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 heuristic. Default: 89.79
ps_ppl_threshold (float): Threshold value for prefix/suffix perplexity heuristic. Default: 1845.65
"""
prompt: str
lp_threshold: Optional[float] = 89.79
ps_ppl_threshold: Optional[float] = 1845.65
def ps_ppl_heuristic_check(request: JailbreakCheckRequest):
return checks.check_jailbreak_prefix_suffix_perplexity(
request.prompt, request.ps_ppl_threshold
) | 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 heuristic. Default: 89.79
ps_ppl_threshold (float): Threshold value for prefix/suffix perplexity heuristic. Default: 1845.65
"""
prompt: str
lp_threshold: Optional[float] = 89.79
ps_ppl_threshold: Optional[float] = 1845.65
def run_all_heuristics(request: JailbreakCheckRequest):
# Will add other heuristics as they become available
lp_check = checks.check_jailbreak_length_per_perplexity(
request.prompt, request.lp_threshold
)
ps_ppl_check = checks.check_jailbreak_prefix_suffix_perplexity(
request.prompt, request.ps_ppl_threshold
)
jailbreak = any([lp_check["jailbreak"], ps_ppl_check["jailbreak"]])
heuristic_checks = {
"jailbreak": jailbreak,
"length_per_perplexity": lp_check["jailbreak"],
"prefix_suffix_perplexity": ps_ppl_check["jailbreak"],
}
return heuristic_checks | 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(default="0.0.0.0", help="IP address of the host"),
):
uvicorn.run(app, host=host, port=port) | 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.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.explain import LLMCallInfo
def parse_llama_guard_response(response: str) -> Tuple[bool, Optional[List[str]]]:
"""
Parses the response from the Llama Guard LLM and returns a tuple of:
- Whether the response is safe or not.
- If not safe, a list of the violated policies.
"""
response = response.lower().strip()
log.info(f"Llama Guard response: {response}.")
if response.startswith("safe"):
return True, None
# If unsafe, extract the violated policy numbers and return it as an array.
elif response.startswith("unsafe"):
policy_violations = response.split("unsafe")[1].strip().split(" ")
log.info(f"Violated policies: {policy_violations}")
return False, policy_violations
log.warning(
f"""Unexpected Llama Guard response: {response}\n
If prompted correctly, it should always start with 'safe' or 'unsafe'"""
)
return False, []
async def llm_call(
llm: BaseLanguageModel,
prompt: Union[str, List[dict]],
stop: Optional[List[str]] = None,
custom_callback_handlers: Optional[List[AsyncCallbackHandler]] = None,
) -> str:
"""Calls the LLM with a prompt and returns the generated text."""
# We initialize a new LLM call if we don't have one already
llm_call_info = llm_call_info_var.get()
if llm_call_info is None:
llm_call_info = LLMCallInfo()
llm_call_info_var.set(llm_call_info)
if custom_callback_handlers and custom_callback_handlers != [None]:
all_callbacks = BaseCallbackManager(
handlers=logging_callbacks.handlers + custom_callback_handlers,
inheritable_handlers=logging_callbacks.handlers + custom_callback_handlers,
)
else:
all_callbacks = logging_callbacks
if isinstance(prompt, str):
# stop sinks here
result = await llm.agenerate_prompt(
[StringPromptValue(text=prompt)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
# TODO: error handling
return result.generations[0][0].text
else:
# We first need to translate the array of messages into LangChain message format
messages = []
for _msg in prompt:
if _msg["type"] == "user":
messages.append(HumanMessage(content=_msg["content"]))
elif _msg["type"] in ["bot", "assistant"]:
messages.append(AIMessage(content=_msg["content"]))
elif _msg["type"] == "system":
messages.append(SystemMessage(content=_msg["content"]))
else:
raise ValueError(f"Unknown message type {_msg['type']}")
result = await llm.agenerate_prompt(
[ChatPromptValue(messages=messages)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
return result.generations[0][0].text
llm_call_info_var = contextvars.ContextVar("llm_call_info", default=None)
def llm_params(llm: BaseLanguageModel, **kwargs):
"""Returns a parameter manager for the given language model."""
_llm_params = _param_managers.get(llm.__class__, LLMParams)
return _llm_params(llm, **kwargs)
class LLMTaskManager:
"""Interface for interacting with an LLM in a task-oriented way."""
def __init__(self, config: RailsConfig):
# Save the config as we need access to instructions and sample conversations.
self.config = config
# Initialize the environment for rendering templates.
self.env = Environment()
# Register the default filters.
self.env.filters["colang"] = colang
self.env.filters["colang_without_identifiers"] = colang_without_identifiers
self.env.filters["remove_text_messages"] = remove_text_messages
self.env.filters["first_turns"] = first_turns
self.env.filters["last_turns"] = last_turns
self.env.filters["indent"] = indent
self.env.filters["user_assistant_sequence"] = user_assistant_sequence
self.env.filters[
"user_assistant_sequence_nemollm"
] = user_assistant_sequence_nemollm
self.env.filters["to_messages"] = to_messages
self.env.filters["to_messages_nemollm"] = to_messages_nemollm
self.env.filters["verbose_v1"] = verbose_v1
self.output_parsers = {
"user_intent": user_intent_parser,
"bot_intent": bot_intent_parser,
"bot_message": bot_message_parser,
"verbose_v1": verbose_v1_parser,
}
# The prompt context will hold additional variables that ce also be included
# in the prompt.
self.prompt_context = {}
def _get_general_instructions(self):
"""Helper to extract the general instructions."""
text = ""
for instruction in self.config.instructions:
if instruction.type == "general":
text = instruction.content
# We stop at the first one for now
break
return text
def _render_string(
self,
template_str: str,
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> str:
"""Render a template using the provided context information.
:param template_str: The template to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered template.
:rtype: str.
"""
template = self.env.from_string(template_str)
# First, we extract all the variables from the template.
variables = meta.find_undeclared_variables(self.env.parse(template_str))
# This is the context that will be passed to the template when rendering.
render_context = {
"history": events,
"general_instructions": self._get_general_instructions(),
"sample_conversation": self.config.sample_conversation,
"sample_conversation_two_turns": self.config.sample_conversation,
}
# Copy the context variables to the render context.
if context:
for variable in variables:
if variable in context:
render_context[variable] = context[variable]
# Last but not least, if we have variables from the prompt context, we add them
# to the render context.
if self.prompt_context:
for variable in variables:
if variable in self.prompt_context:
value = self.prompt_context[variable]
# If it's a callable, we compute the value, otherwise we just use it
# as is.
if callable(value):
value = value()
render_context[variable] = value
return template.render(render_context)
def _render_messages(
self,
message_templates: List[Union[str, MessageTemplate]],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> List[dict]:
"""Render a sequence of messages.
:param message_templates: The message templates to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered messages.
"""
messages = []
# We iterate each template and render it.
# If it's a string, it must be a list of messages in JSON format.
# If it's a MessageTemplate, we render it as a message.
for message_template in message_templates:
if isinstance(message_template, str):
str_messages = self._render_string(
message_template, context=context, events=events
)
try:
new_messages = literal_eval(str_messages)
except SyntaxError:
raise ValueError(f"Invalid message template: {message_template}")
messages.extend(new_messages)
else:
content = self._render_string(
message_template.content, context=context, events=events
)
# Don't add empty messages.
if content.strip():
messages.append(
{
"type": message_template.type,
"content": content,
}
)
return messages
def _get_messages_text_length(self, messages: List[dict]) -> int:
"""Return the length of the text in the messages."""
text = ""
for message in messages:
text += message["content"] + "\n"
return len(text)
def render_task_prompt(
self,
task: Union[str, Task],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> Union[str, List[dict]]:
"""Render the prompt for a specific task.
:param task: The name of the task.
:param context: The context for rendering the prompt
:param events: The history of events so far.
:return: A string, for completion models, or an array of messages for chat models.
"""
prompt = get_prompt(self.config, task)
if prompt.content:
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
while len(task_prompt) > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
return task_prompt
else:
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
while task_prompt_length > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
return task_messages
def parse_task_output(self, task: Task, output: str):
"""Parses the output for the provided tasks.
If an output parser is associated with the prompt, it will be used.
Otherwise, the output is returned as is.
"""
prompt = get_prompt(self.config, task)
output_parser = None
if prompt.output_parser:
output_parser = self.output_parsers.get(prompt.output_parser)
if not output_parser:
logging.warning("No output parser found for %s", prompt.output_parser)
if output_parser:
return output_parser(output)
else:
return output
def get_stop_tokens(self, task: Union[str, Task]) -> List[str]:
"""Return the stop sequence for the given task."""
prompt = get_prompt(self.config, task)
return prompt.stop
def register_filter(self, filter_fn: callable, name: Optional[str] = None):
"""Register a custom filter for the rails configuration."""
name = name or filter_fn.__name__
self.env.filters[name] = filter_fn
def register_output_parser(self, output_parser: callable, name: str):
"""Register a custom output parser for the rails configuration."""
self.output_parsers[name] = output_parser
def register_prompt_context(self, name: str, value_or_fn: Any):
"""Register a value to be included in the prompt context.
:name: The name of the variable or function that will be used.
:value_or_fn: The value or function that will be used to generate the value.
"""
self.prompt_context[name] = value_or_fn
class Task(Enum):
"""The various tasks that can be performed by the LLM."""
# Core LLM tasks
GENERAL = "general"
GENERATE_USER_INTENT = "generate_user_intent"
GENERATE_NEXT_STEPS = "generate_next_steps"
GENERATE_BOT_MESSAGE = "generate_bot_message"
GENERATE_INTENT_STEPS_MESSAGE = "generate_intent_steps_message"
GENERATE_VALUE = "generate_value"
GENERATE_VALUE_FROM_INSTRUCTION = "generate_value_from_instruction"
GENERATE_USER_INTENT_FROM_USER_ACTION = "generate_user_intent_from_user_action"
GENERATE_FLOW_FROM_INSTRUCTIONS = "generate_flow_from_instructions"
GENERATE_FLOW_FROM_NAME = "generate_flow_from_name"
GENERATE_FLOW_CONTINUATION = "generate_flow_continuation"
# Tasks for various rails
SELF_CHECK_INPUT = "self_check_input"
SELF_CHECK_OUTPUT = "self_check_output"
LLAMA_GUARD_CHECK_INPUT = "llama_guard_check_input"
LLAMA_GUARD_CHECK_OUTPUT = "llama_guard_check_output"
SELF_CHECK_FACTS = "fact_checking"
CHECK_HALLUCINATION = "check_hallucination"
class LLMCallInfo(LLMCallSummary):
prompt: Optional[str] = Field(
default=None, description="The prompt that was used for the LLM call."
)
completion: Optional[str] = Field(
default=None, description="The completion generated by the LLM."
)
raw_response: Optional[dict] = Field(
default=None,
description="The raw response received from the LLM. "
"May contain additional information, e.g. logprobs.",
)
The provided code snippet includes necessary dependencies for implementing the `llama_guard_check_input` function. Write a Python function `async def llama_guard_check_input( llm_task_manager: LLMTaskManager, context: Optional[dict] = None, llama_guard_llm: Optional[BaseLLM] = None, ) -> dict` to solve the following problem:
Checks user messages using the configured Llama Guard model and the configured prompt containing the safety guidelines.
Here is the function:
async def llama_guard_check_input(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llama_guard_llm: Optional[BaseLLM] = None,
) -> dict:
"""
Checks user messages using the configured Llama Guard model
and the configured prompt containing the safety guidelines.
"""
user_input = context.get("user_message")
check_input_prompt = llm_task_manager.render_task_prompt(
task=Task.LLAMA_GUARD_CHECK_INPUT,
context={
"user_input": user_input,
},
)
stop = llm_task_manager.get_stop_tokens(task=Task.LLAMA_GUARD_CHECK_INPUT)
# Initialize the LLMCallInfo object
llm_call_info_var.set(LLMCallInfo(task=Task.SELF_CHECK_INPUT.value))
with llm_params(llama_guard_llm, temperature=0.0):
result = await llm_call(llama_guard_llm, check_input_prompt, stop=stop)
allowed, policy_violations = parse_llama_guard_response(result)
return {"allowed": allowed, "policy_violations": policy_violations} | 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.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.explain import LLMCallInfo
def parse_llama_guard_response(response: str) -> Tuple[bool, Optional[List[str]]]:
"""
Parses the response from the Llama Guard LLM and returns a tuple of:
- Whether the response is safe or not.
- If not safe, a list of the violated policies.
"""
response = response.lower().strip()
log.info(f"Llama Guard response: {response}.")
if response.startswith("safe"):
return True, None
# If unsafe, extract the violated policy numbers and return it as an array.
elif response.startswith("unsafe"):
policy_violations = response.split("unsafe")[1].strip().split(" ")
log.info(f"Violated policies: {policy_violations}")
return False, policy_violations
log.warning(
f"""Unexpected Llama Guard response: {response}\n
If prompted correctly, it should always start with 'safe' or 'unsafe'"""
)
return False, []
async def llm_call(
llm: BaseLanguageModel,
prompt: Union[str, List[dict]],
stop: Optional[List[str]] = None,
custom_callback_handlers: Optional[List[AsyncCallbackHandler]] = None,
) -> str:
"""Calls the LLM with a prompt and returns the generated text."""
