Adding Gemini API due to openrouter's limitations. (#34)
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README.md
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README.md
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@ -127,6 +127,9 @@ export OPENROUTER_API_KEY=your_api_key_here
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# For OpenAI-compatible providers (optional)
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export OPENAI_API_BASE=your_api_base_url
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# For Gemini provider (optional)
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export GEMINI_API_KEY=your_api_key_here
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# For web research capabilities
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export TAVILY_API_KEY=your_api_key_here
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```
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@ -140,6 +143,7 @@ You can get your API keys from:
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- Anthropic API key: https://console.anthropic.com/
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- OpenAI API key: https://platform.openai.com/api-keys
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- OpenRouter API key: https://openrouter.ai/keys
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- Gemini API key: https://aistudio.google.com/app/apikey
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## Usage
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@ -165,7 +169,7 @@ ra-aid -m "Add new feature" --verbose
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- `--provider`: Specify the model provider (See Model Configuration section)
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- `--model`: Specify the model name (See Model Configuration section)
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- `--expert-provider`: Specify the provider for the expert tool (defaults to OpenAI)
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- `--expert-model`: Specify the model name for the expert tool (defaults to o1-preview for OpenAI)
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- `--expert-model`: Specify the model name for the expert tool (defaults to o1 for OpenAI)
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- `--chat`: Enable chat mode for interactive assistance
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- `--verbose`: Enable detailed logging output for debugging and monitoring
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@ -276,7 +280,7 @@ RA.Aid supports multiple AI providers and models. The default model is Anthropic
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The programmer tool (aider) automatically selects its model based on your available API keys. It will use Claude models if ANTHROPIC_API_KEY is set, or fall back to OpenAI models if only OPENAI_API_KEY is available.
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Note: The expert tool can be configured to use different providers (OpenAI, Anthropic, OpenRouter) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables. Each provider requires its own API key set through the appropriate environment variable.
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Note: The expert tool can be configured to use different providers (OpenAI, Anthropic, OpenRouter, Gemini) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables. Each provider requires its own API key set through the appropriate environment variable.
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#### Environment Variables
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@ -286,12 +290,14 @@ RA.Aid supports multiple providers through environment variables:
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- `OPENAI_API_KEY`: Required for OpenAI provider
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- `OPENROUTER_API_KEY`: Required for OpenRouter provider
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- `OPENAI_API_BASE`: Required for OpenAI-compatible providers along with `OPENAI_API_KEY`
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- `GEMINI_API_KEY`: Required for Gemini provider
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Expert Tool Environment Variables:
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- `EXPERT_OPENAI_API_KEY`: API key for expert tool using OpenAI provider
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- `EXPERT_ANTHROPIC_API_KEY`: API key for expert tool using Anthropic provider
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- `EXPERT_OPENROUTER_API_KEY`: API key for expert tool using OpenRouter provider
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- `EXPERT_OPENAI_API_BASE`: Base URL for expert tool using OpenAI-compatible provider
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- `EXPERT_GEMINI_API_KEY`: API key for expert tool using Gemini provider
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You can set these permanently in your shell's configuration file (e.g., `~/.bashrc` or `~/.zshrc`):
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@ -307,6 +313,9 @@ export OPENROUTER_API_KEY=your_api_key_here
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# For OpenAI-compatible providers
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export OPENAI_API_BASE=your_api_base_url
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# For Gemini provider
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export GEMINI_API_KEY=your_api_key_here
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```
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### Custom Model Examples
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@ -332,7 +341,7 @@ export OPENAI_API_BASE=your_api_base_url
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4. **Configuring Expert Provider**
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The expert tool is used by the agent for complex logic and debugging tasks. It can be configured to use different providers (OpenAI, Anthropic, OpenRouter) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables.
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The expert tool is used by the agent for complex logic and debugging tasks. It can be configured to use different providers (OpenAI, Anthropic, OpenRouter, Gemini, openai-compatible) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables.
