mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-22 05:37:48 +00:00
49 KiB
49 KiB
Information for personality.py
Classes
AIPersonality
class AIPersonality:
def __init__(self, personality_package_path: str | Path, lollms_paths: LollmsPaths, config: LOLLMSConfig, model: LLMBinding = None, app: LoLLMsCom = None, run_scripts = True, selected_language = None, ignore_discussion_documents_rag = False, is_relative_path = True, installation_option: InstallOption = InstallOption.INSTALL_IF_NECESSARY, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def InfoMessage(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
def ShowBlockingMessage(self, content, client_id = None, verbose: bool = True) -> Any
def HideBlockingMessage(self, client_id = None, verbose: bool = True) -> Any
def info(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
def warning(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
def success(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
def error(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
def notify(self, content, notification_type: NotificationType = NotificationType.NOTIF_SUCCESS, duration: int = 4, client_id = None, display_type: NotificationDisplayType = NotificationDisplayType.TOAST, verbose = True) -> Any
def new_message(self, message_text: str, message_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL, metadata = [], callback: Callable[([str, int, dict, list, Any], bool)] = None) -> Any
def full(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def ui(self, ui_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def full_invisible_to_ai(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def full_invisible_to_user(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def build_prompt(self, prompt_parts: List[str], sacrifice_id: int = -1, context_size: int = None, minimum_spare_context_size: int = None) -> Any
def add_collapsible_entry(self, title, content) -> Any
def internet_search_with_vectorization(self, query, quick_search: bool = False, asses_using_llm = True) -> Any
def sink(self, s = None, i = None, d = None) -> Any
def yes_no(self, question: str, context: str = '', max_answer_length: int = 50, conditionning = '') -> bool
def multichoice_question(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
def multichoice_ranking(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
def step_start(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def step_end(self, step_text, status = True, callback: Callable[([str, int, dict, list], bool)] = None) -> Any
def step(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def print_prompt(self, title, prompt) -> Any
def fast_gen_with_images(self, prompt: str, images: list, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
def fast_gen(self, prompt: str, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None) -> str
def process(self, text: str, message_type: MSG_TYPE, callback = None, show_progress = False) -> Any
def generate_with_images(self, prompt, images, max_size, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False, show_progress = False) -> Any
def generate(self, prompt, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False, show_progress = False) -> Any
def setCallback(self, callback: Callable[([str, MSG_TYPE, dict, list], bool)]) -> Any
def __str__(self) -> Any
def load_personality(self, package_path = None) -> Any
def remove_file(self, file_name, callback = None) -> Any
def remove_all_files(self, callback = None) -> Any
def add_file(self, path, client: Client, callback = None, process = True) -> Any
def save_personality(self, package_path = None) -> Any
def as_dict(self) -> Any
def conditionning_commands(self) -> Any
def logo(self) -> Any
def version(self) -> Any
def version(self, value) -> Any
def author(self) -> Any
def author(self, value) -> Any
def name(self) -> str
def name(self, value: str) -> Any
def user_name(self) -> str
def user_name(self, value: str) -> Any
def language(self) -> str
def category(self) -> str
def category_desc(self) -> str
def language(self, value: str) -> Any
def category(self, value: str) -> Any
def category_desc(self, value: str) -> Any
def supported_languages(self) -> str
def supported_languages(self, value: str) -> Any
def selected_language(self) -> str
def selected_language(self, value: str) -> Any
def ignore_discussion_documents_rag(self) -> str
def ignore_discussion_documents_rag(self, value: str) -> Any
def personality_description(self) -> str
def personality_description(self, description: str) -> Any
def personality_conditioning(self) -> str
def personality_conditioning(self, conditioning: str) -> Any
def prompts_list(self) -> str
def prompts_list(self, prompts: str) -> Any
def welcome_message(self) -> str