# We initialize a new LLM call if we don't have one already
llm_call_info = llm_call_info_var.get()
if llm_call_info is None:
llm_call_info = LLMCallInfo()
llm_call_info_var.set(llm_call_info)
if custom_callback_handlers and custom_callback_handlers != [None]:
all_callbacks = BaseCallbackManager(
handlers=logging_callbacks.handlers + custom_callback_handlers,
inheritable_handlers=logging_callbacks.handlers + custom_callback_handlers,
)
else:
all_callbacks = logging_callbacks
if isinstance(prompt, str):
# stop sinks here
result = await llm.agenerate_prompt(
[StringPromptValue(text=prompt)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
# TODO: error handling
return result.generations[0][0].text
else:
# We first need to translate the array of messages into LangChain message format
messages = []
for _msg in prompt:
if _msg["type"] == "user":
messages.append(HumanMessage(content=_msg["content"]))
elif _msg["type"] in ["bot", "assistant"]:
messages.append(AIMessage(content=_msg["content"]))
elif _msg["type"] == "system":
messages.append(SystemMessage(content=_msg["content"]))
else:
raise ValueError(f"Unknown message type {_msg['type']}")
result = await llm.agenerate_prompt(
[ChatPromptValue(messages=messages)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
return result.generations[0][0].text
llm_call_info_var = contextvars.ContextVar("llm_call_info", default=None)
def llm_params(llm: BaseLanguageModel, **kwargs):
"""Returns a parameter manager for the given language model."""
_llm_params = _param_managers.get(llm.__class__, LLMParams)
return _llm_params(llm, **kwargs)
class LLMTaskManager:
"""Interface for interacting with an LLM in a task-oriented way."""
def __init__(self, config: RailsConfig):
# Save the config as we need access to instructions and sample conversations.
self.config = config
# Initialize the environment for rendering templates.
self.env = Environment()
# Register the default filters.
self.env.filters["colang"] = colang
self.env.filters["colang_without_identifiers"] = colang_without_identifiers
self.env.filters["remove_text_messages"] = remove_text_messages
self.env.filters["first_turns"] = first_turns
self.env.filters["last_turns"] = last_turns
self.env.filters["indent"] = indent
self.env.filters["user_assistant_sequence"] = user_assistant_sequence
self.env.filters[
"user_assistant_sequence_nemollm"
] = user_assistant_sequence_nemollm
self.env.filters["to_messages"] = to_messages
self.env.filters["to_messages_nemollm"] = to_messages_nemollm
self.env.filters["verbose_v1"] = verbose_v1
self.output_parsers = {
"user_intent": user_intent_parser,
"bot_intent": bot_intent_parser,
"bot_message": bot_message_parser,
"verbose_v1": verbose_v1_parser,
}
# The prompt context will hold additional variables that ce also be included
# in the prompt.
self.prompt_context = {}
def _get_general_instructions(self):
"""Helper to extract the general instructions."""
text = ""
for instruction in self.config.instructions:
if instruction.type == "general":
text = instruction.content
# We stop at the first one for now
break
return text
def _render_string(
self,
template_str: str,
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> str:
"""Render a template using the provided context information.
:param template_str: The template to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered template.
:rtype: str.
"""
template = self.env.from_string(template_str)
# First, we extract all the variables from the template.
variables = meta.find_undeclared_variables(self.env.parse(template_str))
# This is the context that will be passed to the template when rendering.
render_context = {
"history": events,
"general_instructions": self._get_general_instructions(),
"sample_conversation": self.config.sample_conversation,
"sample_conversation_two_turns": self.config.sample_conversation,
}
# Copy the context variables to the render context.
if context:
for variable in variables:
if variable in context:
render_context[variable] = context[variable]
# Last but not least, if we have variables from the prompt context, we add them
# to the render context.
if self.prompt_context:
for variable in variables:
if variable in self.prompt_context:
value = self.prompt_context[variable]
# If it's a callable, we compute the value, otherwise we just use it
# as is.
if callable(value):
value = value()
render_context[variable] = value
return template.render(render_context)
def _render_messages(
self,
message_templates: List[Union[str, MessageTemplate]],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> List[dict]:
"""Render a sequence of messages.
:param message_templates: The message templates to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered messages.
"""
messages = []
# We iterate each template and render it.
# If it's a string, it must be a list of messages in JSON format.
# If it's a MessageTemplate, we render it as a message.
for message_template in message_templates:
if isinstance(message_template, str):
str_messages = self._render_string(
message_template, context=context, events=events
)
try:
new_messages = literal_eval(str_messages)
except SyntaxError:
raise ValueError(f"Invalid message template: {message_template}")
messages.extend(new_messages)
else:
content = self._render_string(
message_template.content, context=context, events=events
)
# Don't add empty messages.
if content.strip():
messages.append(
{
"type": message_template.type,
"content": content,
}
)
return messages
def _get_messages_text_length(self, messages: List[dict]) -> int:
"""Return the length of the text in the messages."""
text = ""
for message in messages:
text += message["content"] + "\n"
return len(text)
def render_task_prompt(
self,
task: Union[str, Task],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> Union[str, List[dict]]:
"""Render the prompt for a specific task.
:param task: The name of the task.
:param context: The context for rendering the prompt
:param events: The history of events so far.
:return: A string, for completion models, or an array of messages for chat models.
"""
prompt = get_prompt(self.config, task)
if prompt.content:
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
while len(task_prompt) > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
return task_prompt
else:
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
while task_prompt_length > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
return task_messages
def parse_task_output(self, task: Task, output: str):
"""Parses the output for the provided tasks.
If an output parser is associated with the prompt, it will be used.
Otherwise, the output is returned as is.
"""
prompt = get_prompt(self.config, task)
output_parser = None
if prompt.output_parser:
output_parser = self.output_parsers.get(prompt.output_parser)
if not output_parser:
logging.warning("No output parser found for %s", prompt.output_parser)
if output_parser:
return output_parser(output)
else:
return output
def get_stop_tokens(self, task: Union[str, Task]) -> List[str]:
"""Return the stop sequence for the given task."""
prompt = get_prompt(self.config, task)
return prompt.stop
def register_filter(self, filter_fn: callable, name: Optional[str] = None):
"""Register a custom filter for the rails configuration."""
name = name or filter_fn.__name__
self.env.filters[name] = filter_fn
def register_output_parser(self, output_parser: callable, name: str):
"""Register a custom output parser for the rails configuration."""
self.output_parsers[name] = output_parser
def register_prompt_context(self, name: str, value_or_fn: Any):
"""Register a value to be included in the prompt context.
:name: The name of the variable or function that will be used.
:value_or_fn: The value or function that will be used to generate the value.
"""
self.prompt_context[name] = value_or_fn
class Task(Enum):
"""The various tasks that can be performed by the LLM."""
# Core LLM tasks
GENERAL = "general"
GENERATE_USER_INTENT = "generate_user_intent"
GENERATE_NEXT_STEPS = "generate_next_steps"
GENERATE_BOT_MESSAGE = "generate_bot_message"
GENERATE_INTENT_STEPS_MESSAGE = "generate_intent_steps_message"
GENERATE_VALUE = "generate_value"
GENERATE_VALUE_FROM_INSTRUCTION = "generate_value_from_instruction"
GENERATE_USER_INTENT_FROM_USER_ACTION = "generate_user_intent_from_user_action"
GENERATE_FLOW_FROM_INSTRUCTIONS = "generate_flow_from_instructions"
GENERATE_FLOW_FROM_NAME = "generate_flow_from_name"
GENERATE_FLOW_CONTINUATION = "generate_flow_continuation"
# Tasks for various rails
SELF_CHECK_INPUT = "self_check_input"
SELF_CHECK_OUTPUT = "self_check_output"
LLAMA_GUARD_CHECK_INPUT = "llama_guard_check_input"
LLAMA_GUARD_CHECK_OUTPUT = "llama_guard_check_output"
SELF_CHECK_FACTS = "fact_checking"
CHECK_HALLUCINATION = "check_hallucination"
class LLMCallInfo(LLMCallSummary):
prompt: Optional[str] = Field(
default=None, description="The prompt that was used for the LLM call."
)
completion: Optional[str] = Field(
default=None, description="The completion generated by the LLM."
)
raw_response: Optional[dict] = Field(
default=None,
description="The raw response received from the LLM. "
"May contain additional information, e.g. logprobs.",
)
The provided code snippet includes necessary dependencies for implementing the `llama_guard_check_output` function. Write a Python function `async def llama_guard_check_output( llm_task_manager: LLMTaskManager, context: Optional[dict] = None, llama_guard_llm: Optional[BaseLLM] = None, ) -> dict` to solve the following problem:
Check the bot response using the configured Llama Guard model and the configured prompt containing the safety guidelines.
Here is the function:
async def llama_guard_check_output(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llama_guard_llm: Optional[BaseLLM] = None,
) -> dict:
"""
Check the bot response using the configured Llama Guard model
and the configured prompt containing the safety guidelines.
"""
user_input = context.get("user_message")
bot_response = context.get("bot_message")
check_output_prompt = llm_task_manager.render_task_prompt(
task=Task.LLAMA_GUARD_CHECK_OUTPUT,
context={
"user_input": user_input,
"bot_response": bot_response,
},
)
stop = llm_task_manager.get_stop_tokens(task=Task.LLAMA_GUARD_CHECK_OUTPUT)
# Initialize the LLMCallInfo object
llm_call_info_var.set(LLMCallInfo(task=Task.SELF_CHECK_OUTPUT.value))
with llm_params(llama_guard_llm, temperature=0.0):
result = await llm_call(llama_guard_llm, check_output_prompt, stop=stop)
allowed, policy_violations = parse_llama_guard_response(result)
return {"allowed": allowed, "policy_violations": policy_violations} | 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 import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.explain import LLMCallInfo
log = logging.getLogger(__name__)
async def llm_call(
llm: BaseLanguageModel,
prompt: Union[str, List[dict]],
stop: Optional[List[str]] = None,
custom_callback_handlers: Optional[List[AsyncCallbackHandler]] = None,
) -> str:
"""Calls the LLM with a prompt and returns the generated text."""
# We initialize a new LLM call if we don't have one already
llm_call_info = llm_call_info_var.get()
if llm_call_info is None:
llm_call_info = LLMCallInfo()
llm_call_info_var.set(llm_call_info)
if custom_callback_handlers and custom_callback_handlers != [None]:
all_callbacks = BaseCallbackManager(
handlers=logging_callbacks.handlers + custom_callback_handlers,
inheritable_handlers=logging_callbacks.handlers + custom_callback_handlers,
)
else:
all_callbacks = logging_callbacks
if isinstance(prompt, str):
# stop sinks here
result = await llm.agenerate_prompt(
[StringPromptValue(text=prompt)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
# TODO: error handling
return result.generations[0][0].text
else:
# We first need to translate the array of messages into LangChain message format
messages = []
for _msg in prompt:
if _msg["type"] == "user":
messages.append(HumanMessage(content=_msg["content"]))
elif _msg["type"] in ["bot", "assistant"]:
messages.append(AIMessage(content=_msg["content"]))
elif _msg["type"] == "system":
messages.append(SystemMessage(content=_msg["content"]))
else:
raise ValueError(f"Unknown message type {_msg['type']}")
result = await llm.agenerate_prompt(
[ChatPromptValue(messages=messages)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
return result.generations[0][0].text
llm_call_info_var = contextvars.ContextVar("llm_call_info", default=None)
def llm_params(llm: BaseLanguageModel, **kwargs):
"""Returns a parameter manager for the given language model."""
_llm_params = _param_managers.get(llm.__class__, LLMParams)
return _llm_params(llm, **kwargs)
class LLMTaskManager:
"""Interface for interacting with an LLM in a task-oriented way."""
def __init__(self, config: RailsConfig):
# Save the config as we need access to instructions and sample conversations.
self.config = config
# Initialize the environment for rendering templates.
self.env = Environment()
# Register the default filters.
self.env.filters["colang"] = colang
self.env.filters["colang_without_identifiers"] = colang_without_identifiers
self.env.filters["remove_text_messages"] = remove_text_messages
self.env.filters["first_turns"] = first_turns
self.env.filters["last_turns"] = last_turns
self.env.filters["indent"] = indent
self.env.filters["user_assistant_sequence"] = user_assistant_sequence
self.env.filters[
"user_assistant_sequence_nemollm"
] = user_assistant_sequence_nemollm
self.env.filters["to_messages"] = to_messages
self.env.filters["to_messages_nemollm"] = to_messages_nemollm
self.env.filters["verbose_v1"] = verbose_v1
self.output_parsers = {
"user_intent": user_intent_parser,
"bot_intent": bot_intent_parser,
"bot_message": bot_message_parser,
"verbose_v1": verbose_v1_parser,
}
# The prompt context will hold additional variables that ce also be included
# in the prompt.
self.prompt_context = {}
def _get_general_instructions(self):
"""Helper to extract the general instructions."""
text = ""
for instruction in self.config.instructions:
if instruction.type == "general":
text = instruction.content
# We stop at the first one for now
break
return text
def _render_string(
self,
template_str: str,
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> str:
"""Render a template using the provided context information.
:param template_str: The template to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered template.
:rtype: str.
"""
template = self.env.from_string(template_str)
# First, we extract all the variables from the template.
variables = meta.find_undeclared_variables(self.env.parse(template_str))
# This is the context that will be passed to the template when rendering.
render_context = {
"history": events,
"general_instructions": self._get_general_instructions(),
"sample_conversation": self.config.sample_conversation,
"sample_conversation_two_turns": self.config.sample_conversation,
}
# Copy the context variables to the render context.
if context:
for variable in variables:
if variable in context:
render_context[variable] = context[variable]
# Last but not least, if we have variables from the prompt context, we add them
# to the render context.
if self.prompt_context:
for variable in variables:
if variable in self.prompt_context:
value = self.prompt_context[variable]
# If it's a callable, we compute the value, otherwise we just use it
# as is.
if callable(value):
value = value()
render_context[variable] = value
return template.render(render_context)
def _render_messages(
self,
message_templates: List[Union[str, MessageTemplate]],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> List[dict]:
"""Render a sequence of messages.
:param message_templates: The message templates to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered messages.
