/hundehausen/tor-hidden-service:latest
volumes:
- tor-data:/var/lib/tor
environment:
- HS_ROUTER=routstr:8000:80
depends_on:
- routstr
volumes:
tor-data:
```
Start with:
```bash
docker compose up -d
```
### Getting Your Onion Address
After starting, retrieve your hidden service address:
```bash
# View Tor logs
docker compose logs tor
# Or directly from the hostname file
docker exec tor cat /var/lib/tor/hidden_service/hostname
```
Your onion address will look like:
```
roustrjfsdgfiueghsklchg.onion
```
## Manual Tor Setup
### Install Tor
```bash
# Ubuntu/Debian
sudo apt-get install tor
# macOS
brew install tor
# Start Tor
sudo systemctl start tor
```
### Configure Hidden Service
Edit `/etc/tor/torrc`:
```bash
# Hidden service configuration
HiddenServiceDir /var/lib/tor/routstr/
HiddenServicePort 80 127.0.0.1:8000
# Optional: Restrict to v3 addresses
HiddenServiceVersion 3
```
Restart Tor:
```bash
sudo systemctl restart tor
```
Get onion address:
```bash
sudo cat /var/lib/tor/routstr/hostname
```
## Client Configuration
### Using Tor with Python
```python
import httpx
from openai import OpenAI
# Configure SOCKS proxy
proxies = {
"http://": "socks5://127.0.0.1:9050",
"https://": "socks5://127.0.0.1:9050"
}
# Create client with Tor
http_client = httpx.Client(proxies=proxies)
client = OpenAI(
api_key="rstr_your_key",
base_url="http://roustrjfsdgfiueghsklchg.onion/v1",
http_client=http_client
)
# Use normally
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello via Tor!"}]
)
```
### Using Tor with cURL
```bash
# Install torify
sudo apt-get install torsocks
# Make request through Tor
torify curl http://roustrjfsdgfiueghsklchg.onion/v1/models
# Or with explicit proxy
curl --socks5 127.0.0.1:9050 http://roustrjfsdgfiueghsklchg.onion/v1/models
```
### JavaScript/Node.js
```javascript
import { SocksProxyAgent } from 'socks-proxy-agent';
import OpenAI from 'openai';
// Create SOCKS agent
const agent = new SocksProxyAgent('socks5://127.0.0.1:9050');
// Configure OpenAI client
const openai = new OpenAI({
apiKey: 'rstr_your_key',
baseURL: 'http://roustrjfsdgfiueghsklchg.onion/v1',
httpAgent: agent
});
// Use normally
const response = await openai.chat.completions.create({
model: 'gpt-3.5-turbo',
messages: [{ role: 'user', content: 'Hello via Tor!' }]
});
```
## Configuration
### Environment Variables
Configure Tor proxy for outgoing connections:
```bash
# .env
TOR_PROXY_URL=socks5://tor:9050 # Docker
# or
TOR_PROXY_URL=socks5://127.0.0.1:9050 # Local
```
### Publishing Onion Address
Make your onion address discoverable:
```bash
# .env
ONION_URL=http://roustrjfsdgfiueghsklchg.onion
```
This will be included in:
- `/v1/info` endpoint
- Nostr announcements
- Admin dashboard
## Security Considerations
### Hidden Service Security
1. **Keep Private Key Secure**
```bash
# Backup hidden service keys
sudo tar -czf tor-keys-backup.tar.gz /var/lib/tor/routstr/
# Restore to maintain same address
sudo tar -xzf tor-keys-backup.tar.gz -C /
```
2. **Access Control**
```bash
# Restrict to authenticated clients
HiddenServiceAuthorizeClient stealth client1,client2
```
3. **Rate Limiting**
```bash
# In torrc
HiddenServiceMaxStreams 100
HiddenServiceMaxStreamsCloseCircuit 1
```
### Operational Security
1. **Separate Tor Instance**
```yaml
# Use dedicated Tor container
tor:
image: ghcr.io/hundehausen/tor-hidden-service:latest
restart: always
networks:
- tor_network
```
2. **Monitor Tor Health**
```python
async def check_tor_connection():
"""Verify Tor connectivity."""
try:
async with httpx.AsyncClient(
proxies={"all://": TOR_PROXY_URL}
) as client:
response = await client.get(
"https://check.torproject.org/api/ip"
)
data = response.json()
return data.get("IsTor", False)
except Exception:
return False
```
3. **Logging Considerations**
```python
# Don't log .onion addresses with IPs
def sanitize_logs(message: str) -> str:
# Remove IP addresses when .onion is present
if ".onion" in message:
message = re.sub(r'\d+\.\d+\.\d+\.\d+', '[IP]', message)
return message
```
## Performance Optimization
### Connection Pooling
```python
# Reuse Tor circuits
class TorConnectionPool:
def __init__(self, proxy_url: str):
self.proxy_url = proxy_url
self._clients = []
async def get_client(self) -> httpx.AsyncClient:
if not self._clients:
client = httpx.AsyncClient(
proxies={"all://": self.proxy_url},
timeout=httpx.Timeout(30.0),
limits=httpx.Limits(
max_keepalive_connections=5,
max_connections=10
)
)
self._clients.append(client)
return self._clients[0]
```
### Circuit Management
```python
# Rotate Tor circuits periodically
async def rotate_tor_circuit():
"""Signal Tor to create new circuit."""
async with httpx.AsyncClient() as client:
# Tor control port (requires configuration)
response = await client.post(
"http://localhost:9051",
data="AUTHENTICATE\r\nSIGNAL NEWNYM\r\n"
)
```
### Caching Strategies
```python
# Cache responses for Tor users
@lru_cache(maxsize=1000)
def get_cached_response(
endpoint: str,
params_hash: str
) -> Optional[dict]:
"""Cache frequently accessed data."""
# Longer cache for Tor users due to latency
return cache.get(f"tor:{endpoint}:{params_hash}")
```
## Monitoring
### Tor Metrics
Track Tor-specific metrics:
```python
class TorMetrics:
def __init__(self):
self.tor_requests = 0
self.tor_errors = 0
self.circuit_builds = 0
self.average_latency = 0
async def record_request(
self,
duration: float,
success: bool
):
self.tor_requests += 1
if not success:
self.tor_errors += 1
# Update average latency
self.average_latency = (
(self.average_latency * (self.tor_requests - 1) + duration)
/ self.tor_requests
)
```
### Health Checks
```python
@router.get("/health/tor")
async def tor_health():
"""Check Tor service health."""
checks = {
"tor_proxy": await check_tor_proxy(),
"hidden_service": await check_hidden_service(),
"circuit_established": await check_circuit()
}
status = "healthy" if all(checks.values()) else "unhealthy"
return {
"status": status,
"checks": checks,
"metrics": {
"tor_requests_total": metrics.tor_requests,
"tor_error_rate": metrics.tor_errors / max(metrics.tor_requests, 1),
"average_latency_ms": metrics.average_latency * 1000
}
}
```
## Troubleshooting
### Common Issues
**Hidden Service Not Accessible**
```bash
# Check Tor logs
docker compose logs tor
# or
sudo journalctl -u tor
# Verify service is running
sudo systemctl status tor
# Test locally
curl --socks5 127.0.0.1:9050 http://your-onion.onion/v1/info
```
**Slow Connection**
- Tor adds 3+ hops of latency
- Use connection pooling
- Implement aggressive caching
- Consider increasing timeouts
**Connection Errors**
```python
# Implement Tor-specific retry logic
async def tor_retry(func, max_retries=5):
for attempt in range(max_retries):
try:
return await func()
except httpx.ProxyError:
if attempt < max_retries - 1:
# Exponential backoff for circuit building
await asyncio.sleep(2 ** attempt)
else:
raise
```
### Debugging
Enable Tor debug logging:
```bash
# In torrc
Log debug file /var/log/tor/debug.log
# Monitor in real-time
tail -f /var/log/tor/debug.log
```
## Best Practices
### For Operators
1. **Backup Hidden Service Keys**
- Store securely offline
- Enables service recovery
- Maintains same .onion address
2. **Monitor Tor Health**
- Check circuit establishment
- Track request latency
- Alert on failures
3. **Separate Concerns**
- Run Tor in separate container
- Isolate from main application
- Use internal networks
### For Users
1. **Verify Onion Addresses**
- Check against multiple sources
- Bookmark verified addresses
- Watch for phishing
2. **Handle Higher Latency**
- Increase client timeouts
- Implement retries
- Use connection pooling
3. **Enhance Privacy**
- Use Tor Browser for web access
- Avoid mixing Tor/clearnet
- Don't include identifying info
## Advanced Configuration
### Multi-Hop Onion Services
For extra security, chain multiple Tor instances:
```yaml
# compose.yml
services:
tor-entry:
image: tor:latest
command: tor -f /etc/tor/torrc.entry
tor-middle:
image: tor:latest
command: tor -f /etc/tor/torrc.middle
tor-exit:
image: tor:latest
command: tor -f /etc/tor/torrc.exit
```
### Onion Service Authentication
Require client authorization:
```bash
# Generate client auth
openssl rand -base64 32 > client_auth_key
# In torrc
HiddenServiceDir /var/lib/tor/routstr/
HiddenServicePort 80 127.0.0.1:8000
HiddenServiceAuthorizeClient stealth payments
```
### Load Balancing
Distribute load across multiple instances:
```nginx
# Onion service nginx config
upstream routstr_backends {
server routstr1:8000;
server routstr2:8000;
server routstr3:8000;
}
server {
listen 80;
location / {
proxy_pass http://routstr_backends;
}
}
```
## Next Steps
- [Nostr Discovery](nostr.md) - Announce your onion service
- [Security Guide](../contributing/guidelines.md#security) - Security best practices
- [Docker Setup](../getting-started/docker.md) - Container configuration
docs/api/authentication.md
+427
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# Authentication
Routstr uses API key authentication for all protected endpoints. This guide covers how to create, use, and manage API keys.
## API Key Creation
### From eCash Token
Create an API key by depositing an eCash token:
```bash
POST /v1/wallet/create
Content-Type: application/json
{
"cashu_token": "cashuAeyJ0b2tlbiI6W3sibWludCI6Imh0dHBzOi8vbWlu...",
"name": "Production Key",
"expires_at": "2024-12-31T23:59:59Z",
"refund_npub": "npub1abcdef..."
}
```
**Request Parameters:**
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `cashu_token` | string | Yes | Base64-encoded Cashu token |
| `name` | string | No | Friendly name for the key |
| `expires_at` | string | No | ISO 8601 expiration timestamp |
| `refund_npub` | string | No | Nostr pubkey for refunds |
**Response:**
```json
{
"api_key": "rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p",
"balance": 10000,
"created_at": "2024-01-01T00:00:00Z",
"expires_at": "2024-12-31T23:59:59Z",
"key_id": "key_123456"
}
```
### From Lightning Invoice (Coming Soon)
```bash
POST /v1/wallet/create/lightning
Content-Type: application/json
{
"amount_sats": 10000,
"name": "Lightning Key"
}
```
Response includes Lightning invoice for payment.
## Using API Keys
### Header Authentication
Include the API key in the Authorization header:
```bash
curl https://your-node.com/v1/chat/completions \
-H "Authorization: Bearer rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-3.5-turbo","messages":[{"role":"user","content":"Hello"}]}'
```
### Query Parameter (Not Recommended)
For tools that don't support headers:
```bash
GET /v1/models?api_key=rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p
```
⚠️ **Warning**: Query parameters may be logged. Use headers when possible.
## Key Management
### Check Balance
Get current balance and usage statistics:
```bash
GET /v1/wallet/balance
Authorization: Bearer rstr_your_api_key
Response:
{
"balance": 8546,
"total_deposited": 10000,
"total_spent": 1454,
"last_used": "2024-01-01T12:34:56Z",
"created_at": "2024-01-01T00:00:00Z",
"expires_at": null,
"key_info": {
"name": "Production Key",
"key_id": "key_123456"
}
}
```
### Top Up Balance
Add funds to existing key:
```bash
POST /v1/wallet/topup
Authorization: Bearer rstr_your_api_key
Content-Type: application/json
{
"cashu_token": "cashuAeyJ0b2tlbiI6W3..."
}
Response:
{
"old_balance": 8546,
"added_amount": 5000,
"new_balance": 13546,
"transaction_id": "txn_789"
}
```
### List Transactions
View transaction history:
```bash
GET /v1/wallet/transactions?limit=10
Authorization: Bearer rstr_your_api_key
Response:
{
"transactions": [
{
"id": "txn_123",
"type": "usage",
"amount": -154,
"balance_after": 8546,
"description": "gpt-3.5-turbo: 50 prompt + 150 completion tokens",
"timestamp": "2024-01-01T12:34:56Z"
},
{
"id": "txn_122",
"type": "deposit",
"amount": 10000,
"balance_after": 10000,
"description": "Initial deposit",
"timestamp": "2024-01-01T00:00:00Z"
}
],
"has_more": false,
"total": 2
}
```
## Security Best Practices
### API Key Storage
**Do:**
- Store keys in environment variables
- Use secret management systems
- Encrypt keys at rest
- Implement key rotation
**Don't:**
- Commit keys to version control
- Share keys between environments
- Log keys in plain text
- Expose keys in client-side code
### Environment Variables
```bash
# .env file
ROUTSTR_API_KEY=rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p
ROUTSTR_BASE_URL=https://your-node.com/v1
# Usage in code
import os
api_key = os.getenv("ROUTSTR_API_KEY")
```
### Key Rotation
Regularly rotate API keys:
```python
# 1. Create new key
new_key = create_api_key(balance=old_key_balance)
# 2. Update applications
update_environment_variable("ROUTSTR_API_KEY", new_key)
# 3. Test new key
test_api_connection(new_key)
# 4. Withdraw old key balance
withdraw_balance(old_key)
```
## Authentication Errors
### Invalid API Key
```json
{
"error": {
"type": "authentication_failed",
"message": "Invalid API key",
"code": "invalid_api_key"
}
}
```
**Status Code:** 401
**Common Causes:**
- Typo in API key
- Key doesn't exist
- Key has been deleted
### Expired API Key
```json
{
"error": {
"type": "authentication_failed",
"message": "API key has expired",
"code": "key_expired",
"details": {
"expired_at": "2024-01-01T00:00:00Z"
}
}
}
```
**Status Code:** 401
**Resolution:**
- Create a new API key
- Contact admin if refund address was set
### Insufficient Balance
```json
{
"error": {
"type": "insufficient_balance",
"message": "Insufficient balance for request",
"code": "payment_required",
"details": {
"balance": 100,
"required": 154,
"shortfall": 54
}
}
}
```
**Status Code:** 402
**Resolution:**
- Top up the API key balance
- Use a more economical model
- Optimize request parameters
## Advanced Authentication
### Per-Request Tokens (Coming Soon)
Pay per request without maintaining a balance:
```bash
curl https://your-node.com/v1/chat/completions \
-H "X-Cashu: cashuAeyJ0b2tlbiI6W3..." \
-H "Content-Type: application/json" \
-d '{"model":"gpt-3.5-turbo","messages":[...]}'
```
Response includes change:
```
X-Cashu: cashuAeyJjaGFuZ2UiOlt7...
```
### Multi-Key Authentication
Use multiple keys for different purposes:
```python
# Production key for main app
PROD_KEY = os.getenv("ROUTSTR_PROD_KEY")
# Development key for testing
DEV_KEY = os.getenv("ROUTSTR_DEV_KEY")
# Analytics key with restricted permissions
ANALYTICS_KEY = os.getenv("ROUTSTR_ANALYTICS_KEY")
# Choose key based on environment
api_key = PROD_KEY if is_production() else DEV_KEY
```
### Delegated Authentication
Create sub-keys with limited permissions:
```bash
POST /v1/wallet/create/subkey
Authorization: Bearer rstr_parent_key
Content-Type: application/json
{
"name": "Limited Subkey",
"balance_limit": 1000,
"allowed_models": ["gpt-3.5-turbo"],
"expires_in_hours": 24
}
```
## Rate Limiting
Rate limits are applied per API key:
### Default Limits
| Metric | Limit | Window |
|--------|-------|--------|
| Requests | 1000 | 1 minute |
| Tokens | 1,000,000 | 1 hour |
| Concurrent | 10 | - |
### Rate Limit Headers
```
X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 999
X-RateLimit-Reset: 1640995200
X-RateLimit-Type: requests_per_minute
```
### Handling Rate Limits
```python
import time
from typing import Optional
def make_request_with_retry(
client,
max_retries: int = 3
) -> Optional[Response]:
for attempt in range(max_retries):
try:
response = client.chat.completions.create(...)
return response
except RateLimitError as e:
if attempt < max_retries - 1:
# Extract retry-after from error
retry_after = e.retry_after or 60
print(f"Rate limited. Waiting {retry_after}s...")
time.sleep(retry_after)
else:
raise
```
## IP Whitelisting
Restrict API key usage by IP:
```bash
POST /v1/wallet/update
Authorization: Bearer rstr_your_api_key
Content-Type: application/json
{
"allowed_ips": [
"192.168.1.100",
"10.0.0.0/24"
]
}
```
## Monitoring
### Usage Alerts
Set up usage notifications:
```bash
POST /v1/wallet/alerts
Authorization: Bearer rstr_your_api_key
Content-Type: application/json
{
"low_balance_threshold": 1000,
"daily_spend_limit": 5000,
"webhook_url": "https://your-app.com/webhook"
}
```
### Audit Logging
All API key usage is logged:
```json
{
"timestamp": "2024-01-01T12:34:56Z",
"api_key_id": "key_123456",
"endpoint": "/v1/chat/completions",
"method": "POST",
"ip_address": "192.168.1.100",
"user_agent": "OpenAI-Python/1.0",
"cost_sats": 154,
"response_status": 200
}
```
## Next Steps
- [Endpoints](endpoints.md) - Complete endpoint reference
- [Errors](errors.md) - Error handling guide
- [Using the API](../user-guide/using-api.md) - Integration examples
docs/api/endpoints.md
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# API Endpoints
Complete reference for all available endpoints in Routstr Core.
## Chat Completions
### Create Chat Completion
Generate a model response for a conversation.
```http
POST /v1/chat/completions
```
**Request Body:**
```json
{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
],
"temperature": 0.7,
"max_tokens": 150,
"stream": false
}
```
**Parameters:**
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `model` | string | Yes | - | Model ID to use |
| `messages` | array | Yes | - | Conversation messages |
| `temperature` | number | No | 1.0 | Sampling temperature (0-2) |
| `max_tokens` | integer | No | Unlimited | Maximum tokens to generate |
| `stream` | boolean | No | false | Stream response |
| `top_p` | number | No | 1.0 | Nucleus sampling |
| `n` | integer | No | 1 | Number of completions |
| `stop` | string/array | No | null | Stop sequences |
| `presence_penalty` | number | No | 0 | Presence penalty (-2 to 2) |
| `frequency_penalty` | number | No | 0 | Frequency penalty (-2 to 2) |
| `logit_bias` | object | No | null | Token bias |
| `user` | string | No | null | End-user identifier |
**Response:**
```json
{
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "gpt-3.5-turbo",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 10,
"total_tokens": 19
}
}
```
### Streaming Response
When `stream: true`:
```
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
data: [DONE]
```
## Completions
### Create Completion
Generate text completion (legacy endpoint).
```http
POST /v1/completions
```
**Request Body:**
```json
{
"model": "gpt-3.5-turbo-instruct",
"prompt": "Once upon a time",
"max_tokens": 50,
"temperature": 0.7
}
```
**Response:**
```json
{
"id": "cmpl-123",
"object": "text_completion",
"created": 1677652288,
"model": "gpt-3.5-turbo-instruct",
"choices": [{
"text": " in a faraway land, there lived a brave knight...",
"index": 0,
"logprobs": null,
"finish_reason": "length"
}],
"usage": {
"prompt_tokens": 4,
"completion_tokens": 50,
"total_tokens": 54
}
}
```
## Embeddings
### Create Embeddings
Generate vector representations of text.
```http
POST /v1/embeddings
```
**Request Body:**
```json
{
"model": "text-embedding-3-small",
"input": "The quick brown fox jumps over the lazy dog",
"encoding_format": "float"
}
```
**Parameters:**
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `model` | string | Yes | - | Embedding model ID |
| `input` | string/array | Yes | - | Text(s) to embed |
| `encoding_format` | string | No | "float" | Format: "float" or "base64" |
| `dimensions` | integer | No | Model default | Output dimensions |
**Response:**
```json
{
"object": "list",
"data": [{
"object": "embedding",
"index": 0,
"embedding": [0.0023064255, -0.009327292, ...]
}],
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 9,
"total_tokens": 9
}
}
```
## Images
### Create Image
Generate images from text prompts.
```http
POST /v1/images/generations
```
**Request Body:**
```json
{
"model": "dall-e-3",
"prompt": "A white siamese cat wearing a space helmet",
"n": 1,
"size": "1024x1024",
"quality": "standard"
}
```
**Parameters:**
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `model` | string | Yes | - | Model: dall-e-2, dall-e-3 |
| `prompt` | string | Yes | - | Text description |
| `n` | integer | No | 1 | Number of images |
| `size` | string | No | "1024x1024" | Image dimensions |
| `quality` | string | No | "standard" | Quality: standard, hd |
| `style` | string | No | "vivid" | Style: vivid, natural |
| `response_format` | string | No | "url" | Format: url, b64_json |
**Response:**
```json
{
"created": 1677652288,
"data": [{
"url": "https://generated-image-url.com/image.png",
"revised_prompt": "A white Siamese cat wearing a detailed space helmet..."
