id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
16,601 | import os
from typing import Optional
import typer
import uvicorn
from fastapi import FastAPI
from heuristics import checks
from pydantic import BaseModel
class JailbreakCheckRequest(BaseModel):
"""
prompt (str): User utterance to the model
lp_threshold (float): Threshold value for length-perplexity heurist... | null |
16,602 | import os
from typing import Optional
import typer
import uvicorn
from fastapi import FastAPI
from heuristics import checks
from pydantic import BaseModel
class JailbreakCheckRequest(BaseModel):
"""
prompt (str): User utterance to the model
lp_threshold (float): Threshold value for length-perplexity heurist... | null |
16,603 | import os
from typing import Optional
import typer
import uvicorn
from fastapi import FastAPI
from heuristics import checks
from pydantic import BaseModel
app = FastAPI()
def start(
port: int = typer.Option(
default=1337, help="The port that the server should listen on."
),
host: str = typer.Option... | null |
16,604 | import logging
from typing import List, Optional, Tuple
from langchain.llms import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskman... | Checks user messages using the configured Llama Guard model and the configured prompt containing the safety guidelines. |
16,605 | import logging
from typing import List, Optional, Tuple
from langchain.llms import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskman... | Check the bot response using the configured Llama Guard model and the configured prompt containing the safety guidelines. |
16,606 | import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager imp... | Checks if the output from the bot. Prompt the LLM, using the `self_check_output` task prompt, to determine if the output from the bot should be allowed or not. The LLM call should return "yes" if the output is bad and should be blocked (this is consistent with self_check_input_prompt). Returns: True if the output shoul... |
16,607 | import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.llm.params import llm_params
from nemoguardrai... | Checks the input from the user. Prompt the LLM, using the `check_input` task prompt, to determine if the input from the user should be allowed or not. Returns: True if the input should be allowed, False otherwise. |
16,608 | import logging
from typing import Dict, Optional
from fastapi import FastAPI
from pydantic import BaseModel, Field
from nemoguardrails.actions.action_dispatcher import ActionDispatcher
log = logging.getLogger(__name__)
app = FastAPI(
title="Guardrails Action Server API",
description=api_description,
version... | Execute the specified action and return the result. Args: body (RequestBody): The request body containing action_name and action_parameters. Returns: dict: The response containing the execution status and result. |
16,609 | import logging
from typing import Dict, Optional
from fastapi import FastAPI
from pydantic import BaseModel, Field
from nemoguardrails.actions.action_dispatcher import ActionDispatcher
app = FastAPI(
title="Guardrails Action Server API",
description=api_description,
version="0.1.0",
license_info={"name"... | Returns the list of available actions. |
16,610 | import copy
import logging
import random
import re
import time
from collections import deque
from datetime import datetime, timedelta
from functools import partial
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from nemoguardrails.colang.v2_x.lang.colang_ast import (
Abort,
Assignment,
... | Initialize the state to make it ready for the story start. |
16,611 | import copy
import logging
import random
import re
import time
from collections import deque
from datetime import datetime, timedelta
from functools import partial
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from nemoguardrails.colang.v2_x.lang.colang_ast import (
Abort,
Assignment,
... | Compute the next state of the flow-driven system. |
16,612 | import copy
import logging
import random
import re
import time
from collections import deque
from datetime import datetime, timedelta
from functools import partial
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from nemoguardrails.colang.v2_x.lang.colang_ast import (
Abort,
Assignment,
... | Return a list of all active heads that point to an event 'match' element. |
16,613 | import copy
import logging
import random
import re
import time
from collections import deque
from datetime import datetime, timedelta
from functools import partial
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from nemoguardrails.colang.v2_x.lang.colang_ast import (
Abort,
Assignment,
... | Returns 'FinishFlow' internal event |
16,614 | import copy
import logging
import random
import re
import time
from collections import deque
from datetime import datetime, timedelta
from functools import partial
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from nemoguardrails.colang.v2_x.lang.colang_ast import (
Abort,
Assignment,
... | Returns 'StopFlow' internal event |
16,615 | from functools import lru_cache
from lark import Lark
from lark.indenter import PythonIndenter
The provided code snippet includes necessary dependencies for implementing the `load_lark_parser` function. Write a Python function `def load_lark_parser(grammar_path: str)` to solve the following problem:
Helper to load a L... | Helper to load a Lark parser. The result is cached so that it's faster in subsequent times. Args: grammar_path: The path to the .lark file with the grammar. Returns: A Lark parser instance. |
16,616 | import uuid
from dataclasses import asdict, is_dataclass
from typing import Any
The provided code snippet includes necessary dependencies for implementing the `new_uuid` function. Write a Python function `def new_uuid() -> str` to solve the following problem:
Helper to generate new UUID v4. In testing mode, it will ge... | Helper to generate new UUID v4. In testing mode, it will generate a predictable set of UUIDs to help debugging. |
16,617 | import uuid
from dataclasses import asdict, is_dataclass
from typing import Any
def dataclass_to_dict(obj: Any) -> Any:
if is_dataclass(obj):
return {k: dataclass_to_dict(v) for k, v in asdict(obj).items()}
elif isinstance(obj, list):
return [dataclass_to_dict(v) for v in obj]
elif isinstan... | null |
16,618 | from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Union
from dataclasses_json import dataclass_json
class Element:
"""Base class for all elements in the AST."""
