`gmuse.llm`#

LLM client for interacting with various LLM providers.

This module provides a unified interface for calling LLM APIs using LiteLLM, which supports 100+ providers including OpenAI, Anthropic, Cohere, and more.

Public API: - detect_provider: Auto-detect LLM provider from environment - resolve_model: Resolve model name with auto-detection - is_llm_available: Check if LLM is configured - LLMClient: Client for generating text

Note: All providers are supported out of the box via LiteLLM.

Module Contents#

Classes#

LLMClient

Client for generating text using LLM providers.

Functions#

`_suppress_litellm_output`	Context manager to suppress litellm’s noisy stdout/stderr output.
`detect_provider`	Detect LLM provider from environment variables or model.
`resolve_provider_from_key_env_vars`	Resolve provider by checking for known API key environment variables.
`resolve_model`	Resolve model name, using provider auto-detection if needed.
`_convert_to_llm_error`	Convert various exceptions to appropriate LLMError messages.

Data#

`logger`
`_DEFAULT_MODELS`

API#

gmuse.llm.logger = 'get_logger(...)'#

gmuse.llm._DEFAULT_MODELS: Final[dict[str, str]] = None#

gmuse.llm._suppress_litellm_output() → Generator[None, None, None]#

Context manager to suppress litellm’s noisy stdout/stderr output.

LiteLLM prints debug info like “Provider List: …” that clutters output. This context manager captures and discards that output unless debug mode is enabled.

Yields: None

gmuse.llm.detect_provider(*, model: str | None = None, credential_lookup_timeout: float | None = None) → Optional[str]#

Detect LLM provider from environment variables or model.

Checks for common API key environment variables in priority order:

OPENAI_API_KEY -> “openai”
ANTHROPIC_API_KEY -> “anthropic”
COHERE_API_KEY -> “cohere”
AZURE_API_KEY -> “azure”
GEMINI_API_KEY or GOOGLE_API_KEY -> “gemini”
GMUSE_MODEL containing “gemini” -> “gemini”

Returns: Provider name if API key found, None otherwise

Example: >>> os.environ[“OPENAI_API_KEY”] = “sk-…” >>> detect_provider() ‘openai’

gmuse.llm.resolve_provider_from_key_env_vars() → Optional[str]#

Resolve provider by checking for known API key environment variables.

Returns: Provider name if found, None otherwise

gmuse.llm.resolve_model(provider: str, model: Optional[str] = None) → str#

Resolve model name, using provider auto-detection if needed.

Resolution priority:

Explicit model parameter
GMUSE_MODEL environment variable
Auto-detect from provider API keys

Args: model: Explicit model name (e.g., “gpt-4”, “claude-3-opus”) provider: Explicit provider override

Returns: Resolved model name

Raises: LLMError: If no model can be resolved

Example: >>> resolve_model(“gpt-4”) ‘gpt-4’ >>> os.environ[“OPENAI_API_KEY”] = “sk-…” >>> resolve_model() # Auto-detects ‘gpt-4o-mini’

class gmuse.llm.LLMClient(model: Optional[str] = None, timeout: int = 30, credential_lookup_timeout: float | None = None)#

Client for generating text using LLM providers.

This class wraps LiteLLM to provide a simple interface for generating commit messages using various LLM providers.

Attributes: model: LLM model identifier timeout: Request timeout in seconds

Example: >>> client = LLMClient(model=”gpt-4”, timeout=30) >>> response = client.generate( … system_prompt=”You are a commit message generator.”, … user_prompt=”Generate a commit message for: Added tests” … ) >>> print(response) ‘Add unit tests for authentication module’

Initialization

Initialize LLM client.

Args: model: LLM model identifier (auto-detects if None) timeout: Request timeout in seconds (default: 30)

Raises: LLMError: If no provider is configured

generate(system_prompt: str, user_prompt: str, temperature: float = 0.7, max_tokens: int = 500) → str#

Generate text using the LLM.

Args: system_prompt: System message defining role and constraints user_prompt: User message with context and task temperature: Sampling temperature (0.0-1.0, default: 0.7) max_tokens: Maximum tokens in response (default: 500)

Returns: Generated text from LLM

Raises: LLMError: If API call fails

Example: >>> client = LLMClient(model=”gpt-4”) >>> message = client.generate( … system_prompt=”You are a helpful assistant.”, … user_prompt=”Say hello” … ) >>> print(message) ‘Hello! How can I help you today?’

gmuse.llm._convert_to_llm_error(error: Exception, timeout: int) → gmuse.exceptions.LLMError#

Convert various exceptions to appropriate LLMError messages.

Args: error: The original exception timeout: Request timeout value for error messages

Returns: LLMError with user-friendly message

gmuse.llm#