}]
}
```
## Audio
### Create Transcription
Convert audio to text.
```http
POST /v1/audio/transcriptions
Content-Type: multipart/form-data
```
**Form Data:**
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `file` | file | Yes | Audio file (mp3, mp4, mpeg, mpga, m4a, wav, webm) |
| `model` | string | Yes | Model ID (whisper-1) |
| `language` | string | No | Language code (ISO-639-1) |
| `prompt` | string | No | Context prompt |
| `response_format` | string | No | Format: json, text, srt, verbose_json, vtt |
| `temperature` | number | No | Sampling temperature |
**Response:**
```json
{
"text": "Hello, this is the transcribed audio content."
}
```
### Create Translation
Translate audio to English.
```http
POST /v1/audio/translations
Content-Type: multipart/form-data
```
Same parameters as transcription, but always translates to English.
## Models
### List Models
Get available models and pricing.
```http
GET /v1/models
```
**Response:**
```json
{
"object": "list",
"data": [
{
"id": "gpt-3.5-turbo",
"object": "model",
"created": 1677652288,
"owned_by": "openai",
"pricing": {
"prompt": 0.0015,
"completion": 0.002,
"prompt_sats_per_1k": 3,
"completion_sats_per_1k": 4
}
},
{
"id": "gpt-4",
"object": "model",
"created": 1677652288,
"owned_by": "openai",
"pricing": {
"prompt": 0.03,
"completion": 0.06,
"prompt_sats_per_1k": 60,
"completion_sats_per_1k": 120
}
}
]
}
```
### Get Model
Get details for a specific model.
```http
GET /v1/models/{model_id}
```
**Response:**
```json
{
"id": "gpt-3.5-turbo",
"object": "model",
"created": 1677652288,
"owned_by": "openai",
"permission": [{
"allow_create_engine": false,
"allow_sampling": true,
"allow_logprobs": true,
"allow_search_indices": false,
"allow_view": true,
"allow_fine_tuning": false
}],
"root": "gpt-3.5-turbo",
"parent": null,
"pricing": {
"prompt": 0.0015,
"completion": 0.002,
"image": 0,
"request": 0
}
}
```
## Wallet Management
### Create API Key
Create a new API key with eCash deposit.
```http
POST /v1/wallet/create
```
**Request Body:**
```json
{
"cashu_token": "cashuAeyJ0b2tlbiI6W3...",
"name": "My API Key",
"expires_at": "2024-12-31T23:59:59Z",
"refund_npub": "npub1..."
}
```
**Response:**
```json
{
"api_key": "rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p",
"balance": 10000,
"created_at": "2024-01-01T00:00:00Z",
"expires_at": "2024-12-31T23:59:59Z"
}
```
### Check Balance
Get current balance and usage stats.
```http
GET /v1/wallet/balance
Authorization: Bearer {api_key}
```
**Response:**
```json
{
"balance": 8546,
"total_deposited": 10000,
"total_spent": 1454,
"last_used": "2024-01-01T12:34:56Z",
"created_at": "2024-01-01T00:00:00Z"
}
```
### Top Up Balance
Add funds to existing API key.
```http
POST /v1/wallet/topup
Authorization: Bearer {api_key}
```
**Request Body:**
```json
{
"cashu_token": "cashuAeyJ0b2tlbiI6W3..."
}
```
**Response:**
```json
{
"old_balance": 8546,
"added_amount": 5000,
"new_balance": 13546
}
```
### Withdraw Balance
Generate eCash token from balance.
```http
POST /v1/wallet/withdraw
Authorization: Bearer {api_key}
```
**Request Body:**
```json
{
"amount": 5000,
"mint_url": "https://mint.minibits.cash/Bitcoin"
}
```
**Response:**
```json
{
"cashu_token": "cashuAeyJ0b2tlbiI6W3...",
"amount": 5000,
"mint_url": "https://mint.minibits.cash/Bitcoin"
}
```
## Node Information
### Get Node Info
Get public information about the Routstr node.
```http
GET /v1/info
```
**Response:**
```json
{
"name": "Lightning AI Gateway",
"description": "Fast AI API access with Bitcoin payments",
"version": "0.1.1b",
"npub": "npub1abc...",
"mints": [
"https://mint.minibits.cash/Bitcoin",
"https://testnut.cashu.space"
],
"http_url": "https://api.lightning-ai.com",
"onion_url": "http://lightningai.onion",
"models": {
"gpt-3.5-turbo": {
"name": "GPT-3.5 Turbo",
"pricing": {
"prompt": 0.0015,
"completion": 0.002
}
}
}
}
```
## Discovery
### List Providers
Discover Routstr providers from Nostr relays.
```http
GET /v1/providers
```
**Query Parameters:**
| Parameter | Type | Description |
|-----------|------|-------------|
| `relay` | string | Specific relay URL |
| `limit` | integer | Maximum results |
**Response:**
```json
{
"providers": [
{
"name": "Fast AI Node",
"npub": "npub1xyz...",
"url": "https://fast-ai.com",
"description": "Low latency AI API access",
"models": ["gpt-3.5-turbo", "gpt-4"],
"pricing": {
"gpt-3.5-turbo": {
"prompt_sats_per_1k": 3,
"completion_sats_per_1k": 4
}
}
}
]
}
```
## Admin Endpoints
### Admin Dashboard
Access the web-based admin interface.
```http
GET /admin/
```
Requires password authentication via web form.
### Admin API
Protected endpoints for node management.
```http
POST /admin/api/withdraw
X-Admin-Password: {admin_password}
```
**Request Body:**
```json
{
"api_key": "rstr_123...",
"amount": 5000
}
```
## Health & Status
### Health Check
Monitor service health.
```http
GET /health
```
**Response:**
```json
{
"status": "healthy",
"version": "0.1.1b",
"timestamp": "2024-01-01T00:00:00Z",
"checks": {
"database": "ok",
"upstream": "ok",
"mint": "ok"
}
}
```
### Metrics
Get service metrics.
```http
GET /metrics
```
Returns Prometheus-compatible metrics.
## Deprecated Endpoints
### Legacy Balance Check
```http
GET /v1/balance
Authorization: Bearer {api_key}
```
⚠️ **Deprecated**: Use `/v1/wallet/balance` instead.
## Rate Limits
All endpoints are subject to rate limiting:
| Endpoint Type | Limit | Window |
|---------------|-------|--------|
| AI Generation | 100/min | 1 minute |
| Wallet Operations | 10/min | 1 minute |
| Info/Discovery | 60/min | 1 minute |
Rate limit information is included in response headers.
## Next Steps
- [Errors](errors.md) - Error handling reference
- [Authentication](authentication.md) - Auth details
- [Examples](../user-guide/using-api.md) - Code examples
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# Error Handling
This guide covers error responses, codes, and handling strategies for the Routstr API.
## Error Response Format
All errors follow a consistent JSON structure:
```json
{
"error": {
"type": "error_type",
"message": "Human-readable error message",
"code": "error_code",
"details": {
"additional": "context-specific information"
}
}
}
```
## HTTP Status Codes
| Status | Meaning | Common Causes |
|--------|---------|---------------|
| 400 | Bad Request | Invalid parameters, malformed JSON |
| 401 | Unauthorized | Invalid or missing API key |
| 402 | Payment Required | Insufficient balance |
| 403 | Forbidden | Access denied to resource |
| 404 | Not Found | Endpoint or resource doesn't exist |
| 422 | Unprocessable Entity | Validation errors |
| 429 | Too Many Requests | Rate limit exceeded |
| 500 | Internal Server Error | Server-side error |
| 502 | Bad Gateway | Upstream API error |
| 503 | Service Unavailable | Temporary outage |
## Error Types
### Authentication Errors
#### Invalid API Key
```json
{
"error": {
"type": "authentication_failed",
"message": "Invalid API key provided",
"code": "invalid_api_key"
}
}
```
**Status:** 401
**Resolution:** Check API key format and validity
#### Expired API Key
```json
{
"error": {
"type": "authentication_failed",
"message": "API key has expired",
"code": "key_expired",
"details": {
"expired_at": "2024-01-01T00:00:00Z",
"refund_available": true
}
}
}
```
**Status:** 401
**Resolution:** Create new API key or contact admin for refund
#### Missing Authorization
```json
{
"error": {
"type": "authentication_failed",
"message": "Authorization header required",
"code": "missing_auth"
}
}
```
**Status:** 401
**Resolution:** Include `Authorization: Bearer {api_key}` header
### Payment Errors
#### Insufficient Balance
```json
{
"error": {
"type": "insufficient_balance",
"message": "Insufficient balance for request",
"code": "payment_required",
"details": {
"balance": 100,
"required": 154,
"shortfall": 54,
"estimated_tokens": {
"prompt": 50,
"completion": 150
}
}
}
}
```
**Status:** 402
**Resolution:** Top up API key balance
#### Invalid Token
```json
{
"error": {
"type": "payment_error",
"message": "Invalid Cashu token",
"code": "invalid_token",
"details": {
"reason": "Token already spent"
}
}
}
```
**Status:** 400
**Resolution:** Use a valid, unspent token
#### Mint Unavailable
```json
{
"error": {
"type": "payment_error",
"message": "Cannot connect to Cashu mint",
"code": "mint_unavailable",
"details": {
"mint_url": "https://mint.example.com",
"retry_after": 60
}
}
}
```
**Status:** 503
**Resolution:** Try again later or use different mint
### Validation Errors
#### Invalid Parameters
```json
{
"error": {
"type": "invalid_request",
"message": "Invalid request parameters",
"code": "validation_error",
"details": {
"errors": [
{
"field": "temperature",
"message": "Must be between 0 and 2",
"value": 3.5
},
{
"field": "model",
"message": "Model 'gpt-5' not found",
"value": "gpt-5"
}
]
}
}
}
```
**Status:** 422
**Resolution:** Fix parameter values
#### Missing Required Fields
```json
{
"error": {
"type": "invalid_request",
"message": "Missing required fields",
"code": "missing_fields",
"details": {
"missing": ["model", "messages"]
}
}
}
```
**Status:** 400
**Resolution:** Include all required fields
### Rate Limiting
#### Rate Limit Exceeded
```json
{
"error": {
"type": "rate_limit_exceeded",
"message": "Too many requests",
"code": "rate_limit",
"details": {
"limit": 100,
"window": "1 minute",
"retry_after": 45
}
}
}
```
**Status:** 429
**Headers:**
```
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1640995200
Retry-After: 45
```
**Resolution:** Wait for retry_after seconds
### Upstream Errors
#### Model Overloaded
```json
{
"error": {
"type": "upstream_error",
"message": "Model is currently overloaded",
"code": "model_overloaded",
"details": {
"model": "gpt-4",
"retry_after": 5
}
}
}
```
**Status:** 503
**Resolution:** Retry request after delay
#### Upstream Timeout
```json
{
"error": {
"type": "upstream_error",
"message": "Request to upstream API timed out",
"code": "upstream_timeout",
"details": {
"timeout": 30,
"endpoint": "chat/completions"
}
}
}
```
**Status:** 504
**Resolution:** Retry with shorter prompt or max_tokens
### Content Policy
#### Content Filtered
```json
{
"error": {
"type": "content_policy_violation",
"message": "Content filtered due to policy violation",
"code": "content_filtered",
"details": {
"reason": "harmful_content",
"categories": ["violence", "hate"]
}
}
}
```
**Status:** 400
**Resolution:** Modify prompt to comply with policies
## Error Handling Best Practices
### Retry Logic
Implement exponential backoff with jitter:
```python
import time
import random
from typing import Optional, Callable
def retry_with_backoff(
func: Callable,
max_retries: int = 3,
base_delay: float = 1.0,
max_delay: float = 60.0
) -> Optional[Any]:
"""Retry function with exponential backoff."""
for attempt in range(max_retries):
try:
return func()
except Exception as e:
if attempt == max_retries - 1:
raise
# Check if error is retryable
if hasattr(e, 'status_code'):
if e.status_code in [429, 502, 503, 504]:
# Calculate delay with jitter
delay = min(
base_delay * (2 ** attempt) + random.uniform(0, 1),
max_delay
)
# Use retry_after if provided
if hasattr(e, 'retry_after'):
delay = e.retry_after
time.sleep(delay)
else:
# Non-retryable error
raise
```
### Error Categories
Group errors for handling:
```python
class ErrorHandler:
# Errors that should be retried
RETRYABLE_ERRORS = {
'rate_limit',
'upstream_timeout',
'model_overloaded',
'mint_unavailable'
}
# Errors requiring user action
USER_ACTION_ERRORS = {
'insufficient_balance',
'invalid_api_key',
'key_expired'
}
# Errors requiring code changes
CLIENT_ERRORS = {
'validation_error',
'missing_fields',
'invalid_request'
}
@classmethod
def handle_error(cls, error_response: dict) -> None:
error_code = error_response['error']['code']
if error_code in cls.RETRYABLE_ERRORS:
# Implement retry logic
pass
elif error_code in cls.USER_ACTION_ERRORS:
# Alert user
pass
elif error_code in cls.CLIENT_ERRORS:
# Log for debugging
pass
```
### Graceful Degradation
Handle errors without breaking application flow:
```python
async def get_ai_response(prompt: str) -> str:
"""Get AI response with fallback handling."""
try:
# Try primary model
response = await client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
except InsufficientBalanceError:
# Fall back to cheaper model
try:
response = await client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
max_tokens=100 # Limit tokens
)
return response.choices[0].message.content
except Exception as e:
logger.error(f"Fallback failed: {e}")
return "Service temporarily unavailable"
except Exception as e:
logger.error(f"Unexpected error: {e}")
return "An error occurred processing your request"
```
### Logging Errors
Structure error logs for debugging:
```python
import logging
import json
def log_api_error(error_response: dict, context: dict) -> None:
"""Log API errors with context."""
logger = logging.getLogger(__name__)
error_data = {
'timestamp': datetime.utcnow().isoformat(),
'error': error_response['error'],
'context': {
'endpoint': context.get('endpoint'),
'api_key_id': context.get('api_key_id'),
'request_id': context.get('request_id'),
'model': context.get('model')
}
}
logger.error(
"API Error",
extra={'structured_data': json.dumps(error_data)}
)
```
### User-Friendly Messages
Map technical errors to user messages:
```python
ERROR_MESSAGES = {
'insufficient_balance': "Your account balance is too low. Please add funds to continue.",
'invalid_api_key': "Invalid API key. Please check your configuration.",
'rate_limit': "Too many requests. Please wait a moment and try again.",
'model_overloaded': "The AI service is busy. Please try again in a few seconds.",
'validation_error': "Invalid request. Please check your input and try again."
}
def get_user_message(error_code: str) -> str:
"""Get user-friendly error message."""
return ERROR_MESSAGES.get(
error_code,
"An unexpected error occurred. Please try again later."
)
```
## Common Scenarios
### Handling Balance Errors
```python
async def make_request_with_balance_check():
try:
# Check balance first
balance_info = await client.get("/v1/wallet/balance")
# Estimate cost
estimated_cost = calculate_cost(model, prompt_length)
if balance_info['balance'] < estimated_cost * 1.1: # 10% buffer
# Proactively top up
await top_up_balance()
# Make request
return await client.chat.completions.create(...)
except InsufficientBalanceError as e:
# Handle insufficient balance
shortfall = e.details['shortfall']
await top_up_balance(amount=shortfall * 2)
# Retry request
```
### Handling Rate Limits
```python
from datetime import datetime, timedelta
class RateLimitTracker:
def __init__(self):
self.reset_times = {}
def is_limited(self, endpoint: str) -> bool:
reset_time = self.reset_times.get(endpoint)
if reset_time and datetime.now() < reset_time:
return True
return False
def set_limit(self, endpoint: str, reset_timestamp: int):
self.reset_times[endpoint] = datetime.fromtimestamp(reset_timestamp)
def wait_time(self, endpoint: str) -> float:
reset_time = self.reset_times.get(endpoint)
if reset_time:
return max(0, (reset_time - datetime.now()).total_seconds())
return 0
```
## Testing Error Handling
### Unit Tests
```python
import pytest
from unittest.mock import Mock
async def test_insufficient_balance_handling():
# Mock API client
mock_client = Mock()
mock_client.chat.completions.create.side_effect = InsufficientBalanceError(
required=100,
available=50
)
# Test error handling
handler = ErrorHandler(mock_client)
result = await handler.safe_request(
model="gpt-4",
messages=[{"role": "user", "content": "test"}]
)
# Verify fallback behavior
assert result.fallback_used is True
assert result.model == "gpt-3.5-turbo"
```
### Integration Tests
```python
async def test_real_error_scenarios():
# Test with invalid API key
invalid_client = OpenAI(
api_key="rstr_invalid",
base_url=test_url
)
with pytest.raises(AuthenticationError) as exc_info:
await invalid_client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "test"}]
)
assert exc_info.value.status_code == 401
assert "invalid_api_key" in str(exc_info.value)
```
## Monitoring Errors
Track error rates and patterns:
```python
class ErrorMetrics:
def __init__(self):
self.error_counts = defaultdict(int)
self.error_timestamps = defaultdict(list)
def record_error(self, error_code: str):
self.error_counts[error_code] += 1
self.error_timestamps[error_code].append(datetime.now())
def get_error_rate(self, error_code: str, window_minutes: int = 60) -> float:
cutoff = datetime.now() - timedelta(minutes=window_minutes)
recent_errors = [
ts for ts in self.error_timestamps[error_code]
if ts > cutoff
]
return len(recent_errors) / window_minutes
```
## Next Steps
- [Authentication](authentication.md) - Auth error details
- [Endpoints](endpoints.md) - Endpoint-specific errors
- [Examples](../user-guide/using-api.md) - Error handling examples
docs/api/overview.md
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# API Reference Overview
Routstr Core provides a complete OpenAI-compatible API with additional endpoints for payment management. This reference covers all available endpoints, authentication methods, and response formats.
## Base URL
```
https://your-routstr-node.com/v1
```
All API endpoints are prefixed with `/v1` for versioning.
## Authentication
Routstr uses API keys for authentication. Include your key in the Authorization header:
```bash
Authorization: Bearer rstr_your_api_key_here
```
### API Key Format
- Prefix: `rstr_`
- Length: 32 characters
- Example: `rstr_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p`
## Content Types
### Request
- **Required**: `Content-Type: application/json`
- **Encoding**: UTF-8
- **Maximum Size**: 10MB (configurable)
### Response
- **Type**: `application/json` or `text/event-stream` (for streaming)
- **Encoding**: UTF-8
- **Compression**: gzip (if accepted)
## Rate Limiting
Rate limits are applied per API key:
- **Requests**: 1000 per minute
- **Tokens**: 1,000,000 per hour
- **Concurrent**: 10 simultaneous requests
Rate limit headers:
```
X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 999
X-RateLimit-Reset: 1640995200
```
## Error Responses
All errors follow a consistent format:
```json
{
"error": {
"type": "insufficient_balance",
"message": "Insufficient balance for request",
"code": "payment_required",
"details": {
"required": 154,
"available": 100
}
}
}
```
### Error Types
| Type | Status Code | Description |
|------|-------------|-------------|
| `invalid_request` | 400 | Malformed request |
| `authentication_failed` | 401 | Invalid or missing API key |
| `insufficient_balance` | 402 | Not enough balance |
| `forbidden` | 403 | Access denied |
| `not_found` | 404 | Resource not found |
| `rate_limit_exceeded` | 429 | Too many requests |
| `internal_error` | 500 | Server error |
| `upstream_error` | 502 | Upstream API error |
## Endpoint Categories
### AI/ML Endpoints
Standard OpenAI-compatible endpoints:
- **Chat Completions**: `/v1/chat/completions`
- **Completions**: `/v1/completions`
- **Embeddings**: `/v1/embeddings`
- **Images**: `/v1/images/generations`
- **Audio**: `/v1/audio/transcriptions`
- **Models**: `/v1/models`
### Payment Endpoints
Routstr-specific payment management:
- **Wallet**: `/v1/wallet/*`
- **Balance**: `/v1/balance`
- **Node Info**: `/v1/info`
### Admin Endpoints
Protected administrative functions:
- **Dashboard**: `/admin/`
- **API Management**: `/admin/api/*`
## Request Headers
### Standard Headers
| Header | Required | Description |
|--------|----------|-------------|
| `Authorization` | Yes | Bearer token with API key |
| `Content-Type` | Yes | Must be `application/json` |
| `Accept` | No | Response format preference |
| `Accept-Encoding` | No | Compression support |
| `X-Request-ID` | No | Client-provided request ID |
### Custom Headers
| Header | Description |
|--------|-------------|
| `X-Routstr-Version` | API version override |
| `X-Cashu` | eCash token for per-request payment |
| `X-Max-Cost` | Maximum acceptable cost in sats |
## Response Headers
### Standard Headers
| Header | Description |
|--------|-------------|
| `Content-Type` | Response format |
| `Content-Length` | Response size |
| `X-Routstr-Request-ID` | Unique request identifier |
| `X-Routstr-Version` | API version used |
### Cost Headers
| Header | Description |
|--------|-------------|
| `X-Routstr-Cost` | Request cost in sats |
| `X-Routstr-Balance` | Remaining balance |
| `X-Cashu` | Change token (if applicable) |
## Streaming Responses
For endpoints supporting streaming, responses use Server-Sent Events:
```
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"role":"assistant","content":"Hello"},"finish_reason":null}]}
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":" there"},"finish_reason":null}]}
data: [DONE]
```
## OpenAPI Specification
The complete OpenAPI 3.0 specification is available at:
```
GET /openapi.json
```
Interactive documentation:
```
GET /docs # Swagger UI
GET /redoc # ReDoc
```
## SDK Support
Routstr is compatible with official OpenAI SDKs:
### Python
```python
from openai import OpenAI
client = OpenAI(
api_key="rstr_your_key",
base_url="https://your-node.com/v1"
)
```
### JavaScript/TypeScript
```javascript
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: 'rstr_your_key',
baseURL: 'https://your-node.com/v1'
});
```
### cURL
```bash
curl https://your-node.com/v1/chat/completions \
-H "Authorization: Bearer rstr_your_key" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-3.5-turbo","messages":[{"role":"user","content":"Hello"}]}'
```
## Webhook Support
Configure webhooks for events:
```json
POST /v1/webhooks
{
"url": "https://your-app.com/webhook",
"events": ["balance.low", "key.expired"],
"secret": "whsec_your_secret"
}
```
Events are sent with signature verification:
```
X-Webhook-Signature: sha256=...
