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Model Configuration

This section provides comprehensive guidance on configuring AI models in the Radicalbit AI Gateway based on the actual codebase structure.

Overview​

The Radicalbit AI Gateway supports multiple AI providers and models through a flexible configuration system.

With the new configuration structure:

  • Models are defined at top-level under:
    • chat_models (for chat/completions)
    • embedding_models (for embeddings)
  • Routes do not contain full model objects anymore.
    • Routes reference models by model ID (string lists)

Model Structure​

Basic Model Configuration (Chat)​

chat_models:
  - model_id: openai-4o
    model: openai/gpt-4o
    credentials:
      api_key: !secret OPENAI_API_KEY
    params:
      temperature: 1
      max_tokens: 20

routes:
  production:
    chat_models:
      - openai-4o

Model Fields​

Required Fields​

  • model_id: Unique identifier for the model (used by routes and fallbacks)
  • model: Model identifier in format provider/model_name (e.g., openai/gpt-4o)

Optional Fields​

  • credentials: API credentials for accessing the model
  • params: Model parameters (temperature, max_tokens, etc.)
  • retry_attempts: Number of retry attempts (default: 3)
  • prompt: Optional inline system/developer prompt (mutually exclusive with prompt_ref)
  • prompt_ref: Optional reference to a Markdown file containing the prompt
  • role: Role used when injecting prompt/prompt_ref (allowed: system or developer when prompt is set)
  • input_cost_per_million_tokens: Cost per million input tokens
  • output_cost_per_million_tokens: Cost per million output tokens

Supported Providers​

The gateway has native support for the following providers. The model field always follows the provider/model-name format.

OpenAI​

chat_models:
  - model_id: gpt-4o
    model: openai/gpt-4o
    credentials:
      api_key: !secret OPENAI_API_KEY

Common models: openai/gpt-4o, openai/gpt-4o-mini, openai/o1, openai/o3-mini

Anthropic​

chat_models:
  - model_id: claude
    model: anthropic/claude-3-5-sonnet-latest
    credentials:
      api_key: !secret ANTHROPIC_API_KEY

Common models: anthropic/claude-3-5-sonnet-latest, anthropic/claude-3-haiku-20240307

note

The gateway automatically handles Anthropic-specific streaming behavior and prompt caching.

Google Gemini​

chat_models:
  - model_id: gemini
    model: google-genai/gemini-2.5-flash
    credentials:
      api_key: !secret GOOGLE_API_KEY   # required — no env fallback

Common models: google-genai/gemini-2.5-flash, google-genai/gemini-2.5-pro

Embedding models use a models/ prefix:

embedding_models:
  - model_id: gemini-embedding
    model: google-genai/models/gemini-embedding-001
    credentials:
      api_key: !secret GOOGLE_API_KEY
    params:
      task_type: RETRIEVAL_QUERY  # RETRIEVAL_DOCUMENT | SEMANTIC_SIMILARITY | CLASSIFICATION | CLUSTERING

DeepSeek​

chat_models:
  - model_id: deepseek
    model: deepseek/deepseek-chat
    credentials:
      api_key: !secret DEEPSEEK_API_KEY

Common models: deepseek/deepseek-chat, deepseek/deepseek-reasoner

note

DeepSeek uses its own tokenizer for accurate token counting — this is handled automatically.

Mistral​

chat_models:
  - model_id: mistral
    model: mistralai/mistral-large-latest
    credentials:
      api_key: !secret MISTRAL_API_KEY

Common models: mistralai/mistral-large-latest, mistralai/mistral-nemo-latest

Azure OpenAI​

chat_models:
  - model_id: azure-gpt4o
    model: azure/my-deployment-name
    credentials:
      api_key: !secret AZURE_OPENAI_KEY
      api_version: "2024-02-01"
      # azure_ad_token: !secret AZURE_AD_TOKEN  # alternative to api_key

OpenAI-compatible endpoints​

Any provider that exposes an OpenAI-compatible API — Ollama, vLLM, OpenRouter, or any on-premises deployment — works by using the openai/ prefix with a base_url:

chat_models:
  # Ollama (local)
  - model_id: llama3
    model: openai/llama3.2:3b
    credentials:
      base_url: "http://host.docker.internal:11434/v1"

  # vLLM (on-premises)
  - model_id: vllm-model
    model: openai/your-deployed-model
    credentials:
      base_url: "http://vllm-server:8000/v1"
      api_key: !secret VLLM_API_KEY  # if required

  # OpenRouter
  - model_id: openrouter-model
    model: openai/meta-llama/llama-3.1-8b-instruct
    credentials:
      base_url: "https://openrouter.ai/api/v1"
      api_key: !secret OPENROUTER_API_KEY
tip

Need a provider not listed here? Additional integrations can be developed on request — contact us.