def welcome_message(self, message: str) -> Any
def include_welcome_message_in_discussion(self) -> bool
def include_welcome_message_in_discussion(self, message: bool) -> Any
def user_message_prefix(self) -> str
def user_message_prefix(self, prefix: str) -> Any
def link_text(self) -> str
def link_text(self, text: str) -> Any
def ai_message_prefix(self) -> Any
def ai_message_prefix(self, prefix) -> Any
def dependencies(self) -> List[str]
def dependencies(self, dependencies: List[str]) -> Any
def disclaimer(self) -> str
def disclaimer(self, disclaimer: str) -> Any
def help(self) -> str
def help(self, help: str) -> Any
def commands(self) -> str
def commands(self, commands: str) -> Any
def model_temperature(self) -> float
def model_temperature(self, value: float) -> Any
def model_top_k(self) -> int
def model_top_k(self, value: int) -> Any
def model_top_p(self) -> float
def model_top_p(self, value: float) -> Any
def model_repeat_penalty(self) -> float
def model_repeat_penalty(self, value: float) -> Any
def model_repeat_last_n(self) -> int
def model_repeat_last_n(self, value: int) -> Any
def assets_list(self) -> list
def assets_list(self, value: list) -> Any
def processor(self) -> APScript
def processor(self, value: APScript) -> Any
def processor_cfg(self) -> list
def processor_cfg(self, value: dict) -> Any
def start_header_id_template(self) -> str
def end_header_id_template(self) -> str
def system_message_template(self) -> str
def separator_template(self) -> str
def start_user_header_id_template(self) -> str
def end_user_header_id_template(self) -> str
def end_user_message_id_template(self) -> str
def start_ai_header_id_template(self) -> str
def end_ai_header_id_template(self) -> str
def end_ai_message_id_template(self) -> str
def system_full_header(self) -> str
def user_full_header(self) -> str
def ai_full_header(self) -> str
def system_custom_header(self, ai_name) -> str
def ai_custom_header(self, ai_name) -> str
def detect_antiprompt(self, text: str) -> bool
def replace_keys(input_string, replacements) -> Any
def verify_rag_entry(self, query, rag_entry) -> Any
def translate(self, text_chunk, output_language = 'french', max_generation_size = 3000) -> Any
def summarize_text(self, text, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def smart_data_extraction(self, text, data_extraction_instruction = f'summarize the current chunk.', final_task_instruction = 'reformulate with better wording', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def summarize_chunks(self, chunks, summary_instruction = f'summarize the current chunk.', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def sequencial_chunks_summary(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None) -> Any
StateMachine
class StateMachine:
def __init__(self, states_list) -> Any
def goto_state(self, state) -> Any
def process_state(self, command, full_context, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, context_state: dict = None, client: Client = None) -> Any
LoLLMsActionParameters
class LoLLMsActionParameters:
def __init__(self, name: str, parameter_type: Type, range: Optional[List] = None, options: Optional[List] = None, value: Any = None) -> None
def __str__(self) -> str
def from_str(string: str) -> LoLLMsActionParameters
def from_dict(parameter_dict: dict) -> LoLLMsActionParameters
LoLLMsActionParametersEncoder
class LoLLMsActionParametersEncoder:
def default(self, obj) -> Any
LoLLMsAction
class LoLLMsAction:
def __init__(self, name, parameters: List[LoLLMsActionParameters], callback: Callable, description: str = '') -> None
def __str__(self) -> str
def from_str(string: str) -> LoLLMsAction
def from_dict(action_dict: dict) -> LoLLMsAction
def run(self) -> None
APScript
class APScript:
def __init__(self, personality: AIPersonality, personality_config: TypedConfig, states_list: dict = {}, callback = None) -> None
def sink(self, s = None, i = None, d = None) -> Any
def settings_updated(self) -> Any
def mounted(self) -> Any
def get_welcome(self, welcome_message: str, client: Client) -> Any
def selected(self) -> Any
def execute_command(self, command: str, parameters: list = [], client: Client = None) -> Any
def load_personality_config(self) -> Any
def install(self) -> Any
def uninstall(self) -> Any
def add_file(self, path, client: Client, callback = None, process = True) -> Any
def remove_file(self, path) -> Any
def load_config_file(self, path, default_config = None) -> Any
def save_config_file(self, path, data) -> Any
def generate_with_images(self, prompt, images, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False) -> Any
def generate(self, prompt, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False) -> Any
def run_workflow(self, prompt: str, previous_discussion_text: str = '', callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, context_details: dict = None, client: Client = None) -> Any