_type: str
_source: Optional[Source] = None
def __getitem__(self, key):
retu... | Make all subtypes of Element hashable. |
16,619 | import logging
import os
import re
import yaml
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.lang.grammar.load import load_lark_parser
from nemoguardrails.colang.v2_x.lang.transformer import ColangTransformer
from nemoguardrails.utils import CustomDumper
class ColangParser:... | Parse the content of a .co. |
16,620 | import uuid
from dataclasses import dataclass, field
from enum import Enum
from time import time
from typing import Dict, List, Optional
from nemoguardrails.colang.v1_0.runtime.eval import eval_expression
from nemoguardrails.colang.v1_0.runtime.sliding import slide
from nemoguardrails.utils import new_event_dict
class ... | Computes the next step in a flow-driven system given a history of events. Args: history (List[dict]): The history of events. flow_configs (Dict[str, FlowConfig]): Flow configurations. rails_config (RailsConfig): Rails configuration. processing_log (List[dict]): The processing log so far. This will be mutated. Returns: ... |
16,621 | import uuid
from dataclasses import dataclass, field
from enum import Enum
from time import time
from typing import Dict, List, Optional
from nemoguardrails.colang.v1_0.runtime.eval import eval_expression
from nemoguardrails.colang.v1_0.runtime.sliding import slide
from nemoguardrails.utils import new_event_dict
The p... | Computes the context given a history of events. Special context variables: - $last_user_message: the last message sent by the user. - $last_bot_message: the last message sent by the bot. Args: history (List[dict]): The history of events. Returns: dict: The computed context. |
16,622 | import uuid
from typing import List, Optional, Text, Tuple
def word_split(text: str, word: str):
"""A simple logic that splits by word but takes strings into accounts."""
parts = []
# Edge case
if text == "":
return [""]
# The current position
i = 0
# The start of the current part
... | Helper to returned numbered lines. Comments and empty lines are ignored. |
16,623 | import uuid
from typing import List, Optional, Text, Tuple
def split_max(text, separator, max_instances):
"""Helper to simulate the behavior of .split(..., max_instances).
This implementation is meant to transpile correctly to the JS>
"""
parts = text.split(separator)
if len(parts) > max_instances +... | Helper to remove a token |
16,624 | import uuid
from typing import List, Optional, Text, Tuple
def split_max(text, separator, max_instances):
"""Helper to simulate the behavior of .split(..., max_instances).
This implementation is meant to transpile correctly to the JS>
"""
parts = text.split(separator)
if len(parts) > max_instances +... | Helper to extract the main token from a line |
16,625 | import uuid
from typing import List, Optional, Text, Tuple
The provided code snippet includes necessary dependencies for implementing the `char_split` function. Write a Python function `def char_split( text: str, c: str, ignore_parenthesis=False, ignore_strings=False ) -> List[str]` to solve the following problem:... | Helper method to split a string by a given character. :param text: The text to split. :param c: The character to use as the separator :param ignore_parenthesis: If set, it will now account for lists i.e. starting with [], () or {} :param ignore_strings: If set, it will not take into account strings. |
16,626 | import uuid
from typing import List, Optional, Text, Tuple
The provided code snippet includes necessary dependencies for implementing the `params_tokenize` function. Write a Python function `def params_tokenize(text)` to solve the following problem:
Tokenizer specific to the params parsing.