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```bash
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# Use Anthropic for expert tool
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@ -345,7 +354,11 @@ export OPENAI_API_BASE=your_api_base_url
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# Use default OpenAI for expert tool
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export EXPERT_OPENAI_API_KEY=your_openai_api_key
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ra-aid -m "Your task" --expert-provider openai --expert-model o1-preview
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ra-aid -m "Your task" --expert-provider openai --expert-model o1
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# Use Gemini for expert tool
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export EXPERT_GEMINI_API_KEY=your_gemini_api_key
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ra-aid -m "Your task" --expert-provider gemini --expert-model gemini-2.0-flash-thinking-exp-1219
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```
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Aider specific Environment Variables you can add:
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@ -24,6 +24,7 @@ classifiers = [
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dependencies = [
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"langchain-anthropic>=0.3.1",
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"langchain-openai",
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"langchain-google-genai",
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"langgraph>=0.2.60",
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"langgraph-checkpoint>=2.0.9",
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"langgraph-sdk>=0.1.48",
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@ -33,7 +33,7 @@ import os
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logger = get_logger(__name__)
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def parse_arguments(args=None):
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VALID_PROVIDERS = ['anthropic', 'openai', 'openrouter', 'openai-compatible']
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VALID_PROVIDERS = ['anthropic', 'openai', 'openrouter', 'openai-compatible', 'gemini']
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ANTHROPIC_DEFAULT_MODEL = 'claude-3-5-sonnet-20241022'
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OPENAI_DEFAULT_MODEL = 'gpt-4o'
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@ -46,6 +46,10 @@ def copy_base_to_expert_vars(base_provider: str, expert_provider: str) -> None:
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},
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'openrouter': {
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'OPENROUTER_API_KEY': 'EXPERT_OPENROUTER_API_KEY'
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},
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'gemini': {
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'GEMINI_API_KEY': 'EXPERT_GEMINI_API_KEY',
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'GEMINI_MODEL': 'EXPERT_GEMINI_MODEL'
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}
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}
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@ -2,6 +2,9 @@ import os
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_core.language_models import BaseChatModel
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from langchain_google_genai import ChatGoogleGenerativeAI
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def initialize_llm(provider: str, model_name: str, temperature: float | None = None) -> BaseChatModel:
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"""Initialize a language model client based on the specified provider and model.
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@ -10,7 +13,7 @@ def initialize_llm(provider: str, model_name: str, temperature: float | None = N
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Use validate_environment() to ensure all required variables are set.
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Args:
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provider: The LLM provider to use ('openai', 'anthropic', 'openrouter', 'openai-compatible')
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provider: The LLM provider to use ('openai', 'anthropic', 'openrouter', 'openai-compatible', 'gemini')
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model_name: Name of the model to use
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temperature: Optional temperature setting for controlling randomness (0.0-2.0).
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If not specified, provider-specific defaults are used.
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@ -47,19 +50,25 @@ def initialize_llm(provider: str, model_name: str, temperature: float | None = N
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temperature=temperature if temperature is not None else 0.3,
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model=model_name,
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)
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elif provider == "gemini":
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return ChatGoogleGenerativeAI(
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api_key=os.getenv("GEMINI_API_KEY"),
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model=model_name,
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**({"temperature": temperature} if temperature is not None else {})
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)
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else:
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raise ValueError(f"Unsupported provider: {provider}")
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def initialize_expert_llm(provider: str = "openai", model_name: str = "o1-preview") -> BaseChatModel:
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def initialize_expert_llm(provider: str = "openai", model_name: str = "o1") -> BaseChatModel:
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"""Initialize an expert language model client based on the specified provider and model.
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Note: Environment variables must be validated before calling this function.
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Use validate_environment() to ensure all required variables are set.
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Args:
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provider: The LLM provider to use ('openai', 'anthropic', 'openrouter', 'openai-compatible').
