Providers API

The built-in providers implement the interface expected by the top-level helper functions.

`llm_feature_gen.providers`

`providers`

Provider implementations for remote and local LLM backends.

Modules:

local_provider –
openai_provider –

Classes:

LocalProvider –

Thin adapter around OpenAI-compatible LOCAL endpoints.
OpenAIProvider –

Thin adapter around OpenAI (Azure or personal) for feature discovery/generation.

`LocalProvider(base_url: Optional[str] = None, api_key: Optional[str] = None, default_text_model: Optional[str] = None, default_vision_model: Optional[str] = None, max_retries: int = 5, temperature: float = 0.0, max_tokens: int = 2048)`

Thin adapter around OpenAI-compatible LOCAL endpoints. Supports: - Ollama - vLLM - LM Studio - Any OpenAI-compatible local server

Reads configuration from .env: LOCAL_OPENAI_BASE_URL LOCAL_OPENAI_API_KEY LOCAL_MODEL_TEXT LOCAL_MODEL_VISION LOCAL_WHISPER_MODEL_SIZE LOCAL_WHISPER_DEVICE
Two entry points: image_features(image_base64_list, prompt=None, deployment_name=None, feature_gen=False, as_set=False) text_features(text_list, prompt=None, deployment_name=None, feature_gen=False) transcribe_audio(audio_path)
Returns a list of dicts (one per input item) in the usual case. If as_set=True, returns a list with a single dict corresponding to the joint call.

Provider is configured via environment variables.

Methods:

image_features –

For each base64 image, ask the LLM to extract features.
text_features –

For each text, ask the LLM to extract features.
transcribe_audio –

Transcribes audio file using local Faster-Whisper.

`image_features(image_base64_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False, as_set: bool = False, extra_context: Optional[str] = None) -> List[Dict[str, Any]]`

For each base64 image, ask the LLM to extract features.

If as_set=False (default): behaves as before — one request per image.
If as_set=True: sends ALL images in ONE request.

`text_features(text_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False) -> List[Dict[str, Any]]`

For each text, ask the LLM to extract features. If feature_gen=True, a JSON-only system prompt is enforced.

`transcribe_audio(audio_path: str) -> str`

Transcribes audio file using local Faster-Whisper.

`OpenAIProvider(api_key: Optional[str] = None, api_version: Optional[str] = None, endpoint: Optional[str] = None, default_deployment_name: Optional[str] = None, max_retries: int = 5, temperature: float = 0.0, max_tokens: int = 2048, default_audio_model: Optional[str] = None)`

Thin adapter around OpenAI (Azure or personal) for feature discovery/generation. Supports: - Azure OpenAI - Personal / private OpenAI API

Reads credentials from .env: AZURE_OPENAI_API_KEY AZURE_OPENAI_API_VERSION AZURE_OPENAI_ENDPOINT AZURE_OPENAI_GPT41_DEPLOYMENT_NAME (default deployment/model name)
Two entry points: image_features(image_base64_list, prompt=None, deployment_name=None, feature_gen=False, as_set=False) text_features(text_list, prompt=None, deployment_name=None, feature_gen=False)
Returns a list of dicts (one per input item) in the usual case. If as_set=True, returns a list with a single dict corresponding to the joint call.

Provider is auto-detected from environment variables.

Methods:

image_features –

For each base64 image, ask the LLM to extract features.
text_features –

For each text, ask the LLM to extract features.
transcribe_audio –

Transcribes audio file using OpenAI Whisper (Cloud).

`image_features(image_base64_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False, as_set: bool = False, extra_context: Optional[str] = None) -> List[Dict[str, Any]]`

For each base64 image, ask the LLM to extract features.

If as_set=False (default): behaves as before — one request per image, returns a list of dicts.
If as_set=True: sends ALL images in ONE request (for comparative / discovery prompts) and returns a list with a single dict.

feature_gen=True can be used to enforce a strict JSON schema prompt on the system side.

`text_features(text_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False) -> List[Dict[str, Any]]`

For each text, ask the LLM to extract features. If feature_gen=True, a JSON-only system prompt is enforced and your custom prompt is appended (preserving your colleagues’ behavior).

`transcribe_audio(audio_path: str) -> str`

Transcribes audio file using OpenAI Whisper (Cloud).