```
## API Versioning
- Current version: `v1`
- Version in URL path: `/v1/endpoint`
- Override with header: `X-Routstr-Version: v2`
- Deprecation notices: 6 months
## Status Codes
| Code | Meaning |
|------|---------|
| 200 | Success |
| 201 | Created |
| 204 | No content |
| 400 | Bad request |
| 401 | Unauthorized |
| 402 | Payment required |
| 403 | Forbidden |
| 404 | Not found |
| 429 | Rate limited |
| 500 | Server error |
| 502 | Upstream error |
| 503 | Service unavailable |
## CORS Support
CORS is enabled with configurable origins:
```
Access-Control-Allow-Origin: *
Access-Control-Allow-Methods: GET, POST, PUT, DELETE, OPTIONS
Access-Control-Allow-Headers: Authorization, Content-Type
Access-Control-Max-Age: 86400
```
## Compression
Responses are compressed with gzip when:
- Client sends `Accept-Encoding: gzip`
- Response is larger than 1KB
- Content type is compressible
## Pagination
List endpoints support pagination:
```
GET /v1/transactions?limit=50&offset=100
```
Response includes pagination metadata:
```json
{
"data": [...],
"has_more": true,
"total": 500,
"limit": 50,
"offset": 100
}
```
## Field Filtering
Select specific fields in responses:
```
GET /v1/models?fields=id,name,pricing
```
## Batch Requests
Process multiple operations in one request:
```json
POST /v1/batch
{
"requests": [
{"method": "POST", "endpoint": "/chat/completions", "body": {...}},
{"method": "GET", "endpoint": "/models"},
{"method": "GET", "endpoint": "/balance"}
]
}
```
## Idempotency
Prevent duplicate operations:
```
Idempotency-Key: unique-request-id
```
Keys are stored for 24 hours.
## Health Check
Monitor service status:
```
GET /health
Response:
{
"status": "healthy",
"version": "0.1.1b",
"timestamp": "2024-01-01T00:00:00Z",
"checks": {
"database": "ok",
"upstream": "ok",
"mint": "ok"
}
}
```
## Next Steps
- [Authentication](authentication.md) - Detailed auth guide
- [Endpoints](endpoints.md) - Complete endpoint reference
- [Errors](errors.md) - Error handling guide
- [Examples](../user-guide/using-api.md) - Code examples
docs/contributing/architecture.md
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# Architecture Overview
This document describes the high-level architecture of Routstr Core, helping contributors understand how the system works.
## System Overview
Routstr Core is a FastAPI-based reverse proxy that adds Bitcoin micropayments to OpenAI-compatible APIs.
```mermaid
graph TB
subgraph "External Services"
Client[API Client]
Mint[Cashu Mint]
Provider[AI Provider]
Nostr[Nostr Relays]
end
subgraph "Routstr Core"
API[FastAPI Server]
Auth[Auth Module]
Payment[Payment Module]
Proxy[Proxy Module]
DB[(SQLite DB)]
API --> Auth
Auth --> Payment
Auth --> DB
Payment --> Proxy
Proxy --> Provider
Payment --> Mint
API --> Nostr
end
Client --> API
```
## Core Components
### FastAPI Application
The main application is initialized in `routstr/core/main.py`:
- **Lifespan Management**: Handles startup/shutdown tasks
- **Middleware**: CORS, logging, error handling
- **Routers**: Modular endpoint organization
- **Background Tasks**: Price updates, automatic payouts
### Authentication System
Located in `routstr/auth.py`, handles:
- **API Key Validation**: Hashed key storage and lookup
- **Balance Checking**: Ensures sufficient funds
- **Rate Limiting**: Optional request throttling
- **Token Redemption**: Converts eCash to balance
### Payment Processing
The `routstr/payment/` module manages:
- **Cost Calculation**: Token-based or fixed pricing
- **Model Pricing**: Dynamic pricing from models.json
- **Currency Conversion**: BTC/USD rate management
- **Fee Application**: Exchange and provider fees
### Request Proxying
`routstr/proxy.py` handles:
- **Request Forwarding**: Preserves headers and body
- **Response Streaming**: Efficient memory usage
- **Usage Tracking**: Counts tokens and costs
- **Error Handling**: Graceful upstream failures
### Database Layer
Using SQLModel in `routstr/core/db.py`:
```python
# Core models
APIKey:
- id: Primary key
- key_hash: Hashed API key
- balance: Current balance (msats)
- created_at: Timestamp
- metadata: JSON field
Transaction:
- id: Primary key
- api_key_id: Foreign key
- amount: Transaction amount
- type: deposit/usage/withdrawal
- timestamp: When occurred
```
## Request Flow
### Standard API Request
```mermaid
sequenceDiagram
participant C as Client
participant R as Routstr
participant D as Database
participant P as AI Provider
C->>R: API Request + Key
R->>D: Validate Key
D-->>R: Key Info + Balance
R->>R: Check Balance
R->>P: Forward Request
P-->>R: AI Response
R->>D: Deduct Cost
R-->>C: Return Response
```
### Payment Flow
```mermaid
sequenceDiagram
participant C as Client
participant R as Routstr
participant W as Wallet Module
participant M as Cashu Mint
participant D as Database
C->>R: Create Key Request + Token
R->>W: Validate Token
W->>M: Verify with Mint
M-->>W: Token Valid
W-->>R: Token Amount
R->>D: Create Key + Balance
R-->>C: Return API Key
```
## Key Design Decisions
### 1. Async Architecture
Everything is async for maximum performance:
```python
async def handle_request(request: Request) -> Response:
# Non-blocking database queries
api_key = await get_api_key(request.headers["Authorization"])
# Concurrent operations
balance_check, rate_limit = await asyncio.gather(
check_balance(api_key),
check_rate_limit(api_key)
)
# Stream response without blocking
async for chunk in proxy_request(request):
yield chunk
```
### 2. Modular Design
Components are loosely coupled:
- **Routers**: Separate files for different endpoints
- **Dependencies**: Injected via FastAPI's DI system
- **Models**: Shared data structures
- **Services**: Business logic separated from routes
### 3. Error Handling
Graceful degradation and clear error messages:
```python
class RoustrError(Exception):
"""Base exception with structured error response"""
status_code: int = 500
error_type: str = "internal_error"
class InsufficientBalanceError(RoustrError):
status_code = 402
error_type = "insufficient_balance"
```
### 4. Database Migrations
Using Alembic for schema management:
- Auto-migrations on startup
- Version control for schema changes
- Rollback capability
- Zero-downtime updates
## Security Architecture
### API Key Security
- **Storage**: SHA-256 hashed keys
- **Generation**: Cryptographically secure random
- **Validation**: Constant-time comparison
- **Rotation**: Support for key expiry
### Payment Security
- **Token Validation**: Cryptographic verification
- **Double-Spend Prevention**: Mint verification
- **Balance Protection**: Atomic transactions
- **Audit Trail**: All transactions logged
### Network Security
- **HTTPS**: Enforced in production
- **CORS**: Configurable origins
- **Rate Limiting**: Per-key limits
- **Input Validation**: Pydantic models
## Performance Considerations
### Caching Strategy
```python
# Model pricing cache
@lru_cache(maxsize=100)
def get_model_price(model_id: str) -> ModelPrice:
return MODELS.get(model_id)
# Balance cache with TTL
balance_cache = TTLCache(maxsize=1000, ttl=60)
```
### Database Optimization
- **Connection Pooling**: Reuse connections
- **Indexed Queries**: Key lookups are O(1)
- **Batch Operations**: Group updates
- **Async I/O**: Non-blocking queries
### Streaming Responses
Efficient memory usage for large responses:
```python
async def stream_response(upstream_response):
async for chunk in upstream_response.aiter_bytes():
# Process chunk without loading full response
yield process_chunk(chunk)
```
## Extension Points
### Adding New Endpoints
1. Create router module
2. Define Pydantic models
3. Implement business logic
4. Register with main app
5. Add tests
### Custom Pricing Models
1. Extend `ModelPrice` class
2. Implement calculation logic
3. Add to pricing registry
4. Update configuration
### Payment Methods
1. Create payment handler
2. Implement validation
3. Add to payment router
4. Update balance logic
## Testing Strategy
### Unit Tests
- Mock external dependencies
- Test business logic in isolation
- Fast execution (< 1 second per test)
- High coverage target (> 80%)
### Integration Tests
- Test component interactions
- Use test database
- Mock external services
- Verify end-to-end flows
### Performance Tests
- Load testing with locust
- Memory profiling
- Database query optimization
- Response time benchmarks
## Monitoring and Observability
### Structured Logging
```python
logger.info("api_request", extra={
"request_id": request_id,
"api_key": api_key_id,
"endpoint": endpoint,
"model": model,
"tokens": token_count,
"cost_sats": cost,
"duration_ms": duration
})
```
### Metrics Collection
Key metrics tracked:
- Request rate by endpoint
- Token usage by model
- Balance changes
- Error rates
- Response times
### Health Checks
```python
@app.get("/health")
async def health_check():
return {
"status": "healthy",
"version": __version__,
"database": await check_db(),
"upstream": await check_upstream(),
"mints": await check_mints()
}
```
## Deployment Architecture
### Container Structure
```dockerfile
# Multi-stage build
FROM python:3.11-slim AS builder
# Install dependencies
FROM python:3.11-slim
# Copy only runtime needs
# Run as non-root user
```
### Environment Configuration
- **Development**: Local SQLite, debug logging
- **Testing**: In-memory database, mock services
- **Production**: Persistent storage, structured logs
### Scaling Considerations
- **Horizontal**: Multiple instances behind load balancer
- **Vertical**: Async handles high concurrency
- **Database**: Consider PostgreSQL for scale
- **Caching**: Redis for distributed cache
## Future Architecture
### Planned Improvements
1. **WebSocket Support**: Real-time balance updates
2. **Plugin System**: Extensible pricing/auth
3. **Multi-Region**: Geographic distribution
4. **Event Sourcing**: Complete audit trail
### Technical Debt
Areas for improvement:
- Database query optimization
- Response caching layer
- Metric aggregation
- API versioning strategy
## Next Steps
- Review [Code Structure](code-structure.md) for detailed organization
- See [Testing Guide](testing.md) for test architecture
- Check [Database Guide](database.md) for schema details
- Read [Guidelines](guidelines.md) for coding standards
docs/contributing/code-structure.md
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# Code Structure
This guide provides a detailed overview of Routstr Core's codebase organization and key modules.
## Directory Layout
```
routstr-core/
├── routstr/ # Main application package
│ ├── __init__.py # Package initialization, loads .env
│ ├── auth.py # Authentication and authorization
│ ├── balance.py # Balance management endpoints
│ ├── discovery.py # Nostr relay discovery
│ ├── proxy.py # Request proxying logic
│ ├── wallet.py # Cashu wallet operations
│ │
│ ├── core/ # Core infrastructure
│ │ ├── __init__.py
│ │ ├── admin.py # Admin dashboard and API
│ │ ├── db.py # Database models and connection
│ │ ├── exceptions.py # Custom exception classes
│ │ ├── logging.py # Structured logging setup
│ │ ├── main.py # FastAPI app initialization
│ │ └── middleware.py # HTTP middleware components
│ │
│ └── payment/ # Payment processing
│ ├── __init__.py
│ ├── cost_calculation.py # Usage cost calculation
│ ├── helpers.py # Payment utilities
│ ├── lnurl.py # Lightning URL support
│ ├── models.py # Model pricing management
│ ├── price.py # BTC/USD price handling
│ └── x_cashu.py # Cashu header protocol
│
├── tests/ # Test suite
│ ├── __init__.py
│ ├── conftest.py # Pytest configuration
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
│
├── migrations/ # Alembic database migrations
│ ├── alembic.ini
│ ├── env.py
│ ├── script.py.mako
│ └── versions/ # Migration files
│
├── scripts/ # Utility scripts
│ └── models_meta.py # Fetch model pricing
│
├── docs/ # Documentation
├── logs/ # Application logs (git ignored)
│
├── .github/ # GitHub Actions workflows
├── .env.example # Environment variable template
├── .gitignore # Git ignore rules
├── .dockerignore # Docker ignore rules
├── Dockerfile # Container definition
├── Makefile # Development commands
├── README.md # Project overview
├── alembic.ini # Migration configuration
├── compose.yml # Docker Compose setup
├── compose.testing.yml # Testing environment
├── pyproject.toml # Project configuration
└── uv.lock # Locked dependencies
```
## Key Modules
### Application Entry Point
#### `routstr/__init__.py`
```python
# Loads environment variables
import dotenv
dotenv.load_dotenv()
# Exports FastAPI app
from .core.main import app as fastapi_app
```
#### `routstr/core/main.py`
```python
# FastAPI application setup
app = FastAPI(
title="Routstr Node",
lifespan=lifespan, # Manages startup/shutdown
)
# Middleware registration
app.add_middleware(CORSMiddleware, ...)
app.add_middleware(LoggingMiddleware)
# Router inclusion
app.include_router(admin_router)
app.include_router(balance_router)
app.include_router(proxy_router)
```
### Authentication Module
#### `routstr/auth.py`
Handles API key validation and authorization:
```python
class APIKeyAuth:
"""FastAPI dependency for API key authentication"""
async def __call__(self, request: Request) -> APIKey:
# Extract and validate API key
# Check balance
# Return authenticated key object
# Usage in routes:
@router.get("/protected")
async def protected_route(api_key: APIKey = Depends(APIKeyAuth())):
pass
```
Key functions:
- `create_api_key()` - Generate new API keys
- `validate_api_key()` - Verify and retrieve key
- `check_balance()` - Ensure sufficient funds
- `update_last_used()` - Track usage
### Payment Processing
#### `routstr/payment/cost_calculation.py`
Calculates request costs:
```python
def calculate_request_cost(
model: str,
prompt_tokens: int,
completion_tokens: int,
**kwargs
) -> CostData:
"""Calculate cost in millisatoshis"""
# Model-based or fixed pricing
# Token counting
# Fee application
# Currency conversion
```
#### `routstr/payment/models.py`
Manages model pricing data:
```python
class ModelPrice:
id: str
name: str
pricing: dict[str, float] # USD prices
context_length: int
# Global model registry
MODELS: dict[str, ModelPrice] = load_models()
# Dynamic price updates
async def update_sats_pricing():
"""Background task to update BTC prices"""
```
#### `routstr/payment/x_cashu.py`
Implements Cashu payment protocol:
```python
class XCashuHandler:
"""Handle x-cashu header payments"""
async def process_request_payment(
self,
token: str,
estimated_cost: int
) -> PaymentResult:
# Validate token
# Check minimum amount
# Process payment
# Generate change
```
### Request Proxying
#### `routstr/proxy.py`
Core proxy functionality:
```python
@router.api_route("/{path:path}", methods=ALL_METHODS)
async def proxy_request(
request: Request,
path: str,
api_key: APIKey = Depends(APIKeyAuth())
) -> Response:
"""Forward requests to upstream provider"""
# Build upstream request
# Stream response
# Track usage
# Deduct costs
```
Key features:
- Streaming support
- Header preservation
- Error handling
- Usage tracking
### Database Layer
#### `routstr/core/db.py`
SQLModel definitions:
```python
class APIKey(SQLModel, table=True):
id: int | None = Field(primary_key=True)
key_hash: str = Field(index=True, unique=True)
balance: int # millisatoshis
total_deposited: int = 0
total_spent: int = 0
created_at: datetime
expires_at: datetime | None = None
metadata: dict = Field(default_factory=dict, sa_column=Column(JSON))
class Transaction(SQLModel, table=True):
id: int | None = Field(primary_key=True)
api_key_id: int = Field(foreign_key="apikey.id")
amount: int # can be negative
balance_after: int
type: TransactionType
description: str
timestamp: datetime
```
### Admin Interface
#### `routstr/core/admin.py`
Web dashboard and admin API:
```python
@admin_router.get("/admin/")
async def admin_dashboard(request: Request):
"""Render admin HTML interface"""
# Authentication check
# Load statistics
# Render template
@admin_router.post("/admin/api/withdraw")
async def withdraw_balance(
api_key: str,
amount: int | None = None
) -> WithdrawalResponse:
"""Generate eCash token for withdrawal"""
```
Features:
- HTML dashboard
- API key management
- Balance withdrawals
- Usage statistics
### Wallet Integration
#### `routstr/wallet.py`
Cashu wallet operations:
```python
class WalletManager:
"""Manage Cashu wallet instances"""
async def redeem_token(
self,
token: str,
mint_url: str | None = None
) -> int:
"""Redeem eCash token and return value"""
async def create_token(
self,
amount: int,
mint_url: str
) -> str:
"""Create eCash token for withdrawal"""
```
### Utility Modules
#### `routstr/core/logging.py`
Structured logging configuration:
```python
def setup_logging():
"""Configure JSON structured logging"""
# Set log level
# Configure formatters
# Add handlers
class RequestIdMiddleware:
"""Add request ID to all logs"""
```
#### `routstr/core/middleware.py`
HTTP middleware components:
```python
class LoggingMiddleware:
"""Log all HTTP requests/responses"""
class ErrorHandlingMiddleware:
"""Consistent error responses"""
```
#### `routstr/core/exceptions.py`
Custom exception hierarchy:
```python
class RoustrError(Exception):
"""Base exception with error details"""
status_code: int
error_type: str
detail: str
class PaymentError(RoustrError):
"""Payment-related errors"""
class UpstreamError(RoustrError):
"""Upstream API errors"""
```
## Configuration Files
### `pyproject.toml`
Project metadata and dependencies:
```toml
[project]
name = "routstr"
version = "0.1.1b"
dependencies = [
"fastapi[standard]>=0.115",
"sqlmodel>=0.0.24",
"cashu",
# ...
]
[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"
[tool.ruff.lint]
select = ["E", "F", "I"]
```
### `alembic.ini`
Database migration configuration:
```ini
[alembic]
script_location = migrations
prepend_sys_path = .
version_path_separator = os
[loggers]
keys = root,sqlalchemy,alembic
```
### `Makefile`
Development commands:
```makefile
# Setup commands
setup:
uv sync
uv pip install -e .
# Development server
dev:
fastapi dev routstr --host 0.0.0.0
# Testing
test:
uv run pytest
# Code quality
lint:
uv run ruff check .
```
## Code Patterns
### Dependency Injection
Using FastAPI's DI system:
```python
# Define dependency
async def get_db() -> AsyncSession:
async with async_session() as session:
yield session
# Use in routes
@router.get("/items")
async def get_items(db: AsyncSession = Depends(get_db)):
result = await db.execute(select(Item))
return result.scalars().all()
```
### Async Context Managers
For resource management:
```python
async with httpx.AsyncClient() as client:
response = await client.get(url)
async with database.transaction():
# Atomic operations
```
### Type Safety
Leveraging Python 3.11+ features:
```python
# Union types with |
def process(value: str | int) -> dict[str, Any]:
pass
# Type aliases
Balance = int # millisatoshis
TokenList = list[dict[str, str]]
```
### Error Handling
Consistent error responses:
```python
try:
result = await risky_operation()
except SpecificError as e:
logger.error("Operation failed", exc_info=True)
raise HTTPException(
status_code=400,
detail={
"error": "specific_error",
"message": str(e)
}
)
```
## Best Practices
### Module Organization
1. **Single Responsibility**: Each module has one clear purpose
2. **Minimal Imports**: Import only what's needed
3. **Circular Dependencies**: Avoid by using dependency injection
4. **Public API**: Expose through `__init__.py`
### Function Design
1. **Type Hints**: Always include complete type annotations
2. **Async First**: Use async/await for I/O operations
3. **Error Handling**: Raise specific exceptions
4. **Documentation**: Docstrings for public functions
### Testing Structure
1. **Mirror Source**: Test structure matches source
2. **Fixtures**: Reusable test data in conftest.py
3. **Mocking**: Mock external dependencies
4. **Coverage**: Aim for >80% coverage
## Next Steps
- Review [Testing Guide](testing.md) for test structure
- See [Database Guide](database.md) for schema details
- Check [Guidelines](guidelines.md) for coding standards
- Read [Architecture](architecture.md) for system design
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# Database Guide
This guide covers database design, migrations, and best practices for Routstr Core.