Here is the function:
def... | Tokenizer specific to the params parsing. |
16,627 | import uuid
from typing import List, Optional, Text, Tuple
The provided code snippet includes necessary dependencies for implementing the `get_first_key` function. Write a Python function `def get_first_key(d: dict)` to solve the following problem:
Helper to get the first key, which transpiles correctly.
Here is the ... | Helper to get the first key, which transpiles correctly. |
16,628 | import uuid
from typing import List, Optional, Text, Tuple
def split_max(text, separator, max_instances):
"""Helper to simulate the behavior of .split(..., max_instances).
This implementation is meant to transpile correctly to the JS>
"""
parts = text.split(separator)
if len(parts) > max_instances +... | Helper to extract the object from the definition of a topic. Supported expressions is_open_source is_open_source for @roboself is_open_source for $company is_open_source($roboself) is_open_source(@roboself) |
16,629 | import uuid
from typing import List, Optional, Text, Tuple
def split_max(text, separator, max_instances):
"""Helper to simulate the behavior of .split(..., max_instances).
This implementation is meant to transpile correctly to the JS>
"""
parts = text.split(separator)
if len(parts) > max_instances +... | Helper to extract a normalized package name. |
16,630 | import uuid
from typing import List, Optional, Text, Tuple
The provided code snippet includes necessary dependencies for implementing the `string_hash` function. Write a Python function `def string_hash(s)` to solve the following problem:
A simple string hash with an equivalent implementation in javascript. module.exp... | A simple string hash with an equivalent implementation in javascript. module.exports.string_hash = function(s){ let hash = 0; if (s.length === 0) return hash; for (let i = 0; i < s.length; i++) { let char = s.charCodeAt(i); hash = ((hash<<5)-hash)+char; hash = hash & hash; // Convert to 32bit integer } if (hash < 0) ha... |
16,631 | import logging
import textwrap
from typing import List, Optional
from nemoguardrails.colang.v1_0.lang.colang_parser import (
parse_coflows_to_yml_flows,
parse_snippets_and_imports,
)
from nemoguardrails.colang.v1_0.lang.comd_parser import parse_md_file
from nemoguardrails.colang.v1_0.lang.coyml_parser import pa... | Parse the content of a .co file into the CoYML format. |
16,632 | import json
import re
from ast import literal_eval
from typing import List
from .utils import get_stripped_tokens, split_args, split_max, word_split
def _dict_to_element(d):
"""Helper to turn a short-hand dictionary into an event structure.
:param d: A dictionary in one of the supported formats
:return:
... | Helper to convert a list of events data to 'full events |
16,633 | import functools
import hashlib
import json
import logging
from abc import ABC, abstractmethod
from functools import singledispatchmethod
from pathlib import Path
from typing import Dict, List
from nemoguardrails.rails.llm.config import EmbeddingsCacheConfig
class EmbeddingsCache:
def __init__(
self,
... | Decorator to cache the embeddings. This decorator caches the embeddings in the cache store. It uses the `cache_config` attribute of the class to configure the cache. If the class does not have a `cache_config` attribute, it will use the `EmbeddingsCacheConfig` by default. This decorator can be applied to the `_get_embe... |
16,634 | import logging
from typing import Dict, Type
from langchain.base_language import BaseLanguageModel
class LLMParams:
"""Context manager to temporarily modify the parameters of a language model."""
def __init__(self, llm: BaseLanguageModel, **kwargs):
self.llm = llm
self.altered_params = kwargs
... | Register a parameter manager. |
16,635 | def _replace_prefix(s: str, prefix: str, repl: str):
"""Helper function to replace a prefix from a string."""
if s.startswith(prefix):
return repl + s[len(prefix) :].strip()
return s
The provided code snippet includes necessary dependencies for implementing the `user_intent_parser` function. Write ... | Parses the user intent. |
16,636 | def _replace_prefix(s: str, prefix: str, repl: str):
"""Helper function to replace a prefix from a string."""