`llm_feature_gen.providers.openai_provider`

`openai_provider`

Classes:

OpenAIProvider –

Thin adapter around OpenAI (Azure or personal) for feature discovery/generation.

`OpenAIProvider(api_key: Optional[str] = None, api_version: Optional[str] = None, endpoint: Optional[str] = None, default_deployment_name: Optional[str] = None, max_retries: int = 5, temperature: float = 0.0, max_tokens: int = 2048, default_audio_model: Optional[str] = None)`

Thin adapter around OpenAI (Azure or personal) for feature discovery/generation. Supports: - Azure OpenAI - Personal / private OpenAI API

Reads credentials from .env: AZURE_OPENAI_API_KEY AZURE_OPENAI_API_VERSION AZURE_OPENAI_ENDPOINT AZURE_OPENAI_GPT41_DEPLOYMENT_NAME (default deployment/model name)
Two entry points: image_features(image_base64_list, prompt=None, deployment_name=None, feature_gen=False, as_set=False) text_features(text_list, prompt=None, deployment_name=None, feature_gen=False)
Returns a list of dicts (one per input item) in the usual case. If as_set=True, returns a list with a single dict corresponding to the joint call.

Provider is auto-detected from environment variables.

Methods:

image_features –

For each base64 image, ask the LLM to extract features.
text_features –

For each text, ask the LLM to extract features.
transcribe_audio –

Transcribes audio file using OpenAI Whisper (Cloud).

`image_features(image_base64_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False, as_set: bool = False, extra_context: Optional[str] = None) -> List[Dict[str, Any]]`

For each base64 image, ask the LLM to extract features.

If as_set=False (default): behaves as before — one request per image, returns a list of dicts.
If as_set=True: sends ALL images in ONE request (for comparative / discovery prompts) and returns a list with a single dict.

feature_gen=True can be used to enforce a strict JSON schema prompt on the system side.

`text_features(text_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False) -> List[Dict[str, Any]]`

For each text, ask the LLM to extract features. If feature_gen=True, a JSON-only system prompt is enforced and your custom prompt is appended (preserving your colleagues’ behavior).

`transcribe_audio(audio_path: str) -> str`

Transcribes audio file using OpenAI Whisper (Cloud).

`llm_feature_gen.providers.local_provider`

`local_provider`

Classes:

LocalProvider –

Thin adapter around OpenAI-compatible LOCAL endpoints.

`LocalProvider(base_url: Optional[str] = None, api_key: Optional[str] = None, default_text_model: Optional[str] = None, default_vision_model: Optional[str] = None, max_retries: int = 5, temperature: float = 0.0, max_tokens: int = 2048)`

Thin adapter around OpenAI-compatible LOCAL endpoints. Supports: - Ollama - vLLM - LM Studio - Any OpenAI-compatible local server

Reads configuration from .env: LOCAL_OPENAI_BASE_URL LOCAL_OPENAI_API_KEY LOCAL_MODEL_TEXT LOCAL_MODEL_VISION LOCAL_WHISPER_MODEL_SIZE LOCAL_WHISPER_DEVICE
Two entry points: image_features(image_base64_list, prompt=None, deployment_name=None, feature_gen=False, as_set=False) text_features(text_list, prompt=None, deployment_name=None, feature_gen=False) transcribe_audio(audio_path)
Returns a list of dicts (one per input item) in the usual case. If as_set=True, returns a list with a single dict corresponding to the joint call.

Provider is configured via environment variables.

Methods:

image_features –

For each base64 image, ask the LLM to extract features.
text_features –

For each text, ask the LLM to extract features.
transcribe_audio –

Transcribes audio file using local Faster-Whisper.

`image_features(image_base64_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False, as_set: bool = False, extra_context: Optional[str] = None) -> List[Dict[str, Any]]`

For each base64 image, ask the LLM to extract features.

If as_set=False (default): behaves as before — one request per image.
If as_set=True: sends ALL images in ONE request.

`text_features(text_list: List[str], prompt: Optional[str] = None, deployment_name: Optional[str] = None, feature_gen: bool = False) -> List[Dict[str, Any]]`

For each text, ask the LLM to extract features. If feature_gen=True, a JSON-only system prompt is enforced.

`transcribe_audio(audio_path: str) -> str`

Transcribes audio file using local Faster-Whisper.