Source code for camel.configs.gemini_config
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# See the License for the specific language governing permissions and
# limitations under the License.
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
from collections.abc import Iterable
from typing import Any, Optional
from pydantic import model_validator
from camel.configs.base_config import BaseConfig
[docs]
class GeminiConfig(BaseConfig):
r"""A simple dataclass used to configure the generation parameters of
`GenerativeModel.generate_content`.
Args:
candidate_count (int, optional): Number of responses to return.
stop_sequences (Iterable[str], optional): The set of character
sequences (up to 5) that will stop output generation. If specified
the API will stop at the first appearance of a stop sequence.
The stop sequence will not be included as part of the response.
max_output_tokens (int, optional): The maximum number of tokens to
include in a candidate. If unset, this will default to
output_token_limit specified in the model's specification.
temperature (float, optional): Controls the randomness of the output.
Note: The default value varies by model, see the
`Model.temperature` attribute of the `Model` returned
the `genai.get_model` function. Values can range from [0.0,1.0],
inclusive. A value closer to 1.0 will produce responses that are
more varied and creative, while a value closer to 0.0 will
typically result in more straightforward responses from the model.
top_p (int, optional): The maximum cumulative probability of tokens to
consider when sampling. The model uses combined Top-k and nucleus
sampling. Tokens are sorted based on their assigned probabilities
so that only the most likely tokens are considered. Top-k sampling
directly limits the maximum number of tokens to consider, while
Nucleus sampling limits number of tokens
based on the cumulative probability. Note: The default value varies
by model, see the `Model.top_p` attribute of the `Model` returned
the `genai.get_model` function.
top_k (int, optional): The maximum number of tokens to consider when
sampling. The model uses combined Top-k and nucleus sampling.Top-k
sampling considers the set of `top_k` most probable tokens.
Defaults to 40. Note: The default value varies by model, see the
`Model.top_k` attribute of the `Model` returned the
`genai.get_model` function.
response_mime_type (str, optional): Output response mimetype of the
generated candidate text. Supported mimetype:
`text/plain`: (default) Text output.
`application/json`: JSON response in the candidates.
response_schema (Schema, optional): Specifies the format of the
JSON requested if response_mime_type is `application/json`.
safety_settings (SafetySettingOptions, optional):
Overrides for the model's safety settings.
tools (FunctionLibraryType, optional):
`protos.Tools` more info coming soon.
tool_config (ToolConfigType, optional):
more info coming soon.
request_options (RequestOptionsType, optional):
Options for the request.
"""
candidate_count: Optional[int] = None
stop_sequences: Optional[Iterable[str]] = None
max_output_tokens: Optional[int] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
top_k: Optional[int] = None
response_mime_type: Optional[str] = None
response_schema: Optional[Any] = None
safety_settings: Optional[Any] = None
tool_config: Optional[Any] = None
request_options: Optional[Any] = None
[docs]
@model_validator(mode="before")
@classmethod
def model_type_checking(cls, data: Any):
if isinstance(data, dict):
response_schema = data.get("response_schema")
safety_settings = data.get("safety_settings")
tools = data.get("tools")
tool_config = data.get("tool_config")
request_options = data.get("request_options")
if response_schema:
from google.generativeai.protos import Schema
from google.generativeai.types.content_types import (
FunctionLibraryType,
ToolConfigType,
)
from google.generativeai.types.helper_types import (
RequestOptionsType,
)
from google.generativeai.types.safety_types import (
SafetySettingOptions,
)
else:
return data
if response_schema and not isinstance(response_schema, Schema):
raise ValueError(
"The response_schema should be "
"an instance of `google.generativeai.protos.Schema`."
)
if safety_settings and not isinstance(
safety_settings, SafetySettingOptions
):
raise ValueError(
"The response_schema should be an instance of "
"`google.generativeai.types.safety_types.SafetySettingOptions`."
)
if tools is not None:
for tool in tools:
if not isinstance(tool, FunctionLibraryType):
raise ValueError(
"The tool should be an instance of "
"`google.generativeai.types.content_types.FunctionLibraryType`."
)
if tool_config and not isinstance(tool_config, ToolConfigType):
raise ValueError(
"The response_schema should be an instance of "
"`google.generativeai.types.content_types.ToolConfigType`."
)
if request_options and not isinstance(
request_options, RequestOptionsType
):
raise ValueError(
"The response_schema should be an instance of "
"`google.generativeai.types.helper_types.RequestOptionsType`."
)
return data
Gemini_API_PARAMS = {param for param in GeminiConfig().model_fields.keys()}