if s.startswith(prefix):
return repl + s[len(prefix) :].strip()
return s
The provided code snippet includes necessary dependencies for implementing the `bot_intent_parser` function. Write a... | Parses the bot intent. |
16,637 | def _replace_prefix(s: str, prefix: str, repl: str):
"""Helper function to replace a prefix from a string."""
if s.startswith(prefix):
return repl + s[len(prefix) :].strip()
return s
The provided code snippet includes necessary dependencies for implementing the `bot_message_parser` function. Write ... | Parses the bot messages. |
16,638 | def _replace_prefix(s: str, prefix: str, repl: str):
"""Helper function to replace a prefix from a string."""
if s.startswith(prefix):
return repl + s[len(prefix) :].strip()
return s
The provided code snippet includes necessary dependencies for implementing the `verbose_v1_parser` function. Write a... | Parses completions generated using the `verbose_v1` formatter. This will convert text from the following format: User message: "Hello" User intent: express greeting Bot intent: express greeting Bot message: "Hi" To: user "Hello" express greeting bot express greeting "Hi" |
16,639 | import os
from typing import List, Union
import yaml
from nemoguardrails.llm.types import Task
from nemoguardrails.rails.llm.config import RailsConfig, TaskPrompt
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
class TaskPrompt(BaseModel):
"""Configuration for prompts that will be used for a specific task... | Load the predefined prompts from the `prompts` directory. |
16,640 | import os
from typing import List, Union
import yaml
from nemoguardrails.llm.types import Task
from nemoguardrails.rails.llm.config import RailsConfig, TaskPrompt
_prompts = _load_prompts()
def _get_prompt(
task_name: str, model: str, prompting_mode: str, prompts: List
) -> TaskPrompt:
"""Return the prompt for ... | Return the prompt for the given task. |
16,641 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
def get_colang_history(
events: List[dict],
include_texts: bool = True,
remove_retrieval_events: bool = False,
) -> str:
"""Creates a history ... | Filter that turns an array of events into a colang history. |
16,642 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
def get_colang_history(
events: List[dict],
include_texts: bool = True,
remove_retrieval_events: bool = False,
) -> str:
"""Creates a history ... | Filter that turns an array of events into a colang history. |
16,643 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `to_messages` function. Write a Python function `def to_messages(colang_history... | Filter that given a history in colang format, returns all messages. |
16,644 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `verbose_v1` function. Write a Python function `def verbose_v1(colang_history: ... | Filter that given a history in colang format, returns a verbose version of the history. |
16,645 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `user_assistant_sequence` function. Write a Python function `def user_assistant... | Filter that turns an array of events into a sequence of user/assistant messages. The output will look like: ``` User: hi Assistant: Hello there! User: What can you do? Assistant: I can help with many things. ``` |
16,646 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `remove_text_messages` function. Write a Python function `def remove_text_messa... | Filters that given a history in colang format, removes all texts. |
16,647 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `first_turns` function. Write a Python function `def first_turns(colang_history... | Returns the first n turns from a given colang history. |
16,648 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `last_turns` function. Write a Python function `def last_turns(colang_history: ... | Returns the last n turns from a given colang history. |
16,649 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `indent` function. Write a Python function `def indent(text: str, n_spaces: int... | Indents the provided text with the provided number of spaces. |
16,650 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `user_assistant_sequence_nemollm` function. Write a Python function `def user_a... | Filter that turns an array of events into a sequence of user/assistant messages. The output will look like: ``` <extra_id_1>User hi <extra_id_1>Assistant Hello there! <extra_id_1>User What can you do? <extra_id_1>Assistant I can help with many things. ``` |
16,651 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
def _previous_line(lines: List[str], i: int):
"""Returns the previous lines, skipping comments."""