"""
messages = []
# We iterate each template and render it.
# If it's a string, it must be a list of messages in JSON format.
# If it's a MessageTemplate, we render it as a message.
for message_template in message_templates:
if isinstance(message_template, str):
str_messages = self._render_string(
message_template, context=context, events=events
)
try:
new_messages = literal_eval(str_messages)
except SyntaxError:
raise ValueError(f"Invalid message template: {message_template}")
messages.extend(new_messages)
else:
content = self._render_string(
message_template.content, context=context, events=events
)
# Don't add empty messages.
if content.strip():
messages.append(
{
"type": message_template.type,
"content": content,
}
)
return messages
def _get_messages_text_length(self, messages: List[dict]) -> int:
"""Return the length of the text in the messages."""
text = ""
for message in messages:
text += message["content"] + "\n"
return len(text)
def render_task_prompt(
self,
task: Union[str, Task],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> Union[str, List[dict]]:
"""Render the prompt for a specific task.
:param task: The name of the task.
:param context: The context for rendering the prompt
:param events: The history of events so far.
:return: A string, for completion models, or an array of messages for chat models.
"""
prompt = get_prompt(self.config, task)
if prompt.content:
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
while len(task_prompt) > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
return task_prompt
else:
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
while task_prompt_length > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
return task_messages
def parse_task_output(self, task: Task, output: str):
"""Parses the output for the provided tasks.
If an output parser is associated with the prompt, it will be used.
Otherwise, the output is returned as is.
"""
prompt = get_prompt(self.config, task)
output_parser = None
if prompt.output_parser:
output_parser = self.output_parsers.get(prompt.output_parser)
if not output_parser:
logging.warning("No output parser found for %s", prompt.output_parser)
if output_parser:
return output_parser(output)
else:
return output
def get_stop_tokens(self, task: Union[str, Task]) -> List[str]:
"""Return the stop sequence for the given task."""
prompt = get_prompt(self.config, task)
return prompt.stop
def register_filter(self, filter_fn: callable, name: Optional[str] = None):
"""Register a custom filter for the rails configuration."""
name = name or filter_fn.__name__
self.env.filters[name] = filter_fn
def register_output_parser(self, output_parser: callable, name: str):
"""Register a custom output parser for the rails configuration."""
self.output_parsers[name] = output_parser
def register_prompt_context(self, name: str, value_or_fn: Any):
"""Register a value to be included in the prompt context.
:name: The name of the variable or function that will be used.
:value_or_fn: The value or function that will be used to generate the value.
"""
self.prompt_context[name] = value_or_fn
class Task(Enum):
"""The various tasks that can be performed by the LLM."""
# Core LLM tasks
GENERAL = "general"
GENERATE_USER_INTENT = "generate_user_intent"
GENERATE_NEXT_STEPS = "generate_next_steps"
GENERATE_BOT_MESSAGE = "generate_bot_message"
GENERATE_INTENT_STEPS_MESSAGE = "generate_intent_steps_message"
GENERATE_VALUE = "generate_value"
GENERATE_VALUE_FROM_INSTRUCTION = "generate_value_from_instruction"
GENERATE_USER_INTENT_FROM_USER_ACTION = "generate_user_intent_from_user_action"
GENERATE_FLOW_FROM_INSTRUCTIONS = "generate_flow_from_instructions"
GENERATE_FLOW_FROM_NAME = "generate_flow_from_name"
GENERATE_FLOW_CONTINUATION = "generate_flow_continuation"
# Tasks for various rails
SELF_CHECK_INPUT = "self_check_input"
SELF_CHECK_OUTPUT = "self_check_output"
LLAMA_GUARD_CHECK_INPUT = "llama_guard_check_input"
LLAMA_GUARD_CHECK_OUTPUT = "llama_guard_check_output"
SELF_CHECK_FACTS = "fact_checking"
CHECK_HALLUCINATION = "check_hallucination"
class LLMCallInfo(LLMCallSummary):
prompt: Optional[str] = Field(
default=None, description="The prompt that was used for the LLM call."
)
completion: Optional[str] = Field(
default=None, description="The completion generated by the LLM."
)
raw_response: Optional[dict] = Field(
default=None,
description="The raw response received from the LLM. "
"May contain additional information, e.g. logprobs.",
)
The provided code snippet includes necessary dependencies for implementing the `self_check_output` function. Write a Python function `async def self_check_output( llm_task_manager: LLMTaskManager, context: Optional[dict] = None, llm: Optional[BaseLLM] = None, )` to solve the following problem:
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 should be allowed, False otherwise.
Here is the function:
async def self_check_output(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""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 should be allowed, False otherwise.
"""
bot_response = context.get("bot_message")
user_input = context.get("user_message")
if bot_response:
prompt = llm_task_manager.render_task_prompt(
task=Task.SELF_CHECK_OUTPUT,
context={
"user_input": user_input,
"bot_response": bot_response,
},
)
stop = llm_task_manager.get_stop_tokens(task=Task.SELF_CHECK_OUTPUT)
# Initialize the LLMCallInfo object
llm_call_info_var.set(LLMCallInfo(task=Task.SELF_CHECK_OUTPUT.value))
with llm_params(llm, temperature=0.0):
response = await llm_call(llm, prompt, stop=stop)
response = response.lower().strip()
log.info(f"Output self-checking result is: `{response}`.")
if "yes" in response:
return False
return True | 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 should be allowed, False otherwise. |
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 nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.explain import LLMCallInfo
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
class ActionResult:
"""Data class representing the result of an action.
Attributes:
return_value (Optional[Any]): The value returned by the action.
events (Optional[List[dict]]): The events to be added to the stream.
context_updates (Optional[dict]): Updates made to the context by this action.
"""
# The value returned by the action
return_value: Optional[Any] = None
# The events that should be added to the stream
events: Optional[List[dict]] = None
# The updates made to the context by this action
context_updates: Optional[dict] = field(default_factory=dict)
async def llm_call(
llm: BaseLanguageModel,
prompt: Union[str, List[dict]],
stop: Optional[List[str]] = None,
custom_callback_handlers: Optional[List[AsyncCallbackHandler]] = None,
) -> str:
"""Calls the LLM with a prompt and returns the generated text."""
# We initialize a new LLM call if we don't have one already
llm_call_info = llm_call_info_var.get()
if llm_call_info is None:
llm_call_info = LLMCallInfo()
llm_call_info_var.set(llm_call_info)
if custom_callback_handlers and custom_callback_handlers != [None]:
all_callbacks = BaseCallbackManager(
handlers=logging_callbacks.handlers + custom_callback_handlers,
inheritable_handlers=logging_callbacks.handlers + custom_callback_handlers,
)
else:
all_callbacks = logging_callbacks
if isinstance(prompt, str):
# stop sinks here
result = await llm.agenerate_prompt(
[StringPromptValue(text=prompt)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
# TODO: error handling
return result.generations[0][0].text
else:
# We first need to translate the array of messages into LangChain message format
messages = []
for _msg in prompt:
if _msg["type"] == "user":
messages.append(HumanMessage(content=_msg["content"]))
elif _msg["type"] in ["bot", "assistant"]:
messages.append(AIMessage(content=_msg["content"]))
elif _msg["type"] == "system":
messages.append(SystemMessage(content=_msg["content"]))
else:
raise ValueError(f"Unknown message type {_msg['type']}")
result = await llm.agenerate_prompt(
[ChatPromptValue(messages=messages)], callbacks=all_callbacks, stop=stop
)
llm_call_info.raw_response = result.llm_output
return result.generations[0][0].text
llm_call_info_var = contextvars.ContextVar("llm_call_info", default=None)
def llm_params(llm: BaseLanguageModel, **kwargs):
"""Returns a parameter manager for the given language model."""
_llm_params = _param_managers.get(llm.__class__, LLMParams)
return _llm_params(llm, **kwargs)
class LLMTaskManager:
"""Interface for interacting with an LLM in a task-oriented way."""
def __init__(self, config: RailsConfig):
# Save the config as we need access to instructions and sample conversations.
self.config = config
# Initialize the environment for rendering templates.
self.env = Environment()
# Register the default filters.
self.env.filters["colang"] = colang
self.env.filters["colang_without_identifiers"] = colang_without_identifiers
self.env.filters["remove_text_messages"] = remove_text_messages
self.env.filters["first_turns"] = first_turns
self.env.filters["last_turns"] = last_turns
self.env.filters["indent"] = indent
self.env.filters["user_assistant_sequence"] = user_assistant_sequence
self.env.filters[
"user_assistant_sequence_nemollm"
] = user_assistant_sequence_nemollm
self.env.filters["to_messages"] = to_messages
self.env.filters["to_messages_nemollm"] = to_messages_nemollm
self.env.filters["verbose_v1"] = verbose_v1
self.output_parsers = {
"user_intent": user_intent_parser,
"bot_intent": bot_intent_parser,
"bot_message": bot_message_parser,
"verbose_v1": verbose_v1_parser,
}
# The prompt context will hold additional variables that ce also be included
# in the prompt.
self.prompt_context = {}
def _get_general_instructions(self):
"""Helper to extract the general instructions."""
text = ""
for instruction in self.config.instructions:
if instruction.type == "general":
text = instruction.content
# We stop at the first one for now
break
return text
def _render_string(
self,
template_str: str,
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> str:
"""Render a template using the provided context information.
:param template_str: The template to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered template.
:rtype: str.
"""
template = self.env.from_string(template_str)
# First, we extract all the variables from the template.
variables = meta.find_undeclared_variables(self.env.parse(template_str))
# This is the context that will be passed to the template when rendering.
render_context = {
"history": events,
"general_instructions": self._get_general_instructions(),
"sample_conversation": self.config.sample_conversation,
"sample_conversation_two_turns": self.config.sample_conversation,
}
# Copy the context variables to the render context.
if context:
for variable in variables:
if variable in context:
render_context[variable] = context[variable]
# Last but not least, if we have variables from the prompt context, we add them
# to the render context.
if self.prompt_context:
for variable in variables:
if variable in self.prompt_context:
value = self.prompt_context[variable]
# If it's a callable, we compute the value, otherwise we just use it
# as is.
if callable(value):
value = value()
render_context[variable] = value
return template.render(render_context)
def _render_messages(
self,
message_templates: List[Union[str, MessageTemplate]],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> List[dict]:
"""Render a sequence of messages.
:param message_templates: The message templates to render.
:param context: The context for rendering the prompt.
:param events: The history of events so far.
:return: The rendered messages.
"""
messages = []
# We iterate each template and render it.
# If it's a string, it must be a list of messages in JSON format.
# If it's a MessageTemplate, we render it as a message.
for message_template in message_templates:
if isinstance(message_template, str):
str_messages = self._render_string(
message_template, context=context, events=events
)
try:
new_messages = literal_eval(str_messages)
except SyntaxError:
raise ValueError(f"Invalid message template: {message_template}")
messages.extend(new_messages)
else:
content = self._render_string(
message_template.content, context=context, events=events
)
# Don't add empty messages.
if content.strip():
messages.append(
{
"type": message_template.type,
"content": content,
}
)
return messages
def _get_messages_text_length(self, messages: List[dict]) -> int:
"""Return the length of the text in the messages."""
text = ""
for message in messages:
text += message["content"] + "\n"
return len(text)
def render_task_prompt(
self,
task: Union[str, Task],
context: Optional[dict] = None,
events: Optional[List[dict]] = None,
) -> Union[str, List[dict]]:
"""Render the prompt for a specific task.
:param task: The name of the task.
:param context: The context for rendering the prompt
:param events: The history of events so far.
:return: A string, for completion models, or an array of messages for chat models.
"""
prompt = get_prompt(self.config, task)
if prompt.content:
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
while len(task_prompt) > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_prompt = self._render_string(
prompt.content, context=context, events=events
)
return task_prompt
else:
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
while task_prompt_length > prompt.max_length:
if not events:
raise Exception(
f"Prompt exceeds max length of {prompt.max_length} characters even without history"
)
# Remove events from the beginning of the history until the prompt fits.
events = events[1:]
task_messages = self._render_messages(
prompt.messages, context=context, events=events
)
task_prompt_length = self._get_messages_text_length(task_messages)
return task_messages
def parse_task_output(self, task: Task, output: str):
"""Parses the output for the provided tasks.
If an output parser is associated with the prompt, it will be used.
Otherwise, the output is returned as is.
"""
prompt = get_prompt(self.config, task)
output_parser = None
if prompt.output_parser:
output_parser = self.output_parsers.get(prompt.output_parser)
if not output_parser:
logging.warning("No output parser found for %s", prompt.output_parser)
if output_parser:
return output_parser(output)
else:
return output
def get_stop_tokens(self, task: Union[str, Task]) -> List[str]:
"""Return the stop sequence for the given task."""
prompt = get_prompt(self.config, task)
return prompt.stop
def register_filter(self, filter_fn: callable, name: Optional[str] = None):
"""Register a custom filter for the rails configuration."""
name = name or filter_fn.__name__
self.env.filters[name] = filter_fn
def register_output_parser(self, output_parser: callable, name: str):
"""Register a custom output parser for the rails configuration."""
self.output_parsers[name] = output_parser
def register_prompt_context(self, name: str, value_or_fn: Any):
"""Register a value to be included in the prompt context.
:name: The name of the variable or function that will be used.
:value_or_fn: The value or function that will be used to generate the value.
"""
self.prompt_context[name] = value_or_fn
class Task(Enum):
"""The various tasks that can be performed by the LLM."""
# Core LLM tasks
GENERAL = "general"
GENERATE_USER_INTENT = "generate_user_intent"
GENERATE_NEXT_STEPS = "generate_next_steps"
GENERATE_BOT_MESSAGE = "generate_bot_message"
GENERATE_INTENT_STEPS_MESSAGE = "generate_intent_steps_message"
GENERATE_VALUE = "generate_value"
GENERATE_VALUE_FROM_INSTRUCTION = "generate_value_from_instruction"
GENERATE_USER_INTENT_FROM_USER_ACTION = "generate_user_intent_from_user_action"
GENERATE_FLOW_FROM_INSTRUCTIONS = "generate_flow_from_instructions"
GENERATE_FLOW_FROM_NAME = "generate_flow_from_name"
GENERATE_FLOW_CONTINUATION = "generate_flow_continuation"
# Tasks for various rails
SELF_CHECK_INPUT = "self_check_input"
SELF_CHECK_OUTPUT = "self_check_output"
LLAMA_GUARD_CHECK_INPUT = "llama_guard_check_input"
LLAMA_GUARD_CHECK_OUTPUT = "llama_guard_check_output"
SELF_CHECK_FACTS = "fact_checking"
CHECK_HALLUCINATION = "check_hallucination"
class LLMCallInfo(LLMCallSummary):
prompt: Optional[str] = Field(
default=None, description="The prompt that was used for the LLM call."