## Database Overview
Routstr uses:
- **SQLite** for local development and single-node deployments
- **PostgreSQL** (optional) for production scale
- **SQLModel** for ORM with type safety
- **Alembic** for schema migrations
- **Async SQLAlchemy** for non-blocking I/O
## Schema Design
### Core Tables
#### APIKey Table
```python
class APIKey(SQLModel, table=True):
"""API key with balance tracking"""
# Primary key
id: int | None = Field(default=None, primary_key=True)
# Key data (indexed for fast lookups)
key_hash: str = Field(index=True, unique=True)
# Balance in millisatoshis (1 sat = 1000 msats)
balance: int = Field(default=0)
total_deposited: int = Field(default=0)
total_spent: int = Field(default=0)
# Timestamps
created_at: datetime = Field(default_factory=datetime.utcnow)
last_used_at: datetime | None = None
expires_at: datetime | None = None
# Metadata (JSON field)
metadata: dict = Field(
default_factory=dict,
sa_column=Column(JSON)
)
# Relationships
transactions: list["Transaction"] = Relationship(back_populates="api_key")
```
#### Transaction Table
```python
class Transaction(SQLModel, table=True):
"""Transaction log for audit trail"""
# Primary key
id: int | None = Field(default=None, primary_key=True)
# Foreign key to API key
api_key_id: int = Field(foreign_key="apikey.id", index=True)
# Transaction details
amount: int # Can be negative for deductions
balance_after: int # Balance snapshot
type: TransactionType # Enum: deposit, usage, withdrawal
description: str
# Timestamp (indexed for range queries)
timestamp: datetime = Field(
default_factory=datetime.utcnow,
index=True
)
# Request details (optional)
request_data: dict | None = Field(
default=None,
sa_column=Column(JSON)
)
# Relationships
api_key: APIKey = Relationship(back_populates="transactions")
```
#### Withdrawal Table
```python
class Withdrawal(SQLModel, table=True):
"""Track withdrawal requests"""
id: int | None = Field(default=None, primary_key=True)
api_key_id: int = Field(foreign_key="apikey.id")
# Withdrawal details
amount: int
token: str # Encrypted eCash token
mint_url: str
# Status tracking
status: WithdrawalStatus # pending, completed, failed
created_at: datetime = Field(default_factory=datetime.utcnow)
completed_at: datetime | None = None
# Error handling
error_message: str | None = None
retry_count: int = Field(default=0)
```
### Indexes
Critical indexes for performance:
```python
# In models
key_hash: str = Field(index=True, unique=True) # Fast key lookup
timestamp: datetime = Field(index=True) # Range queries
# Composite indexes (in migrations)
Index('idx_transactions_key_time', 'api_key_id', 'timestamp')
Index('idx_apikey_expires', 'expires_at').where(expires_at.isnot(None))
```
## Migrations
### Creating Migrations
#### Auto-generate from Model Changes
```bash
# After modifying SQLModel classes
make db-migrate
# Enter descriptive message
> Add withdrawal status field
```
#### Manual Migration
```bash
# Create empty migration
alembic revision -m "custom migration"
# Edit the generated file
```
#### Migration Template
```python
"""Add withdrawal status field
Revision ID: abc123
Revises: def456
Create Date: 2024-01-01 12:00:00
"""
from alembic import op
import sqlalchemy as sa
import sqlmodel
# revision identifiers
revision = 'abc123'
down_revision = 'def456'
def upgrade() -> None:
"""Apply migration"""
op.add_column(
'withdrawal',
sa.Column(
'status',
sa.String(),
nullable=False,
server_default='pending'
)
)
# Add index
op.create_index(
'idx_withdrawal_status',
'withdrawal',
['status']
)
def downgrade() -> None:
"""Revert migration"""
op.drop_index('idx_withdrawal_status', 'withdrawal')
op.drop_column('withdrawal', 'status')
```
### Running Migrations
#### Development
```bash
# Apply all migrations
make db-upgrade
# Check current version
make db-current
# Rollback one version
make db-downgrade
# View history
make db-history
```
#### Production
Migrations run automatically on startup:
```python
# In routstr/core/main.py
def run_migrations():
"""Run database migrations on startup"""
from alembic import command
from alembic.config import Config
alembic_cfg = Config("alembic.ini")
command.upgrade(alembic_cfg, "head")
```
### Migration Best Practices
1. **Always Review Generated Migrations**
- Check for data loss
- Verify index creation
- Test rollback
2. **Handle Data Migrations**
```python
def upgrade():
# Schema change
op.add_column('apikey', sa.Column('status', sa.String()))
# Data migration
connection = op.get_bind()
connection.execute(
"UPDATE apikey SET status = 'active' WHERE expires_at IS NULL"
)
```
3. **Make Migrations Idempotent**
```python
def upgrade():
# Check if column exists
inspector = sa.inspect(op.get_bind())
columns = [col['name'] for col in inspector.get_columns('apikey')]
if 'new_field' not in columns:
op.add_column('apikey', sa.Column('new_field', sa.String()))
```
## Database Operations
### Connection Management
```python
# Database session factory
async_session = sessionmaker(
engine,
class_=AsyncSession,
expire_on_commit=False
)
# Dependency injection
async def get_db() -> AsyncSession:
async with async_session() as session:
yield session
```
### Query Patterns
#### Basic Queries
```python
# Get by primary key
api_key = await session.get(APIKey, key_id)
# Get by unique field
result = await session.execute(
select(APIKey).where(APIKey.key_hash == hash_value)
)
api_key = result.scalar_one_or_none()
# Get multiple with filter
result = await session.execute(
select(APIKey)
.where(APIKey.balance > 0)
.where(APIKey.expires_at > datetime.utcnow())
)
active_keys = result.scalars().all()
```
#### Joins and Relationships
```python
# Eager loading
result = await session.execute(
select(APIKey)
.options(selectinload(APIKey.transactions))
.where(APIKey.id == key_id)
)
api_key = result.scalar_one()
# Join query
result = await session.execute(
select(Transaction)
.join(APIKey)
.where(APIKey.key_hash == hash_value)
.order_by(Transaction.timestamp.desc())
.limit(10)
)
recent_transactions = result.scalars().all()
```
#### Aggregations
```python
# Sum total spent
result = await session.execute(
select(func.sum(Transaction.amount))
.where(Transaction.api_key_id == key_id)
.where(Transaction.type == TransactionType.USAGE)
)
total_spent = result.scalar() or 0
# Count active keys
result = await session.execute(
select(func.count(APIKey.id))
.where(APIKey.balance > 0)
)
active_count = result.scalar()
```
### Transactions
#### Atomic Operations
```python
async def transfer_balance(
session: AsyncSession,
from_key: int,
to_key: int,
amount: int
):
"""Atomic balance transfer"""
async with session.begin():
# Lock rows to prevent race conditions
from_api_key = await session.execute(
select(APIKey)
.where(APIKey.id == from_key)
.with_for_update()
)
from_api_key = from_api_key.scalar_one()
to_api_key = await session.execute(
select(APIKey)
.where(APIKey.id == to_key)
.with_for_update()
)
to_api_key = to_api_key.scalar_one()
# Check balance
if from_api_key.balance < amount:
raise InsufficientBalanceError()
# Update balances
from_api_key.balance -= amount
to_api_key.balance += amount
# Log transactions
session.add(Transaction(
api_key_id=from_key,
amount=-amount,
balance_after=from_api_key.balance,
type=TransactionType.TRANSFER_OUT
))
session.add(Transaction(
api_key_id=to_key,
amount=amount,
balance_after=to_api_key.balance,
type=TransactionType.TRANSFER_IN
))
# Commit happens automatically
```
#### Optimistic Locking
```python
class APIKey(SQLModel, table=True):
# Add version field
version: int = Field(default=1)
async def update_with_version_check(
session: AsyncSession,
api_key: APIKey,
new_balance: int
):
"""Update with optimistic locking"""
result = await session.execute(
update(APIKey)
.where(APIKey.id == api_key.id)
.where(APIKey.version == api_key.version)
.values(
balance=new_balance,
version=APIKey.version + 1
)
)
if result.rowcount == 0:
raise ConcurrentModificationError()
```
## Performance Optimization
### Query Optimization
1. **Use Indexes Effectively**
```python
# Good: Uses index
where(APIKey.key_hash == value)
# Bad: Function prevents index use
where(func.lower(APIKey.key_hash) == value.lower())
```
2. **Limit Results**
```python
# Always limit when possible
query.limit(100)
# Use pagination
query.offset(page * page_size).limit(page_size)
```
3. **Select Only Needed Columns**
```python
# Select specific columns
result = await session.execute(
select(APIKey.id, APIKey.balance)
.where(APIKey.key_hash == hash_value)
)
```
### Connection Pooling
```python
# Configure connection pool
engine = create_async_engine(
DATABASE_URL,
pool_size=20, # Number of connections
max_overflow=10, # Extra connections when needed
pool_timeout=30, # Wait time for connection
pool_recycle=3600, # Recycle connections after 1 hour
pool_pre_ping=True, # Check connection health
)
```
### Batch Operations
```python
# Batch insert
async def bulk_create_transactions(
session: AsyncSession,
transactions: list[dict]
):
"""Efficient bulk insert"""
await session.execute(
insert(Transaction),
transactions
)
await session.commit()
# Batch update
await session.execute(
update(APIKey)
.where(APIKey.expires_at < datetime.utcnow())
.values(active=False)
)
```
## Testing Database Code
### Test Database Setup
```python
@pytest.fixture
async def test_engine():
"""Create test database engine"""
engine = create_async_engine(
"sqlite+aiosqlite:///:memory:",
echo=True # Log SQL for debugging
)
async with engine.begin() as conn:
await conn.run_sync(SQLModel.metadata.create_all)
yield engine
await engine.dispose()
```
### Testing Queries
```python
async def test_balance_update(test_session):
"""Test atomic balance update"""
# Create test data
api_key = APIKey(key_hash="test", balance=1000)
test_session.add(api_key)
await test_session.commit()
# Test update
await deduct_balance(test_session, api_key.id, 100)
# Verify
await test_session.refresh(api_key)
assert api_key.balance == 900
# Verify transaction log
result = await test_session.execute(
select(Transaction)
.where(Transaction.api_key_id == api_key.id)
)
transactions = result.scalars().all()
assert len(transactions) == 1
assert transactions[0].amount == -100
```
### Testing Migrations
```python
def test_migration_upgrade():
"""Test migration applies correctly"""
# Create database at previous version
alembic_cfg = Config("alembic.ini")
command.downgrade(alembic_cfg, "-1")
# Apply migration
command.upgrade(alembic_cfg, "+1")
# Verify schema changes
engine = create_engine(DATABASE_URL)
inspector = inspect(engine)
columns = [col['name'] for col in inspector.get_columns('apikey')]
assert 'new_column' in columns
```
## Database Maintenance
### Monitoring Queries
```sql
-- Slow queries (SQLite)
EXPLAIN QUERY PLAN
SELECT * FROM apikey WHERE balance > 0;
-- Table sizes
SELECT
name,
COUNT(*) as row_count
FROM sqlite_master
WHERE type='table'
GROUP BY name;
-- Index usage
SELECT * FROM sqlite_stat1;
```
### Cleanup Tasks
```python
async def cleanup_expired_keys(session: AsyncSession):
"""Remove expired API keys"""
result = await session.execute(
delete(APIKey)
.where(APIKey.expires_at < datetime.utcnow())
.where(APIKey.balance == 0)
)
logger.info(f"Cleaned up {result.rowcount} expired keys")
await session.commit()
async def vacuum_database(session: AsyncSession):
"""Optimize database file size (SQLite)"""
await session.execute(text("VACUUM"))
```
### Backup Strategies
```python
async def backup_database(source_url: str, backup_path: str):
"""Create database backup"""
if "sqlite" in source_url:
# SQLite backup
import shutil
db_path = source_url.split("///")[1]
shutil.copy2(db_path, backup_path)
else:
# PostgreSQL backup
import subprocess
subprocess.run([
"pg_dump",
source_url,
"-f", backup_path
])
```
## PostgreSQL Migration
### Configuration
```python
# PostgreSQL connection
DATABASE_URL = "postgresql+asyncpg://user:pass@localhost/routstr"
# Additional PostgreSQL-specific settings
engine = create_async_engine(
DATABASE_URL,
server_settings={
"jit": "off",
"statement_timeout": "30s"
}
)
```
### PostgreSQL-Specific Features
```python
# Use PostgreSQL arrays
from sqlalchemy.dialects.postgresql import ARRAY
class APIKey(SQLModel, table=True):
allowed_models: list[str] = Field(
default_factory=list,
sa_column=Column(ARRAY(String))
)
# Use JSONB for better performance
metadata: dict = Field(
default_factory=dict,
sa_column=Column(JSONB)
)
# Full-text search
from sqlalchemy.dialects.postgresql import TSVECTOR
search_vector = Column(TSVECTOR)
```
## Security Considerations
### SQL Injection Prevention
```python
# Always use parameterized queries
# Good
await session.execute(
select(APIKey).where(APIKey.key_hash == key_hash)
)
# Bad - SQL injection risk
await session.execute(
text(f"SELECT * FROM apikey WHERE key_hash = '{key_hash}'")
)
```
### Data Encryption
```python
from cryptography.fernet import Fernet
class EncryptedField(TypeDecorator):
"""Encrypt sensitive data at rest"""
impl = String
def __init__(self, key: bytes, *args, **kwargs):
self.cipher = Fernet(key)
super().__init__(*args, **kwargs)
def process_bind_param(self, value, dialect):
if value is not None:
return self.cipher.encrypt(value.encode()).decode()
return value
def process_result_value(self, value, dialect):
if value is not None:
return self.cipher.decrypt(value.encode()).decode()
return value
```
## Next Steps
- Review [Testing Guide](testing.md) for database testing
- Check [Guidelines](guidelines.md) for code standards
- See [Architecture](architecture.md) for system design
- Read [Setup Guide](setup.md) for development setup
docs/contributing/guidelines.md
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# Contribution Guidelines
Thank you for considering contributing to Routstr Core! This document provides guidelines and standards for contributing to the project.
## Code of Conduct
### Our Pledge
We pledge to make participation in our project a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
### Expected Behavior
- Be respectful and inclusive
- Accept constructive criticism gracefully
- Focus on what's best for the community
- Show empathy towards other contributors
## Getting Started
### First Time Contributors
1. **Find an Issue**
- Look for issues labeled `good first issue`
- Check `help wanted` labels
- Ask in discussions if unsure
2. **Claim the Issue**
- Comment on the issue to claim it
- Wait for maintainer acknowledgment
- Ask questions if needed
3. **Fork and Branch**
```bash
git clone https://github.com/YOUR_USERNAME/routstr-core.git
cd routstr-core
git checkout -b feat/your-feature-name
```
## Code Standards
### Python Style Guide
We follow modern Python practices with strict type checking:
#### Type Annotations
Always use complete type hints:
```python
# ✅ Good - Complete type hints
async def process_payment(
token: str,
amount: int,
mint_url: str | None = None
) -> PaymentResult:
"""Process an eCash payment."""
pass
# ❌ Bad - Missing or incomplete types
async def process_payment(token, amount, mint_url=None):
pass
```
#### Modern Python Features
Use Python 3.11+ syntax:
```python
# ✅ Good - Modern union types
def handle_value(data: str | int | None) -> dict[str, Any]:
pass
# ❌ Bad - Old-style typing
from typing import Union, Dict, Optional
def handle_value(data: Optional[Union[str, int]]) -> Dict[str, Any]:
pass
```
#### Error Handling
Be explicit with exceptions:
```python
# ✅ Good - Specific exceptions with context
class InsufficientBalanceError(RoustrError):
"""Raised when balance is insufficient for operation."""
def __init__(self, required: int, available: int):
super().__init__(
f"Insufficient balance: required {required}, available {available}"
)
self.required = required
self.available = available
# ❌ Bad - Generic exceptions
raise Exception("Not enough balance")
```
### Async/Await Patterns
All I/O operations must be async:
```python
# ✅ Good - Async all the way
async def fetch_user_data(user_id: int) -> UserData:
async with get_db() as session:
result = await session.execute(
select(User).where(User.id == user_id)
)
return result.scalar_one()
# ❌ Bad - Blocking I/O
def fetch_user_data(user_id: int) -> UserData:
with get_db() as session:
return session.query(User).filter_by(id=user_id).first()
```
### Documentation Standards
#### Module Documentation
```python
"""Payment processing module.
This module handles all payment-related operations including:
- Token validation and redemption
- Balance management
- Cost calculation
- Transaction logging
"""
```
#### Function Documentation
```python
async def redeem_token(
token: str,
mint_url: str | None = None,
*,
verify: bool = True
) -> RedemptionResult:
"""Redeem a Cashu eCash token.
Args:
token: Base64-encoded Cashu token
mint_url: Optional mint URL override
verify: Whether to verify token with mint
Returns:
RedemptionResult containing amount and token details
Raises:
TokenInvalidError: If token format is invalid
TokenExpiredError: If token has expired
MintConnectionError: If mint is unreachable
Example:
>>> result = await redeem_token("cashuAey...")
>>> print(f"Redeemed {result.amount} sats")
"""
```
### Comments
Only add comments for non-obvious logic:
```python
# ✅ Good - Explains complex business logic
# Apply exponential backoff with jitter to prevent thundering herd
# when multiple clients retry simultaneously
delay = min(base_delay * (2 ** attempt) + random.uniform(0, 1), max_delay)
# ❌ Bad - States the obvious
# Increment the counter by 1
counter += 1
```
## Testing Requirements
### Test Coverage
- New features must include tests
- Maintain >80% code coverage
- Test edge cases and error conditions
### Test Structure
```python
class TestFeatureName:
"""Test suite for FeatureName functionality."""
async def test_happy_path(self):
"""Test normal operation succeeds."""
# Arrange
input_data = create_test_data()
# Act
result = await function_under_test(input_data)
# Assert
assert result.success is True
assert result.value == expected_value
async def test_error_condition(self):
"""Test appropriate error is raised."""
with pytest.raises(SpecificError) as exc_info:
await function_under_test(invalid_data)
assert "descriptive message" in str(exc_info.value)
```
## Commit Messages
Follow [Conventional Commits](https://www.conventionalcommits.org/):
### Format
```
<type>(<scope>): <subject>
<body>
<footer>
```
### Types
- `feat`: New feature
- `fix`: Bug fix
- `docs`: Documentation only
- `style`: Code style (formatting, etc.)
- `refactor`: Code refactoring
- `test`: Test additions or fixes
- `chore`: Maintenance tasks
- `perf`: Performance improvements
### Examples
```bash
# Feature
feat(payment): add support for multi-mint tokens
Implement token validation across multiple trusted mints.
This allows users to pay with tokens from any configured mint.
Closes #123
# Bug fix
fix(proxy): handle streaming timeouts correctly
Previously, long-running streaming requests would timeout after 30s.
This change implements proper timeout handling for streams.
# Documentation
docs(api): update authentication examples
Add examples for bearer token authentication and
update curl commands to match current API.