i = i - 1
while i > 0 and lines[i].strip().start... | Filter that given a history in colang format, returns a messages string in the chat format used by NeMo LLM models. |
16,652 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
def remove_trailing_new_line(s: str):
if s.endswith("\n"):
s = s[:-1]
return s | null |
16,653 | import re
import textwrap
from typing import List
from nemoguardrails.actions.llm.utils import (
get_colang_history,
remove_action_intent_identifiers,
)
The provided code snippet includes necessary dependencies for implementing the `conversation_to_events` function. Write a Python function `def conversation_to... | Filter that given a conversation, returns a list of events. |
16,654 | import asyncio
import logging
from typing import Any, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.llms.huggingface_pipeline... | Automatically discover all LLM providers from LangChain. |
16,655 | import asyncio
import logging
from typing import Any, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.llms.huggingface_pipeline... | Register an additional LLM provider. |
16,656 | import asyncio
import logging
from typing import Any, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.llms.huggingface_pipeline... | null |
16,657 | import asyncio
import logging
from typing import Any, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.llms.huggingface_pipeline... | Returns the list of supported LLM providers. |
16,658 | import asyncio
import os
from typing import Dict, List, Optional
import aiohttp
from prompt_toolkit import PromptSession
from prompt_toolkit.patch_stdout import patch_stdout
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.logging i... | Run a chat session in the terminal. Args: config_path (Optional[str]): The path to the configuration file. Defaults to None. verbose (bool): Whether to run in verbose mode. Defaults to False. verbose_llm_calls (bool): Whether to print the prompts and the completions. Defaults to False. streaming (bool): Whether to enab... |
16,659 | import asyncio
import os
import nest_asyncio
nest_asyncio_patch_applied = False
def apply():
global nest_asyncio_patch_applied
if os.environ.get("DISABLE_NEST_ASYNCIO", "true").lower() not in [
"true",
"1",
"yes",
]:
nest_asyncio.apply()
nest_asyncio_patch_applied =... | null |
16,660 | import asyncio
import os
import nest_asyncio
nest_asyncio_patch_applied = False
The provided code snippet includes necessary dependencies for implementing the `check_sync_call_from_async_loop` function. Write a Python function `def check_sync_call_from_async_loop()` to solve the following problem:
Helper to check if a... | Helper to check if a sync call is made from an async loop. Returns True if a sync call is made from an async loop. |
16,661 | import logging
from typing import Optional
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.utils import new_event_dict
class ActionResult:
"""Data class representing the result of an action.
Attributes:
return_value (Optional[Any]): The value returned by the action.... | Creates an event for the bot based on the provided data. Args: event (dict): The input event data. context (Optional[dict]): The context for the action. Defaults to None. Returns: ActionResult: An action result containing the created event. |
16,662 | import logging
import os
from typing import Optional
from urllib import parse
import aiohttp
from nemoguardrails.actions import action
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
APP_ID = os.environ.get("WOLFRAM_ALPHA_APP_ID")... | Makes a request to the Wolfram Alpha API. Args: query (Optional[str]): The query for Wolfram Alpha. Defaults to None. context (Optional[dict]): The context for the execution of the action. Defaults to None. Returns: ActionResult or str: The result of the Wolfram Alpha request. Raises: Exception: If no query is provided... |
16,663 | from dataclasses import dataclass, field
from typing import Any, List, Optional
The provided code snippet includes necessary dependencies for implementing the `action` function. Write a Python function `def action( is_system_action: bool = False, name: Optional[str] = None, execute_async: bool = False, )` ... | Decorator to mark a function or class as an action. Args: is_system_action (bool): Flag indicating if the action is a system action. name (Optional[str]): The name to associate with the action. execute_async: Whether the function should be executed in async mode. Returns: callable: The decorated function or class. |
16,664 | import logging
import re
from ast import literal_eval
from typing import Any, List, Optional
from langchain.llms import BaseLLM
from nemoguardrails.actions.actions import action
from nemoguardrails.actions.llm.generation import LLMGenerationActions
from nemoguardrails.actions.llm.utils import (
escape_flow_name,
... | Remove the leading empty lines if they exist. A line is considered empty if it has only white spaces. |
16,665 | import json
import re
from typing import List
from urllib.parse import quote
from .filter_secrets import contains_secrets
MAX_LEN = 50
The provided code snippet includes necessary dependencies for implementing the `validate_input` function. Write a Python function `def validate_input(attribute: str, validators: List[s... | A generic decorator that can be used by any action (class method or function) for input validation. Supported validation choices are: length and quote. |
16,666 | import json
import re
from typing import List
from urllib.parse import quote
from .filter_secrets import contains_secrets
MAX_LEN = 50
def _is_default_resp(resp):
"""Helper for detecting a default response from LangChain tools."""
pattern = re.compile(r"^No good.*result(?: was)? found$", re.IGNORECASE)
matc... | A generic decorator that can be used by any action (class method or function) for response validation. Supported validation choices are: length, ip_filter, is_default_resp |
16,667 | import logging
from typing import Optional
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.kb.kb import KnowledgeBase
class ActionResult:
"""Data class representing the result of an action.