)
completion: Optional[str] = Field(
default=None, description="The completion generated by the LLM."
)
raw_response: Optional[dict] = Field(
default=None,
description="The raw response received from the LLM. "
"May contain additional information, e.g. logprobs.",
)
def new_event_dict(event_type: str, **payload) -> Dict[str, Any]:
"""Helper to create a generic event structure."""
event: Dict[str, Any] = {
"type": event_type,
"uid": new_uid(),
"event_created_at": datetime.now(timezone.utc).isoformat(),
"source_uid": "NeMoGuardrails",
}
event = {**event, **payload}
if "Action" in event_type:
_add_modality_info(event)
_update_action_properties(event)
ensure_valid_event(event)
return event
The provided code snippet includes necessary dependencies for implementing the `self_check_input` function. Write a Python function `async def self_check_input( llm_task_manager: LLMTaskManager, context: Optional[dict] = None, llm: Optional[BaseLLM] = None, )` to solve the following problem:
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.
Here is the function:
async def self_check_input(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""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.
"""
user_input = context.get("user_message")
if user_input:
prompt = llm_task_manager.render_task_prompt(
task=Task.SELF_CHECK_INPUT,
context={
"user_input": user_input,
},
)
stop = llm_task_manager.get_stop_tokens(task=Task.SELF_CHECK_INPUT)
# Initialize the LLMCallInfo object
llm_call_info_var.set(LLMCallInfo(task=Task.SELF_CHECK_INPUT.value))
with llm_params(llm, temperature=0.0):
check = await llm_call(llm, prompt, stop=stop)
check = check.lower().strip()
log.info(f"Input self-checking result is: `{check}`.")
if "yes" in check:
return ActionResult(
return_value=False,
events=[
new_event_dict(
"mask_prev_user_message", intent="unanswerable message"
)
],
)
return True | 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="0.1.0",
license_info={"name": "Apache License, Version 2.0"},
)
app.action_dispatcher = ActionDispatcher(load_all_actions=True)
class RequestBody(BaseModel):
"""Request body for executing an action."""
action_name: str = ""
action_parameters: Dict = Field(
default={}, description="The list of action parameters."
)
The provided code snippet includes necessary dependencies for implementing the `run_action` function. Write a Python function `async def run_action(body: RequestBody)` to solve the following problem:
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.
Here is the function:
async def run_action(body: RequestBody):
"""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.
"""
log.info(f"Request body: {body}")
result, status = await app.action_dispatcher.execute_action(
body.action_name, body.action_parameters
)
resp = {"status": status, "result": result}
log.info(f"Response: {resp}")
return resp | 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": "Apache License, Version 2.0"},
)
app.action_dispatcher = ActionDispatcher(load_all_actions=True)
The provided code snippet includes necessary dependencies for implementing the `get_actions_list` function. Write a Python function `async def get_actions_list()` to solve the following problem:
Returns the list of available actions.
Here is the function:
async def get_actions_list():
"""Returns the list of available actions."""
return app.action_dispatcher.get_registered_actions() | 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,
BeginScope,
Break,
CatchPatternFailure,
Continue,
ElementType,
EndScope,
ForkHead,
Global,
Goto,
Label,
Log,
MergeHeads,
Print,
Priority,
Return,
Spec,
SpecOp,
SpecType,
WaitForHeads,
)
from nemoguardrails.colang.v2_x.lang.expansion import expand_elements
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.colang.v2_x.runtime.flows import (
Action,
ActionEvent,
ActionStatus,
ColangRuntimeError,
ColangValueError,
Event,
FlowConfig,
FlowHead,
FlowHeadStatus,
FlowState,
FlowStatus,
InteractionLoopType,
InternalEvent,
InternalEvents,
State,
)
from nemoguardrails.colang.v2_x.runtime.utils import new_readable_uid
from nemoguardrails.utils import new_event_dict, new_uid
def initialize_flow(state: State, flow_config: FlowConfig) -> None:
"""Initialize a flow before it can be used and instantiated."""
# Transform and resolve flow configuration element notation (actions, flows, ...)
flow_config.elements = expand_elements(flow_config.elements, state.flow_configs)
# Extract flow loop id if available
if flow_config.source_code:
match = re.search(r"#\W*meta:\W*loop_id\W*=\W*(\w*)", flow_config.source_code)
if match:
flow_config.loop_id = match.group(1)
# Extract all the label elements
for idx, element in enumerate(flow_config.elements):
if isinstance(element, Label):
flow_config.element_labels.update({element["name"]: idx})
def create_flow_instance(
flow_config: FlowConfig, flow_hierarchy_position: str
) -> FlowState:
"""Create a new flow instance that can be added."""
loop_uid: Optional[str] = None
if flow_config.loop_type == InteractionLoopType.NEW:
loop_uid = new_uid()
elif flow_config.loop_type == InteractionLoopType.NAMED:
assert flow_config.loop_id is not None
loop_uid = flow_config.loop_id
# For type InteractionLoopType.PARENT we keep it None to infer loop_id at run_time from parent
flow_uid = new_readable_uid(flow_config.id)
head_uid = new_uid()
flow_state = FlowState(
uid=flow_uid,
flow_id=flow_config.id,
loop_id=loop_uid,
hierarchy_position=flow_hierarchy_position,
heads={
head_uid: FlowHead(
uid=head_uid,
flow_state_uid=flow_uid,
matching_scores=[],
)
},
)
# Add all the flow parameters
for idx, param in enumerate(flow_config.parameters):
flow_state.arguments.append(param.name)
flow_state.context.update(
{
param.name: (
eval_expression(param.default_value_expr, {})
if param.default_value_expr
else None
),
}
)
# Add the positional flow parameter identifiers
for idx, param in enumerate(flow_config.parameters):
flow_state.arguments.append(f"${idx}")
# Add all flow return members
for idx, member in enumerate(flow_config.return_members):
flow_state.context.update(
{
member.name: (
eval_expression(member.default_value_expr, {})
if member.default_value_expr
else None
),
}
)
return flow_state
def add_new_flow_instance(state: State, flow_state: FlowState) -> FlowState:
"""Add a new flow instance to the current state."""
# Update state structures
state.flow_states.update({flow_state.uid: flow_state})
if flow_state.flow_id in state.flow_id_states:
state.flow_id_states[flow_state.flow_id].append(flow_state)
else:
state.flow_id_states.update({flow_state.flow_id: [flow_state]})
flow_head = next(iter(flow_state.heads.values()))
flow_head.position_changed_callback = partial(_flow_head_changed, state, flow_state)
flow_head.status_changed_callback = partial(_flow_head_changed, state, flow_state)
_flow_head_changed(state, flow_state, flow_head)
return flow_state
class State:
"""The state of a flow-driven system."""
# The current set of flow instances with their uid as key.
flow_states: Dict[str, FlowState]
# The configuration of all the flows that are available.
flow_configs: Dict[str, FlowConfig]
# All actions that were instantiated in a flow that is still referenced somewhere
actions: Dict[str, Action] = field(default_factory=dict)
# Queue of internal events
internal_events: Deque[Event] = field(default_factory=deque)
# The main flow state
main_flow_state: Optional[FlowState] = None
# The global context that contains all flow variables defined as global
context: Dict[str, Any] = field(default_factory=dict)
# The resulting events of event-driven system
outgoing_events: List[dict] = field(default_factory=list)
# The most recent N events that have been processed. Will be capped at a
# reasonable limit e.g. 500. The history is needed when prompting the LLM for example.
# TODO: Clean this up to only use one type
last_events: List[Union[dict, Event]] = field(default_factory=list)
# The updates to the context that should be applied before the next step
# TODO: This would be needed if we decide to implement assignments of global variables via context updates
# context_updates: dict = field(default_factory=dict)
########################
# Helper data structures
########################
# Helper dictionary that maps from flow_id (name) to all available flow states
flow_id_states: Dict[str, List[FlowState]] = field(default_factory=dict)
# Helper dictionary () that maps active event matchers (by event names) to relevant heads (flow_state_uid, head_uid)
event_matching_heads: Dict[str, List[Tuple[str, str]]] = field(default_factory=dict)
# Helper dictionary that maps active heads (flow_state_uid, head_uid) to event matching names
event_matching_heads_reverse_map: Dict[Tuple[str, str], str] = field(
default_factory=dict
)
def new_readable_uid(name: str) -> str:
"""Creates a new uuid with a human readable prefix."""
return f"({name}){str(uuid.uuid4())}"
The provided code snippet includes necessary dependencies for implementing the `initialize_state` function. Write a Python function `def initialize_state(state: State) -> None` to solve the following problem:
Initialize the state to make it ready for the story start.
Here is the function:
def initialize_state(state: State) -> None:
"""
Initialize the state to make it ready for the story start.
"""
state.internal_events = deque()
assert "main" in state.flow_configs, "No main flow found!"
state.flow_states = dict()
# TODO: Think about where to put this
for flow_config in state.flow_configs.values():
initialize_flow(state, flow_config)
# Create main flow state first
main_flow_config = state.flow_configs["main"]
main_flow = add_new_flow_instance(
state, create_flow_instance(main_flow_config, "0")
)
if main_flow_config.loop_id is None:
main_flow.loop_id = new_readable_uid("main")
else:
main_flow.loop_id = main_flow_config.loop_id
state.main_flow_state = main_flow | 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,
BeginScope,
Break,
CatchPatternFailure,
Continue,
ElementType,
EndScope,
ForkHead,
Global,
Goto,
Label,
Log,
MergeHeads,
Print,
Priority,
Return,
Spec,
SpecOp,
SpecType,
WaitForHeads,
)
from nemoguardrails.colang.v2_x.lang.expansion import expand_elements
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.colang.v2_x.runtime.flows import (
Action,
ActionEvent,
ActionStatus,
ColangRuntimeError,
ColangValueError,
Event,
FlowConfig,
FlowHead,
FlowHeadStatus,
FlowState,
FlowStatus,
InteractionLoopType,
InternalEvent,
InternalEvents,
State,
)
from nemoguardrails.colang.v2_x.runtime.utils import new_readable_uid
from nemoguardrails.utils import new_event_dict, new_uid
log = logging.getLogger(__name__)
def _context_log(flow_state: FlowState) -> str:
return str(
[
{key: value}
for key, value in flow_state.context.items()
if not isinstance(value, InternalEvent) and not isinstance(value, FlowState)
]
)
def _clean_up_state(state: State) -> None:
"""Perform a clean up of the state to avoid growing memory footprint."""
# Clear all matching scores
for flow_state in state.flow_states.values():
for head in flow_state.heads.values():
head.matching_scores.clear()
# Remove all old flow states based on last status update to limit their number
# TODO: Refactor, we need to have reference based clean up approach
states_to_be_removed = []
for flow_state in state.flow_states.values():
if _is_done_flow(flow_state) and (
datetime.now() - flow_state.status_updated
) > timedelta(seconds=5):
states_to_be_removed.append(flow_state.uid)
for flow_state_uid in states_to_be_removed:
flow_state = state.flow_states[flow_state_uid]
if flow_state.parent_uid and flow_state.parent_uid in state.flow_states:
state.flow_states[flow_state.parent_uid].child_flow_uids.remove(
flow_state_uid
)
flow_states = state.flow_id_states[state.flow_states[flow_state_uid].flow_id]
flow_states.remove(flow_state)
del state.flow_states[flow_state_uid]
# Remove all actions that are no longer referenced
# TODO: Refactor to use no more ids to simplify memory management
new_action_dict: Dict[str, Action] = {}
for flow_state in state.flow_states.values():
for action_uid in flow_state.action_uids:
if action_uid not in new_action_dict:
new_action_dict.update({action_uid: state.actions[action_uid]})
state.actions = new_action_dict
def _process_internal_events_without_default_matchers(
state: State, event: Event
) -> Set[str]:
"""
Process internal events that have no default matchers in flows yet.
Return a set of all the event loop ids that handled the event.