Mock Models (Testing)​

chat_models:
  - model_id: mock-chat
    model: mock/gateway
    params:
      latency_ms: 150
      response_text: "mocked response"

embedding_models:
  - model_id: mock-embed
    model: mock/embeddings
    params:
      latency_ms: 100
      vector_size: 8

Model Types​

Chat Models​

Used for conversational AI and text generation.

Definition:

chat_models:
  - model_id: assistant
    model: openai/gpt-4o
    credentials:
      api_key: !secret OPENAI_API_KEY
    params:
      temperature: 0.7
      max_tokens: 1000

Usage in routes:

routes:
  customer-service:
    chat_models:
      - assistant

Embedding Models​

Used for embeddings and vector operations.

Definition:

embedding_models:
  - model_id: emb-small
    model: openai/text-embedding-3-small
    credentials:
      api_key: !secret OPENAI_API_KEY

Usage in routes:

routes:
  semantic-cache-demo:
    chat_models:
      - assistant
    embedding_models:
      - emb-small

Credentials Configuration​

API Key Authentication​

credentials:
  api_key: !secret OPENAI_API_KEY

Custom Base URL​

credentials:
  base_url: "http://localhost:11434/v1"
  api_key: "dummy-api-key"  # May be required by some OpenAI-compatible servers/clients

Model Parameters​

Common Parameters​

  • temperature: Controls randomness (0.0-2.0)
  • max_tokens: Maximum tokens to generate
  • top_p: Nucleus sampling parameter
  • frequency_penalty: Penalty for frequent tokens
  • presence_penalty: Penalty for new tokens

Provider-Specific Parameters​

Each provider may support additional parameters. Refer to the provider's documentation for complete details.

Cost Configuration​

Automatic Cost Assignment​

The gateway can assign costs from a price list (e.g. model_prices.json) if not explicitly configured:

chat_models:
  - model_id: gpt-4o
    model: openai/gpt-4o
    # Costs can be automatically assigned if supported

Manual Cost Configuration​

chat_models:
  - model_id: custom-model
    model: openai/gpt-4o
    input_cost_per_million_tokens: 5.0
    output_cost_per_million_tokens: 15.0

Retry Configuration​

Default Retry Policy​

chat_models:
  - model_id: gpt-4o
    model: openai/gpt-4o
    retry_attempts: 3  # Default value

Custom Retry Policy​

chat_models:
  - model_id: unreliable-model
    model: openai/gpt-4o-mini
    retry_attempts: 5

Prompts​

Basic Prompt​

chat_models:
  - model_id: assistant
    model: openai/gpt-4o
    prompt: "You are a helpful assistant."
    role: system

File-based Prompt (prompt_ref)​

You can load the default prompt from a Markdown file mounted at runtime.

chat_models:
  - model_id: assistant
    model: openai/gpt-4o
    # Use either `prompt` OR `prompt_ref` (mutually exclusive)
    prompt_ref: "assistant.md"
    role: system

Role-Based Prompt​

chat_models:
  - model_id: developer-assistant
    model: openai/gpt-4o
    prompt: "You are a senior software developer."
    role: developer

Model Validation​

Unique Model IDs​

Model IDs should be unique within each top-level section.

chat_models:
  - model_id: gpt-4o        # ✅ Unique
    model: openai/gpt-4o
  - model_id: gpt-4o-mini # ✅ Unique
    model: openai/gpt-4o-mini

Route References​

  • Every ID listed in routes.<route>.chat_models must match a chat_models[].model_id
  • Every ID listed in routes.<route>.embedding_models must match an embedding_models[].model_id

Credential Validation​

  • Many hosted providers require API keys
  • OpenAI-compatible servers may require a dummy api_key depending on the client/adapter

Best Practices​

Model Organization​

  • Use descriptive model_id names
  • Separate production and testing models
  • Keep prompts short and consistent across environments
  • Prefer prompt_ref for environment-specific prompts (mount different prompt folders per environment without changing config.yaml).

Cost Management​

  • Configure cost information for accurate billing
  • Use automatic cost assignment when possible
  • Monitor token usage through metrics

Error Handling​

  • Configure appropriate retry attempts
  • Use fallback models for critical routes
  • Monitor model availability

Troubleshooting​

Common Issues​

  1. Model Not Found: Verify the model_id exists in chat_models / embedding_models and is correctly referenced by routes/fallbacks
  2. Authentication Errors: Check API keys and credentials configuration
  3. Prompt File Not Found: If using prompt_ref, ensure the Markdown file exists in the mounted PROMPTS_DIR inside the container and the filename matches the configured value.
  4. Cost Assignment: Ensure model names match those in the price list file (if used)

Debug Configuration​

chat_models:
  - model_id: debug-model
    model: openai/gpt-4o-mini
    retry_attempts: 1  # Reduce retries for faster debugging

routes:
  debug:
    chat_models:
      - debug-model

Next Steps​