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provider: The LLM provider to use ('openai', 'anthropic', 'openrouter', 'openai-compatible', 'gemini').
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Defaults to 'openai'.
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model_name: Name of the model to use. Defaults to 'o1-preview'.
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model_name: Name of the model to use. Defaults to 'o1'.
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Returns:
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BaseChatModel: Configured expert language model client
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@ -89,5 +98,10 @@ def initialize_expert_llm(provider: str = "openai", model_name: str = "o1-previe
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base_url=os.getenv("EXPERT_OPENAI_API_BASE"),
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model=model_name,
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)
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elif provider == "gemini":
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return ChatGoogleGenerativeAI(
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api_key=os.getenv("EXPERT_GEMINI_API_KEY"),
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model=model_name,
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)
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else:
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raise ValueError(f"Unsupported provider: {provider}")
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raise ValueError(f"Unsupported provider: {provider}")
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@ -213,6 +213,32 @@ class OpenRouterStrategy(ProviderStrategy):
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return ValidationResult(valid=len(missing) == 0, missing_vars=missing)
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class GeminiStrategy(ProviderStrategy):
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"""Gemini provider validation strategy."""
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def validate(self, args: Optional[Any] = None) -> ValidationResult:
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"""Validate Gemini environment variables."""
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missing = []
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# Check if we're validating expert config
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if args and hasattr(args, 'expert_provider') and args.expert_provider == 'gemini':
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key = os.environ.get('EXPERT_GEMINI_API_KEY')
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if not key or key == '':
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# Try to copy from base if not set
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base_key = os.environ.get('GEMINI_API_KEY')
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if base_key:
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os.environ['EXPERT_GEMINI_API_KEY'] = base_key
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key = base_key
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if not key:
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missing.append('EXPERT_GEMINI_API_KEY environment variable is not set')
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else:
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key = os.environ.get('GEMINI_API_KEY')
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if not key:
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missing.append('GEMINI_API_KEY environment variable is not set')
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return ValidationResult(valid=len(missing) == 0, missing_vars=missing)
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class OllamaStrategy(ProviderStrategy):
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"""Ollama provider validation strategy."""
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@ -245,6 +271,7 @@ class ProviderFactory:
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'openai-compatible': OpenAICompatibleStrategy(),
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'anthropic': AnthropicStrategy(),
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'openrouter': OpenRouterStrategy(),
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'gemini': GeminiStrategy(),
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'ollama': OllamaStrategy()
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}
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strategy = strategies.get(provider)
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@ -15,7 +15,7 @@ def get_model():
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try:
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if _model is None:
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provider = _global_memory['config']['expert_provider'] or 'openai'
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model = _global_memory['config']['expert_model'] or 'o1-preview'
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model = _global_memory['config']['expert_model'] or 'o1'
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_model = initialize_expert_llm(provider, model)
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except Exception as e:
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_model = None
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@ -3,6 +3,7 @@ import pytest
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from unittest.mock import patch, Mock
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from langchain_openai.chat_models import ChatOpenAI
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from langchain_anthropic.chat_models import ChatAnthropic
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from langchain_google_genai.chat_models import ChatGoogleGenerativeAI
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from langchain_core.messages import HumanMessage
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from dataclasses import dataclass
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from ra_aid.agents.ciayn_agent import CiaynAgent
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@ -16,7 +17,7 @@ def clean_env(monkeypatch):
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env_vars = [
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'ANTHROPIC_API_KEY', 'OPENAI_API_KEY', 'OPENROUTER_API_KEY',
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'OPENAI_API_BASE', 'EXPERT_ANTHROPIC_API_KEY', 'EXPERT_OPENAI_API_KEY',
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'EXPERT_OPENROUTER_API_KEY', 'EXPERT_OPENAI_API_BASE'
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'EXPERT_OPENROUTER_API_KEY', 'EXPERT_OPENAI_API_BASE', 'GEMINI_API_KEY', 'EXPERT_GEMINI_API_KEY'
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]
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for var in env_vars:
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monkeypatch.delenv(var, raising=False)
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@ -38,7 +39,7 @@ def test_initialize_expert_defaults(clean_env, mock_openai, monkeypatch):
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mock_openai.assert_called_once_with(
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api_key="test-key",
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model="o1-preview"
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model="o1"
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)
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def test_initialize_expert_openai_custom(clean_env, mock_openai, monkeypatch):
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@ -51,6 +52,16 @@ def test_initialize_expert_openai_custom(clean_env, mock_openai, monkeypatch):
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model="gpt-4-preview"
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)
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def test_initialize_expert_gemini(clean_env, mock_gemini, monkeypatch):
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"""Test expert Gemini initialization."""