"""
handled_event_loops = set()
if event.name == InternalEvents.START_FLOW:
# Start new flow state instance if flow exists
flow_id = event.arguments["flow_id"]
if flow_id in state.flow_configs and flow_id != "main":
add_new_flow_instance(
state,
create_flow_instance(
state.flow_configs[flow_id],
event.arguments["flow_hierarchy_position"],
),
)
elif event.name == InternalEvents.FINISH_FLOW:
if "flow_instance_uid" in event.arguments:
flow_instance_uid = event.arguments["flow_instance_uid"]
if flow_instance_uid in state.flow_states:
flow_state = state.flow_states[event.arguments["flow_instance_uid"]]
if not _is_inactive_flow(flow_state):
_finish_flow(
state,
flow_state,
event.matching_scores,
)
assert flow_state.loop_id
handled_event_loops.add(flow_state.loop_id)
elif "flow_id" in event.arguments:
flow_id = event.arguments["flow_id"]
if flow_id in state.flow_id_states:
for flow_state in state.flow_id_states[flow_id]:
if not _is_inactive_flow(flow_state):
_finish_flow(
state,
flow_state,
event.matching_scores,
event.arguments.get("deactivate", "False"),
)
assert flow_state.loop_id
handled_event_loops.add(flow_state.loop_id)
elif event.name == InternalEvents.STOP_FLOW:
if "flow_instance_uid" in event.arguments:
flow_instance_uid = event.arguments["flow_instance_uid"]
if flow_instance_uid in state.flow_states:
flow_state = state.flow_states[flow_instance_uid]
if not _is_inactive_flow(flow_state):
_abort_flow(
state=state,
flow_state=flow_state,
matching_scores=event.matching_scores,
deactivate_flow=flow_state.activated,
)
assert flow_state.loop_id
handled_event_loops.add(flow_state.loop_id)
elif "flow_id" in event.arguments:
flow_id = event.arguments["flow_id"]
if flow_id in state.flow_id_states:
for flow_state in state.flow_id_states[flow_id]:
if not _is_inactive_flow(flow_state):
_abort_flow(
state=state,
flow_state=flow_state,
matching_scores=event.matching_scores,
deactivate_flow=flow_state.activated,
)
assert flow_state.loop_id
handled_event_loops.add(flow_state.loop_id)
# TODO: Add support for all flow instances of same flow with "flow_id"
# elif event.name == "ResumeFlow":
# pass
# elif event.name == "PauseFlow":
# pass
elif (
event.name == InternalEvents.BOT_INTENT_LOG
or event.name == InternalEvents.USER_INTENT_LOG
or event.name == InternalEvents.BOT_ACTION_LOG
or event.name == InternalEvents.USER_ACTION_LOG
):
# We also record the flow finished events in the history
state.last_events.append(event)
handled_event_loops.add("all_loops")
return handled_event_loops
def _get_all_head_candidates(state: State, event: Event) -> List[Tuple[str, str]]:
"""
Find all heads that are on a potential match with the event.
Returns those heads in a flow hierarchical order.
"""
# Find all heads of flows where the event is relevant
head_candidates = state.event_matching_heads.get(event.name, []).copy()
# TODO: We still need to check for those events since they could fail
# Let's implement that by an explicit keyword for mismatching, e.g. 'not'
if event.name == InternalEvents.FLOW_FINISHED:
head_candidates.extend(
state.event_matching_heads.get(InternalEvents.FLOW_STARTED, [])
)
head_candidates.extend(
state.event_matching_heads.get(InternalEvents.FLOW_FAILED, [])
)
elif event.name == InternalEvents.FLOW_FAILED:
head_candidates.extend(
state.event_matching_heads.get(InternalEvents.FLOW_STARTED, [])
)
head_candidates.extend(
state.event_matching_heads.get(InternalEvents.FLOW_FINISHED, [])
)
# Ensure that event order is related to flow hierarchy
sorted_head_candidates = sorted(
head_candidates,
key=lambda s: state.flow_states[s[0]].hierarchy_position,
)
return sorted_head_candidates
def _handle_internal_event_matching(
state: State, event: Event, heads_matching: List[FlowHead]
) -> None:
for head in heads_matching:
element = get_element_from_head(state, head)
flow_state = get_flow_state_from_head(state, head)
# Create a potential reference from the match
if (
element is not None
and isinstance(element, SpecOp)
and isinstance(element.spec, Spec)
and element.spec.ref is not None
):
flow_state.context.update(
_create_event_reference(state, flow_state, element, event)
)
if (
event.name == InternalEvents.START_FLOW
and event.arguments["flow_id"] == flow_state.flow_id
and head.position == 0
):
_start_flow(state, flow_state, event.arguments)
elif event.name == InternalEvents.FLOW_STARTED:
# Add started flow to active scopes
for scope_uid in head.scope_uids:
if scope_uid in flow_state.scopes:
flow_state.scopes[scope_uid][0].append(
event.arguments["source_flow_instance_uid"]
)
# elif event.name == InternalEvents.FINISH_FLOW:
# _finish_flow(new_state, flow_state)
# TODO: Introduce default matching statements with heads for all flows
# elif event.name == InternalEvents.ABORT_FLOW:
# _abort_flow(new_state, flow_state)
# elif event.name == "ResumeFlow":
# pass
# elif event.name == "PauseFlow":
# pass
def _resolve_action_conflicts(
state: State, actionable_heads: List[FlowHead]
) -> List[FlowHead]:
"""Resolve all conflicting action conflicts from actionable heads."""
# Check for potential conflicts between actionable heads
advancing_heads: List[FlowHead] = []
if len(actionable_heads) == 1:
# If we have only one actionable head there is no conflict
advancing_heads = actionable_heads
_generate_action_event_from_actionable_element(state, list(actionable_heads)[0])
elif len(actionable_heads) > 1:
# Group all actionable heads by their flows interaction loop
head_groups: Dict[str, List[FlowHead]] = {}
for head in actionable_heads:
flow_state = get_flow_state_from_head(state, head)
assert flow_state.loop_id
if flow_state.loop_id in head_groups:
head_groups[flow_state.loop_id].append(head)
else:
head_groups.update({flow_state.loop_id: [head]})
for group in head_groups.values():
max_length = max(len(head.matching_scores) for head in group)
ordered_heads = sorted(
group,
key=lambda head: head.matching_scores
+ [1.0] * (max_length - len(head.matching_scores)),
reverse=True,
)
# Check if we have heads with the exact same matching scores and pick one at random (or-group)
equal_heads_index = next(
(
i
for i, h in enumerate(ordered_heads)
if h.matching_scores != ordered_heads[0].matching_scores
),
len(ordered_heads),
)
picked_head = random.choice(ordered_heads[:equal_heads_index])
winning_element = get_flow_config_from_head(state, picked_head).elements[
picked_head.position
]
assert isinstance(winning_element, SpecOp)
flow_state = get_flow_state_from_head(state, picked_head)
winning_event = get_event_from_element(state, flow_state, winning_element)
log.info(
"Winning action at head: %s scores=%s",
picked_head,
picked_head.matching_scores,
)
advancing_heads.append(picked_head)
_generate_action_event_from_actionable_element(state, picked_head)
for head in ordered_heads:
if head == picked_head:
continue
competing_element = get_flow_config_from_head(state, head).elements[
head.position
]
assert isinstance(competing_element, SpecOp)
competing_flow_state = get_flow_state_from_head(state, head)
competing_event = get_event_from_element(
state, competing_flow_state, competing_element
)
if winning_event.is_equal(competing_event):
if (
isinstance(winning_event, ActionEvent)
and winning_event.action_uid
and isinstance(competing_event, ActionEvent)
and competing_event.action_uid
):
# All heads that are on the exact same action as the winning head
# need to replace their action references with the winning heads action reference
for (
key,
context_variable,
) in competing_flow_state.context.items():
if (
isinstance(context_variable, Action)
and context_variable.uid == competing_event.action_uid
):
competing_flow_state.context[key] = state.actions[
winning_event.action_uid
]
index = competing_flow_state.action_uids.index(
competing_event.action_uid
)
competing_flow_state.action_uids[
index
] = winning_event.action_uid
del state.actions[competing_event.action_uid]
advancing_heads.append(head)
log.info(
"Co-winning action at head: %s scores=%s",
head,
head.matching_scores,
)
elif head.catch_pattern_failure_label:
# If a head defines a pattern failure catch label,
# it will forward the head to the label rather the aborting the flow
head.position = get_flow_config_from_head(
state, head
).element_labels[head.catch_pattern_failure_label[-1]]
advancing_heads.append(head)
log.info(
"Caught loosing action head: %s scores=%s",
head,
head.matching_scores,
)
else:
# Loosing heads will abort the flow
flow_state = get_flow_state_from_head(state, head)
log.info(
"Loosing action at head: %s scores=%s",
head,
head.matching_scores,
)
_abort_flow(state, flow_state, head.matching_scores)
return advancing_heads
def _advance_head_front(state: State, heads: List[FlowHead]) -> List[FlowHead]:
"""
Advance all provided heads to the next blocking elements (actionable, matching, head merge)
and returns all heads on actionable elements.
"""
actionable_heads: List[FlowHead] = []
for head in heads:
log.debug("Advancing head: %s flow_state_uid: %s", head, head.flow_state_uid)
flow_state = get_flow_state_from_head(state, head)
flow_config = get_flow_config_from_head(state, head)
if head.status == FlowHeadStatus.INACTIVE or not _is_listening_flow(flow_state):
continue
elif head.status == FlowHeadStatus.MERGING and len(state.internal_events) > 0:
# We only advance merging heads if all internal events were processed
actionable_heads.append(head)
continue
elif head.status == FlowHeadStatus.ACTIVE:
head.position += 1
if flow_state.status == FlowStatus.WAITING:
flow_state.status = FlowStatus.STARTING
flow_finished = False
flow_aborted = False
try:
new_heads = slide(state, flow_state, flow_config, head)
# Advance all new heads created by a head fork
if len(new_heads) > 0:
for new_head in _advance_head_front(state, new_heads):
if new_head not in actionable_heads:
actionable_heads.append(new_head)
# Add merging heads to the actionable heads since they need to be advanced in the next iteration
if head.status == FlowHeadStatus.MERGING:
actionable_heads.append(head)
if head.position >= len(flow_config.elements):
if flow_state.status == FlowStatus.STOPPING:
flow_aborted = True
else:
flow_finished = True
all_heads_are_waiting = False
if not flow_finished and not flow_aborted:
# Check if all all flow heads are waiting at a 'match' or a 'wait_for_heads' element
all_heads_are_waiting = True
for temp_head in flow_state.active_heads.values():
element = flow_config.elements[temp_head.position]
if not isinstance(element, WaitForHeads) and (
not is_match_op_element(element)
or (isinstance(element, SpecOp) and "internal" in element.info)
):
all_heads_are_waiting = False
break
if flow_finished or all_heads_are_waiting:
if flow_state.status == FlowStatus.STARTING:
flow_state.status = FlowStatus.STARTED
event = create_internal_flow_event(
InternalEvents.FLOW_STARTED, flow_state, head.matching_scores
)
_push_internal_event(state, event)
elif not flow_aborted:
elem = get_element_from_head(state, head)
if elem and is_action_op_element(elem):
actionable_heads.append(head)
except Exception as e:
# In case there were any runtime error the flow will be aborted (fail)
log.warning(
"Colang error: Flow '%s' failed due to runtime exception!",
flow_state.flow_id,
exc_info=True,
)
colang_error_event = Event(
name="ColangError",
arguments={
"error_type": str(type(e).__name__),
"error": str(e),
},
)
_push_internal_event(state, colang_error_event)
flow_aborted = True
if flow_finished:
_finish_flow(state, flow_state, head.matching_scores)
flow_finished = True
log.debug("Flow finished: %s with last element", head.flow_state_uid)
elif flow_aborted:
_abort_flow(state, flow_state, head.matching_scores)
flow_aborted = True
log.debug("Flow aborted: %s by 'abort' statement", head.flow_state_uid)
# Make sure that all actionable heads still exist in flows, otherwise remove them
actionable_heads = [
head
for head in actionable_heads
if head in state.flow_states[head.flow_state_uid].active_heads.values()
]
return actionable_heads
def _abort_flow(
state: State,
flow_state: FlowState,
matching_scores: List[float],
deactivate_flow: bool = False,
) -> None:
"""Abort a flow instance and all its active child flows."""
# abort all running child flows
for child_flow_uid in flow_state.child_flow_uids:
child_flow_state = state.flow_states[child_flow_uid]
if _is_listening_flow(child_flow_state):
_abort_flow(state, child_flow_state, matching_scores, True)
# Abort all started actions that have not finished yet
for action_uid in flow_state.action_uids:
action = state.actions[action_uid]
if (
action.status == ActionStatus.STARTING
or action.status == ActionStatus.STARTED
):
action_event = action.stop_event({})
action.status = ActionStatus.STOPPING
_generate_umim_event(state, action_event)
# Cleanup all head from flow
for head in flow_state.heads.values():
_remove_head_from_event_matching_structures(state, flow_state, head)
flow_state.heads.clear()
flow_state.status = FlowStatus.STOPPED
# Generate FlowFailed event
event = create_internal_flow_event(
InternalEvents.FLOW_FAILED, flow_state, matching_scores
)
_push_internal_event(state, event)
log.info(
"Flow aborted/failed: '%s'",
_get_readable_flow_state_hierarchy(state, flow_state.uid),
)
if (
flow_state.activated
and not deactivate_flow
and not flow_state.new_instance_started
):
event = _create_restart_flow_internal_event(flow_state, matching_scores)
_push_left_internal_event(state, event)
flow_state.new_instance_started = True
def _update_action_status_by_event(state: State, event: ActionEvent) -> None:
for flow_state in state.flow_states.values():
if not _is_listening_flow(flow_state):
# Don't process flows that are not active
continue
for action_uid in flow_state.action_uids:
# TODO: Make sure that the state.action are deleted so we don't need this check
if action_uid in state.actions:
action = state.actions[action_uid]
if action.status != ActionStatus.FINISHED:
action.process_event(event)
def _is_listening_flow(flow_state: FlowState) -> bool:
return (
flow_state.status == FlowStatus.WAITING
or flow_state.status == FlowStatus.STARTED
or flow_state.status == FlowStatus.STARTING
)
def _is_active_flow(flow_state: FlowState) -> bool:
return (
flow_state.status == FlowStatus.STARTED
or flow_state.status == FlowStatus.STARTING
)
def _push_internal_event(state: State, event: Event) -> None:
state.internal_events.append(event)
log.debug("Created internal event: %s", event)
def get_element_from_head(state: State, head: FlowHead) -> Optional[ElementType]:
"""Returns the element at the flow head position"""
flow_config = get_flow_config_from_head(state, head)
if head.position >= 0 and head.position < len(flow_config.elements):
return flow_config.elements[head.position]
else:
return None
def get_flow_config_from_head(state: State, head: FlowHead) -> FlowConfig:
"""Return the flow config of the flow of the head"""
return state.flow_configs[get_flow_state_from_head(state, head).flow_id]
def get_flow_state_from_head(state: State, head: FlowHead) -> FlowState:
"""Return the flow state of the flow head"""
return state.flow_states[head.flow_state_uid]
def is_match_op_element(element: ElementType) -> bool:
"""Check if the given element is a match statement."""
return isinstance(element, SpecOp) and element.op == "match"
def _compute_event_matching_score(
state: State, flow_state: FlowState, head: FlowHead, event: Event
) -> float:
"""Check if the element matches with given event."""
element = get_element_from_head(state, head)
assert (
element is not None
and isinstance(element, SpecOp)
and is_match_op_element(element)
), f"Element '{element}' is not a match element!"
ref_event = get_event_from_element(state, flow_state, element)
if not isinstance(ref_event, type(event)):
return 0.0
return _compute_event_comparison_score(state, event, ref_event, flow_state.priority)
def create_internal_event(
event_name: str, event_args: dict, matching_scores: List[float]
) -> InternalEvent:
"""Returns an internal event for the provided event data"""
event = InternalEvent(
name=event_name,
arguments=event_args,
matching_scores=matching_scores,
)
return event
class InternalEvents:
"""All internal event types. This event will not appear in the event stream and have priority over them."""