```
## Pull Request Process
### Before Submitting
1. **Run Quality Checks**
```bash
make format # Auto-format code
make lint # Check style
make type-check # Verify types
make test # Run all tests
```
2. **Update Documentation**
- Add/update docstrings
- Update README if needed
- Add to changelog
3. **Verify Changes**
- Only include relevant changes
- No commented code
- No debug prints
- No temporary files
### PR Template
```markdown
## Description
Brief description of changes
## Type of Change
- [ ] Bug fix (non-breaking change fixing an issue)
- [ ] New feature (non-breaking change adding functionality)
- [ ] Breaking change (fix or feature causing existing functionality to change)
## Testing
- [ ] Unit tests pass
- [ ] Integration tests pass
- [ ] Manual testing completed
## Checklist
- [ ] Code follows project style guidelines
- [ ] Self-review completed
- [ ] Comments added for complex logic
- [ ] Documentation updated
- [ ] No new warnings generated
```
### Review Process
1. **Automated Checks**
- CI/CD must pass
- Coverage maintained
- No linting errors
2. **Peer Review**
- At least one approval required
- Address all feedback
- Resolve all conversations
3. **Final Steps**
- Squash commits if needed
- Update branch with main
- Maintainer merges
## Code Organization
### File Naming
```python
# Use lowercase with underscores
payment_processor.py # ✅ Good
PaymentProcessor.py # ❌ Bad
payment-processor.py # ❌ Bad
```
### Import Organization
```python
# Standard library imports
import asyncio
import json
from datetime import datetime
from typing import Any
# Third-party imports
import httpx
from fastapi import FastAPI, Depends
from sqlmodel import Session, select
# Local imports
from .auth import validate_token
from .models import User, Transaction
from .utils import calculate_hash
```
### Constants
```python
# Module-level constants in UPPER_CASE
DEFAULT_TIMEOUT = 30
MAX_RETRIES = 3
SUPPORTED_MODELS = ["gpt-3.5-turbo", "gpt-4"]
# Class constants
class PaymentProcessor:
MIN_AMOUNT = 1
MAX_AMOUNT = 1_000_000
```
## Best Practices
### Security
1. **Never commit secrets**
```python
# ❌ Bad
API_KEY = "sk-1234567890"
# ✅ Good
API_KEY = os.environ.get("API_KEY")
```
2. **Validate all inputs**
```python
from pydantic import BaseModel, validator
class PaymentRequest(BaseModel):
amount: int
token: str
@validator("amount")
def validate_amount(cls, v):
if v <= 0:
raise ValueError("Amount must be positive")
return v
```
3. **Use constant-time comparisons**
```python
import secrets
# For sensitive comparisons
if secrets.compare_digest(provided_hash, expected_hash):
# Authenticated
```
### Performance
1. **Use async for I/O**
```python
# Concurrent operations
results = await asyncio.gather(
fetch_user(user_id),
fetch_balance(user_id),
fetch_transactions(user_id)
)
```
2. **Implement caching**
```python
from functools import lru_cache
@lru_cache(maxsize=1000)
def get_model_pricing(model_id: str) -> dict:
return MODELS.get(model_id, DEFAULT_PRICING)
```
3. **Use generators for large data**
```python
async def stream_transactions() -> AsyncGenerator[Transaction, None]:
async for transaction in get_transactions():
yield transaction
```
### Error Handling
1. **Be specific**
```python
try:
result = await risky_operation()
except NetworkError:
# Handle network issues
logger.error("Network error during operation")
raise
except ValueError as e:
# Handle value errors
logger.warning(f"Invalid value: {e}")
return default_value
```
2. **Provide context**
```python
class PaymentError(RoustrError):
def __init__(self, token: str, reason: str):
super().__init__(f"Payment failed for token {token[:8]}: {reason}")
self.token = token
self.reason = reason
```
## Release Process
### Version Numbering
We use [Semantic Versioning](https://semver.org/):
- MAJOR: Breaking API changes
- MINOR: New features, backwards compatible
- PATCH: Bug fixes
### Release Checklist
1. Update version in `pyproject.toml`
2. Update CHANGELOG.md
3. Run full test suite
4. Create git tag
5. Push tag to trigger release
## Getting Help
### Resources
- [Development Setup](setup.md)
- [Architecture Overview](architecture.md)
- [Testing Guide](testing.md)
- [Database Guide](database.md)
### Communication
- **GitHub Issues**: Bug reports and feature requests
- **GitHub Discussions**: Questions and ideas
- **Pull Requests**: Code contributions
### Response Times
- Issues: 2-3 business days
- PRs: 2-3 business days
- Security issues: <24 hours
## Recognition
Contributors are recognized in:
- CONTRIBUTORS.md file
- Release notes
- Project documentation
Thank you for contributing to Routstr Core! 🎉
docs/contributing/setup.md
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# Development Setup
This guide will help you set up a development environment for contributing to Routstr Core.
## Prerequisites
Before you begin, ensure you have:
- **Python 3.11+** - Required for type hints and modern features
- **Git** - For version control
- **Docker** (optional) - For running integration tests
- **Make** - For running development commands
## Quick Start
### 1. Fork and Clone
First, fork the repository on GitHub, then clone your fork:
```bash
git clone https://github.com/YOUR_USERNAME/routstr-core.git
cd routstr-core
```
### 2. Install uv
We use [uv](https://github.com/astral-sh/uv) for fast, reliable Python package management:
```bash
# Using the installer script
curl -LsSf https://astral.sh/uv/install.sh | sh
# Or with pip
pip install uv
# Or with Homebrew (macOS)
brew install uv
```
### 3. Set Up Environment
Run the setup command:
```bash
make setup
```
This will:
- ✅ Install uv if not present
- ✅ Create a virtual environment
- ✅ Install all dependencies
- ✅ Install dev tools (mypy, ruff, pytest)
- ✅ Install project in editable mode
### 4. Configure Environment
Create your environment file:
```bash
cp .env.example .env
```
Edit `.env` with your configuration:
```bash
# Minimum required for development
UPSTREAM_BASE_URL=https://api.openai.com/v1
UPSTREAM_API_KEY=your-api-key # Optional for mock testing
ADMIN_PASSWORD=development-password
DATABASE_URL=sqlite+aiosqlite:///dev.db
```
### 5. Verify Installation
Run these commands to verify your setup:
```bash
# Check dependencies
make check-deps
# Run unit tests
make test-unit
# Start development server
make dev
```
## Development Workflow
### Running the Server
For development with auto-reload:
```bash
make dev
# Server starts at http://localhost:8000
# Auto-reloads on code changes
```
For production-like environment:
```bash
make run
```
### Code Quality
Before committing, always run:
```bash
# Format code
make format
# Check linting
make lint
# Type checking
make type-check
# All checks at once
make check
```
### Testing
Run different test suites:
```bash
# Unit tests only (fast)
make test-unit
# Integration tests with mocks
make test-integration
# All tests
make test
# With coverage report
make test-coverage
# Run specific test
uv run pytest tests/unit/test_auth.py::test_token_validation -v
```
### Database Management
Work with database migrations:
```bash
# Create new migration
make db-migrate
# Apply migrations
make db-upgrade
# Rollback one migration
make db-downgrade
# View current revision
make db-current
```
## Project Structure
Understanding the codebase:
```
routstr-core/
├── routstr/ # Main package
│ ├── __init__.py # Package initialization
│ ├── auth.py # Authentication logic
│ ├── balance.py # Balance management
│ ├── discovery.py # Nostr discovery
│ ├── proxy.py # Request proxying
│ ├── wallet.py # Cashu wallet integration
│ │
│ ├── core/ # Core modules
│ │ ├── admin.py # Admin dashboard
│ │ ├── db.py # Database models
│ │ ├── exceptions.py # Custom exceptions
│ │ ├── logging.py # Logging setup
│ │ ├── main.py # FastAPI app
│ │ └── middleware.py # HTTP middleware
│ │
│ └── payment/ # Payment processing
│ ├── cost_calculation.py # Cost logic
│ ├── helpers.py # Utilities
│ ├── lnurl.py # Lightning URLs
│ ├── models.py # Model pricing
│ ├── price.py # BTC pricing
│ └── x_cashu.py # Cashu headers
│
├── tests/ # Test suite
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
│
├── migrations/ # Database migrations
├── scripts/ # Utility scripts
├── docs/ # Documentation
│
├── Makefile # Dev commands
├── pyproject.toml # Project config
└── compose.yml # Docker setup
```
## Common Tasks
### Adding a New Endpoint
1. Create route in appropriate module
2. Add request/response models
3. Write unit tests
4. Update API documentation
5. Add integration tests
Example:
```python
# In routstr/core/main.py or appropriate router
@app.get("/v1/stats")
async def get_stats(
user: User = Depends(get_current_user)
) -> StatsResponse:
"""Get usage statistics for the current user."""
# Implementation
pass
```
### Adding a Database Model
1. Define model in `routstr/core/db.py`
2. Create migration: `make db-migrate`
3. Review generated migration
4. Apply: `make db-upgrade`
Example:
```python
class Transaction(SQLModel, table=True):
id: int | None = Field(default=None, primary_key=True)
api_key_id: int = Field(foreign_key="apikey.id")
amount: int # millisatoshis
timestamp: datetime = Field(default_factory=datetime.utcnow)
description: str
```
### Writing Tests
Follow the AAA pattern:
```python
async def test_balance_deduction():
# Arrange
api_key = await create_test_api_key(balance=1000)
# Act
result = await deduct_balance(api_key.key, amount=100)
# Assert
assert result.success
assert result.new_balance == 900
assert result.deducted == 100
```
## Development Tools
### Makefile Commands
Key commands for development:
```bash
make help # Show all commands
make setup # Initial setup
make dev # Run dev server
make test # Run all tests
make lint # Check code style
make format # Fix code style
make type-check # Check types
make clean # Clean temp files
make docker-build # Build Docker image
```
### IDE Setup
#### VS Code
Recommended extensions:
- Python
- Pylance
- Ruff
- GitLens
Settings (`.vscode/settings.json`):
```json
{
"python.linting.enabled": true,
"python.linting.ruffEnabled": true,
"python.formatting.provider": "ruff",
"python.analysis.typeCheckingMode": "strict",
"editor.formatOnSave": true
}
```
#### PyCharm
1. Set Python interpreter to uv venv
2. Enable type checking
3. Configure Ruff as external tool
4. Set up file watchers for formatting
### Debugging
#### Debug Server
```bash
# Run with debug logging
LOG_LEVEL=DEBUG make dev
# Or with debugger
uv run python -m debugpy --listen 5678 --wait-for-client \
-m uvicorn routstr:fastapi_app --reload
```
#### Debug Tests
```bash
# Run specific test with output
uv run pytest tests/unit/test_auth.py -v -s
# With debugger
uv run pytest tests/unit/test_auth.py --pdb
```
## Troubleshooting
### Common Issues
**Import Errors**
```bash
# Ensure project is installed in editable mode
uv sync
uv pip install -e .
```
**Database Errors**
```bash
# Reset database
rm dev.db
make db-upgrade
```
**Type Checking Fails**
```bash
# Clear mypy cache
make clean
make type-check
```
**Tests Fail Locally**
```bash
# Ensure test dependencies are installed
uv sync --dev
# Check for leftover test data
rm -rf test_*.db
```
### Getting Help
- Check existing [GitHub Issues](https://github.com/routstr/routstr-core/issues)
- Review [Contributing Guidelines](guidelines.md)
- Ask in [GitHub Discussions](https://github.com/routstr/routstr-core/discussions)
- Read the [Architecture Guide](architecture.md)
## Next Steps
Now that you're set up:
1. Read the [Architecture Overview](architecture.md)
2. Review [Code Standards](guidelines.md)
3. Check [open issues](https://github.com/routstr/routstr-core/issues)
4. Start with a small contribution
Happy coding! 🚀
docs/contributing/testing.md
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# Testing Guide
This guide covers testing practices, patterns, and tools used in Routstr Core development.
## Testing Philosophy
We follow these principles:
- **Test Behavior, Not Implementation** - Tests should survive refactoring
- **Fast Feedback** - Unit tests run in milliseconds
- **Reliable Tests** - No flaky tests allowed
- **Clear Failures** - Tests should clearly indicate what broke
## Test Structure
```
tests/
├── __init__.py
├── conftest.py # Shared fixtures and configuration
├── unit/ # Fast, isolated unit tests
│ ├── test_auth.py
│ ├── test_balance.py
│ ├── test_cost_calculation.py
│ ├── test_models.py
│ └── test_wallet.py
└── integration/ # Component integration tests
├── test_api_endpoints.py
├── test_payment_flow.py
├── test_proxy_streaming.py
└── test_real_mint.py
```
## Running Tests
### Quick Test Commands
```bash
# Run all tests
make test
# Run unit tests only (fast)
make test-unit
# Run integration tests
make test-integration
# Run with coverage
make test-coverage
# Run specific test file
uv run pytest tests/unit/test_auth.py -v
# Run specific test
uv run pytest tests/unit/test_auth.py::test_create_api_key -v
# Run tests matching pattern
uv run pytest -k "balance" -v
```
### Test Markers
Use markers to categorize tests:
```python
@pytest.mark.slow
async def test_heavy_computation():
pass
@pytest.mark.requires_docker
async def test_real_services():
pass
# Run without slow tests
pytest -m "not slow"
# Run only integration tests
pytest -m "integration"
```
## Writing Tests
### Unit Test Example
```python
# tests/unit/test_auth.py
import pytest
from routstr.auth import create_api_key, validate_api_key
class TestAPIKeyAuth:
"""Test API key authentication functionality"""
async def test_create_api_key(self, test_db):
"""Test creating a new API key"""
# Arrange
initial_balance = 10000
# Act
api_key = await create_api_key(
balance=initial_balance,
name="Test Key"
)
# Assert
assert api_key.key.startswith("rstr_")
assert len(api_key.key) == 32
assert api_key.balance == initial_balance
async def test_validate_invalid_key(self, test_db):
"""Test validation fails for invalid key"""
# Act & Assert
with pytest.raises(InvalidAPIKeyError):
await validate_api_key("invalid_key")
```
### Integration Test Example
```python
# tests/integration/test_payment_flow.py
import pytest
from httpx import AsyncClient
class TestPaymentFlow:
"""Test end-to-end payment flows"""
@pytest.mark.asyncio
async def test_token_redemption_flow(
self,
app_client: AsyncClient,
mock_cashu_wallet
):
"""Test complete token redemption and API usage"""
# Arrange
token = create_test_token(amount=5000)
mock_cashu_wallet.redeem.return_value = 5000
# Act - Create API key
response = await app_client.post(
"/v1/wallet/create",
json={"cashu_token": token}
)
# Assert - Key created
assert response.status_code == 200
api_key = response.json()["api_key"]
assert response.json()["balance"] == 5000
# Act - Use API key
response = await app_client.post(
"/v1/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hi"}]
}
)
# Assert - Request successful
assert response.status_code == 200
assert "choices" in response.json()
```
## Test Fixtures
### Common Fixtures
Located in `tests/conftest.py`:
```python
@pytest.fixture
async def test_db():
"""Provide a clean test database"""
# Create in-memory SQLite database
engine = create_async_engine("sqlite+aiosqlite:///:memory:")
async with engine.begin() as conn:
await conn.run_sync(SQLModel.metadata.create_all)
async_session = sessionmaker(engine, class_=AsyncSession)
async with async_session() as session:
yield session
await engine.dispose()
@pytest.fixture
async def app_client(test_db):
"""Provide test client with test database"""
app.dependency_overrides[get_db] = lambda: test_db
async with AsyncClient(app=app, base_url="http://test") as client:
yield client
app.dependency_overrides.clear()
@pytest.fixture
def mock_cashu_wallet(mocker):
"""Mock Cashu wallet for testing"""
mock = mocker.patch("routstr.wallet.Wallet")
mock.return_value.redeem.return_value = 1000
return mock
```
### Using Fixtures
```python
async def test_with_fixtures(
test_db, # Get test database
app_client, # Get test HTTP client
mock_cashu_wallet # Get mocked wallet
):
# Use fixtures in test
pass
```
## Mocking Strategies
### Mocking External Services
```python
# Mock upstream API
@pytest.fixture
def mock_openai(mocker):
mock_response = mocker.Mock()
mock_response.json.return_value = {
"choices": [{
"message": {"content": "Hello!"},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
}
}
mocker.patch(
"httpx.AsyncClient.post",
return_value=mock_response
)
# Mock Cashu mint
@pytest.fixture
def mock_mint(mocker):
mint = mocker.patch("routstr.wallet.Mint")
mint.return_value.check_proof_state.return_value = True
return mint
```
### Mocking Time
```python
from freezegun import freeze_time
@freeze_time("2024-01-01 12:00:00")
async def test_time_dependent():
# Time is frozen during test
key = await create_api_key(expires_in_days=30)
assert key.expires_at == datetime(2024, 1, 31, 12, 0, 0)
```
## Test Patterns
### Testing Async Code
```python
# Always mark async tests
@pytest.mark.asyncio
async def test_async_function():
result = await async_operation()
assert result == expected
# Test async context managers
async def test_async_context():
async with create_resource() as resource:
assert resource.is_active
```
### Testing Exceptions
```python
# Test specific exception
async def test_raises_specific_error():
with pytest.raises(InsufficientBalanceError) as exc_info:
await deduct_balance(api_key, amount=999999)
assert "Insufficient balance" in str(exc_info.value)
assert exc_info.value.status_code == 402
# Test exception details
async def test_exception_details():
with pytest.raises(RoustrError) as exc_info:
await risky_operation()
error = exc_info.value
assert error.error_type == "validation_error"
assert error.detail == "Invalid input"
```
### Testing Streaming Responses
```python
async def test_streaming_response():
"""Test streaming chat completion"""
chunks = []
async with app_client.stream(
"POST",
"/v1/chat/completions",
json={"model": "gpt-3.5-turbo", "stream": True}
) as response:
async for line in response.aiter_lines():
if line.startswith("data: "):
chunks.append(json.loads(line[6:]))
# Verify chunks
assert len(chunks) > 0
assert chunks[-1] == "[DONE]"
```
### Database Testing
```python
async def test_database_transaction(test_db):
"""Test atomic transactions"""
async with test_db.begin():
# Create test data
api_key = APIKey(key_hash="test", balance=1000)
test_db.add(api_key)
await test_db.flush()
# Test rollback
try:
async with test_db.begin_nested():
api_key.balance = -100 # Invalid
await test_db.flush()
raise ValueError("Rollback")
except ValueError:
pass
# Verify rollback worked
await test_db.refresh(api_key)
assert api_key.balance == 1000
```
## Performance Testing
### Benchmark Tests
```python
@pytest.mark.benchmark
def test_performance(benchmark):
"""Benchmark critical functions"""
result = benchmark(expensive_function, arg1, arg2)
assert result == expected
# Run benchmarks
pytest --benchmark-only
```
### Load Testing
```python
# tests/integration/test_load.py
async def test_concurrent_requests(app_client):
"""Test handling multiple concurrent requests"""
async def make_request(i):
response = await app_client.get(f"/test/{i}")
return response.status_code
# Make 100 concurrent requests
tasks = [make_request(i) for i in range(100)]
results = await asyncio.gather(*tasks)
# All should succeed
assert all(status == 200 for status in results)
```
## Test Data
### Factories
```python
# tests/factories.py
from datetime import datetime, timedelta
def create_test_api_key(**kwargs):
"""Factory for test API keys"""
defaults = {
"key": f"rstr_test_{uuid4().hex[:8]}",
"balance": 10000,
"created_at": datetime.utcnow(),
"expires_at": datetime.utcnow() + timedelta(days=30)
}
defaults.update(kwargs)
return APIKey(**defaults)
def create_test_token(amount: int = 1000, mint: str = None):
"""Factory for test Cashu tokens"""
# Create valid test token structure
return base64.encode(...)
```
### Test Constants
```python
# tests/constants.py
TEST_MODELS = {
"gpt-3.5-turbo": {
"prompt": 0.0015,
"completion": 0.002
},
"gpt-4": {
"prompt": 0.03,
"completion": 0.06
}
}
TEST_API_KEY = "rstr_test_1234567890"
TEST_MINT_URL = "https://testmint.example.com"
```
## Coverage
### Running Coverage
```bash
# Generate coverage report
make test-coverage
# View HTML report
open htmlcov/index.html
# Coverage with specific tests
pytest --cov=routstr --cov-report=html tests/unit/
```
### Coverage Configuration
In `pyproject.toml`:
```toml
[tool.coverage.run]
source = ["routstr"]
omit = ["tests/*", "*/migrations/*"]
[tool.coverage.report]
exclude_lines = [
"pragma: no cover",
"def __repr__",
"raise AssertionError",
"raise NotImplementedError",
"if TYPE_CHECKING:"
]
```
## Debugging Tests
### Print Debugging
```python
# Use -s flag to see print output
pytest -s tests/unit/test_auth.py
# In test
async def test_debug():
print(f"Value: {value}") # Will show with -s
assert value == expected
```
### Interactive Debugging
```python
# Drop into debugger on failure
pytest --pdb
# Set breakpoint in test
async def test_debug():
import pdb; pdb.set_trace()
# Execution stops here
```
### Logging in Tests
```python
# Enable debug logging in tests
import logging
logging.basicConfig(level=logging.DEBUG)
# Or use caplog fixture
async def test_logging(caplog):
with caplog.at_level(logging.INFO):
await function_that_logs()
assert "Expected message" in caplog.text
```
## CI/CD Integration
### GitHub Actions
Tests run automatically on:
- Pull requests
- Pushes to main
- Nightly schedules
See `.github/workflows/test.yml` for configuration.
### Pre-commit Hooks
Install pre-commit hooks:
```bash
pre-commit install
# Run manually
pre-commit run --all-files
```
## Best Practices
### Do's
1. ✅ Write tests first (TDD)
2. ✅ Keep tests simple and focused
3. ✅ Use descriptive test names
4. ✅ Test edge cases
5. ✅ Mock external dependencies
6. ✅ Use fixtures for setup
7. ✅ Assert specific values
### Don'ts
1. ❌ Don't test implementation details
2. ❌ Don't use production services
3. ❌ Don't rely on test order
4. ❌ Don't ignore flaky tests
5. ❌ Don't skip error cases
6. ❌ Don't use hard-coded waits
7. ❌ Don't commit commented tests
## Troubleshooting
### Common Issues
**Async Test Errors**
```python
# Wrong
def test_async(): # Missing async
await function()
# Right
async def test_async():
await function()
```
**Database State**
```python
# Ensure clean state
@pytest.fixture(autouse=True)
async def cleanup(test_db):
yield
# Cleanup after each test
await test_db.execute("DELETE FROM apikey")
await test_db.commit()
```
**Mock Not Working**
```python
# Check import path
mocker.patch("routstr.wallet.Wallet") # Full path
# Not just "Wallet"
```
## Next Steps
- Review [Database Guide](database.md) for schema testing
- Check [Guidelines](guidelines.md) for code standards
- See [Architecture](architecture.md) for system design
- Read [Setup Guide](setup.md) for environment setup
docs/getting-started/configuration.md
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# Configuration
Routstr Core is configured through environment variables. This guide covers all available options.
