genaiv2.py

Source: sunholo/genai/genaiv2.py

Classes

GoogleAI

A wrapper class for Google's v2 Generative AI APIs. See https://ai.google.dev/gemini-api/docs/models/gemini-v2

• init(self, config: sunholo.genai.genaiv2.GoogleAIConfig)
  • Initialize the GoogleAI client.

Args: config (GoogleAIConfig): Configuration for client initialization

• _process_responses(self, session) -> List[str]

  • Internal method to process session responses.

• _record_audio(self, duration: float = 5.0, sample_rate: int = 16000) -> bytes

  • Internal method to record audio.

• _record_video(self, duration: float = 5.0) -> List[bytes]

  • Internal method to record video frames.

• count_tokens(self, text: str, model: Optional[str] = None) -> int

  • Count the number of tokens in the text.

Args: text (str): Input text model (Optional[str]): Model to use for tokenization

Returns: int: Number of tokens

• generate_text(self, prompt: str, model: Optional[str] = None, temperature: float = 0.7, max_output_tokens: int = 1024, top_p: float = 0.95, top_k: int = 20, stop_sequences: Optional[List[str]] = None, system_prompt: Optional[str] = None, tools: Optional[List[ForwardRef('types.Tool')]] = None) -> str
  • Generate text using the specified model.

Args: prompt (str): The input prompt model (Optional[str]): Model name to use temperature (float): Controls randomness (0.0-1.0) max_output_tokens (int): Maximum number of tokens to generate top_p (float): Nucleus sampling parameter top_k (int): Top-k sampling parameter stop_sequences (Optional[List[str]]): Sequences that stop generation system_prompt (Optional[str]): System-level instruction tools: list of python functions or Tool objects

Returns: str: Generated text response

• generate_text_async(self, prompt: str, model: Optional[str] = None, **kwargs) -> str

  • Async version of generate_text.

• get_embedding(self, text: Union[str, List[str]], model: str = 'text-embedding-004', output_dim: Optional[int] = None) -> Union[List[float], List[List[float]]]

  • Get text embeddings.

Args: text (Union[str, List[str]]): Text to embed model (str): Embedding model to use output_dim (Optional[int]): Desired embedding dimension

Returns: Union[List[float], List[List[float]]]: Embeddings

• google_search_tool(self) -> 'types.Tool'

  • No docstring available.

• gs_uri(self, uri, mime_type=None)

  • No docstring available.

• live_async(self, prompt: Union[str, List[Union[str, bytes]], NoneType] = None, input_type: str = 'text', duration: Optional[float] = None, model: Optional[str] = None, **kwargs) -> str

  • Live Multimodal API with support for text, audio, and video inputs.

Args: input_type: Type of input ("text", "audio", or "video") prompt: Text prompt or list of text/binary chunks duration: Recording duration for audio/video in seconds model: Optional model name **kwargs: Additional configuration parameters

Returns: str: Generated response text

• local_file(self, filename, mime_type=None)

  • No docstring available.

• stream_text(self, prompt: str, model: Optional[str] = None, **kwargs) -> 'Generator[str, None, None]'

  • Stream text generation responses.

Args: prompt (str): The input prompt model (Optional[str]): Model name to use **kwargs: Additional configuration parameters

Yields: str: Chunks of generated text

• structured_output(self, prompt: str, schema: Union[pydantic.main.BaseModel, Dict, type, TypedDict], model: Optional[str] = None, is_list: bool = False) -> Dict
  • Generate structured output according to a schema.

Args: prompt (str): Input prompt schema (Union[BaseModel, Dict, type]): Schema definition (Pydantic model, TypedDict, or raw schema) model (Optional[str]): Model to use is_list (bool): Whether to wrap the schema in a list

Returns: Dict: Structured response matching schema

GoogleAIConfig

Configuration class for GoogleAI client initialization. See https://ai.google.dev/gemini-api/docs/models/gemini-v2

• copy(self) -> 'Self'

  • Returns a shallow copy of the model.

• deepcopy(self, memo: 'dict[int, Any] | None' = None) -> 'Self'

  • Returns a deep copy of the model.

• delattr(self, item: 'str') -> 'Any'

  • Implement delattr(self, name).

• eq(self, other: 'Any') -> 'bool'

  • Return self==value.

• getattr(self, item: 'str') -> 'Any'

  • No docstring available.

• getstate(self) -> 'dict[Any, Any]'

  • Helper for pickle.

• init(self, /, **data: 'Any') -> 'None'

  • Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

• iter(self) -> 'TupleGenerator'

  • So dict(model) works.

• pretty(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]'

  • Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

• replace(self, **changes: 'Any') -> 'Self'

  • No docstring available.

• repr(self) -> 'str'

  • Return repr(self).

• repr_args(self) -> '_repr.ReprArgs'

  • No docstring available.

• repr_name(self) -> 'str'

  • Name of the instance's class, used in repr.

• repr_recursion(self, object: 'Any') -> 'str'

  • Returns the string representation of a recursive object.

• repr_str(self, join_str: 'str') -> 'str'

  • No docstring available.

• rich_repr(self) -> 'RichReprResult'

  • Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

• setattr(self, name: 'str', value: 'Any') -> 'None'

  • Implement setattr(self, name, value).

• setstate(self, state: 'dict[Any, Any]') -> 'None'

  • No docstring available.

• str(self) -> 'str'

  • Return str(self).

• _calculate_keys(self, *args: 'Any', **kwargs: 'Any') -> 'Any'

  • No docstring available.

• _check_frozen(self, name: 'str', value: 'Any') -> 'None'

  • No docstring available.

• _copy_and_set_values(self, *args: 'Any', **kwargs: 'Any') -> 'Any'

  • No docstring available.

• _iter(self, *args: 'Any', **kwargs: 'Any') -> 'Any'

  • No docstring available.

• copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'

  • Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.

Returns: A copy of the model with included, excluded and updated fields as specified.

• dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'

  • No docstring available.

• json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'

  • No docstring available.

• model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'

  • Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Args: update: Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. deep: Set to True to make a deep copy of the model.

Returns: New model instance.

• model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
  • Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: The mode in which to_python should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError]. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns: A dictionary representation of the model.

• model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: 'bool' = False) -> 'str'
  • Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic's to_json method.

Args: indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError]. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns: A JSON string representation of the model.

• model_post_init(self, _BaseModel__context: 'Any') -> 'None'
  • Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.