Attributes:
return_value (Optional[Any]): The value returned by the action.
... | Retrieve relevant knowledge chunks and update the context. Args: context (Optional[dict]): The context for the execution of the action. Defaults to None. kb (Optional[KnowledgeBase]): The KnowledgeBase to search for relevant chunks. Defaults to None. Returns: ActionResult: An action result containing the retrieved rele... |
16,668 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Converts a flow to colang format. Example flow: ``` - user: ask capabilities - bot: inform capabilities ``` to colang: ``` user ask capabilities bot inform capabilities ``` |
16,669 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the retrieved chunks for current user utterance from the events. |
16,670 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the last user utterance from the events. |
16,671 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the last user utterance from the events. |
16,672 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the last user intent from the events. |
16,673 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the last user intent from the events. |
16,674 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the last bot utterance from the events. |
16,675 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Helper that given a history in colang format, removes all texts. |
16,676 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Helper that returns the first non-empty line from a string |
16,677 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Helper that returns a list with the top k non-empty lines from a string. If there are less than k non-empty lines, it returns a smaller number of lines. |
16,678 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Returns the first action before an empty line. |
16,679 | import re
from typing import List, Optional, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIM... | Escape invalid keywords in flow names. |
16,680 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Returns the list of available rails configurations. |
16,681 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Chat completion for the provided conversation. TODO: add support for explicit state object. |
16,682 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Returns the list of available challenges for red teaming. |
16,683 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Registers a DataStore to be used by the server. |
16,684 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Register any additional challenges, if available at startup. |
16,685 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | Register an additional logger |
16,686 | import asyncio
import contextvars
import importlib.util
import json
import logging
import os.path
import time
from typing import List, Optional
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Fi... | null |
16,687 | import json
from nemoguardrails.llm.providers import get_llm_provider, get_llm_provider_names
from nemoguardrails.rails.llm.config import Model
class Model(BaseModel):
"""Configuration of a model used by the rails engine.
Typically, the main model is configured e.g.:
{
"type": "main",
"eng... | Initializes the model from LLM provider. |
16,688 | import json
from nemoguardrails.llm.providers import get_llm_provider, get_llm_provider_names
from nemoguardrails.rails.llm.config import Model
The provided code snippet includes necessary dependencies for implementing the `load_dataset` function. Write a Python function `def load_dataset(dataset_path: str)` to solve ... | Loads a dataset from a file. |
16,689 | import json
import typer
def load_dataset(input_path, split="harmful"):
"""
Loads the dataset from the given path.
Args:
input_path (str): The path to the dataset.
split (str, optional): The split of the dataset (harmful or helpful). Defaults to "harmful".
Returns:
dict or list: ... | Extracts the first turn harmful prompts from the red team attempts dataset. Args: input_path (str): The path to the dataset. rating (float): The harmfulness rating. Returns: None |
16,690 | import json
import typer
def load_dataset(input_path, split="harmful"):
"""
Loads the dataset from the given path.
Args:
input_path (str): The path to the dataset.
split (str, optional): The split of the dataset (harmful or helpful). Defaults to "harmful".