def compile_latex(self, file_path, pdf_latex_path = None) -> Any
def find_numeric_value(self, text) -> Any
def remove_backticks(self, text) -> Any
def search_duckduckgo(self, query: str, max_results: int = 10, instant_answers: bool = True, regular_search_queries: bool = True, get_webpage_content: bool = False) -> List[Dict[(str, Union[str, None])]]
def translate(self, text_chunk, output_language = 'french', max_generation_size = 3000) -> Any
def summarize_text(self, text, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def smart_data_extraction(self, text, data_extraction_instruction = 'summarize', final_task_instruction = 'reformulate with better wording', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def summarize_chunks(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
def sequencial_chunks_summary(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None) -> Any
def build_prompt_from_context_details(self, context_details: dict, custom_entries = '', suppress = []) -> Any
def build_prompt(self, prompt_parts: List[str], sacrifice_id: int = -1, context_size: int = None, minimum_spare_context_size: int = None) -> Any
def add_collapsible_entry(self, title, content, subtitle = '') -> Any
def internet_search_with_vectorization(self, query, quick_search: bool = False) -> Any
def vectorize_and_query(self, title, url, text, query, max_chunk_size = 512, overlap_size = 20, internet_vectorization_nb_chunks = 3) -> Any
def step_start(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def step_end(self, step_text, status = True, callback: Callable[([str, int, dict, list], bool)] = None) -> Any
def step(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def exception(self, ex, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def warning(self, warning: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def json(self, title: str, json_infos: dict, callback: Callable[([str, int, dict, list], bool)] = None, indent = 4) -> Any
def ui(self, html_ui: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def ui_in_iframe(self, html_ui: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def code(self, code: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def chunk(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def full(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, msg_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL) -> Any
def full_invisible_to_ai(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def full_invisible_to_user(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def execute_python(self, code, code_folder = None, code_file_name = None) -> Any
def build_python_code(self, prompt, max_title_length = 4096) -> Any
def make_title(self, prompt, max_title_length: int = 50) -> Any
def plan_with_images(self, request: str, images: list, actions_list: list = [LoLLMsAction], context: str = '', max_answer_length: int = 512) -> List[LoLLMsAction]
def plan(self, request: str, actions_list: list = [LoLLMsAction], context: str = '', max_answer_length: int = 512) -> List[LoLLMsAction]
def parse_directory_structure(self, structure) -> Any
def extract_code_blocks(self, text: str) -> List[dict]
def build_and_execute_python_code(self, context, instructions, execution_function_signature, extra_imports = '') -> Any
def yes_no(self, question: str, context: str = '', max_answer_length: int = 50, conditionning = '') -> bool
def multichoice_question(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
def multichoice_ranking(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
def build_html5_integration(self, html, ifram_name = 'unnamed') -> Any
def InfoMessage(self, content, client_id = None, verbose: bool = None) -> Any
def info(self, info_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def step_progress(self, step_text: str, progress: float, callback: Callable[([str, MSG_TYPE, dict, list, AIPersonality], bool)] = None) -> Any
def new_message(self, message_text: str, message_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL, metadata = [], callback: Callable[([str, int, dict, list, AIPersonality], bool)] = None) -> Any
def finished_message(self, message_text: str = '', callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
def print_prompt(self, title, prompt) -> Any
def fast_gen_with_images(self, prompt: str, images: list, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
def fast_gen(self, prompt: str, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
def mix_it_up(self, prompt: str, models, master_model, nb_rounds = 2, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> dict
def generate_with_function_calls(self, context_details: dict, functions: List[Dict[(str, Any)]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[(str, Any)]]