## Environment Variables
### Core Settings
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `UPSTREAM_BASE_URL` | Base URL of the OpenAI-compatible API to proxy | - | ✅ |
| `UPSTREAM_API_KEY` | API key for the upstream service | - | ❌ |
| `DATABASE_URL` | SQLite database connection string | `sqlite+aiosqlite:///keys.db` | ❌ |
| `ADMIN_PASSWORD` | Password for admin dashboard access | - | ⚠️ |
### Node Information
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `NAME` | Public name of your Routstr node | `ARoutstrNode` | ❌ |
| `DESCRIPTION` | Description of your node | `A Routstr Node` | ❌ |
| `NPUB` | Nostr public key for node identity | - | ❌ |
| `HTTP_URL` | Public HTTP URL of your node | - | ❌ |
| `ONION_URL` | Tor hidden service URL | - | ❌ |
### Cashu Configuration
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `CASHU_MINTS` | Comma-separated list of trusted Cashu mint URLs | `https://mint.minibits.cash/Bitcoin` | ❌ |
| `RECEIVE_LN_ADDRESS` | Lightning address for automatic payouts | - | ❌ |
### Pricing Configuration
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `MODEL_BASED_PRICING` | Enable model-specific pricing from models.json | `false` | ❌ |
| `COST_PER_REQUEST` | Fixed cost per API request in sats | `1` | ❌ |
| `COST_PER_1K_INPUT_TOKENS` | Cost per 1000 input tokens in sats | `0` | ❌ |
| `COST_PER_1K_OUTPUT_TOKENS` | Cost per 1000 output tokens in sats | `0` | ❌ |
| `EXCHANGE_FEE` | Exchange rate markup (1.005 = 0.5% fee) | `1.005` | ❌ |
| `UPSTREAM_PROVIDER_FEE` | Provider fee markup (1.05 = 5% fee) | `1.05` | ❌ |
### Network Configuration
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `CORS_ORIGINS` | Comma-separated list of allowed CORS origins | `*` | ❌ |
| `TOR_PROXY_URL` | SOCKS5 proxy URL for Tor connections | `socks5://127.0.0.1:9050` | ❌ |
### Logging Configuration
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `LOG_LEVEL` | Logging level (DEBUG, INFO, WARNING, ERROR) | `INFO` | ❌ |
| `ENABLE_CONSOLE_LOGGING` | Enable console log output | `true` | ❌ |
### Model Management
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `MODELS_PATH` | Path to custom models.json file | `models.json` | ❌ |
| `BASE_URL` | Base URL for fetching model info | `https://openrouter.ai/api/v1` | ❌ |
| `SOURCE` | Filter models by source provider | - | ❌ |
## Configuration Examples
### Basic OpenAI Proxy
```bash
# .env
UPSTREAM_BASE_URL=https://api.openai.com/v1
UPSTREAM_API_KEY=sk-...
ADMIN_PASSWORD=my-secure-password
```
### Custom AI Provider
```bash
# .env
UPSTREAM_BASE_URL=https://api.anthropic.com/v1
UPSTREAM_API_KEY=your-anthropic-key
MODEL_BASED_PRICING=true
MODELS_PATH=/app/config/anthropic-models.json
```
### High-Security Setup
```bash
# .env
UPSTREAM_BASE_URL=https://api.openai.com/v1
UPSTREAM_API_KEY=sk-...
ADMIN_PASSWORD=very-long-secure-password-here
CORS_ORIGINS=https://myapp.com,https://app.myapp.com
TOR_PROXY_URL=socks5://tor:9050
LOG_LEVEL=WARNING
```
### Public Node Configuration
```bash
# .env
NAME=Lightning AI Gateway
DESCRIPTION=Fast and reliable AI API access with Bitcoin payments
NPUB=npub1abcd...
HTTP_URL=https://api.lightning-ai.com
ONION_URL=http://lightningai.onion
CASHU_MINTS=https://mint1.com,https://mint2.com
```
## Pricing Models
### Fixed Pricing
Simple per-request pricing:
```bash
MODEL_BASED_PRICING=false
COST_PER_REQUEST=10 # 10 sats per request
```
### Token-Based Pricing
Charge based on token usage:
```bash
MODEL_BASED_PRICING=false
COST_PER_REQUEST=1 # 1 sat base fee
COST_PER_1K_INPUT_TOKENS=5 # 5 sats per 1k input
COST_PER_1K_OUTPUT_TOKENS=15 # 15 sats per 1k output
```
### Model-Based Pricing
Use dynamic pricing from models.json:
```bash
MODEL_BASED_PRICING=true
EXCHANGE_FEE=1.01 # 1% exchange fee
UPSTREAM_PROVIDER_FEE=1.00 # No additional markup
```
## Custom Models Configuration
Create a `models.json` file:
```json
{
"models": [
{
"id": "gpt-4",
"name": "GPT-4",
"pricing": {
"prompt": "0.00003",
"completion": "0.00006",
"request": "0"
}
},
{
"id": "gpt-3.5-turbo",
"name": "GPT-3.5 Turbo",
"pricing": {
"prompt": "0.0000015",
"completion": "0.000002",
"request": "0"
}
}
]
}
```
## Security Best Practices
### Admin Password
Generate a strong password:
```bash
openssl rand -base64 32
```
### API Keys
- Rotate upstream API keys regularly
- Use read-only keys when possible
- Monitor key usage
### Network Security
- Restrict CORS origins in production
- Use HTTPS for public endpoints
- Enable Tor for anonymity
### Database Security
- Regular backups
- Encrypted storage volumes
- Restricted file permissions
## Troubleshooting
### Check Current Configuration
```bash
# View all environment variables
docker exec routstr env | sort
# Test configuration
curl http://localhost:8000/v1/info
```
### Common Issues
**Missing Upstream URL**
```
ERROR: UPSTREAM_BASE_URL not set
Solution: Set UPSTREAM_BASE_URL in .env
```
**Invalid Cashu Mint**
```
ERROR: Failed to connect to mint
Solution: Verify CASHU_MINTS URLs are accessible
```
**Database Errors**
```
ERROR: Database connection failed
Solution: Check DATABASE_URL and file permissions
```
## Advanced Configuration
### Multiple Mints
Configure fallback mints:
```bash
CASHU_MINTS=https://primary.mint,https://backup1.mint,https://backup2.mint
```
### Custom Database
Use PostgreSQL instead of SQLite:
```bash
DATABASE_URL=postgresql+asyncpg://user:pass@localhost/routstr
```
### Proxy Settings
For corporate environments:
```bash
HTTP_PROXY=http://proxy.company.com:8080
HTTPS_PROXY=http://proxy.company.com:8080
```
## Next Steps
- [User Guide](../user-guide/introduction.md) - Start using Routstr
- [Admin Dashboard](../user-guide/admin-dashboard.md) - Manage your node
- [Custom Pricing](../advanced/custom-pricing.md) - Advanced pricing strategies
docs/getting-started/docker.md
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# Docker Setup
This guide covers deploying Routstr Core using Docker for production environments.
## Docker Images
Official images are available on GitHub Container Registry:
```bash
ghcr.io/routstr/proxy:latest
```
## Basic Docker Run
### Minimal Setup
```bash
docker run -d \
--name routstr \
-p 8000:8000 \
-e UPSTREAM_BASE_URL=https://api.openai.com/v1 \
-e UPSTREAM_API_KEY=sk-... \
-e ADMIN_PASSWORD=secure-password \
ghcr.io/routstr/proxy:latest
```
### With Persistent Storage
```bash
docker run -d \
--name routstr \
-p 8000:8000 \
-v routstr-data:/app/data \
-v routstr-logs:/app/logs \
-e UPSTREAM_BASE_URL=https://api.openai.com/v1 \
-e UPSTREAM_API_KEY=sk-... \
-e DATABASE_URL=sqlite+aiosqlite:///data/keys.db \
ghcr.io/routstr/proxy:latest
```
## Docker Compose Setup
### Basic Configuration
Create `compose.yml`:
```yaml
version: '3.8'
services:
routstr:
image: ghcr.io/routstr/proxy:latest
ports:
- "8000:8000"
volumes:
- ./data:/app/data
- ./logs:/app/logs
env_file:
- .env
restart: unless-stopped
```
### With Tor Hidden Service
The included `compose.yml` provides Tor support:
```yaml
version: '3.8'
services:
routstr:
build: . # Or use image: ghcr.io/routstr/proxy:latest
volumes:
- .:/app
- ./logs:/app/logs
env_file:
- .env
environment:
- TOR_PROXY_URL=socks5://tor:9050
ports:
- 8000:8000
extra_hosts:
- "host.docker.internal:host-gateway"
tor:
image: ghcr.io/hundehausen/tor-hidden-service:latest
volumes:
- tor-data:/var/lib/tor
environment:
- HS_ROUTER=routstr:8000:80
depends_on:
- routstr
volumes:
tor-data:
```
### Environment File
Create `.env` file:
```bash
# Required
UPSTREAM_BASE_URL=https://api.openai.com/v1
UPSTREAM_API_KEY=your-api-key
ADMIN_PASSWORD=secure-admin-password
# Cashu Configuration
CASHU_MINTS=https://mint.minibits.cash/Bitcoin
# Optional
NAME=My Routstr Node
DESCRIPTION=Pay-per-use AI API proxy
NPUB=npub1...
HTTP_URL=https://api.mynode.com
ONION_URL=http://mynode.onion
# Pricing
MODEL_BASED_PRICING=true
EXCHANGE_FEE=1.005
UPSTREAM_PROVIDER_FEE=1.05
```
## Building Custom Image
### Dockerfile Overview
The provided Dockerfile:
- Uses Alpine Linux for small size
- Installs required dependencies for secp256k1
- Runs as non-root user
- Exposes port 8000
### Build Locally
```bash
# Clone repository
git clone https://github.com/routstr/routstr-core.git
cd routstr-core
# Build image
docker build -t my-routstr:latest .
# Run custom image
docker run -d \
--name routstr \
-p 8000:8000 \
--env-file .env \
my-routstr:latest
```
## Deployment Considerations
### Resource Requirements
- **CPU**: 1-2 cores recommended
- **Memory**: 512MB-1GB
- **Storage**: 1GB + database growth
- **Network**: Low latency to upstream provider
### Health Checks
Add health check to compose.yml:
```yaml
services:
routstr:
# ... other config ...
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/v1/info"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
```
### Reverse Proxy Setup
#### Nginx Example
```nginx
server {
listen 443 ssl http2;
server_name api.yournode.com;
ssl_certificate /path/to/cert.pem;
ssl_certificate_key /path/to/key.pem;
location / {
proxy_pass http://localhost:8000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# For streaming responses
proxy_buffering off;
proxy_cache off;
proxy_set_header Connection '';
proxy_http_version 1.1;
chunked_transfer_encoding off;
}
}
```
#### Caddy Example
```caddy
api.yournode.com {
reverse_proxy localhost:8000 {
flush_interval -1
}
}
```
## Monitoring
### Log Management
View logs:
```bash
# Docker
docker logs -f routstr
# Docker Compose
docker compose logs -f routstr
# Log files
tail -f ./logs/routstr.log
```
### Metrics
Monitor key metrics:
- Request count and latency
- Token validation success rate
- Upstream API errors
- Database size growth
## Backup and Recovery
### Database Backup
```bash
# Backup SQLite database
docker exec routstr sqlite3 /app/data/keys.db ".backup /app/data/backup.db"
# Copy backup locally
docker cp routstr:/app/data/backup.db ./backup-$(date +%Y%m%d).db
```
### Restore from Backup
```bash
# Stop service
docker compose down
# Restore database
docker cp ./backup.db routstr:/app/data/keys.db
# Restart service
docker compose up -d
```
## Security Considerations
### Environment Variables
- Never commit `.env` files
- Use Docker secrets for sensitive data
- Rotate API keys regularly
- Use strong admin passwords
### Network Security
- Use HTTPS/TLS termination
- Restrict admin interface access
- Enable firewall rules
- Monitor for suspicious activity
### Container Security
- Run as non-root user
- Use read-only filesystem where possible
- Limit container capabilities
- Keep base image updated
## Troubleshooting
### Container Won't Start
```bash
# Check logs
docker logs routstr
# Verify environment
docker exec routstr env | grep -E "(UPSTREAM|CASHU|ADMIN)"
# Test database connection
docker exec routstr sqlite3 /app/data/keys.db ".tables"
```
### Permission Issues
```bash
# Fix volume permissions
sudo chown -R 1000:1000 ./data ./logs
```
### Network Issues
```bash
# Test upstream connectivity
docker exec routstr curl -I https://api.openai.com
# Check DNS resolution
docker exec routstr nslookup api.openai.com
```
## Production Checklist
- [ ] Set strong `ADMIN_PASSWORD`
- [ ] Configure proper `UPSTREAM_BASE_URL` and `UPSTREAM_API_KEY`
- [ ] Set up persistent volumes for data and logs
- [ ] Configure reverse proxy with TLS
- [ ] Set up monitoring and alerting
- [ ] Implement backup strategy
- [ ] Test disaster recovery
- [ ] Document deployment process
## Next Steps
- [Configuration Guide](configuration.md) - All environment variables
- [Admin Dashboard](../user-guide/admin-dashboard.md) - Manage your node
- [Monitoring](../advanced/monitoring.md) - Set up observability
docs/getting-started/overview.md
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# Overview
Routstr Core is a powerful payment proxy that brings Bitcoin micropayments to AI APIs. This overview will help you understand the core concepts and architecture.
## Core Concepts
### Payment Proxy
Routstr acts as a transparent proxy between your application and OpenAI-compatible APIs. It:
- Intercepts API requests
- Validates payment tokens
- Forwards requests to the upstream provider
- Tracks usage and deducts costs
- Returns responses to the client
### Cashu eCash Protocol
[Cashu](https://cashu.space) is a Bitcoin eCash protocol that enables:
- **Privacy**: Payments are unlinkable and untraceable
- **Instant Settlement**: No waiting for blockchain confirmations
- **Micropayments**: Send fractions of a satoshi
- **Offline Capability**: Tokens can be transferred without internet
### Lightning Network Integration
Routstr connects to the Lightning Network through Cashu mints, enabling:
- Fast Bitcoin deposits and withdrawals
- Global payment reach
- Low transaction fees
- No minimum payment amounts
## Architecture
### System Components
```mermaid
graph TB
subgraph "Client Side"
A[AI Application]
B[OpenAI SDK]
C[eCash Wallet]
end
subgraph "Routstr Core"
D[FastAPI Server]
E[Auth Module]
F[Payment Module]
G[Proxy Module]
H[SQLite Database]
end
subgraph "External Services"
I[Upstream AI Provider]
J[Cashu Mint]
K[Bitcoin/Lightning]
end
A --> B
B --> D
C --> D
D --> E
E --> F
F --> J
D --> G
G --> I
D --> H
J --> K
```
### Key Modules
1. **Authentication** (`auth.py`)
- API key validation
- Balance checking
- Request authorization
2. **Payment Processing** (`payment/`)
- Token validation
- Cost calculation
- Balance updates
- Pricing models
3. **Proxy Handler** (`proxy.py`)
- Request forwarding
- Response streaming
- Usage tracking
- Error handling
4. **Wallet Management** (`wallet.py`)
- Cashu wallet integration
- Token redemption
- Balance management
- Automatic payouts
5. **Admin Interface** (`core/admin.py`)
- Web dashboard
- Balance viewing
- Key management
- Withdrawal interface
## Payment Flow
### Standard Flow (API Key)
1. User deposits eCash tokens to create an API key
2. Client sends requests with the API key
3. Routstr checks balance and forwards request
4. Cost is deducted based on actual usage
5. Response is returned to client
### Per-Request Flow (Coming Soon)
1. Client includes eCash token in request header
2. Routstr validates token meets minimum amount
3. Request is processed
4. Change is returned in response header
5. No account or balance needed
## Supported Features
### API Compatibility
- ✅ Chat completions (streaming and non-streaming)
- ✅ Text completions
- ✅ Embeddings
- ✅ Image generation
- ✅ Audio transcription/translation
- ✅ Model listing
- ✅ Custom endpoints
### Payment Features
- ✅ Multiple Cashu mint support
- ✅ Automatic balance tracking
- ✅ Model-based pricing
- ✅ USD to BTC conversion
- ✅ Configurable fees
- ✅ Balance withdrawals
### Operational Features
- ✅ Docker deployment
- ✅ Tor hidden service support
- ✅ Nostr relay discovery
- ✅ Database migrations
- ✅ Comprehensive logging
- ✅ Admin dashboard
## Next Steps
- [Quick Start](quickstart.md) - Get running in minutes
- [Docker Setup](docker.md) - Deploy with containers
- [Configuration](configuration.md) - Customize your instance
docs/getting-started/quickstart.md
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# Quick Start
Get Routstr Core up and running in minutes with Docker or local development setup.
## Prerequisites
- Docker and Docker Compose (for production)
- Python 3.11+ (for development)
- A Cashu-compatible wallet (optional for testing)
## Option 1: Docker (Recommended)
### Quick Run
The fastest way to start Routstr Core:
```bash
docker run -d \
--name routstr-proxy \
-p 8000:8000 \
-e UPSTREAM_BASE_URL=https://api.openai.com/v1 \
-e UPSTREAM_API_KEY=your-openai-api-key \
ghcr.io/routstr/proxy:latest
```
### Docker Compose
For a full setup with Tor support:
1. Clone the repository:
```bash
git clone https://github.com/routstr/routstr-core.git
cd routstr-core
```
2. Create environment file:
```bash
cp .env.example .env
# Edit .env with your settings
```
3. Start the services:
```bash
docker compose up -d
```
This will start:
- Routstr proxy on port 8000
- Tor hidden service (optional)
- Automatic database migrations
### Verify Installation
Check that Routstr is running:
```bash
curl http://localhost:8000/v1/info
```
You should see:
```json
{
"name": "ARoutstrNode",
"description": "A Routstr Node",
"version": "0.1.1b",
"npub": "",
"mints": ["https://mint.minibits.cash/Bitcoin"],
"models": {...}
}
```
## Option 2: Local Development
### Install Dependencies
1. Install [uv](https://github.com/astral-sh/uv) package manager:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Clone and setup:
```bash
git clone https://github.com/routstr/routstr-core.git
cd routstr-core
uv sync
```
3. Configure environment:
```bash
cp .env.example .env
# Edit .env with your settings
```
### Run the Server
```bash
fastapi run routstr --host 0.0.0.0 --port 8000
```
## First API Call
### 1. Get an eCash Token
You'll need a Cashu token to pay for API calls. Options:
- Use a [Cashu wallet](https://cashu.space) to create tokens
- Get test tokens from a testnet mint
- Use the example token (for testing only)
### 2. Create an API Key
Send your eCash token to create an API key:
```bash
curl -X POST http://localhost:8000/v1/wallet/create \
-H "Content-Type: application/json" \
-d '{
"cashu_token": "cashuAeyJ0b2..."
}'
```
Response:
```json
{
"api_key": "rUvK7...",
"balance": 10000
}
```
### 3. Make an API Call
Use your API key like a normal OpenAI key:
```python
import openai
client = openai.OpenAI(
api_key="rUvK7...", # Your Routstr API key
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
```
## Example Client
Run the included example:
```bash
CASHU_TOKEN="your-token" python example.py
```
This demonstrates:
- Creating an API key from a token
- Making streaming chat requests
- Automatic balance deduction
## Testing the Setup
### Check Available Models
```bash
curl http://localhost:8000/v1/models
```
### View Admin Dashboard
Open http://localhost:8000/admin/ in your browser.
Default password is set in `ADMIN_PASSWORD` environment variable.
### Monitor Logs
Docker:
```bash
docker compose logs -f routstr
```
Local:
```bash
# Logs are in ./logs/ directory
tail -f logs/routstr.log
```
## Common Issues
### Connection Refused
- Ensure the service is running: `docker ps`
- Check firewall settings
- Verify port 8000 is not in use
### Invalid API Key
- Ensure you've created an API key with sufficient balance
- Check the token was valid and had value
- Verify the mint URL is accessible
### Upstream Errors
- Check `UPSTREAM_BASE_URL` is correct
- Verify `UPSTREAM_API_KEY` if required
- Test upstream service directly
## Next Steps
- [Configuration Guide](configuration.md) - Customize settings
- [Docker Setup](docker.md) - Production deployment
- [User Guide](../user-guide/introduction.md) - Detailed usage
docs/index.md
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# Routstr Core Documentation
Welcome to the official documentation for **Routstr Core** - a FastAPI-based reverse proxy that enables Bitcoin micropayments for OpenAI-compatible APIs using the Cashu eCash protocol.