START_FLOW = "StartFlow" # Starts a new flow instance
FINISH_FLOW = "FinishFlow" # Flow will be finished successfully
STOP_FLOW = "StopFlow" # Flow will be stopped and failed
FLOW_STARTED = "FlowStarted" # Flow has started (reached first official match statement or end)
FLOW_FINISHED = "FlowFinished" # Flow has finished successfully
FLOW_FAILED = "FlowFailed" # Flow has failed
UNHANDLED_EVENT = "UnhandledEvent" # For any unhandled event in a specific interaction loop we create an unhandled event
# TODO: Check if we could convert them into just an internal list to track action/intents
BOT_INTENT_LOG = "BotIntentLog"
USER_INTENT_LOG = "UserIntentLog"
BOT_ACTION_LOG = "BotActionLog"
USER_ACTION_LOG = "UserActionLog"
ALL = {
START_FLOW,
FINISH_FLOW,
STOP_FLOW,
FLOW_STARTED,
FLOW_FINISHED,
FLOW_FAILED,
UNHANDLED_EVENT,
BOT_INTENT_LOG,
USER_INTENT_LOG,
BOT_ACTION_LOG,
USER_ACTION_LOG,
}
class Event:
"""The base event class."""
# Name of the event
name: str
# Context that contains all relevant event arguments
arguments: dict
# A list of matching scores from the event sequence triggered by an external event
matching_scores: List[float] = field(default_factory=list)
def is_equal(self, other: Event) -> bool:
"""Compares two events in terms of their name and arguments."""
if isinstance(other, Event):
return self.name == other.name and self.arguments == other.arguments
return NotImplemented
def __eq__(self, other: object) -> bool:
if isinstance(other, Event):
return self.is_equal(other)
return False
def __str__(self) -> str:
return f"[bold blue]{self.name}[/] {self.arguments}"
def from_umim_event(cls, event: dict) -> Event:
"""Creates an event from a flat dictionary."""
new_event = Event(event["type"], {})
new_event.arguments = dict(
[(key, event[key]) for key in event if key not in ["type"]]
)
return new_event
class ActionEvent(Event):
"""The action event class."""
# The event can belong to an action
action_uid: Optional[str] = None
# This is the action reference to enable direct access via expressions
# This needs to be consistent with the action_uid
action: Optional[Action] = None
def from_umim_event(cls, event: dict) -> ActionEvent:
"""Creates an event from a flat dictionary."""
new_event = ActionEvent(event["type"], {})
new_event.arguments = dict(
[(key, event[key]) for key in event if key not in ["type"]]
)
if "action_uid" in event:
new_event.action_uid = event["action_uid"]
return new_event
class FlowHeadStatus(Enum):
"""The status of a flow head."""
ACTIVE = "active" # The head is active and either waiting or progressing
INACTIVE = "inactive" # The head is no longer progressing (e.g. is a parent of an active child head)
MERGING = "merging" # The head arrived at a head merging element and will progress only in the next iteration
class FlowHead:
"""The flow head that points to a certain element in the flow"""
# The unique id of a flow head
uid: str
# The flow of the head
flow_state_uid: str
# Matching score history of previous matches that resulted in this head to be advanced
matching_scores: List[float]
# List of all scopes that are relevant for the head
# TODO: Check if scopes are really needed or if they could be replaced by the head forking/merging
scope_uids: List[str] = field(default_factory=list)
# If a flow head is forked it will create new child heads
child_head_uids: List[str] = field(default_factory=list)
# If set, a flow failure will be forwarded to the label, otherwise it will abort/fail the flow
# Mainly used to simplify inner flow logic
catch_pattern_failure_label: List[str] = field(default_factory=list)
# Callback that can be registered to get informed about head position updates
position_changed_callback: Optional[Callable[[FlowHead], None]] = None
# Callback that can be registered to get informed about head status updates
status_changed_callback: Optional[Callable[[FlowHead], None]] = None
# The position of the flow element the head is pointing to
_position: int = 0
def position(self) -> int:
"""Return the current position of the head."""
return self._position
def position(self, position: int) -> None:
"""Set the position of the head."""
if position != self._position:
self._position = position
if self.position_changed_callback is not None:
self.position_changed_callback(self)
# Whether a head is active or not (a head fork will deactivate the parent head)
_status: FlowHeadStatus = FlowHeadStatus.ACTIVE
def status(self) -> FlowHeadStatus:
"""Return the current status of the head."""
return self._status
def status(self, status: FlowHeadStatus) -> None:
"""Set the status of the head."""
if status != self._status:
self._status = status
if self.status_changed_callback is not None:
self.status_changed_callback(self)
def get_child_head_uids(self, state: State) -> List[str]:
"""Return uids of all child heads (recursively)."""
flow_state = state.flow_states[self.flow_state_uid]
child_uids: List[str] = []
for uid in self.child_head_uids:
child_uids.append(uid)
# TODO: Make sure that child head uids are kept up-to-date
if uid in flow_state.heads:
child_uids.extend(flow_state.heads[uid].get_child_head_uids(state))
return child_uids
def __eq__(self, other: Any) -> bool:
if isinstance(other, FlowHead):
return self.uid == other.uid
return NotImplemented
def __hash__(self) -> int:
return hash(self.uid)
def __str__(self) -> str:
return f"flow='{self.flow_state_uid.split(')',1)[0][1:]}' pos={self.position}"
def __repr__(self) -> str:
return f"FlowHead[uid={self.uid}, flow_state_uid={self.flow_state_uid}]"
class State:
"""The state of a flow-driven system."""
# The current set of flow instances with their uid as key.
flow_states: Dict[str, FlowState]
# The configuration of all the flows that are available.
flow_configs: Dict[str, FlowConfig]
# All actions that were instantiated in a flow that is still referenced somewhere
actions: Dict[str, Action] = field(default_factory=dict)
# Queue of internal events
internal_events: Deque[Event] = field(default_factory=deque)
# The main flow state
main_flow_state: Optional[FlowState] = None
# The global context that contains all flow variables defined as global
context: Dict[str, Any] = field(default_factory=dict)
# The resulting events of event-driven system
outgoing_events: List[dict] = field(default_factory=list)
# The most recent N events that have been processed. Will be capped at a
# reasonable limit e.g. 500. The history is needed when prompting the LLM for example.
# TODO: Clean this up to only use one type
last_events: List[Union[dict, Event]] = field(default_factory=list)
# The updates to the context that should be applied before the next step
# TODO: This would be needed if we decide to implement assignments of global variables via context updates
# context_updates: dict = field(default_factory=dict)
########################
# Helper data structures
########################
# Helper dictionary that maps from flow_id (name) to all available flow states
flow_id_states: Dict[str, List[FlowState]] = field(default_factory=dict)
# Helper dictionary () that maps active event matchers (by event names) to relevant heads (flow_state_uid, head_uid)
event_matching_heads: Dict[str, List[Tuple[str, str]]] = field(default_factory=dict)
# Helper dictionary that maps active heads (flow_state_uid, head_uid) to event matching names
event_matching_heads_reverse_map: Dict[Tuple[str, str], str] = field(
default_factory=dict
)
The provided code snippet includes necessary dependencies for implementing the `run_to_completion` function. Write a Python function `def run_to_completion(state: State, external_event: Union[dict, Event]) -> State` to solve the following problem:
Compute the next state of the flow-driven system.
Here is the function:
def run_to_completion(state: State, external_event: Union[dict, Event]) -> State:
"""
Compute the next state of the flow-driven system.
"""
log.info("[bold violet]-> External Event[/]: %s", external_event)
# Convert to event type
converted_external_event: Event
if isinstance(external_event, dict):
if "Action" in external_event["type"]:
converted_external_event = ActionEvent.from_umim_event(external_event)
else:
converted_external_event = Event.from_umim_event(external_event)
elif isinstance(external_event, Event):
converted_external_event = external_event
# Initialize the new state
state.internal_events = deque([converted_external_event])
state.outgoing_events.clear()
_clean_up_state(state)
actionable_heads: List[FlowHead] = []
merging_heads: List[FlowHead] = []
# Main processing loop
heads_are_advancing = True
heads_are_merging = True
while heads_are_advancing:
while heads_are_merging:
while state.internal_events:
event = state.internal_events.popleft()
log.info("Process internal event: %s", event)
# Find all active interaction loops
active_interaction_loops = set()
for flow_state in state.flow_states.values():
if _is_listening_flow(flow_state):
active_interaction_loops.add(flow_state.loop_id)
# TODO: Check if we should rather should do this after the event matching step
# or even skip the event processing
if event.name == "ContextUpdate":
# Update the context
if "data" in event.arguments and isinstance(event.arguments, dict):
state.context.update(event.arguments["data"])
handled_event_loops = _process_internal_events_without_default_matchers(
state, event
)
head_candidates = _get_all_head_candidates(state, event)
heads_matching: List[FlowHead] = []
heads_not_matching: List[FlowHead] = []
heads_failing: List[FlowHead] = []
# Iterate over all potential head candidates and check if we have an event match
for flow_state_uid, head_uid in head_candidates:
flow_state = state.flow_states[flow_state_uid]
head = flow_state.heads[head_uid]
element = get_element_from_head(state, head)
if element is not None and is_match_op_element(element):
matching_score = _compute_event_matching_score(
state, flow_state, head, event
)
if matching_score > 0.0:
# Successful event match
head.matching_scores = event.matching_scores.copy()
head.matching_scores.append(matching_score)
heads_matching.append(head)
if event.name == InternalEvents.START_FLOW:
handled_event_loops.add("all_loops")
else:
assert flow_state.loop_id
handled_event_loops.add(flow_state.loop_id)
log.info(
"Matching head: %s context=%s",
head,
_context_log(flow_state),
)
elif matching_score < 0.0:
# Event match mismatch
heads_failing.append(head)
log.info(
"Matching head failed: %s context=%s",
head,
_context_log(flow_state),
)
else:
# No match nor mismatch
heads_not_matching.append(head)
# Create internal events for unhandled events for every independent interaction loop
unhandled_event_loops = active_interaction_loops - handled_event_loops
if (
"all_loops" not in handled_event_loops
and len(unhandled_event_loops) > 0
and event.name != InternalEvents.UNHANDLED_EVENT
):
arguments = event.arguments.copy()
arguments.update(
{"event": event.name, "loop_ids": unhandled_event_loops}
)
internal_event = create_internal_event(
InternalEvents.UNHANDLED_EVENT, arguments, event.matching_scores
)
_push_internal_event(state, internal_event)
# Sort matching heads to prioritize more specific matches over the others
heads_matching = sorted(
heads_matching, key=lambda x: x.matching_scores, reverse=True
)
_handle_internal_event_matching(state, event, heads_matching)
if isinstance(event, ActionEvent):
# Update actions status in all active flows by current action event
_update_action_status_by_event(state, event)
# Abort all flows with a mismatch
for head in heads_failing:
if head.catch_pattern_failure_label:
head.position = get_flow_config_from_head(
state, head
).element_labels[head.catch_pattern_failure_label[-1]]
heads_matching.append(head)
else:
flow_state = get_flow_state_from_head(state, head)
_abort_flow(state, flow_state, [])
# Advance front of all matching heads to actionable or match statements
for new_head in _advance_head_front(state, heads_matching):
if new_head not in actionable_heads:
actionable_heads.append(new_head)
# Separate merging from actionable heads and remove inactive heads
merging_heads = [
head
for head in actionable_heads
if head.status == FlowHeadStatus.MERGING
]
actionable_heads = [
head
for head in actionable_heads
if head.status == FlowHeadStatus.ACTIVE
]
# Advance all merging heads and create potential new internal events
actionable_heads.extend(_advance_head_front(state, merging_heads))
heads_are_merging = len(merging_heads) > 0
# All internal events are processed and flow heads are either on an action or a match statements
log.debug("All internal event processed -> advance actionable heads:")
# Remove heads from stopped or finished flows
actionable_heads = [
head
for head in actionable_heads
if _is_active_flow(get_flow_state_from_head(state, head))
and head.status == FlowHeadStatus.ACTIVE
]
advancing_heads = _resolve_action_conflicts(state, actionable_heads)
heads_are_advancing = len(advancing_heads) > 0
actionable_heads = _advance_head_front(state, advancing_heads)
heads_are_merging = True
return state | 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,
BeginScope,
Break,
CatchPatternFailure,
Continue,
ElementType,
EndScope,
ForkHead,
Global,
Goto,
Label,
Log,
MergeHeads,
Print,
Priority,
Return,
Spec,
SpecOp,
SpecType,
WaitForHeads,
)
from nemoguardrails.colang.v2_x.lang.expansion import expand_elements
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.colang.v2_x.runtime.flows import (
Action,
ActionEvent,
ActionStatus,
ColangRuntimeError,
ColangValueError,
Event,
FlowConfig,
FlowHead,
FlowHeadStatus,
FlowState,
FlowStatus,
InteractionLoopType,
InternalEvent,
InternalEvents,
State,
)
from nemoguardrails.colang.v2_x.runtime.utils import new_readable_uid
from nemoguardrails.utils import new_event_dict, new_uid
def _is_listening_flow(flow_state: FlowState) -> bool:
return (
flow_state.status == FlowStatus.WAITING
or flow_state.status == FlowStatus.STARTED
or flow_state.status == FlowStatus.STARTING
)
def _is_active_flow(flow_state: FlowState) -> bool:
return (
flow_state.status == FlowStatus.STARTED
or flow_state.status == FlowStatus.STARTING
)
def is_match_op_element(element: ElementType) -> bool:
"""Check if the given element is a match statement."""
return isinstance(element, SpecOp) and element.op == "match"
def _compute_event_comparison_score(
state: State, event: Event, ref_event: Event, priority: Optional[float] = None
) -> float:
"""Check if the given element matches the given event.
