YAML Configuration Guide

AIProxy now supports YAML configuration files for managing channels, model configurations, and system options.

Configuration Priority

The configuration system follows this priority order (highest to lowest):

Environment Variables (highest priority)
YAML Configuration File (medium priority)
Database (lowest priority)

This means:

Values set via environment variables will always take precedence
YAML configuration will override database values
Database values are used as defaults when no other configuration is provided

Configuration File Location

By default, AIProxy looks for config.yaml in the current working directory.

You can specify a custom location using the CONFIG_FILE_PATH environment variable:

export CONFIG_FILE_PATH=/path/to/your/config.yaml

Configuration File Structure

The YAML configuration file has three main sections. The channel and modelconfig structures directly correspond to the database model types, making it easy to understand and maintain.

1. Channels Configuration

Define your API provider channels:

channels:
  - name: "openai-primary"
    type_name: "openai"  # Human-readable type name (recommended)
    # OR use numeric type:
    # type: 1  # OpenAI channel type
    key: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
    base_url: "https://api.openai.com"
    models:
      - "gpt-4"
      - "gpt-3.5-turbo"
    model_mapping:
      "gpt-4": "gpt-4-0613"
    status: 1  # 1=Enabled, 2=Disabled
    priority: 0
    balance: 100.0
    balance_threshold: 10.0
    enabled_auto_balance_check: true
    sets:
      - "default"

Channel Type Names

You can use either type_name (human-readable string) or type (numeric code). Using type_name is recommended for better readability:

Supported Type Names:

openai: OpenAI API
azure / azure2: Azure OpenAI
anthropic / claude: Anthropic Claude
gemini / google gemini: Google Gemini
gemini-openai / google gemini (openai): Google Gemini via OpenAI API
zhipu: Zhipu AI
ali / aliyun: Alibaba Cloud
baidu: Baidu Wenxin
baiduv2 / baidu v2: Baidu Wenxin v2
xunfei: iFlytek Spark
tencent: Tencent Hunyuan
moonshot: Moonshot AI
deepseek: DeepSeek
aws: AWS Bedrock
vertexai / vertex: Google Vertex AI
xai: xAI Grok
groq: Groq
mistral: Mistral AI
cohere: Cohere
openrouter: OpenRouter
And many more... (see core/model/yaml_integration.go for the complete list)

Numeric Channel Types:

1: OpenAI
3: Azure
14: Anthropic/Claude
24: Google Gemini
See core/model/chtype.go for complete list

2. Model Configurations

Define model-specific settings:

modelconfigs:
  - model: "gpt-4"
    owner: "openai"
    type_name: "chat"  # Human-readable type name (recommended)
    # OR use numeric type:
    # type: 1  # ChatCompletions
    rpm: 3500  # Requests per minute
    tpm: 80000  # Tokens per minute
    retry_times: 3
    timeout_config:
      request_timeout: 300
      stream_request_timeout: 600
    warn_error_rate: 0.5
    max_error_rate: 0.8
    price:
      input: 0.03  # Price per 1000 input tokens
      output: 0.06  # Price per 1000 output tokens
    config:
      max_context_tokens: 8192
      max_output_tokens: 4096
      vision: false
      tool_choice: true

  - model: "text-embedding-3-small"
    owner: "openai"
    type_name: "embedding"  # Embedding model
    rpm: 3000
    tpm: 1000000
    price:
      input: 0.00002
      output: 0

Model Type Names

You can use either type_name (human-readable string) or type (numeric code). Using type_name is recommended for better readability:

Supported Type Names:

chat / chatcompletions: Chat completion models
completion / completions: Text completion models
embedding / embeddings: Embedding models
moderation / moderations: Moderation models
image / imagegenerations: Image generation models
imageedit / imageedits: Image editing models
audio / speech / audiospeech: Text-to-speech models
transcription / audiotranscription: Audio transcription models
translation / audiotranslation: Audio translation models
rerank: Reranking models
pdf / parsepdf: PDF parsing models
anthropic: Anthropic-specific models
And more... (see core/model/yaml_integration.go for the complete list)