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monkeypatch.setenv("EXPERT_GEMINI_API_KEY", "test-key")
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llm = initialize_expert_llm("gemini", "gemini-2.0-flash-thinking-exp-1219")
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mock_gemini.assert_called_once_with(
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api_key="test-key",
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model="gemini-2.0-flash-thinking-exp-1219"
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)
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def test_initialize_expert_anthropic(clean_env, mock_anthropic, monkeypatch):
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"""Test expert Anthropic initialization."""
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monkeypatch.setenv("EXPERT_ANTHROPIC_API_KEY", "test-key")
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@ -114,6 +125,16 @@ def test_initialize_openai(clean_env, mock_openai):
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model="gpt-4"
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)
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def test_initialize_gemini(clean_env, mock_gemini):
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"""Test Gemini provider initialization"""
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os.environ["GEMINI_API_KEY"] = "test-key"
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model = initialize_llm("gemini", "gemini-2.0-flash-thinking-exp-1219")
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mock_gemini.assert_called_once_with(
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api_key="test-key",
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model="gemini-2.0-flash-thinking-exp-1219"
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)
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def test_initialize_anthropic(clean_env, mock_anthropic):
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"""Test Anthropic provider initialization"""
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os.environ["ANTHROPIC_API_KEY"] = "test-key"
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@ -159,7 +180,7 @@ def test_temperature_defaults(clean_env, mock_openai, mock_anthropic):
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os.environ["OPENAI_API_KEY"] = "test-key"
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os.environ["ANTHROPIC_API_KEY"] = "test-key"
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os.environ["OPENAI_API_BASE"] = "http://test-url"
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os.environ["GEMINI_API_KEY"] = "test-key"
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# Test openai-compatible default temperature
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initialize_llm("openai-compatible", "test-model")
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mock_openai.assert_called_with(
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@ -181,12 +202,19 @@ def test_temperature_defaults(clean_env, mock_openai, mock_anthropic):
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api_key="test-key",
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model_name="test-model"
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)
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initialize_llm("gemini", "test-model")
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mock_gemini.assert_called_with(
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api_key="test-key",
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model="test-model"
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)
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def test_explicit_temperature(clean_env, mock_openai, mock_anthropic):
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"""Test explicit temperature setting for each provider."""
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os.environ["OPENAI_API_KEY"] = "test-key"
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os.environ["ANTHROPIC_API_KEY"] = "test-key"
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os.environ["OPENROUTER_API_KEY"] = "test-key"
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os.environ["OPENROUTER_API_KEY"] = "test-key",
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os.environ["GEMINI_API_KEY"] = "test-key"
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test_temp = 0.7
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@ -198,6 +226,14 @@ def test_explicit_temperature(clean_env, mock_openai, mock_anthropic):
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temperature=test_temp
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)
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# Test Gemini
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initialize_llm("gemini", "test-model", temperature=test_temp)
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mock_gemini.assert_called_with(
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api_key="test-key",
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model="test-model",
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temperature=test_temp
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)
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# Test Anthropic
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initialize_llm("anthropic", "test-model", temperature=test_temp)
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mock_anthropic.assert_called_with(
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@ -234,7 +270,7 @@ def test_temperature_validation(clean_env, mock_openai):
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def test_provider_name_validation():
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"""Test provider name validation and normalization."""