## What is Routstr Core?
Routstr Core is a payment proxy that sits between API clients and OpenAI-compatible services. It enables:
- **Pay-per-request billing** using Bitcoin eCash tokens
- **Seamless integration** with existing OpenAI clients
- **Privacy-preserving payments** through the Cashu protocol
- **Flexible pricing models** with per-token or per-request billing
- **Multi-provider support** for various AI model providers
### Key Features
- 🪙 **Cashu Wallet Integration** - Accept Lightning payments and redeem eCash tokens
- 🔑 **API Key Management** - Secure key storage with balance tracking
- 💰 **Dynamic Pricing** - Model-based pricing with live BTC/USD conversion
- 🎛️ **Admin Dashboard** - Web interface for balance and key management
- 🌐 **Nostr Discovery** - Find providers through decentralized relay network
- 🐋 **Docker Support** - Easy deployment with optional Tor hidden service
- ⚡ **Lightning Fast** - Minimal latency overhead for API requests
## How It Works
```mermaid
sequenceDiagram
participant Client
participant Routstr as Routstr Proxy
participant DB as Database
participant Upstream as AI Provider
participant Wallet as Cashu Wallet
Client->>Routstr: API Request + eCash Token
Routstr->>Wallet: Validate & Redeem Token
Wallet-->>Routstr: Token Value (sats)
Routstr->>DB: Store/Update Balance
Routstr->>Upstream: Forward API Request
Upstream-->>Routstr: API Response + Usage Data
Routstr->>DB: Deduct Actual Cost
Routstr-->>Client: API Response
```
## Quick Links
<div class="grid cards" markdown>
- :rocket: **[Quick Start](getting-started/quickstart.md)**
Get up and running with Docker in minutes
- :gear: **[Configuration](getting-started/configuration.md)**
Learn about environment variables and settings
- :book: **[User Guide](user-guide/introduction.md)**
Comprehensive guide for using Routstr
- :hammer: **[Contributing](contributing/setup.md)**
Help improve Routstr Core
</div>
## Use Cases
- **AI Application Developers** - Add Bitcoin payments to your AI apps without managing infrastructure
- **API Resellers** - Resell API access with custom pricing and profit margins
- **Privacy-Focused Users** - Access AI models without revealing personal information
- **Micropayment Experiments** - Test new business models with instant, small payments
## Getting Help
- 📖 Browse the [User Guide](user-guide/introduction.md) for detailed usage instructions
- 🐛 Report issues on [GitHub](https://github.com/routstr/routstr-core/issues)
- 💬 Join the community discussions
- 🔧 Check the [API Reference](api/overview.md) for technical details
## License
Routstr Core is open source software licensed under the GPLv3. See the [LICENSE](https://github.com/routstr/routstr-core/blob/main/LICENSE) file for details.
docs/requirements.txt
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mkdocs>=1.5.3
mkdocs-material>=9.5.0
mkdocs-mermaid2-plugin>=1.1.1
pymdown-extensions>=10.5
docs/user-guide/admin-dashboard.md
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# Admin Dashboard
The Routstr admin dashboard provides a web interface for managing your node, viewing balances, and handling withdrawals.
## Accessing the Dashboard
### URL Format
The admin dashboard is available at:
```
https://your-routstr-node.com/admin/
```
> **Important**: Always include the trailing slash (`/`) in the URL.
### Authentication
The dashboard is protected by a password set in the `ADMIN_PASSWORD` environment variable.
1. Navigate to `/admin/`
2. Enter the admin password
3. Click "Login"
The password is stored as a secure cookie for the session.
## Dashboard Overview
### Main Interface
The dashboard displays:
- **Node Information**
- Node name and description
- Version number
- Public URLs (HTTP and Onion)
- Supported Cashu mints
- **Statistics**
- Total API keys
- Active keys
- Total balance across all keys
- Recent activity
- **API Key List**
- All keys with balances
- Usage statistics
- Management options
## Features
### Viewing API Keys
The main table shows all API keys with:
| Column | Description |
|--------|-------------|
| API Key | Masked key (first/last 4 chars) |
| Balance | Current balance in sats |
| Created | Creation timestamp |
| Last Used | Most recent API call |
| Total Spent | Lifetime usage |
| Status | Active/Expired/Disabled |
### Searching and Filtering
- **Search**: Find keys by partial match
- **Sort**: Click column headers to sort
- **Filter**: Show only active/expired keys
- **Export**: Download data as CSV
### Key Details
Click on any key to view:
- Full API key (masked by default)
- Complete transaction history
- Usage graphs
- Metadata (name, expiry, refund address)
## Balance Management
### Viewing Balances
Balances are displayed in multiple units:
- **Sats**: Standard satoshi units
- **mSats**: Millisatoshis (internal precision)
- **BTC**: Bitcoin decimal format
- **USD**: Approximate USD value
### Balance History
View balance changes over time:
```
Time | Type | Amount | Balance | Description
-------------|-----------|---------|---------|-------------
12:34:56 | Deposit | +10,000 | 10,000 | Token redemption
12:35:12 | Usage | -154 | 9,846 | gpt-3.5-turbo call
12:36:45 | Usage | -210 | 9,636 | gpt-4 call
```
## Withdrawals
### Manual Withdrawal
To withdraw funds from an API key:
1. Click "Withdraw" next to the key
2. Optionally specify amount (default: full balance)
3. Select target Cashu mint
4. Click "Generate Token"
5. Copy the eCash token
6. Redeem in your Cashu wallet
### Bulk Operations
For multiple withdrawals:
1. Select keys using checkboxes
2. Click "Bulk Actions" → "Withdraw"
3. Tokens are generated for each key
4. Download all tokens as text file
### Automatic Withdrawals
If configured with `RECEIVE_LN_ADDRESS`:
- Balances above threshold auto-convert to Lightning
- Sent to configured Lightning address
- View payout history in dashboard
## Node Configuration
### Viewing Settings
Current node configuration is displayed:
- Upstream provider URL
- Enabled features
- Pricing model
- Fee structure
### Models and Pricing
View supported models and their pricing:
| Model | Input $/1K | Output $/1K | Sats/1K |
|-------|------------|-------------|---------|
| gpt-3.5-turbo | $0.0015 | $0.002 | 3/4 |
| gpt-4 | $0.03 | $0.06 | 60/120 |
| dall-e-3 | - | - | 1000/image |
### Updating Configuration
> **Note**: Configuration changes require node restart.
To update settings:
1. Modify environment variables
2. Restart the node
3. Verify changes in dashboard
## Analytics
### Usage Statistics
View comprehensive usage data:
- **Requests per Day**: Line graph
- **Token Usage**: Stacked bar chart
- **Model Distribution**: Pie chart
- **Cost Analysis**: Breakdown by model
### Performance Metrics
Monitor node performance:
- Average response time
- Request success rate
- Upstream API latency
- Cache hit ratio
### Export Data
Export analytics data:
1. Select date range
2. Choose metrics
3. Click "Export"
4. Download as CSV/JSON
## Security Features
### Access Control
- Password protection
- Session timeout (configurable)
- IP allowlisting (optional)
- Audit logging
### Security Log
View security events:
```
2024-01-15 12:34:56 | Login Success | IP: 192.168.1.1
2024-01-15 12:35:12 | Withdrawal | Key: rstr_abcd | Amount: 5000
2024-01-15 12:40:00 | Session Timeout | IP: 192.168.1.1
```
### Best Practices
1. **Strong Password**: Use a long, random password
2. **HTTPS Only**: Always access via HTTPS
3. **Regular Monitoring**: Check logs frequently
4. **Limited Access**: Restrict dashboard access
## Troubleshooting
### Cannot Access Dashboard
**Issue**: 404 Not Found
- Ensure trailing slash: `/admin/`
- Check if admin routes are enabled
**Issue**: Unauthorized
- Verify `ADMIN_PASSWORD` is set
- Clear browser cookies
- Try incognito/private mode
### Display Issues
**Issue**: Broken Layout
- Clear browser cache
- Disable ad blockers
- Try different browser
**Issue**: Missing Data
- Check database connectivity
- Verify node is running
- Review error logs
### Withdrawal Problems
**Issue**: Token Generation Fails
- Check mint connectivity
- Verify sufficient balance
- Try different mint
**Issue**: Invalid Token
- Ensure complete token copy
- Check token hasn't expired
- Verify mint compatibility
## Advanced Features
### Custom Branding
Customize dashboard appearance:
```bash
# Environment variables
ADMIN_LOGO_URL=https://example.com/logo.png
ADMIN_THEME_COLOR=#FF6B00
ADMIN_CUSTOM_CSS=/path/to/custom.css
```
### API Access
Access admin functions programmatically:
```bash
# Get node stats
curl -X GET https://your-node.com/admin/api/stats \
-H "X-Admin-Password: your-password"
# Export key data
curl -X GET https://your-node.com/admin/api/keys \
-H "X-Admin-Password: your-password" \
-H "Accept: application/json"
```
### Webhooks
Configure notifications:
```bash
ADMIN_WEBHOOK_URL=https://example.com/webhook
ADMIN_WEBHOOK_EVENTS=withdrawal,low_balance,error
```
## Dashboard Shortcuts
### Keyboard Navigation
- `Ctrl+K`: Quick search
- `Ctrl+R`: Refresh data
- `Ctrl+E`: Export current view
- `Escape`: Close modals
### Quick Actions
- Double-click to copy API key
- Right-click for context menu
- Drag to reorder columns
- Shift-click to select multiple
## Mobile Access
The dashboard is mobile-responsive:
- Touch-optimized controls
- Swipe navigation
- Compact view mode
- Offline capability
## Next Steps
- [Models & Pricing](models-pricing.md) - Configure pricing
- [API Reference](../api/overview.md) - Admin API endpoints
- [Advanced Configuration](../advanced/custom-pricing.md) - Advanced settings
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# User Guide Introduction
Welcome to the Routstr Core User Guide. This guide will help you understand how to use Routstr to access AI APIs with Bitcoin micropayments.
## What You'll Learn
- How the payment system works
- Creating and managing API keys
- Making API calls through Routstr
- Using the admin dashboard
- Managing your balance
## Prerequisites
Before starting, you'll need:
1. **A Running Routstr Instance**
- Either your own deployment or access to a public node
- The base URL (e.g., `https://api.yournode.com`)
2. **A Cashu Wallet** (optional but recommended)
- [Nutstash](https://nutstash.app) - Web wallet
- [Minibits](https://www.minibits.cash) - Mobile wallet
- [Cashu.me](https://cashu.me) - Simple web wallet
3. **An API Client**
- OpenAI Python/JavaScript SDK
- Any HTTP client (curl, Postman, etc.)
- Your application code
## How Routstr Works
### Traditional API Access
```mermaid
graph LR
A[Your App] --> B[OpenAI API]
B --> A
```
- Direct connection to provider
- Monthly billing
- Credit card required
- Usage limits
### With Routstr
```mermaid
graph LR
A[Your App] --> B[Routstr Proxy]
B --> C[OpenAI API]
C --> B
B --> A
D[Bitcoin/eCash] --> B
```
- Pay per request with Bitcoin
- No credit card needed
- Anonymous payments
- Instant settlement
## Key Concepts
### eCash Tokens
- Digital bearer tokens backed by Bitcoin
- Can be sent like cash - whoever has the token owns it
- Redeemable at Cashu mints for Bitcoin
- Perfect for micropayments
### API Keys
- Created by depositing eCash tokens
- Track your balance and usage
- Can be topped up anytime
- Optional expiry and refund address
### Balance Management
- Measured in millisatoshis (msats)
- 1 Bitcoin = 100,000,000 sats = 100,000,000,000 msats
- Deducted based on actual usage
- Withdrawable as eCash tokens
## Typical Workflow
### 1. Get Bitcoin/eCash
Options:
- Buy Bitcoin and deposit to a Cashu mint
- Receive eCash tokens from someone else
- Use a testnet mint for testing
### 2. Create API Key
Send your eCash token to Routstr:
```bash
POST /v1/wallet/create
{
"cashu_token": "cashuAeyJ0..."
}
```
Receive your API key and balance.
### 3. Make API Calls
Use your API key like any OpenAI key:
```python
client = OpenAI(
api_key="your-routstr-key",
base_url="https://your-routstr-node/v1"
)
```
### 4. Monitor Usage
- Check balance: `GET /v1/wallet/balance`
- View admin dashboard
- Track costs per request
### 5. Withdraw Funds
When done, withdraw remaining balance as eCash through the admin interface.
## Supported Endpoints
Routstr supports all standard OpenAI endpoints:
- ✅ `/v1/chat/completions` - Chat models
- ✅ `/v1/completions` - Text completion
- ✅ `/v1/embeddings` - Text embeddings
- ✅ `/v1/images/generations` - Image generation
- ✅ `/v1/audio/transcriptions` - Audio to text
- ✅ `/v1/audio/translations` - Audio translation
- ✅ `/v1/models` - List available models
- ✅ Custom provider endpoints
## Cost Structure
### Pricing Models
1. **Fixed Cost Per Request**
- Simple flat fee per API call
- Good for uniform usage
2. **Token-Based Pricing**
- Pay per input/output token
- More accurate for varied usage
3. **Model-Based Pricing**
- Different rates per model
- Reflects actual provider costs
### Cost Calculation
```
Total Cost = Base Fee + (Input Tokens * Input Rate) + (Output Tokens * Output Rate)
```
Fees may include:
- Exchange rate markup (BTC/USD conversion)
- Provider margin
- Node operator fee
## Getting Support
### Documentation
- This user guide for general usage
- [API Reference](../api/overview.md) for technical details
- [Contributing Guide](../contributing/setup.md) for developers
### Community
- GitHub Issues for bugs and features
- Nostr for decentralized discussion
- Node operator contact info
### Troubleshooting
Common issues and solutions:
- [Payment Flow](payment-flow.md) - Understanding the payment process
- [Using the API](using-api.md) - API integration guide
- [Admin Dashboard](admin-dashboard.md) - Managing your node
## Security Considerations
### API Key Security
- Treat API keys like passwords
- Never share or commit them
- Rotate keys regularly
- Use environment variables
### Payment Security
- eCash tokens are bearer instruments
- Verify mint trustworthiness
- Keep backups of tokens
- Use small amounts for testing
### Network Security
- Always use HTTPS connections
- Verify SSL certificates
- Consider using Tor for privacy
- Monitor for unusual activity
## Next Steps
Ready to start? Continue with:
1. [Payment Flow](payment-flow.md) - Detailed payment process
2. [Using the API](using-api.md) - Making your first calls
3. [Admin Dashboard](admin-dashboard.md) - Managing your account
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# Models & Pricing
Understanding how Routstr calculates costs is essential for managing your API usage efficiently. This guide explains the pricing models and how to configure them.
## Pricing Models
Routstr supports three pricing models:
### 1. Fixed Pricing
Simple per-request charging:
```bash
MODEL_BASED_PRICING=false
COST_PER_REQUEST=10 # 10 sats per request
```
**Best for:**
- Uniform API usage
- Simple applications
- Predictable costs
### 2. Token-Based Pricing
Charge based on actual token usage:
```bash
MODEL_BASED_PRICING=false
COST_PER_REQUEST=1 # 1 sat base fee
COST_PER_1K_INPUT_TOKENS=5 # 5 sats per 1K input
COST_PER_1K_OUTPUT_TOKENS=15 # 15 sats per 1K output
```
**Best for:**
- Varied request sizes
- Fair usage billing
- Cost optimization
### 3. Model-Based Pricing
Dynamic pricing based on model costs:
```bash
MODEL_BASED_PRICING=true
EXCHANGE_FEE=1.005 # 0.5% exchange fee
UPSTREAM_PROVIDER_FEE=1.05 # 5% provider fee
```
**Best for:**
- Multiple models
- Market-based pricing
- Automatic updates
## Model Configuration
### Default Models
Routstr includes pricing for popular models:
| Model | Input ($/1K) | Output ($/1K) | Context | Notes |
|-------|--------------|---------------|---------|-------|
| gpt-3.5-turbo | $0.0015 | $0.002 | 16K | Fast, economical |
| gpt-4 | $0.03 | $0.06 | 8K | Advanced reasoning |
| gpt-4-turbo | $0.01 | $0.03 | 128K | Large context |
| claude-3-opus | $0.015 | $0.075 | 200K | Best quality |
| claude-3-sonnet | $0.003 | $0.015 | 200K | Balanced |
| llama-2-70b | $0.0007 | $0.0009 | 4K | Open source |
### Custom Models File
Create `models.json` to override defaults:
```json
{
"models": [
{
"id": "gpt-4-vision",
"name": "GPT-4 Vision",
"pricing": {
"prompt": "0.00003",
"completion": "0.00006",
"request": "0",
"image": "0.00255"
},
"context_length": 128000,
"supports_vision": true
},
{
"id": "custom-model",
"name": "My Custom Model",
"pricing": {
"prompt": "0.001",
"completion": "0.002",
"request": "0.0001"
},
"context_length": 8192
}
]
}
```
### Auto-updating Models
Fetch latest models from OpenRouter:
```bash
# Update models from API
python scripts/models_meta.py
# Or manually
curl https://openrouter.ai/api/v1/models > models.json
```
## Cost Calculation
### Understanding the Formula
```
Base Cost = (Input Tokens × Input Rate) + (Output Tokens × Output Rate) + Request Fee
Bitcoin Price = Current BTC/USD rate (e.g., $50,000)
Sats Cost = (Base Cost / Bitcoin Price) × 100,000,000
Final Cost = Sats Cost × Exchange Fee × Provider Fee
```
### Example Calculations
**Example 1: Simple Chat (gpt-3.5-turbo)**
```
Input: 50 tokens
Output: 150 tokens
Model rates: $0.0015/1K input, $0.002/1K output
USD Cost = (50/1000 × 0.0015) + (150/1000 × 0.002)
= $0.000075 + $0.0003
= $0.000375
At $50,000/BTC: 0.75 sats
With 5.5% total fees: 0.79 sats
```
**Example 2: Large Context (gpt-4)**
```
Input: 2,000 tokens
Output: 500 tokens
Model rates: $0.03/1K input, $0.06/1K output
USD Cost = (2000/1000 × 0.03) + (500/1000 × 0.06)
= $0.06 + $0.03
= $0.09
At $50,000/BTC: 180 sats
With 5.5% total fees: 190 sats
```
**Example 3: Image Generation (dall-e-3)**
```
Model: dall-e-3
Size: 1024x1024
Quality: standard
Cost: $0.04 per image
At $50,000/BTC: 80 sats
With 5.5% fees: 84 sats
```
## Fee Structure
### Exchange Fee
Covers Bitcoin/USD conversion costs:
```bash
EXCHANGE_FEE=1.005 # 0.5% default
```
Factors:
- Exchange rate volatility
- Conversion costs
- Price update frequency
### Provider Fee
Node operator's margin:
```bash
UPSTREAM_PROVIDER_FEE=1.05 # 5% default
```
Covers:
- Infrastructure costs
- Maintenance
- Support
- Profit margin
### Calculating Total Fees
```
Total Multiplier = EXCHANGE_FEE × UPSTREAM_PROVIDER_FEE
Example: 1.005 × 1.05 = 1.05525 (5.525% total)
```
## Special Pricing
### Image Models
Image generation uses per-image pricing:
| Model | Size | Quality | Price |
|-------|------|---------|-------|
| dall-e-2 | 256x256 | - | $0.016 |
| dall-e-2 | 512x512 | - | $0.018 |
| dall-e-2 | 1024x1024 | - | $0.02 |
| dall-e-3 | 1024x1024 | standard | $0.04 |
| dall-e-3 | 1024x1024 | hd | $0.08 |
| dall-e-3 | 1024x1792 | standard | $0.08 |
| dall-e-3 | 1024x1792 | hd | $0.12 |
### Audio Models
Audio pricing by duration:
| Model | Type | Price |
|-------|------|-------|
| whisper-1 | Transcription | $0.006/minute |
| whisper-1 | Translation | $0.006/minute |
| tts-1 | Text-to-speech | $0.015/1K chars |
| tts-1-hd | HD speech | $0.03/1K chars |
### Embedding Models
Lower costs for embeddings:
| Model | Price/1K tokens |
|-------|-----------------|
| text-embedding-3-small | $0.00002 |
| text-embedding-3-large | $0.00013 |
| text-embedding-ada-002 | $0.0001 |
## Monitoring Costs
### Per-Request Tracking
Each API response includes usage data:
```json
{
"usage": {
"prompt_tokens": 50,
"completion_tokens": 150,
"total_tokens": 200
},
"x-routstr-cost": {
"sats": 79,
"usd": 0.000375,
"breakdown": {
"prompt_cost": 15,
"completion_cost": 60,
"fees": 4
}
}
}
```
### Daily Summaries
View in admin dashboard:
- Total requests
- Token usage by model
- Cost distribution
- Trending patterns
### Cost Alerts
Set up notifications:
```python
# Example monitoring script
def check_daily_spend(api_key):
balance_start = get_balance(api_key, "00:00")
balance_now = get_balance(api_key)
spent = balance_start - balance_now
if spent > DAILY_LIMIT:
send_alert(f"Daily spend exceeded: {spent} sats")
```
## Optimization Strategies
### Model Selection
Choose the right model for each task:
| Task | Recommended Model | Why |
|------|-------------------|-----|
| Simple Q&A | gpt-3.5-turbo | Fast, cheap, sufficient |
| Code generation | gpt-4 | Better reasoning |
| Summarization | claude-3-haiku | Good balance |
| Creative writing | claude-3-opus | Best quality |
| Embeddings | text-embedding-3-small | Optimized for vectors |
### Prompt Engineering
Reduce costs with efficient prompts:
```python
# Expensive
prompt = """
You are an AI assistant. Your task is to help users.
Please provide detailed, comprehensive answers.
Now, answer this question: What is 2+2?