Factors that determine the final score:
- match event parameter specificity
- flow priority [0.0-1.0]
- definition order of flow
Returns:
1.0: Exact match (all parameters match)
< 1.0: Fuzzy match (some parameters are missing, but all the others match)
0.0: No match
-1.0: Mismatch -> Event will fail the current match
"""
# Compute matching score based on event argument matching
match_score: float = 1.0
if (
event.name == InternalEvents.START_FLOW
and ref_event.name == InternalEvents.START_FLOW
):
match_score = _compute_arguments_dict_matching_score(
event.arguments, ref_event.arguments
)
if "flow_id" not in ref_event.arguments:
match_score *= 0.9
else:
match_score = float(
ref_event.name == InternalEvents.START_FLOW
and ref_event.arguments["flow_id"] == event.arguments["flow_id"]
)
elif event.name in InternalEvents.ALL and ref_event.name in InternalEvents.ALL:
assert isinstance(event, InternalEvent) and isinstance(ref_event, InternalEvent)
if (
"flow_id" in ref_event.arguments
and "flow_id" in event.arguments
and _compute_arguments_dict_matching_score(
event.arguments["flow_id"], ref_event.arguments["flow_id"]
)
!= 1.0
) or (
ref_event.flow is not None
and "source_flow_instance_uid" in event.arguments
and _compute_arguments_dict_matching_score(
event.arguments["source_flow_instance_uid"], ref_event.flow.uid
)
!= 1.0
):
return 0.0
match_score = _compute_arguments_dict_matching_score(
event.arguments, ref_event.arguments
)
# TODO: Generalize this with mismatch using e.g. the 'not' keyword
if match_score > 0.0:
if "flow_start_uid" in ref_event.arguments and (
(
ref_event.name == InternalEvents.FLOW_FINISHED
and event.name == InternalEvents.FLOW_FAILED
)
or (
ref_event.name == InternalEvents.FLOW_FAILED
and event.name == InternalEvents.FLOW_FINISHED
)
or (
ref_event.name == InternalEvents.FLOW_STARTED
and (
event.name == InternalEvents.FLOW_FINISHED
or event.name == InternalEvents.FLOW_FAILED
)
)
):
# Match failure
return -1.0
elif ref_event.name != event.name:
# Match success
return 0.0
else:
# Its an UMIM event
if ref_event.name != event.name:
return 0.0
event_copy = copy.deepcopy(event)
if hasattr(event, "action_uid") and hasattr(ref_event, "action_uid"):
if (
ref_event.action_uid is not None
and ref_event.action_uid != event.action_uid
):
return 0.0
# TODO: Action event matches can also fail for certain events, e.g. match Started(), received Finished()
if event.action_uid is not None and event.action_uid in state.actions:
action_arguments = state.actions[event.action_uid].start_event_arguments
event_copy.arguments["action_arguments"] = action_arguments
match_score = _compute_arguments_dict_matching_score(
event_copy.arguments, ref_event.arguments
)
# Take into account the priority of the flow
if priority:
match_score *= priority
return match_score
def get_event_from_element(
state: State, flow_state: FlowState, element: SpecOp
) -> Event:
"""
Converts the element into the corresponding event if possible.
Cases:
1) Event as member of an action or flow reference: send/match $ref.Finished(args) (This is action/flow specific)
2) Event as member of an action or flow constructor: send/match UtteranceBotAction(args).Finished(args)
3) Bare event: send/match UtteranceBotActionFinished(args)
"""
assert isinstance(element.spec, Spec)
element_spec: Spec = element.spec
action: Action
if element_spec["var_name"] is not None:
# Case 1)
variable_name = element_spec["var_name"]
if variable_name not in flow_state.context:
raise ColangRuntimeError((f"Unknown variable: '{variable_name}'!"))
# Resolve variable and member attributes
obj = flow_state.context[variable_name]
member = None
if element_spec.members is not None:
for member in element_spec.members[:-1]:
if isinstance(obj, dict):
if member.name not in obj:
raise ColangValueError(f"No attribute '{member.name}' in {obj}")
obj = obj[member.name]
else:
if not member.name or not hasattr(obj, member.name):
raise ColangValueError(f"No attribute '{member.name}' in {obj}")
obj = getattr(obj, member.name)
if element_spec.members is not None:
member = element_spec.members[-1]
if isinstance(obj, Event):
if element_spec.members is not None:
raise ColangValueError("Events have no event attributes!")
return obj
elif member is not None and (
isinstance(obj, Action) or isinstance(obj, FlowState)
):
if element_spec.members is None:
raise ColangValueError("Missing event attributes!")
event_name = member["name"]
event_arguments = member["arguments"]
event_arguments = _evaluate_arguments(
event_arguments, _get_eval_context(state, flow_state)
)
event = obj.get_event(event_name, event_arguments)
if isinstance(event, InternalEvent) and isinstance(obj, FlowState):
event.flow = obj
elif isinstance(event, ActionEvent):
event.action_uid = obj.uid
event.action = None
return event
else:
raise ColangRuntimeError(f"Unsupported type '{type(obj)}'")
elif element_spec.members is not None:
# Case 2)
assert element_spec.name
if element_spec.spec_type == SpecType.FLOW:
# Flow object
flow_config = state.flow_configs[element_spec.name]
temp_flow_state = create_flow_instance(flow_config, "")
flow_event_name = element_spec.members[0]["name"]
flow_event_arguments = element_spec.members[0]["arguments"]
flow_event_arguments = _evaluate_arguments(
flow_event_arguments, _get_eval_context(state, flow_state)
)
flow_event: InternalEvent = temp_flow_state.get_event(
flow_event_name, flow_event_arguments
)
if element["op"] == "match":
# Delete flow reference from event since it is only a helper object
flow_event.flow = None
return flow_event
elif element_spec.spec_type == SpecType.ACTION:
# Action object
action_arguments = _evaluate_arguments(
element_spec.arguments, _get_eval_context(state, flow_state)
)
action = Action(element_spec.name, action_arguments, flow_state.flow_id)
# TODO: refactor the following repetition of code (see above)
event_name = element_spec.members[0]["name"]
event_arguments = element_spec.members[0]["arguments"]
event_arguments = _evaluate_arguments(
event_arguments, _get_eval_context(state, flow_state)
)
action_event: ActionEvent = action.get_event(event_name, event_arguments)
if element["op"] == "match":
# Delete action_uid from event since the action is only a helper object
action_event.action_uid = None
return action_event
else:
# Case 3)
assert element_spec.name
if element_spec.name.islower() or element_spec.name in InternalEvents.ALL:
# Flow event
event_arguments = _evaluate_arguments(
element_spec.arguments, _get_eval_context(state, flow_state)
)
flow_event = InternalEvent(
name=element_spec.name, arguments=event_arguments
)
return flow_event
elif "Action" in element_spec.name:
# Action event
event_arguments = _evaluate_arguments(
element_spec.arguments, _get_eval_context(state, flow_state)
)
action_event = ActionEvent(
name=element_spec.name, arguments=event_arguments
)
return action_event
else:
# Event
event_arguments = _evaluate_arguments(
element_spec.arguments, _get_eval_context(state, flow_state)
)
new_event = Event(name=element_spec.name, arguments=event_arguments)
return new_event
raise ColangRuntimeError("Unsupported case!")
class SpecOp(Element):
"""An operation performed on a spec.
Valid operations are: send, match, start, stop, await, activate, deactivate.
"""
op: str = ""
# TODO: refactor this removing dict and use SpecAnd and SpecOr instead
spec: Union[Spec, dict] = field(default_factory=Spec)
# spec: Union[Spec, SpecAnd, SpecOr] = Spec()
# If the return value of the spec needs to be captured. The return value only makes sense
# for await on flows and actions.
# For compatibility, the return value in all other cases is the same value as the ref.
# TODO: or should it just be None?
return_var_name: Optional[str] = None
# Attribute that can carry SpecOp specific information, currently only used for
# 'match' operations that should not be considered for a flow to have started: info['internal'] = True
info: dict = field(default_factory=dict)
_type: str = "spec_op"
class Event:
"""The base event class."""
# Name of the event
name: str
# Context that contains all relevant event arguments
arguments: dict
# A list of matching scores from the event sequence triggered by an external event
matching_scores: List[float] = field(default_factory=list)
def is_equal(self, other: Event) -> bool:
"""Compares two events in terms of their name and arguments."""
if isinstance(other, Event):
return self.name == other.name and self.arguments == other.arguments
return NotImplemented
def __eq__(self, other: object) -> bool:
if isinstance(other, Event):
return self.is_equal(other)
return False
def __str__(self) -> str:
return f"[bold blue]{self.name}[/] {self.arguments}"
def from_umim_event(cls, event: dict) -> Event:
"""Creates an event from a flat dictionary."""
new_event = Event(event["type"], {})
new_event.arguments = dict(
[(key, event[key]) for key in event if key not in ["type"]]
)
return new_event
class FlowHeadStatus(Enum):
"""The status of a flow head."""
ACTIVE = "active" # The head is active and either waiting or progressing
INACTIVE = "inactive" # The head is no longer progressing (e.g. is a parent of an active child head)
MERGING = "merging" # The head arrived at a head merging element and will progress only in the next iteration
class FlowHead:
"""The flow head that points to a certain element in the flow"""
# The unique id of a flow head
uid: str
# The flow of the head
flow_state_uid: str
# Matching score history of previous matches that resulted in this head to be advanced
matching_scores: List[float]
# List of all scopes that are relevant for the head
# TODO: Check if scopes are really needed or if they could be replaced by the head forking/merging
scope_uids: List[str] = field(default_factory=list)
# If a flow head is forked it will create new child heads
child_head_uids: List[str] = field(default_factory=list)
# If set, a flow failure will be forwarded to the label, otherwise it will abort/fail the flow
# Mainly used to simplify inner flow logic
catch_pattern_failure_label: List[str] = field(default_factory=list)
# Callback that can be registered to get informed about head position updates
position_changed_callback: Optional[Callable[[FlowHead], None]] = None
# Callback that can be registered to get informed about head status updates
status_changed_callback: Optional[Callable[[FlowHead], None]] = None
# The position of the flow element the head is pointing to
_position: int = 0
def position(self) -> int:
"""Return the current position of the head."""
return self._position
def position(self, position: int) -> None:
"""Set the position of the head."""
if position != self._position:
self._position = position
if self.position_changed_callback is not None:
self.position_changed_callback(self)
# Whether a head is active or not (a head fork will deactivate the parent head)
_status: FlowHeadStatus = FlowHeadStatus.ACTIVE
def status(self) -> FlowHeadStatus:
"""Return the current status of the head."""
return self._status
def status(self, status: FlowHeadStatus) -> None:
"""Set the status of the head."""
if status != self._status:
self._status = status
if self.status_changed_callback is not None:
self.status_changed_callback(self)
def get_child_head_uids(self, state: State) -> List[str]:
"""Return uids of all child heads (recursively)."""
flow_state = state.flow_states[self.flow_state_uid]
child_uids: List[str] = []
for uid in self.child_head_uids:
child_uids.append(uid)
# TODO: Make sure that child head uids are kept up-to-date
if uid in flow_state.heads:
child_uids.extend(flow_state.heads[uid].get_child_head_uids(state))
return child_uids
def __eq__(self, other: Any) -> bool:
if isinstance(other, FlowHead):
return self.uid == other.uid
return NotImplemented
def __hash__(self) -> int:
return hash(self.uid)
def __str__(self) -> str:
return f"flow='{self.flow_state_uid.split(')',1)[0][1:]}' pos={self.position}"
def __repr__(self) -> str:
return f"FlowHead[uid={self.uid}, flow_state_uid={self.flow_state_uid}]"
class State:
"""The state of a flow-driven system."""
# The current set of flow instances with their uid as key.
flow_states: Dict[str, FlowState]
# The configuration of all the flows that are available.
flow_configs: Dict[str, FlowConfig]
# All actions that were instantiated in a flow that is still referenced somewhere
actions: Dict[str, Action] = field(default_factory=dict)
# Queue of internal events
internal_events: Deque[Event] = field(default_factory=deque)
# The main flow state
main_flow_state: Optional[FlowState] = None
# The global context that contains all flow variables defined as global
context: Dict[str, Any] = field(default_factory=dict)
# The resulting events of event-driven system
outgoing_events: List[dict] = field(default_factory=list)
# The most recent N events that have been processed. Will be capped at a
# reasonable limit e.g. 500. The history is needed when prompting the LLM for example.
# TODO: Clean this up to only use one type
last_events: List[Union[dict, Event]] = field(default_factory=list)
# The updates to the context that should be applied before the next step
# TODO: This would be needed if we decide to implement assignments of global variables via context updates
# context_updates: dict = field(default_factory=dict)
########################
# Helper data structures
########################
# Helper dictionary that maps from flow_id (name) to all available flow states
flow_id_states: Dict[str, List[FlowState]] = field(default_factory=dict)
# Helper dictionary () that maps active event matchers (by event names) to relevant heads (flow_state_uid, head_uid)
event_matching_heads: Dict[str, List[Tuple[str, str]]] = field(default_factory=dict)
# Helper dictionary that maps active heads (flow_state_uid, head_uid) to event matching names
event_matching_heads_reverse_map: Dict[Tuple[str, str], str] = field(
default_factory=dict
)
The provided code snippet includes necessary dependencies for implementing the `find_all_active_event_matchers` function. Write a Python function `def find_all_active_event_matchers( state: State, event: Optional[Event] = None ) -> List[FlowHead]` to solve the following problem:
Return a list of all active heads that point to an event 'match' element.