def generate_with_function_calls_and_images(self, context_details: dict, images: list, functions: List[Dict[(str, Any)]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[(str, Any)]]
def execute_function(self, code, function_definitions = None) -> Any
def execute_function_calls(self, function_calls: List[Dict[(str, Any)]], function_definitions: List[Dict[(str, Any)]]) -> List[Any]
def transform_functions_to_text(self, functions) -> Any
def transform_functions(self, functions) -> Any
def _upgrade_prompt_with_function_info(self, context_details: dict, functions: List[Dict[(str, Any)]]) -> str
def extract_function_calls_as_json(self, text: str) -> List[Dict[(str, Any)]]
def interact(self, context_details, callback = None) -> Any
def interact_with_function_call(self, context_details, function_definitions, prompt_after_execution = True, callback = None, hide_function_call = False, separate_output = False, max_nested_function_calls = 10) -> Any
def path2url(file) -> Any
def build_a_document_block(self, title = 'Title', link = '', content = 'content') -> Any
def build_a_folder_link(self, folder_path, link_text = 'Open Folder') -> Any
def build_a_file_link(self, file_path, link_text = 'Open Folder') -> Any
def compress_js(self, code) -> Any
def compress_python(self, code) -> Any
def compress_html(self, code) -> Any
def select_model(self, binding_name, model_name) -> Any
def verify_rag_entry(self, query, rag_entry) -> Any
def start_header_id_template(self) -> str
def end_header_id_template(self) -> str
def system_message_template(self) -> str
def separator_template(self) -> str
def start_user_header_id_template(self) -> str
def end_user_header_id_template(self) -> str
def end_user_message_id_template(self) -> str
def start_ai_header_id_template(self) -> str
def end_ai_header_id_template(self) -> str
def end_ai_message_id_template(self) -> str
def system_full_header(self) -> str
def user_full_header(self) -> str
def ai_full_header(self) -> str
def system_custom_header(self, ai_name) -> str
def ai_custom_header(self, ai_name) -> str
AIPersonalityInstaller
class AIPersonalityInstaller:
def __init__(self, personality: AIPersonality) -> None
PersonalityBuilder
class PersonalityBuilder:
def __init__(self, lollms_paths: LollmsPaths, config: LOLLMSConfig, model: LLMBinding, app = None, installation_option: InstallOption = InstallOption.INSTALL_IF_NECESSARY, callback = None) -> Any
def build_personality(self, id: int = None) -> Any
def get_personality(self) -> Any
def extract_function_call(self, query) -> Any
Functions
get_element_id
def get_element_id(url, text) -> Any
craft_a_tag_to_specific_text
def craft_a_tag_to_specific_text(url, text, caption) -> Any
is_package_installed
def is_package_installed(package_name) -> Any
install_package
def install_package(package_name) -> Any
fix_json
def fix_json(json_text) -> Any
generate_actions
def generate_actions(potential_actions: List[LoLLMsAction], parsed_text: dict) -> List[LoLLMsAction]
init
def __init__(self, personality_package_path: str | Path, lollms_paths: LollmsPaths, config: LOLLMSConfig, model: LLMBinding = None, app: LoLLMsCom = None, run_scripts = True, selected_language = None, ignore_discussion_documents_rag = False, is_relative_path = True, installation_option: InstallOption = InstallOption.INSTALL_IF_NECESSARY, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
InfoMessage
def InfoMessage(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
ShowBlockingMessage
def ShowBlockingMessage(self, content, client_id = None, verbose: bool = True) -> Any
HideBlockingMessage
def HideBlockingMessage(self, client_id = None, verbose: bool = True) -> Any
info
def info(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
warning
def warning(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
success
def success(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
error
def error(self, content, duration: int = 4, client_id = None, verbose: bool = True) -> Any
notify
def notify(self, content, notification_type: NotificationType = NotificationType.NOTIF_SUCCESS, duration: int = 4, client_id = None, display_type: NotificationDisplayType = NotificationDisplayType.TOAST, verbose = True) -> Any
new_message
def new_message(self, message_text: str, message_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL, metadata = [], callback: Callable[([str, int, dict, list, Any], bool)] = None) -> Any
full
def full(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
ui
def ui(self, ui_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
full_invisible_to_ai
def full_invisible_to_ai(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
full_invisible_to_user
def full_invisible_to_user(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