"""
# Economical
prompt = "Calculate: 2+2"
```
### Caching Strategies
Implement smart caching:
```python
# Cache embedding results
@lru_cache(maxsize=1000)
def get_embedding(text):
return client.embeddings.create(
model="text-embedding-3-small",
input=text
)
# Cache common responses
COMMON_RESPONSES = {
"greeting": "Hello! How can I help you?",
"goodbye": "Goodbye! Have a great day!"
}
```
### Batch Processing
Process multiple items efficiently:
```python
# Instead of multiple calls
for item in items:
response = client.chat.completions.create(...)
# Use single call with formatted prompt
prompt = "\n".join([f"{i+1}. {item}" for i, item in enumerate(items)])
response = client.chat.completions.create(
messages=[{"role": "user", "content": f"Process these items:\n{prompt}"}]
)
```
## Custom Pricing Rules
### Time-Based Pricing
Implement off-peak discounts:
```python
def calculate_multiplier():
hour = datetime.now().hour
if 2 <= hour <= 6: # 2 AM - 6 AM
return 0.8 # 20% discount
elif 18 <= hour <= 22: # 6 PM - 10 PM
return 1.2 # 20% premium
return 1.0
```
### Volume Discounts
Reward high-volume usage:
```python
def get_discount_rate(monthly_volume_sats):
if monthly_volume_sats > 1_000_000:
return 0.9 # 10% discount
elif monthly_volume_sats > 500_000:
return 0.95 # 5% discount
return 1.0
```
### Model-Specific Rules
Custom pricing logic:
```python
def adjust_model_price(model, base_price):
# Premium for latest models
if "turbo" in model or "latest" in model:
return base_price * 1.1
# Discount for older models
if "legacy" in model:
return base_price * 0.8
return base_price
```
## Pricing Transparency
### Public Pricing Page
Display current rates:
```html
<!-- Available at /pricing -->
<table>
<tr>
<th>Model</th>
<th>Input (sats/1K)</th>
<th>Output (sats/1K)</th>
</tr>
<!-- Dynamically generated from models.json -->
</table>
```
### Cost Estimation API
Provide cost estimates:
```bash
POST /v1/estimate
{
"model": "gpt-4",
"prompt_tokens": 500,
"max_tokens": 200
}
Response:
{
"estimated_cost_sats": 45,
"breakdown": {
"prompt": 30,
"completion": 12,
"fees": 3
}
}
```
## Troubleshooting
### Pricing Mismatches
**Issue**: Costs don't match expectations
- Check current BTC/USD rate
- Verify fee settings
- Review model configuration
**Issue**: Models not found
- Update models.json
- Check model ID spelling
- Verify upstream support
### Fee Calculations
**Issue**: Fees seem too high
- Review EXCHANGE_FEE setting
- Check UPSTREAM_PROVIDER_FEE
- Calculate total multiplier
## Next Steps
- [API Reference](../api/overview.md) - Technical details
- [Custom Pricing](../advanced/custom-pricing.md) - Advanced configuration
- [Contributing](../contributing/setup.md) - Help improve Routstr
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# Payment Flow
Understanding how payments work in Routstr is key to using the system effectively. This guide explains the payment process in detail.
## Overview
Routstr uses a pre-funded account model where:
1. Users deposit eCash tokens to create an API key
2. Each API request deducts from the balance
3. Users can withdraw remaining balance as eCash
## Creating an API Key
### Step 1: Obtain eCash Token
Get a Cashu token from any compatible source:
**Option A: From a Cashu Wallet**
```bash
# Example: Creating a 10,000 sat token
cashu send 10000
```
**Option B: Lightning Invoice**
```bash
# Some mints support direct Lightning deposits
curl -X POST https://mint.example.com/v1/mint/quote/bolt11 \
-d '{"amount": 10000, "unit": "sat"}'
```
**Option C: Test Tokens**
```bash
# Get test tokens from testnet mints
# Check mint documentation for faucets
```
### Step 2: Create API Key
Send your token to Routstr:
```bash
curl -X POST https://your-routstr-node/v1/wallet/create \
-H "Content-Type: application/json" \
-d '{
"cashu_token": "cashuAeyJ0b2tlbiI6W3sibWludCI6Imh0dHBzOi8vbWlu...",
"name": "My Project Key",
"expires_at": "2024-12-31T23:59:59Z",
"refund_npub": "npub1abc..."
}'
```
**Request Parameters:**
- `cashu_token` (required): The eCash token to deposit
- `name` (optional): Friendly name for the key
- `expires_at` (optional): ISO timestamp for key expiration
- `refund_npub` (optional): Nostr pubkey for refunds
**Response:**
```json
{
"api_key": "rstr_1234567890abcdef",
"balance": 10000000,
"expires_at": "2024-12-31T23:59:59Z",
"created_at": "2024-01-01T00:00:00Z"
}
```
### Step 3: Verify Balance
Check your key's balance:
```bash
curl -X GET https://your-routstr-node/v1/wallet/balance \
-H "Authorization: Bearer rstr_1234567890abcdef"
```
Response:
```json
{
"balance": 10000000,
"total_deposited": 10000000,
"total_spent": 0,
"last_used": null
}
```
## Making API Requests
### Cost Calculation
Costs are calculated based on:
1. **Request Type**
- Chat completions
- Embeddings
- Image generation
- Audio processing
2. **Token Usage**
- Input tokens (prompt)
- Output tokens (response)
- Model-specific rates
3. **Additional Costs**
- Base request fee
- Image generation fees
- Audio processing time
### Example: Chat Completion
```python
import openai
client = openai.OpenAI(
api_key="rstr_1234567890abcdef",
base_url="https://your-routstr-node/v1"
)
# Make request
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "Hello, how are you?"}
]
)
# Check usage
print(f"Input tokens: {response.usage.prompt_tokens}")
print(f"Output tokens: {response.usage.completion_tokens}")
print(f"Total tokens: {response.usage.total_tokens}")
```
### Cost Breakdown
For the above request:
```
Model: gpt-3.5-turbo
Input tokens: 13
Output tokens: 27
Model rates: $0.0015/1K input, $0.002/1K output
USD Cost = (13/1000 * 0.0015) + (27/1000 * 0.002) = $0.0000735
BTC/USD Rate: $50,000
BTC Cost = 0.0000735 / 50000 = 0.00000000147 BTC = 147 sats
With fees (5%): 154 sats
Final cost: 154 sats
```
## Balance Management
### Monitoring Usage
Track your usage in real-time:
```bash
# Get current balance
curl -X GET https://your-routstr-node/v1/wallet/balance \
-H "Authorization: Bearer your-api-key"
# View recent transactions (through admin dashboard)
# Access at https://your-routstr-node/admin/
```
### Low Balance Handling
When balance is insufficient:
```json
{
"error": {
"type": "insufficient_balance",
"message": "Insufficient balance. Current: 100 sats, Required: 154 sats",
"code": "payment_required"
}
}
```
### Topping Up
Add funds to existing key:
```bash
curl -X POST https://your-routstr-node/v1/wallet/topup \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"cashu_token": "cashuAeyJ0b2..."
}'
```
## Withdrawing Balance
### Via Admin Dashboard
1. Navigate to `/admin/`
2. Enter admin password
3. Find your API key
4. Click "Withdraw"
5. Receive eCash token
### Via API (if enabled)
```bash
curl -X POST https://your-routstr-node/v1/wallet/withdraw \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"amount": 5000,
"mint": "https://mint.minibits.cash/Bitcoin"
}'
```
## Payment Security
### Token Validation
Routstr validates tokens by:
1. Checking signature validity
2. Verifying with the issuing mint
3. Ensuring no double-spending
4. Confirming sufficient value
### Failed Payments
Common failure reasons:
- Invalid token signature
- Already spent token
- Untrusted mint
- Network issues with mint
### Refund Policy
- Unused balance can be withdrawn anytime
- Expired keys with balance can be refunded to `refund_npub`
- Node operators may have additional policies
## Advanced Features
### Multi-Mint Support
Routstr accepts tokens from multiple mints:
```bash
CASHU_MINTS=https://mint1.com,https://mint2.com,https://mint3.com
```
Benefits:
- Redundancy if one mint is down
- User choice of mints
- Geographic distribution
### Automatic Payouts
Configure automatic Lightning payouts:
```bash
RECEIVE_LN_ADDRESS=satoshi@getalby.com
```
When enabled:
- Balances above threshold are swept
- Converted to Lightning payments
- Sent to configured address
### Per-Request Payments (Coming Soon)
Future support for Nut-24 headers:
```bash
curl -X POST https://your-routstr-node/v1/chat/completions \
-H "x-cashu: cashuAeyJ0..." \
-H "Content-Type: application/json" \
-d '{...}'
```
Response includes change:
```
HTTP/1.1 200 OK
x-cashu: cashuAeyJjaGFuZ2Ui...
```
## Best Practices
### API Key Management
1. **Separate Keys per Application**
- Easier tracking
- Better security
- Independent budgets
2. **Set Expiration Dates**
- Automatic cleanup
- Security improvement
- Budget control
3. **Monitor Balances**
- Set up alerts
- Regular checks
- Usage analytics
### Cost Optimization
1. **Choose Appropriate Models**
- Smaller models for simple tasks
- Larger models only when needed
2. **Optimize Prompts**
- Concise, clear instructions
- Avoid unnecessary tokens
3. **Use Streaming**
- Early termination possible
- Better user experience
### Security
1. **Secure Storage**
- Environment variables
- Secrets management
- Never in code
2. **Network Security**
- Always use HTTPS
- Verify certificates
- Consider Tor for privacy
3. **Regular Rotation**
- Change keys periodically
- Withdraw unused funds
- Audit usage logs
## Troubleshooting
### Payment Rejected
**Error:** "Invalid token"
- Check token format
- Verify mint is trusted
- Ensure not already spent
**Error:** "Insufficient value"
- Token value too low
- Check current pricing
- Add larger token
### Balance Discrepancies
- Allow for price fluctuations
- Check model pricing updates
- Review transaction history
### Mint Issues
- Try different mint from list
- Check mint status
- Contact mint operator
## Next Steps
- [Using the API](using-api.md) - Integration guide
- [Admin Dashboard](admin-dashboard.md) - Account management
- [Models & Pricing](models-pricing.md) - Cost details
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# Using the API
This guide shows how to integrate Routstr with your applications using various programming languages and tools.
## API Compatibility
Routstr maintains full compatibility with the OpenAI API, meaning:
- Existing OpenAI client libraries work without modification
- Only the base URL and API key need to change
- All parameters and responses match OpenAI's format
## Basic Setup
### Python
Using the official OpenAI Python library:
```python
from openai import OpenAI
# Initialize client with Routstr endpoint
client = OpenAI(
api_key="rstr_your_api_key_here",
base_url="https://your-routstr-node.com/v1"
)
# Use exactly like OpenAI
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)
```
### JavaScript/TypeScript
Using the official OpenAI Node.js library:
```javascript
import OpenAI from 'openai';
// Initialize client
const openai = new OpenAI({
apiKey: 'rstr_your_api_key_here',
baseURL: 'https://your-routstr-node.com/v1'
});
// Make a request
async function main() {
const completion = await openai.chat.completions.create({
model: 'gpt-3.5-turbo',
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'Hello!' }
]
});
console.log(completion.choices[0].message.content);
}
main();
```
### cURL
Direct HTTP requests:
```bash
curl https://your-routstr-node.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer rstr_your_api_key_here" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'
```
## Common Use Cases
### Chat Completions
Standard chat with conversation history:
```python
messages = []
def chat(user_input):
# Add user message
messages.append({"role": "user", "content": user_input})
# Get AI response
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=messages,
temperature=0.7,
max_tokens=150
)
# Add AI response to history
ai_message = response.choices[0].message
messages.append({"role": "assistant", "content": ai_message.content})
return ai_message.content
# Usage
print(chat("What's the weather like?"))
print(chat("How should I dress?")) # Maintains context
```
### Streaming Responses
For real-time output:
```python
stream = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Write a short story"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="", flush=True)
```
### Function Calling
Using OpenAI's function calling feature:
```python
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}]
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
tools=tools,
tool_choice="auto"
)
# Check if function was called
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
print(f"Function: {tool_call.function.name}")
print(f"Arguments: {tool_call.function.arguments}")
```
### Embeddings
Generate text embeddings:
```python
response = client.embeddings.create(
model="text-embedding-3-small",
input="The quick brown fox jumps over the lazy dog"
)
embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")
```
### Image Generation
Create images with DALL-E:
```python
response = client.images.generate(
model="dall-e-3",
prompt="A futuristic city with flying cars",
size="1024x1024",
quality="standard",
n=1
)
image_url = response.data[0].url
print(f"Image URL: {image_url}")
```
### Audio Transcription
Convert speech to text:
```python
with open("audio.mp3", "rb") as audio_file:
response = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
response_format="text"
)
print(response.text)
```
## Error Handling
### Balance Errors
Handle insufficient balance gracefully:
```python
try:
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}]
)
except Exception as e:
if "insufficient_balance" in str(e):
print("Low balance! Please top up your API key.")
# Implement top-up logic
else:
raise
```
### Rate Limiting
Implement exponential backoff:
```python
import time
from typing import Optional
def make_request_with_retry(
func,
max_retries: int = 3,
initial_delay: float = 1.0
) -> Optional[any]:
for attempt in range(max_retries):
try:
return func()
except Exception as e:
if "rate_limit" in str(e) and attempt < max_retries - 1:
delay = initial_delay * (2 ** attempt)
print(f"Rate limited. Waiting {delay}s...")
time.sleep(delay)
else:
raise
return None
```
### Connection Errors
Handle network issues:
```python
import httpx
# Configure timeout and retries
client = OpenAI(
api_key="rstr_your_key",
base_url="https://your-node.com/v1",
timeout=httpx.Timeout(60.0, connect=5.0),
max_retries=2
)
```
## Advanced Features
### Using Tor
Route requests through Tor for privacy:
```python
import httpx
# Configure Tor proxy
proxies = {
"http://": "socks5://127.0.0.1:9050",
"https://": "socks5://127.0.0.1:9050"
}
http_client = httpx.Client(proxies=proxies)
client = OpenAI(
api_key="rstr_your_key",
base_url="http://your-onion-address.onion/v1",
http_client=http_client
)
```
### Custom Headers
Add custom headers if needed:
```python
import httpx
class CustomClient(httpx.Client):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.headers["X-Custom-Header"] = "value"
client = OpenAI(
api_key="rstr_your_key",
base_url="https://your-node.com/v1",
http_client=CustomClient()
)
```
### Async Operations
For high-performance applications:
```python
import asyncio
from openai import AsyncOpenAI
async_client = AsyncOpenAI(
api_key="rstr_your_key",
base_url="https://your-node.com/v1"
)
async def process_messages(messages):
tasks = []
for msg in messages:
task = async_client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": msg}]
)
tasks.append(task)
responses = await asyncio.gather(*tasks)
return [r.choices[0].message.content for r in responses]
# Run async
messages = ["Hello", "How are you?", "What's 2+2?"]
results = asyncio.run(process_messages(messages))
```
## Best Practices
### 1. Environment Variables
Never hardcode API keys:
```python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("ROUTSTR_API_KEY"),
base_url=os.getenv("ROUTSTR_BASE_URL", "https://api.routstr.com/v1")
)
```
### 2. Error Logging
Implement comprehensive logging:
```python
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
try:
response = client.chat.completions.create(...)
logger.info(f"Request successful. Tokens used: {response.usage.total_tokens}")
except Exception as e:
logger.error(f"API request failed: {e}")
raise
```
### 3. Cost Tracking
Monitor your usage:
```python
class UsageTracker:
def __init__(self):
self.total_tokens = 0
self.total_requests = 0
def track(self, response):
self.total_tokens += response.usage.total_tokens
self.total_requests += 1
# Estimate cost (example rates)
cost_per_1k = 0.002 # $0.002 per 1K tokens
estimated_cost = (self.total_tokens / 1000) * cost_per_1k
logger.info(f"Total usage: {self.total_tokens} tokens, "
f"${estimated_cost:.4f} (~{estimated_cost * 50000:.0f} sats)")
tracker = UsageTracker()
response = client.chat.completions.create(...)
tracker.track(response)
```
### 4. Caching Responses
Reduce costs with intelligent caching:
```python
import hashlib
import json
from functools import lru_cache
@lru_cache(maxsize=100)
def cached_completion(prompt: str, model: str = "gpt-3.5-turbo"):
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
temperature=0 # Deterministic for caching
)
return response.choices[0].message.content
# Repeated calls with same prompt use cache
result1 = cached_completion("What is 2+2?")
result2 = cached_completion("What is 2+2?") # From cache, no API call
```
## Testing
### Mock Responses
For development without spending sats:
```python
class MockOpenAI:
class Completions:
def create(self, **kwargs):
return type('Response', (), {
'choices': [type('Choice', (), {
'message': type('Message', (), {
'content': 'Mock response'
})()
})],
'usage': type('Usage', (), {
'total_tokens': 10
})()
})()
def __init__(self):
self.chat = type('Chat', (), {
'completions': self.Completions()
})()
# Use mock in tests
if os.getenv('TESTING'):
client = MockOpenAI()
else:
client = OpenAI(...)
```
### Integration Tests
Test your Routstr integration:
```python
def test_routstr_connection():
try:
# Test models endpoint
models = client.models.list()
assert len(models.data) > 0
# Test simple completion
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "test"}],
max_tokens=5
)
assert response.choices[0].message.content
print("✅ Routstr integration working!")
return True
except Exception as e:
print(f"❌ Integration test failed: {e}")
return False
```
## Troubleshooting
### Common Issues
**SSL Certificate Errors**
```python
# For development only - not for production!
import ssl
import httpx
client = OpenAI(
api_key="rstr_key",
base_url="https://localhost:8000/v1",
http_client=httpx.Client(verify=False)
)
```
**Timeout Issues**
```python
# Increase timeout for slow connections
client = OpenAI(
api_key="rstr_key",
base_url="https://your-node.com/v1",
timeout=httpx.Timeout(120.0) # 2 minutes
)
```
**Debugging Requests**
```python
import logging
import httpx
# Enable debug logging
logging.basicConfig(level=logging.DEBUG)
httpx_logger = logging.getLogger("httpx")
httpx_logger.setLevel(logging.DEBUG)
```
## Next Steps
- [Admin Dashboard](admin-dashboard.md) - Manage your account
- [Models & Pricing](models-pricing.md) - Understanding costs
- [API Reference](../api/overview.md) - Technical details
mkdocs.yml
+107
View File
@@ -0,0 +1,107 @@
site_name: Routstr Core Documentation
site_url: https://docs.routstr.com
site_description: FastAPI-based reverse proxy for OpenAI-compatible APIs with Bitcoin eCash micropayments
site_author: Routstr Team
repo_name: routstr/routstr-core
repo_url: https://github.com/routstr/routstr-core
edit_uri: tree/main/docs
theme:
name: material
language: en
palette:
- media: "(prefers-color-scheme: light)"
scheme: default
primary: orange
accent: amber
toggle:
icon: material/brightness-7
name: Switch to dark mode
- media: "(prefers-color-scheme: dark)"
scheme: slate
primary: orange
accent: amber
toggle:
icon: material/brightness-4
name: Switch to light mode
features:
- navigation.tabs
- navigation.sections
- navigation.expand
- navigation.top
- navigation.indexes
- search.suggest
- search.highlight
- content.tabs.link
- content.code.copy
- content.code.annotate
icon:
logo: material/lightning-bolt
repo: fontawesome/brands/github
plugins:
- search
- mermaid2
markdown_extensions:
- admonition
- codehilite:
guess_lang: false
- toc:
permalink: true
- pymdownx.superfences:
custom_fences:
- name: mermaid
class: mermaid
format: !!python/name:pymdownx.superfences.fence_code_format
- pymdownx.tabbed:
alternate_style: true
- pymdownx.emoji:
emoji_index: !!python/name:materialx.emoji.twemoji
emoji_generator: !!python/name:materialx.emoji.to_svg
- pymdownx.details
- pymdownx.inlinehilite
- pymdownx.snippets
- pymdownx.tasklist:
custom_checkbox: true
- attr_list
- md_in_html
extra:
social:
- icon: fontawesome/brands/github
link: https://github.com/routstr
- icon: fontawesome/brands/bitcoin
link: https://cashu.space
nav:
- Home: index.md
- Getting Started:
- Overview: getting-started/overview.md
- Quick Start: getting-started/quickstart.md
- Docker Setup: getting-started/docker.md
- Configuration: getting-started/configuration.md
- User Guide:
- Introduction: user-guide/introduction.md
- Payment Flow: user-guide/payment-flow.md
- Using the API: user-guide/using-api.md
- Admin Dashboard: user-guide/admin-dashboard.md
- Models & Pricing: user-guide/models-pricing.md
- Contributing:
- Setup Development: contributing/setup.md
- Architecture: contributing/architecture.md
- Code Structure: contributing/code-structure.md
- Testing: contributing/testing.md
- Database: contributing/database.md
- Guidelines: contributing/guidelines.md
- API Reference:
- Overview: api/overview.md
- Authentication: api/authentication.md
- Endpoints: api/endpoints.md
- Errors: api/errors.md
- Advanced:
- Tor Support: advanced/tor.md
- Nostr Discovery: advanced/nostr.md
- Custom Pricing: advanced/custom-pricing.md
- Migrations: advanced/migrations.md