Returns:
dict or list: ... | Extracts the first turn helpful prompts from the helpful-base dataset. Args: input_path (str): The path to the dataset. Returns: None |
16,691 | import logging
from typing import List
import typer
from nemoguardrails.eval.evaluate_factcheck import FactCheckEvaluation
from nemoguardrails.eval.evaluate_hallucination import HallucinationRailsEvaluation
from nemoguardrails.eval.evaluate_moderation import ModerationRailsEvaluation
from nemoguardrails.eval.evaluate_t... | Evaluates the performance of the topical rails defined in a Guardrails application. Computes accuracy for canonical form detection, next step generation, and next bot message generation. Only a single Guardrails application can be specified in the config option. Args: config (List[str], optional): Path to a directory c... |
16,692 | import logging
from typing import List
import typer
from nemoguardrails.eval.evaluate_factcheck import FactCheckEvaluation
from nemoguardrails.eval.evaluate_hallucination import HallucinationRailsEvaluation
from nemoguardrails.eval.evaluate_moderation import ModerationRailsEvaluation
from nemoguardrails.eval.evaluate_t... | Evaluate the performance of the moderation rails defined in a Guardrails application. This command computes accuracy for jailbreak detection and output moderation. Args: config (str): The path to the guardrails config. Defaults to "config". dataset_path (str): Path to the dataset containing prompts. Defaults to "nemogu... |
16,693 | import logging
from typing import List
import typer
from nemoguardrails.eval.evaluate_factcheck import FactCheckEvaluation
from nemoguardrails.eval.evaluate_hallucination import HallucinationRailsEvaluation
from nemoguardrails.eval.evaluate_moderation import ModerationRailsEvaluation
from nemoguardrails.eval.evaluate_t... | Evaluate the performance of the hallucination rails defined in a Guardrails application. This command computes accuracy for hallucination detection. Args: config (str): The path to the guardrails config. Defaults to "config". dataset_path (str): Dataset path. Defaults to "nemoguardrails/eval/data/hallucination/sample.t... |
16,694 | import logging
from typing import List
import typer
from nemoguardrails.eval.evaluate_factcheck import FactCheckEvaluation
from nemoguardrails.eval.evaluate_hallucination import HallucinationRailsEvaluation
from nemoguardrails.eval.evaluate_moderation import ModerationRailsEvaluation
from nemoguardrails.eval.evaluate_t... | Evaluate the performance of the fact-checking rails defined in a Guardrails application. This command computes accuracy for fact-checking. Negatives can be created synthetically by an LLM that acts as an adversary and modifies the answer to make it incorrect. Args: config (str): The path to the guardrails config. Defau... |
16,695 | import asyncio
import json
import os
import random
import textwrap
from typing import Dict, List, Optional
import numpy as np
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions.llm.utils import (
get_last_bot_intent_event,
get_last_bot_utterance_event,
get_last_user_intent_event,
)... | Wrapper for the evaluate_topical_rails method which is async. |
16,696 | import asyncio
import json
import os
import random
import textwrap
from typing import Dict, List, Optional
import numpy as np
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions.llm.utils import (
get_last_bot_intent_event,
get_last_bot_utterance_event,
get_last_user_intent_event,
)... | Compute the dot product between two embeddings using numpy functions. |
16,697 | import asyncio
import json
import os
import random
import textwrap
from typing import Dict, List, Optional
import numpy as np
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions.llm.utils import (
get_last_bot_intent_event,
get_last_bot_utterance_event,
get_last_user_intent_event,
)... | Extracts a test set of user messages from a config. Args: config: The config from which the test set will be extracted. test_set_percentage: The percentage used for the test set. test_set: A dictionary where the test set will be added. max_samples_per_intent: A limit on the number of samples per intent to be enforced. |
16,698 | import os
import re
import subprocess
from pathlib import Path
import typer
import yaml
def run_nbdoc_build(srcdir, force_all):
try:
# Run the nbdoc_build command with specified arguments
subprocess.run(
["nbdoc_build", "--srcdir", srcdir, "--force_all", str(force_all)],
chec... | Convert a Jupyter notebook in the provided folder to .md. It creates a README.md file next to the Jupyter notebook. |
16,699 | import torch
import numpy as np
from PIL import Image
from controlnet_aux import OpenposeDetector
from model_util import get_torch_device
import cv2
from transformers import DPTImageProcessor, DPTForDepthEstimation
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
feature_extr... | null |
16,700 | import torch
import numpy as np
from PIL import Image
from controlnet_aux import OpenposeDetector
from model_util import get_torch_device
import cv2
from transformers import DPTImageProcessor, DPTForDepthEstimation
def get_canny_image(image, t1=100, t2=200):
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)... | null |
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