Numeric Model Types:

1: ChatCompletions
2: Completions
3: Embeddings
4: Moderations
5: ImagesGenerations
See core/relay/mode/define.go for complete list

Model Config Keys

Common configuration keys:

max_context_tokens: Maximum context window size
max_output_tokens: Maximum output tokens
vision: Whether the model supports vision/image inputs
tool_choice: Whether the model supports function calling

3. System Options

Configure system-wide options:

options:
  # Log retention (in hours)
  LogStorageHours: "168"  # 7 days
  RetryLogStorageHours: "72"  # 3 days
  LogDetailStorageHours: "24"  # 1 day

  # Log settings
  SaveAllLogDetail: "false"
  LogDetailRequestBodyMaxSize: "10000"
  LogDetailResponseBodyMaxSize: "10000"

  # Rate limiting
  IPGroupsThreshold: "100"  # Requests per minute
  IPGroupsBanThreshold: "200"

  # Retry settings
  RetryTimes: "3"

  # Error rate alerts
  DefaultWarnNotifyErrorRate: "0.5"

  # Usage alerts
  UsageAlertThreshold: "100"

Available Options

LogStorageHours: How long to keep logs (hours)
RetryLogStorageHours: How long to keep retry logs (hours)
LogDetailStorageHours: How long to keep detailed logs (hours)
CleanLogBatchSize: Batch size for log cleanup operations
IPGroupsThreshold: Request rate limit per IP
IPGroupsBanThreshold: Ban threshold for IP
SaveAllLogDetail: Whether to save all request/response details
LogDetailRequestBodyMaxSize: Max size of request body to log
LogDetailResponseBodyMaxSize: Max size of response body to log
DisableServe: Disable API serving (for maintenance)
RetryTimes: Number of retry attempts
DefaultChannelModels: Default models for new channels (JSON array)
GroupMaxTokenNum: Max tokens per group
DefaultWarnNotifyErrorRate: Default error rate warning threshold
UsageAlertThreshold: Usage alert threshold
FuzzyTokenThreshold: Fuzzy token matching threshold

Example: Complete Configuration

See config.example.yaml for a complete example configuration file.

Usage

Create a config.yaml file in your project root or specify a custom location via CONFIG_FILE_PATH
Start AIProxy as usual:
```
./aiproxy
```
The configuration will be loaded in this order:
- Database values (if any)
- YAML configuration (overrides database)
- Environment variables (overrides everything)

Updating Configuration at Runtime

Changes to the YAML configuration file require restarting the application to take effect.

However, you can still use the web UI or API to modify configurations at runtime, which will be stored in the database.

Migration from Database-only Configuration

You can extract your current database configuration and convert it to YAML format:

Export your channels via the web UI
Export your model configs via the web UI
Convert to YAML format following the structure in config.example.yaml

Notes

All values in the options section must be strings (they will be parsed according to their type)
Channel and model config IDs are optional in YAML - if omitted, the system will auto-generate them
When both YAML and database contain the same configuration, YAML takes precedence
New configurations from YAML that don't exist in the database will be automatically added

Troubleshooting

Configuration not loading

Check the log output on startup - it will show how many channels/models were loaded from YAML
Verify the YAML syntax is correct (use a YAML validator)
Ensure the file path is correct (check CONFIG_FILE_PATH environment variable)

Environment variables not overriding YAML

Environment variables override YAML values through the config.SetXxx() functions which check for environment variables on every call. Make sure you're using the correct environment variable names (see core/common/config/env.go for the list).

Values reverting to database values

If you modify configuration through the web UI or API, those changes will be written to the database. On next restart, YAML will override those database values again. Use YAML for persistent configuration and the web UI for temporary changes.

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