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# Test all supported providers
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providers = ["openai", "anthropic", "openrouter", "openai-compatible"]
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providers = ["openai", "anthropic", "openrouter", "openai-compatible", "gemini"]
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for provider in providers:
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try:
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with patch(f'ra_aid.llm.ChatOpenAI'), patch('ra_aid.llm.ChatAnthropic'):
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@ -247,7 +283,7 @@ def test_provider_name_validation():
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with pytest.raises(ValueError):
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initialize_llm("OpenAI", "test-model")
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def test_initialize_llm_cross_provider(clean_env, mock_openai, mock_anthropic, monkeypatch):
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def test_initialize_llm_cross_provider(clean_env, mock_openai, mock_anthropic, mock_gemini, monkeypatch):
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"""Test initializing different providers in sequence."""
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# Initialize OpenAI
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monkeypatch.setenv("OPENAI_API_KEY", "openai-key")
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@ -257,6 +293,10 @@ def test_initialize_llm_cross_provider(clean_env, mock_openai, mock_anthropic, m
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monkeypatch.setenv("ANTHROPIC_API_KEY", "anthropic-key")
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llm2 = initialize_llm("anthropic", "claude-3")
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# Initialize Gemini
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monkeypatch.setenv("GEMINI_API_KEY", "gemini-key")
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llm3 = initialize_llm("gemini", "gemini-2.0-flash-thinking-exp-1219")
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# Verify both were initialized correctly
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mock_openai.assert_called_once_with(
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api_key="openai-key",
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@ -266,7 +306,7 @@ def test_initialize_llm_cross_provider(clean_env, mock_openai, mock_anthropic, m
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api_key="anthropic-key",
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model_name="claude-3"
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)
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mock_gemini.assert_called_once_with(
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def test_environment_variable_precedence(clean_env, mock_openai, monkeypatch):
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"""Test environment variable precedence and fallback."""
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from ra_aid.env import validate_environment
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@ -284,6 +324,7 @@ def test_environment_variable_precedence(clean_env, mock_openai, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "base-key")
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monkeypatch.setenv("EXPERT_OPENAI_API_KEY", "expert-key")
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monkeypatch.setenv("TAVILY_API_KEY", "tavily-key")
|
||||
monkeypatch.setenv("GEMINI_API_KEY", "gemini-key")
|
||||
args = Args(provider="openai", expert_provider="openai")
|
||||
expert_enabled, expert_missing, web_enabled, web_missing = validate_environment(args)
|
||||
assert expert_enabled
|
||||
|
|
@ -294,7 +335,7 @@ def test_environment_variable_precedence(clean_env, mock_openai, monkeypatch):
|
|||
llm = initialize_expert_llm()
|
||||
mock_openai.assert_called_with(
|
||||
api_key="expert-key",
|
||||
model="o1-preview"
|
||||
model="o1"
|
||||
)
|
||||
|
||||
# Test empty key validation
|
||||
|
|
@ -302,6 +343,7 @@ def test_environment_variable_precedence(clean_env, mock_openai, monkeypatch):
|
|||
monkeypatch.delenv("OPENAI_API_KEY", raising=False) # Remove fallback
|
||||
monkeypatch.delenv("TAVILY_API_KEY", raising=False) # Remove web research
|
||||
monkeypatch.setenv("ANTHROPIC_API_KEY", "anthropic-key") # Add for provider validation
|
||||
monkeypatch.setenv("GEMINI_API_KEY", "gemini-key") # Add for provider validation
|
||||
monkeypatch.setenv("ANTHROPIC_MODEL", "claude-3-haiku-20240307") # Add model for provider validation
|
||||
args = Args(provider="anthropic", expert_provider="openai") # Change base provider to avoid validation error
|
||||
expert_enabled, expert_missing, web_enabled, web_missing = validate_environment(args)
|
||||
|
|
@ -319,3 +361,10 @@ def mock_anthropic():
|
|||
with patch('ra_aid.llm.ChatAnthropic') as mock:
|
||||
mock.return_value = Mock(spec=ChatAnthropic)
|
||||
yield mock
|
||||
|
||||
@pytest.fixture
|
||||
def mock_gemini():
|
||||
"""Mock ChatGoogleGenerativeAI class for testing Gemini provider initialization."""
