sherpa_ai.config package

sherpa_ai.config package#

Submodules#

sherpa_ai.config.task_config module#

class sherpa_ai.config.task_config.AgentConfig(*, verbose: bool = True, gsite: list[str] = [], do_reflect: bool = False, use_task_agent: bool = False)[source]#

Bases: BaseModel

do_reflect: bool#

classmethod from_config(configs: List[str]) → AgentConfig[source]#

classmethod from_input(input_str: str) → Tuple[str, AgentConfig][source]#: parse input string into AgentConfig. The configurations are at the end of the string

gsite: list[str]#

property invalid_domains: List[str]#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod parse_gsite(value: str | None) → list[str][source]#

property search_domains: List[str]#

use_task_agent: bool#

verbose: bool#

sherpa_ai.config.task_config.validate_url(url: str) → bool[source]#

Module contents#

App configuration settings.

Usage:

First define variables in runtime environment or in your .env file. See .env-sample file for examples and a useful starting point. Then, in your code, use the values like this:

import Config as cfg secret = cfg.SLACK_SIGNING_SECRET another_variable = cfg.ANOTHER_ENVIRONMENT_VARIABLE

To add, remove, or change variables, … 1. Update this file to create the variables 2. Update env-sample to match 3. Update your own .env file and test the changes 4. Update corresponding secrets in Github and deployment environments

class sherpa_ai.config.AgentConfig(*, verbose: bool = True, gsite: list[str] = [], do_reflect: bool = False, use_task_agent: bool = False)[source]#