build_prompt
def build_prompt(self, prompt_parts: List[str], sacrifice_id: int = -1, context_size: int = None, minimum_spare_context_size: int = None) -> Any
add_collapsible_entry
def add_collapsible_entry(self, title, content) -> Any
internet_search_with_vectorization
def internet_search_with_vectorization(self, query, quick_search: bool = False, asses_using_llm = True) -> Any
sink
def sink(self, s = None, i = None, d = None) -> Any
yes_no
def yes_no(self, question: str, context: str = '', max_answer_length: int = 50, conditionning = '') -> bool
multichoice_question
def multichoice_question(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
multichoice_ranking
def multichoice_ranking(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
step_start
def step_start(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
step_end
def step_end(self, step_text, status = True, callback: Callable[([str, int, dict, list], bool)] = None) -> Any
step
def step(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
print_prompt
def print_prompt(self, title, prompt) -> Any
fast_gen_with_images
def fast_gen_with_images(self, prompt: str, images: list, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
fast_gen
def fast_gen(self, prompt: str, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None) -> str
process
def process(self, text: str, message_type: MSG_TYPE, callback = None, show_progress = False) -> Any
generate_with_images
def generate_with_images(self, prompt, images, max_size, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False, show_progress = False) -> Any
generate
def generate(self, prompt, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False, show_progress = False) -> Any
setCallback
def setCallback(self, callback: Callable[([str, MSG_TYPE, dict, list], bool)]) -> Any
str
def __str__(self) -> Any
load_personality
def load_personality(self, package_path = None) -> Any
remove_file
def remove_file(self, file_name, callback = None) -> Any
remove_all_files
def remove_all_files(self, callback = None) -> Any
add_file
def add_file(self, path, client: Client, callback = None, process = True) -> Any
save_personality
def save_personality(self, package_path = None) -> Any
as_dict
def as_dict(self) -> Any
conditionning_commands
def conditionning_commands(self) -> Any
logo
def logo(self) -> Any
version
def version(self) -> Any
version
def version(self, value) -> Any
author
def author(self) -> Any
author
def author(self, value) -> Any
name
def name(self) -> str
name
def name(self, value: str) -> Any
user_name
def user_name(self) -> str
user_name
def user_name(self, value: str) -> Any
language
def language(self) -> str
category
def category(self) -> str
category_desc
def category_desc(self) -> str
language
def language(self, value: str) -> Any
category
def category(self, value: str) -> Any
category_desc
def category_desc(self, value: str) -> Any
supported_languages
def supported_languages(self) -> str
supported_languages
def supported_languages(self, value: str) -> Any
selected_language
def selected_language(self) -> str
selected_language
def selected_language(self, value: str) -> Any
ignore_discussion_documents_rag
def ignore_discussion_documents_rag(self) -> str
ignore_discussion_documents_rag
def ignore_discussion_documents_rag(self, value: str) -> Any
personality_description
def personality_description(self) -> str
personality_description
def personality_description(self, description: str) -> Any
personality_conditioning
def personality_conditioning(self) -> str
personality_conditioning
def personality_conditioning(self, conditioning: str) -> Any
prompts_list
def prompts_list(self) -> str
prompts_list
def prompts_list(self, prompts: str) -> Any
welcome_message
def welcome_message(self) -> str
welcome_message
def welcome_message(self, message: str) -> Any
include_welcome_message_in_discussion
def include_welcome_message_in_discussion(self) -> bool
include_welcome_message_in_discussion
def include_welcome_message_in_discussion(self, message: bool) -> Any
user_message_prefix
def user_message_prefix(self) -> str
user_message_prefix
def user_message_prefix(self, prefix: str) -> Any
link_text
def link_text(self) -> str
link_text
def link_text(self, text: str) -> Any
ai_message_prefix
def ai_message_prefix(self) -> Any
ai_message_prefix
def ai_message_prefix(self, prefix) -> Any
dependencies
def dependencies(self) -> List[str]
dependencies
def dependencies(self, dependencies: List[str]) -> Any
disclaimer
def disclaimer(self) -> str
disclaimer
def disclaimer(self, disclaimer: str) -> Any
help
def help(self) -> str
help
def help(self, help: str) -> Any
commands
def commands(self) -> str
commands
def commands(self, commands: str) -> Any
model_temperature
def model_temperature(self) -> float