|
||||
with patch('ra_aid.llm.ChatGoogleGenerativeAI') as mock:
|
||||
mock.return_value = Mock(spec=ChatGoogleGenerativeAI)
|
||||
yield mock
|
||||
|
|
|
|||
|
|
@ -12,7 +12,8 @@ from ra_aid.provider_strategy import (
|
|||
AnthropicStrategy,
|
||||
OpenAIStrategy,
|
||||
OpenAICompatibleStrategy,
|
||||
OpenRouterStrategy
|
||||
OpenRouterStrategy,
|
||||
GeminiStrategy
|
||||
)
|
||||
|
||||
@dataclass
|
||||
|
|
@ -35,7 +36,10 @@ def clean_env():
|
|||
"EXPERT_OPENAI_API_KEY",
|
||||
"EXPERT_OPENAI_API_BASE",
|
||||
"TAVILY_API_KEY",
|
||||
"ANTHROPIC_MODEL"
|
||||
"ANTHROPIC_MODEL",
|
||||
"GEMINI_API_KEY",
|
||||
"EXPERT_GEMINI_API_KEY",
|
||||
"GEMINI_MODEL"
|
||||
]
|
||||
|
||||
# Store original values
|
||||
|
|
@ -186,6 +190,30 @@ def test_incomplete_openai_compatible_config(clean_env):
|
|||
assert not result.valid
|
||||
assert "OPENAI_API_KEY environment variable is not set" in result.missing_vars
|
||||
|
||||
|
||||
def test_incomplete_gemini_config(clean_env):
|
||||
"""Test Gemini provider with incomplete configuration."""
|
||||
strategy = GeminiStrategy()
|
||||
|
||||
# No configuration
|
||||
result = strategy.validate()
|
||||
assert not result.valid
|
||||
assert "GEMINI_API_KEY environment variable is not set" in result.missing_vars
|
||||
assert "GEMINI_MODEL environment variable is not set" in result.missing_vars
|
||||
|
||||
# Only API key
|
||||
os.environ["GEMINI_API_KEY"] = "test-key"
|
||||
result = strategy.validate()
|
||||
assert not result.valid
|
||||
assert "GEMINI_MODEL environment variable is not set" in result.missing_vars
|
||||
|
||||
# Only model
|
||||
os.environ.pop("GEMINI_API_KEY")
|
||||
os.environ["GEMINI_MODEL"] = "gemini-2.0-flash-exp"
|
||||
result = strategy.validate()
|
||||
assert not result.valid
|
||||
assert "GEMINI_API_KEY environment variable is not set" in result.missing_vars
|
||||
|
||||
def test_incomplete_expert_config(clean_env):
|
||||
"""Test expert provider with incomplete configuration."""
|
||||
# Set main provider but not expert
|
||||
|
|
@ -204,6 +232,7 @@ def test_incomplete_expert_config(clean_env):
|
|||
assert len(expert_missing) == 1
|
||||
assert "EXPERT_OPENAI_API_BASE" in expert_missing[0]
|
||||
|
||||
|
||||
def test_empty_environment_variables(clean_env):
|
||||
"""Test handling of empty environment variables."""
|
||||
# Empty API key
|
||||
|
|
@ -257,3 +286,4 @@ def test_multiple_expert_providers(clean_env):
|
|||
expert_enabled, expert_missing, _, _ = validate_environment(args)
|
||||
assert not expert_enabled
|
||||
assert expert_missing
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue