syndisco package

Submodules

syndisco.actors module

Module defining LLM users in discussions and their characteristics.

class syndisco.actors.Actor(model: BaseModel, persona: Persona, context: str, instructions: str, actor_type: ActorType, stop_words: list[str] = [])

Bases: object

An abstract class representing an actor which responds according to an underlying LLM instance.

describe() → str

Get a description of the actor’s internals.

Returns:

A brief description of the actor

Return type:

dict

final get_name() → str

Get the actor’s assigned name within the conversation.

Returns:

The name of the actor.

Return type:

str

final speak(history: list[str]) → str

Prompt the actor to speak, given a history of previous messages in the conversation.

Parameters:

history (list[str]) – A list of previous messages.

Returns:

The actor’s new message

Return type:

str

class syndisco.actors.ActorType(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: str, Enum

The purpose of the LLMActor, used to determine proper prompt structure

ANNOTATOR = '2'

USER = '1'

class syndisco.actors.Persona(username: str = '', age: int = -1, sex: str = '', sexual_orientation: str = '', demographic_group: str = '', current_employment: str = '', education_level: str = '', special_instructions: str = '', personality_characteristics: list[str] = <factory>)

Bases: object

A dataclass holding information about the synthetic persona of a LLM actor. Includes name, Sociodemographic Background, personality and special instructions.

age: int = -1

current_employment: str = ''

demographic_group: str = ''

education_level: str = ''

static from_json_file(file_path: Path) → list

Generate a list of personas from a properly formatted persona JSON file.

Parameters:

file_path (Path) – the path to the JSON file containing the personas

Returns:

a list of LlmPersona objects for each of the file entries

Return type:

list[LlmPersona]

personality_characteristics: list[str]

sex: str = ''

sexual_orientation: str = ''

special_instructions: str = ''

to_dict()

to_json_file(output_path: str) → None

Serialize the data to a .json file.

Parameters:

output_path (str) – The path of the new file

username: str = ''

syndisco.experiments module

Module automating and managing batches of discussion/annotation tasks defined in the syndisco.jobs module.

class syndisco.experiments.AnnotationExperiment(annotators: list[Actor], history_ctx_len: int = 3, include_mod_comments: bool = True)

Bases: object

An experiment that uses LLM annotators to label synthetic discussion logs.

begin(discussions_dir: Path, output_dir: Path, verbose: bool = True) → None

Start the annotation process over all discussion logs in a directory.

Parameters:

• discussions_dir (Path) – Directory containing discussion logs.

• output_dir (Path) – Directory to write annotation outputs.

• verbose (bool, defaults to True) – Whether to display annotation progress.

class syndisco.experiments.DiscussionExperiment(users: list[Actor], seed_opinions: list[list[str]] | None = None, moderator: Actor | None = None, next_turn_manager: TurnManager | None = None, history_ctx_len: int = 3, num_turns: int = 10, num_active_users: int = 2, num_discussions: int = 5)

Bases: object

An experiment that creates, manages, and executes multiple synthetic discussions using LLM-based agents.

begin(discussions_output_dir: Path = PosixPath('output'), verbose: bool = True) → None

Generate and run all configured discussions.

Parameters:

• discussions_output_dir (Path) – Directory to write output JSON files.

• verbose (bool) – Whether to print intermediate progress and outputs.

syndisco.jobs module

Module handling the execution of LLM discussion and annotation tasks.

class syndisco.jobs.Annotation(annotator: Actor, conv_logs_path: str | Path, include_moderator_comments: bool, history_ctx_len: int = 2)

Bases: object

An annotation job modelled as a discussion between the system writing the logs of a finished discussion, and the LLM Annotator.

begin(verbose=True) → None

Begin the conversation-modelled annotation job.

Parameters:

verbose (bool, optional) – whether to print the results of the annotation to the console, defaults to True

to_dict(timestamp_format: str = '%y-%m-%d-%H-%M') → dict[str, Any]

Get a dictionary view of the data and metadata contained in the annotation.

Parameters:

timestamp_format (str, optional) – the format for the conversation’s creation time, defaults to “%y-%m-%d-%H-%M”

Returns:

a dict representing the conversation

Return type:

dict[str, Any]

to_json_file(output_path: str | Path) → None

Export the data and metadata of the conversation as a json file.

Parameters:

output_path (str) – the path for the exported file

class syndisco.jobs.Discussion(next_turn_manager: TurnManager, users: list[Actor], moderator: Actor | None = None, history_context_len: int = 5, conv_len: int = 5, seed_opinions: list[str] | None = None, seed_opinion_usernames: list[str] | None = None)

Bases: object

A job conducting a discussion between different actors (actors.Actor).

begin(verbose: bool = True) → None

to_dict(timestamp_format: str = '%y-%m-%d-%H-%M') → dict[str, Any]

Get a dictionary view of the data and metadata contained in the discussion.

Parameters:

timestamp_format (str, optional) – the format for the conversation’s creation time, defaults to “%y-%m-%d-%H-%M”

Returns:

a dict representing the conversation

Return type:

dict[str, Any]

to_json_file(output_path: str | Path) → None

Export the data and metadata of the conversation as a json file.

Parameters:

output_path (str) – the path for the exported file

syndisco.logging_util module

Module handling logging for LLM discussion and annotation tasks.

syndisco.logging_util.logging_setup(print_to_terminal: bool, write_to_file: bool, logs_dir: str | Path | None = None, level: str = 'debug', use_colors: bool = True, log_warnings: bool = True) → None

Create the logger configuration.

Parameters:

• print_to_terminal (bool) – whether to print logs to the screen

• write_to_file (bool) – whether to write logs to a file. Needs logs_dir to be specified.

• logs_dir (Optional[str | Path], optional) – the directory where the logs will be placed, defaults to None

• level – the logging level, defaults to logging.DEBUG

• use_colors (bool, defaults to True) – whether to color the output.

• log_warnings (bool, defaults to True) – whether to log library warnings

syndisco.logging_util.timing(f: Callable) → Any

Decorator which logs the execution time of a function.

Parameters:

f (Function) – the function to be timed

Returns:

the result of the function

Return type:

_type_

syndisco.model module

Module containing wrappers for local LLMs loaded with various Python libraries.

class syndisco.model.BaseModel(name: str, max_out_tokens: int, stop_list: list[str] | None = None)

Bases: ABC

Interface for all local LLM wrappers

final get_name() → str

Get the model’s assigned pseudoname.

Returns:

The name of the model.

Return type:

str

final prompt(system_prompt: str, user_prompt: str) → str

Generate the model’s response based on a prompt.

Parameters:

• system_prompt (str) – The system prompt.

• user_prompt (str) – The user prompt.

• stop_words (list[str]) – Strings to be removed after generation.

Returns:

the model’s response

Return type:

str

class syndisco.model.TransformersModel(model_path: str | Path, name: str, max_out_tokens: int, remove_string_list: list[str] | None = None)

Bases: BaseModel

HuggingFace Transformers model wrapper.

syndisco.postprocessing module

Module responsible for exporting discussions and their annotations in CSV format.

syndisco.postprocessing.import_annotations(annot_dir: str | Path) → DataFrame

Import annotation data from JSON files in a directory and process it into a DataFrame.

This function reads JSON files containing annotation data, processes the data to standardize columns, and includes structured user traits.

Parameters:

annot_dir (str | Path) – Directory containing JSON files with annotation data.

Returns:

A DataFrame containing processed annotation data.

Return type:

pd.DataFrame

syndisco.postprocessing.import_discussions(conv_dir: Path) → DataFrame

Import discussion output (logs) from JSON files in a directory and process

it into a DataFrame.

This function reads JSON files containing conversation data, processes the

data to

standardize columns, and adds derived attributes such as user traits and

prompts.

Parameters:

conv_dir (str | Path) – Directory containing JSON files with conversation data.

Returns:

A DataFrame containing processed conversation data.

Return type:

pd.DataFrame

syndisco.turn_manager module

Module handling the turn order of LLM participants in discussions.

class syndisco.turn_manager.RandomWeighted(p_respond: float = -1, names: Iterable[str] | None = None)

Bases: TurnManager

Enable a participant to reply with a set probability, else randomly select another participant.

DEFAULT_RESPOND_PROBABILITY = 0.5

class syndisco.turn_manager.RoundRobin(names: Iterable[str] | None = None)

Bases: TurnManager

A simple turn manager which gives priority to the next user in the queue.

class syndisco.turn_manager.TurnManager(names: Iterable[str] | None = None)

Bases: Iterable

A class that handles which handles turns between users.

final next() → str

Get the username of the next speaker.

Raises:

ValueError – if no names have been provided from the constructor, or from the TurnManager.set() method

Returns:

the next speaker’s username

Return type:

str

final set_names(names: Iterable[str]) → None

Initialize the manager by providing the names of the users.

Parameters:

names (Iterable[str]) – the usernames of the participants

Module contents