model_temperature
def model_temperature(self, value: float) -> Any
model_top_k
def model_top_k(self) -> int
model_top_k
def model_top_k(self, value: int) -> Any
model_top_p
def model_top_p(self) -> float
model_top_p
def model_top_p(self, value: float) -> Any
model_repeat_penalty
def model_repeat_penalty(self) -> float
model_repeat_penalty
def model_repeat_penalty(self, value: float) -> Any
model_repeat_last_n
def model_repeat_last_n(self) -> int
model_repeat_last_n
def model_repeat_last_n(self, value: int) -> Any
assets_list
def assets_list(self) -> list
assets_list
def assets_list(self, value: list) -> Any
processor
def processor(self) -> APScript
processor
def processor(self, value: APScript) -> Any
processor_cfg
def processor_cfg(self) -> list
processor_cfg
def processor_cfg(self, value: dict) -> Any
start_header_id_template
def start_header_id_template(self) -> str
end_header_id_template
def end_header_id_template(self) -> str
system_message_template
def system_message_template(self) -> str
separator_template
def separator_template(self) -> str
start_user_header_id_template
def start_user_header_id_template(self) -> str
end_user_header_id_template
def end_user_header_id_template(self) -> str
end_user_message_id_template
def end_user_message_id_template(self) -> str
start_ai_header_id_template
def start_ai_header_id_template(self) -> str
end_ai_header_id_template
def end_ai_header_id_template(self) -> str
end_ai_message_id_template
def end_ai_message_id_template(self) -> str
system_full_header
def system_full_header(self) -> str
user_full_header
def user_full_header(self) -> str
ai_full_header
def ai_full_header(self) -> str
system_custom_header
def system_custom_header(self, ai_name) -> str
ai_custom_header
def ai_custom_header(self, ai_name) -> str
detect_antiprompt
def detect_antiprompt(self, text: str) -> bool
replace_keys
def replace_keys(input_string, replacements) -> Any
verify_rag_entry
def verify_rag_entry(self, query, rag_entry) -> Any
translate
def translate(self, text_chunk, output_language = 'french', max_generation_size = 3000) -> Any
summarize_text
def summarize_text(self, text, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
smart_data_extraction
def smart_data_extraction(self, text, data_extraction_instruction = f'summarize the current chunk.', final_task_instruction = 'reformulate with better wording', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
summarize_chunks
def summarize_chunks(self, chunks, summary_instruction = f'summarize the current chunk.', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
sequencial_chunks_summary
def sequencial_chunks_summary(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None) -> Any
init
def __init__(self, states_list) -> Any
goto_state
def goto_state(self, state) -> Any
process_state
def process_state(self, command, full_context, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, context_state: dict = None, client: Client = None) -> Any
init
def __init__(self, name: str, parameter_type: Type, range: Optional[List] = None, options: Optional[List] = None, value: Any = None) -> None
str
def __str__(self) -> str
from_str
def from_str(string: str) -> LoLLMsActionParameters
from_dict
def from_dict(parameter_dict: dict) -> LoLLMsActionParameters
default
def default(self, obj) -> Any
init
def __init__(self, name, parameters: List[LoLLMsActionParameters], callback: Callable, description: str = '') -> None
str
def __str__(self) -> str
from_str
def from_str(string: str) -> LoLLMsAction
from_dict
def from_dict(action_dict: dict) -> LoLLMsAction
run
def run(self) -> None
init
def __init__(self, personality: AIPersonality, personality_config: TypedConfig, states_list: dict = {}, callback = None) -> None
sink
def sink(self, s = None, i = None, d = None) -> Any
settings_updated
def settings_updated(self) -> Any
mounted
def mounted(self) -> Any
get_welcome
def get_welcome(self, welcome_message: str, client: Client) -> Any
selected
def selected(self) -> Any
execute_command
def execute_command(self, command: str, parameters: list = [], client: Client = None) -> Any
load_personality_config
def load_personality_config(self) -> Any
install
def install(self) -> Any
uninstall
def uninstall(self) -> Any
add_file
def add_file(self, path, client: Client, callback = None, process = True) -> Any
remove_file
def remove_file(self, path) -> Any
load_config_file
def load_config_file(self, path, default_config = None) -> Any
save_config_file
def save_config_file(self, path, data) -> Any
generate_with_images
def generate_with_images(self, prompt, images, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False) -> Any
generate
def generate(self, prompt, max_size = None, temperature = None, top_k = None, top_p = None, repeat_penalty = None, repeat_last_n = None, callback = None, debug = False) -> Any
run_workflow
def run_workflow(self, prompt: str, previous_discussion_text: str = '', callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, context_details: dict = None, client: Client = None) -> Any
compile_latex
def compile_latex(self, file_path, pdf_latex_path = None) -> Any
find_numeric_value
def find_numeric_value(self, text) -> Any
remove_backticks
def remove_backticks(self, text) -> Any
search_duckduckgo
def search_duckduckgo(self, query: str, max_results: int = 10, instant_answers: bool = True, regular_search_queries: bool = True, get_webpage_content: bool = False) -> List[Dict[(str, Union[str, None])]]
translate
def translate(self, text_chunk, output_language = 'french', max_generation_size = 3000) -> Any
summarize_text
def summarize_text(self, text, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
smart_data_extraction
def smart_data_extraction(self, text, data_extraction_instruction = 'summarize', final_task_instruction = 'reformulate with better wording', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, max_summary_size = 512, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
summarize_chunks
def summarize_chunks(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None, summary_mode = SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL) -> Any
sequencial_chunks_summary
def sequencial_chunks_summary(self, chunks, summary_instruction = 'summarize', doc_name = 'chunk', answer_start = '', max_generation_size = 3000, callback = None, chunk_summary_post_processing = None) -> Any
build_prompt_from_context_details
def build_prompt_from_context_details(self, context_details: dict, custom_entries = '', suppress = []) -> Any
build_prompt
def build_prompt(self, prompt_parts: List[str], sacrifice_id: int = -1, context_size: int = None, minimum_spare_context_size: int = None) -> Any
add_collapsible_entry
def add_collapsible_entry(self, title, content, subtitle = '') -> Any
internet_search_with_vectorization
def internet_search_with_vectorization(self, query, quick_search: bool = False) -> Any
vectorize_and_query
def vectorize_and_query(self, title, url, text, query, max_chunk_size = 512, overlap_size = 20, internet_vectorization_nb_chunks = 3) -> Any
step_start
def step_start(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
step_end
def step_end(self, step_text, status = True, callback: Callable[([str, int, dict, list], bool)] = None) -> Any
step
def step(self, step_text, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
exception
def exception(self, ex, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
warning
def warning(self, warning: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
json
def json(self, title: str, json_infos: dict, callback: Callable[([str, int, dict, list], bool)] = None, indent = 4) -> Any
ui
def ui(self, html_ui: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
ui_in_iframe
def ui_in_iframe(self, html_ui: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
code
def code(self, code: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
chunk
def chunk(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
full
def full(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None, msg_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL) -> Any
full_invisible_to_ai
def full_invisible_to_ai(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
full_invisible_to_user
def full_invisible_to_user(self, full_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
execute_python
def execute_python(self, code, code_folder = None, code_file_name = None) -> Any
build_python_code
def build_python_code(self, prompt, max_title_length = 4096) -> Any
make_title
def make_title(self, prompt, max_title_length: int = 50) -> Any
plan_with_images
def plan_with_images(self, request: str, images: list, actions_list: list = [LoLLMsAction], context: str = '', max_answer_length: int = 512) -> List[LoLLMsAction]
plan
def plan(self, request: str, actions_list: list = [LoLLMsAction], context: str = '', max_answer_length: int = 512) -> List[LoLLMsAction]
parse_directory_structure
def parse_directory_structure(self, structure) -> Any
extract_code_blocks
def extract_code_blocks(self, text: str) -> List[dict]
build_and_execute_python_code
def build_and_execute_python_code(self, context, instructions, execution_function_signature, extra_imports = '') -> Any
yes_no
def yes_no(self, question: str, context: str = '', max_answer_length: int = 50, conditionning = '') -> bool
multichoice_question
def multichoice_question(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
multichoice_ranking
def multichoice_ranking(self, question: str, possible_answers: list, context: str = '', max_answer_length: int = 50, conditionning = '') -> int
build_html5_integration
def build_html5_integration(self, html, ifram_name = 'unnamed') -> Any
InfoMessage
def InfoMessage(self, content, client_id = None, verbose: bool = None) -> Any
info
def info(self, info_text: str, callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
step_progress
def step_progress(self, step_text: str, progress: float, callback: Callable[([str, MSG_TYPE, dict, list, AIPersonality], bool)] = None) -> Any
new_message
def new_message(self, message_text: str, message_type: MSG_TYPE = MSG_TYPE.MSG_TYPE_FULL, metadata = [], callback: Callable[([str, int, dict, list, AIPersonality], bool)] = None) -> Any
finished_message
def finished_message(self, message_text: str = '', callback: Callable[([str, MSG_TYPE, dict, list], bool)] = None) -> Any
print_prompt
def print_prompt(self, title, prompt) -> Any
fast_gen_with_images
def fast_gen_with_images(self, prompt: str, images: list, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
fast_gen
def fast_gen(self, prompt: str, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> str
mix_it_up
def mix_it_up(self, prompt: str, models, master_model, nb_rounds = 2, max_generation_size: int = None, placeholders: dict = {}, sacrifice: list = ['previous_discussion'], debug: bool = False, callback = None, show_progress = False) -> dict
generate_with_function_calls
def generate_with_function_calls(self, context_details: dict, functions: List[Dict[(str, Any)]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[(str, Any)]]
generate_with_function_calls_and_images
def generate_with_function_calls_and_images(self, context_details: dict, images: list, functions: List[Dict[(str, Any)]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[(str, Any)]]
execute_function
def execute_function(self, code, function_definitions = None) -> Any
execute_function_calls
def execute_function_calls(self, function_calls: List[Dict[(str, Any)]], function_definitions: List[Dict[(str, Any)]]) -> List[Any]
transform_functions_to_text
def transform_functions_to_text(self, functions) -> Any
transform_functions
def transform_functions(self, functions) -> Any
_upgrade_prompt_with_function_info
def _upgrade_prompt_with_function_info(self, context_details: dict, functions: List[Dict[(str, Any)]]) -> str
extract_function_calls_as_json
def extract_function_calls_as_json(self, text: str) -> List[Dict[(str, Any)]]
interact
def interact(self, context_details, callback = None) -> Any
interact_with_function_call
def interact_with_function_call(self, context_details, function_definitions, prompt_after_execution = True, callback = None, hide_function_call = False, separate_output = False, max_nested_function_calls = 10) -> Any
path2url
def path2url(file) -> Any
build_a_document_block
def build_a_document_block(self, title = 'Title', link = '', content = 'content') -> Any
build_a_folder_link
def build_a_folder_link(self, folder_path, link_text = 'Open Folder') -> Any
build_a_file_link
def build_a_file_link(self, file_path, link_text = 'Open Folder') -> Any
compress_js
def compress_js(self, code) -> Any
compress_python
def compress_python(self, code) -> Any
compress_html
def compress_html(self, code) -> Any
select_model
def select_model(self, binding_name, model_name) -> Any
verify_rag_entry
def verify_rag_entry(self, query, rag_entry) -> Any
start_header_id_template
def start_header_id_template(self) -> str
end_header_id_template
def end_header_id_template(self) -> str
system_message_template
def system_message_template(self) -> str
separator_template
def separator_template(self) -> str
start_user_header_id_template
def start_user_header_id_template(self) -> str
end_user_header_id_template
def end_user_header_id_template(self) -> str
end_user_message_id_template
def end_user_message_id_template(self) -> str
start_ai_header_id_template
def start_ai_header_id_template(self) -> str
end_ai_header_id_template
def end_ai_header_id_template(self) -> str
end_ai_message_id_template
def end_ai_message_id_template(self) -> str
system_full_header
def system_full_header(self) -> str
user_full_header
def user_full_header(self) -> str
ai_full_header
def ai_full_header(self) -> str
system_custom_header
def system_custom_header(self, ai_name) -> str
ai_custom_header
def ai_custom_header(self, ai_name) -> str
init
def __init__(self, personality: AIPersonality) -> None
init
def __init__(self, lollms_paths: LollmsPaths, config: LOLLMSConfig, model: LLMBinding, app = None, installation_option: InstallOption = InstallOption.INSTALL_IF_NECESSARY, callback = None) -> Any
build_personality
def build_personality(self, id: int = None) -> Any
get_personality
def get_personality(self) -> Any
extract_function_call
def extract_function_call(self, query) -> Any
replace
def replace(match) -> Any