Here is the function:
def find_all_active_event_matchers(
state: State, event: Optional[Event] = None
) -> List[FlowHead]:
"""Return a list of all active heads that point to an event 'match' element."""
event_matchers: List[FlowHead] = []
for flow_state in state.flow_states.values():
if not _is_active_flow(flow_state) or not _is_listening_flow(flow_state):
continue
flow_config = state.flow_configs[flow_state.flow_id]
for head in flow_state.active_heads.values():
if head.status != FlowHeadStatus.INACTIVE:
element = flow_config.elements[head.position]
if is_match_op_element(element):
element = cast(SpecOp, element)
if event:
element_event = get_event_from_element(
state, flow_state, element
)
score = _compute_event_comparison_score(
state,
element_event,
event,
)
if score > 0.0:
event_matchers.append(head)
else:
event_matchers.append(head)
return event_matchers | 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,
BeginScope,
Break,
CatchPatternFailure,
Continue,
ElementType,
EndScope,
ForkHead,
Global,
Goto,
Label,
Log,
MergeHeads,
Print,
Priority,
Return,
Spec,
SpecOp,
SpecType,
WaitForHeads,
)
from nemoguardrails.colang.v2_x.lang.expansion import expand_elements
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.colang.v2_x.runtime.flows import (
Action,
ActionEvent,
ActionStatus,
ColangRuntimeError,
ColangValueError,
Event,
FlowConfig,
FlowHead,
FlowHeadStatus,
FlowState,
FlowStatus,
InteractionLoopType,
InternalEvent,
InternalEvents,
State,
)
from nemoguardrails.colang.v2_x.runtime.utils import new_readable_uid
from nemoguardrails.utils import new_event_dict, new_uid
def create_internal_event(
event_name: str, event_args: dict, matching_scores: List[float]
) -> InternalEvent:
"""Returns an internal event for the provided event data"""
event = InternalEvent(
name=event_name,
arguments=event_args,
matching_scores=matching_scores,
)
return event
class InternalEvents:
"""All internal event types. This event will not appear in the event stream and have priority over them."""
START_FLOW = "StartFlow" # Starts a new flow instance
FINISH_FLOW = "FinishFlow" # Flow will be finished successfully
STOP_FLOW = "StopFlow" # Flow will be stopped and failed
FLOW_STARTED = "FlowStarted" # Flow has started (reached first official match statement or end)
FLOW_FINISHED = "FlowFinished" # Flow has finished successfully
FLOW_FAILED = "FlowFailed" # Flow has failed
UNHANDLED_EVENT = "UnhandledEvent" # For any unhandled event in a specific interaction loop we create an unhandled event
# TODO: Check if we could convert them into just an internal list to track action/intents
BOT_INTENT_LOG = "BotIntentLog"
USER_INTENT_LOG = "UserIntentLog"
BOT_ACTION_LOG = "BotActionLog"
USER_ACTION_LOG = "UserActionLog"
ALL = {
START_FLOW,
FINISH_FLOW,
STOP_FLOW,
FLOW_STARTED,
FLOW_FINISHED,
FLOW_FAILED,
UNHANDLED_EVENT,
BOT_INTENT_LOG,
USER_INTENT_LOG,
BOT_ACTION_LOG,
USER_ACTION_LOG,
}
class InternalEvent(Event):
"""The internal event class."""
# An internal event can belong to a flow
flow: Optional[FlowState] = None
The provided code snippet includes necessary dependencies for implementing the `create_finish_flow_internal_event` function. Write a Python function `def create_finish_flow_internal_event( flow_instance_uid: str, source_flow_instance_uid: str, matching_scores: List[float], ) -> InternalEvent` to solve the following problem:
Returns 'FinishFlow' internal event
Here is the function:
def create_finish_flow_internal_event(
flow_instance_uid: str,
source_flow_instance_uid: str,
matching_scores: List[float],
) -> InternalEvent:
"""Returns 'FinishFlow' internal event"""
arguments = {
"flow_instance_uid": flow_instance_uid,
"source_flow_instance_uid": source_flow_instance_uid,
}
return create_internal_event(
InternalEvents.FINISH_FLOW,
arguments,
matching_scores,
) | 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,
BeginScope,
Break,
CatchPatternFailure,
Continue,
ElementType,
EndScope,
ForkHead,
Global,
Goto,
Label,
Log,
MergeHeads,
Print,
Priority,
Return,
Spec,
SpecOp,
SpecType,
WaitForHeads,
)
from nemoguardrails.colang.v2_x.lang.expansion import expand_elements
from nemoguardrails.colang.v2_x.runtime.eval import eval_expression
from nemoguardrails.colang.v2_x.runtime.flows import (
Action,
ActionEvent,
ActionStatus,
ColangRuntimeError,
ColangValueError,
Event,
FlowConfig,
FlowHead,
FlowHeadStatus,
FlowState,
FlowStatus,
InteractionLoopType,
InternalEvent,
InternalEvents,
State,
)
from nemoguardrails.colang.v2_x.runtime.utils import new_readable_uid
from nemoguardrails.utils import new_event_dict, new_uid
def create_internal_event(
event_name: str, event_args: dict, matching_scores: List[float]
) -> InternalEvent:
"""Returns an internal event for the provided event data"""
event = InternalEvent(
name=event_name,
arguments=event_args,
matching_scores=matching_scores,
)
return event
class InternalEvents:
"""All internal event types. This event will not appear in the event stream and have priority over them."""
START_FLOW = "StartFlow" # Starts a new flow instance
FINISH_FLOW = "FinishFlow" # Flow will be finished successfully
STOP_FLOW = "StopFlow" # Flow will be stopped and failed
FLOW_STARTED = "FlowStarted" # Flow has started (reached first official match statement or end)
FLOW_FINISHED = "FlowFinished" # Flow has finished successfully
FLOW_FAILED = "FlowFailed" # Flow has failed
UNHANDLED_EVENT = "UnhandledEvent" # For any unhandled event in a specific interaction loop we create an unhandled event
# TODO: Check if we could convert them into just an internal list to track action/intents
BOT_INTENT_LOG = "BotIntentLog"
USER_INTENT_LOG = "UserIntentLog"
BOT_ACTION_LOG = "BotActionLog"
USER_ACTION_LOG = "UserActionLog"
ALL = {
START_FLOW,
FINISH_FLOW,
STOP_FLOW,
FLOW_STARTED,
FLOW_FINISHED,
FLOW_FAILED,
UNHANDLED_EVENT,
BOT_INTENT_LOG,
USER_INTENT_LOG,
BOT_ACTION_LOG,
USER_ACTION_LOG,
}
class InternalEvent(Event):
"""The internal event class."""
# An internal event can belong to a flow
flow: Optional[FlowState] = None
The provided code snippet includes necessary dependencies for implementing the `create_stop_flow_internal_event` function. Write a Python function `def create_stop_flow_internal_event( flow_instance_uid: str, source_flow_instance_uid: str, matching_scores: List[float], deactivate_flow: bool = False, ) -> InternalEvent` to solve the following problem:
Returns 'StopFlow' internal event
Here is the function:
def create_stop_flow_internal_event(
flow_instance_uid: str,
source_flow_instance_uid: str,
matching_scores: List[float],
deactivate_flow: bool = False,
) -> InternalEvent:
"""Returns 'StopFlow' internal event"""
arguments: Dict[str, Any] = {
"flow_instance_uid": flow_instance_uid,
"source_flow_instance_uid": source_flow_instance_uid,
}
if deactivate_flow:
arguments["activated"] = False
return create_internal_event(
InternalEvents.STOP_FLOW,
arguments,
matching_scores,
) | 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 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.
Here is the function:
def load_lark_parser(grammar_path: str):
"""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.
"""
with open(grammar_path, "r") as f:
grammar = f.read()
return Lark(
grammar,
start="start",
parser="lalr",
lexer="contextual",
postlex=PythonIndenter(),
propagate_positions=True,
) | 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 generate a predictable set of UUIDs to help debugging.
Here is the function:
def new_uuid() -> str:
"""Helper to generate new UUID v4.
In testing mode, it will generate a predictable set of UUIDs to help debugging.
"""
return str(uuid.uuid4()) | 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 isinstance(obj, dict):
return {k: dataclass_to_dict(v) for k, v in obj.items()}
else:
return obj | 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):
return getattr(self, key, None)
def get(self, key, default_value=None):
"""Getter for backward compatibility with dict elements.
TODO: to remove at some point.
"""
return self[key] or default_value
def __eq__(self, other):
if isinstance(other, self.__class__):
return self.__hash__() == other.__hash__()
return NotImplemented
def hash(self):
"""Return the hash for the current object."""
return hash(_make_hashable(self))
The provided code snippet includes necessary dependencies for implementing the `_make_hashable` function. Write a Python function `def _make_hashable(obj: Any) -> Any` to solve the following problem:
Make all subtypes of Element hashable.
Here is the function:
def _make_hashable(obj: Any) -> Any:
"""Make all subtypes of Element hashable."""
if isinstance(obj, dict):
return tuple((k, _make_hashable(v)) for k, v in sorted(obj.items()))
elif isinstance(obj, list):
return tuple(_make_hashable(x) for x in obj)
elif isinstance(obj, tuple):
return tuple(_make_hashable(x) for x in obj)
elif isinstance(obj, Element):
return tuple((k, _make_hashable(v)) for k, v in sorted(vars(obj).items()))
else:
return obj | 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:
"""Colang 2.x parser class"""
def __init__(self, include_source_mapping: bool = False):
self.include_source_mapping = include_source_mapping
self.grammar_path = os.path.join(
os.path.dirname(__file__), "grammar", "colang.lark"
)
# Initialize the Lark Parser
self._lark_parser = load_lark_parser(self.grammar_path)
def get_parsing_tree(self, content: str) -> dict:
"""Helper to get only the parsing tree.
Args:
content: The Colang content.
Returns:
An instance of a parsing tree as returned by Lark.
"""
# NOTE: dealing with EOF is a bit tricky in Lark; the easiest solution
# to avoid some issues arising from that is to append a new line at the end
return self._lark_parser.parse(content + "\n")
def parse_content(
self, content: str, print_tokens: bool = False, print_parsing_tree: bool = False
) -> dict:
"""Parse the provided content and create element structure."""
if print_tokens:
tokens = list(self._lark_parser.lex(content))
for token in tokens:
print(token.__repr__())
tree = self.get_parsing_tree(content)
if print_parsing_tree:
print(tree.pretty())
exclude_flows_from_llm = self._contains_exclude_from_llm_tag(content)
transformer = ColangTransformer(
source=content, include_source_mapping=self.include_source_mapping
)
data = transformer.transform(tree)
result: dict = {"flows": []}
# For the special case when we only have one flow in the colang file
if isinstance(data, Flow):
data.file_info["exclude_from_llm"] = exclude_flows_from_llm
result["flows"].append(data)
else:
# Otherwise, it's a sequence and we take all the flow elements and return them
for element in data["elements"]:
if element["_type"] == "flow":
element.file_info["exclude_from_llm"] = exclude_flows_from_llm
result["flows"].append(element)
return result
def _contains_exclude_from_llm_tag(self, content: str) -> bool:
pattern = r"^# meta: exclude from llm"
return bool(re.search(pattern, content, re.MULTILINE))
The provided code snippet includes necessary dependencies for implementing the `parse_colang_file` function. Write a Python function `def parse_colang_file( _filename: str, content: str, include_source_mapping: bool = True ) -> dict` to solve the following problem:
Parse the content of a .co.
Here is the function:
def parse_colang_file(
_filename: str, content: str, include_source_mapping: bool = True
) -> dict:
"""Parse the content of a .co."""
colang_parser = ColangParser(include_source_mapping=include_source_mapping)
result = colang_parser.parse_content(content, print_tokens=False)
# flows = []
# for flow_data in result["flows"]:
# # elements = parse_flow_elements(items)
# # TODO: extract the source code here
# source_code = ""
# flows.append(
# {
# "id": flow_data["name"],
# "elements": flow_data["elements"],
# "source_code": source_code,
# }
# )
data = {
"flows": result["flows"],
}
return data | 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 FlowConfig:
"""The configuration of a flow."""
# A unique id of the flow.
id: str
# The sequence of elements that compose the flow.
elements: List[dict]
# The priority of the flow. Higher priority flows are executed first.
priority: float = 1.0
# Whether it is an extension flow or not.
# Extension flows can interrupt other flows on actionable steps.
is_extension: bool = False
# Weather this flow can be interrupted or not
is_interruptible: bool = True
# Weather this flow is a subflow
is_subflow: bool = False
# Weather to allow multiple instances of the same flow
allow_multiple: bool = False
# The events that can trigger this flow to advance.
trigger_event_types: List[str] = field(
default_factory=lambda: [
"UserIntent",
"BotIntent",
"run_action",
"InternalSystemActionFinished",
]
)
# The actual source code, if available
source_code: Optional[str] = None
class State:
"""A state of a flow-driven system."""
# The current set of variables in the state.
context: dict
# The current set of flows in the state.
flow_states: List[FlowState]
# The configuration of all the flows that are available.
flow_configs: Dict[str, FlowConfig]
# The full rails configuration object
rails_config: Optional["RailsConfig"] = None
# The next step of the flow-driven system
next_step: Optional[dict] = None
next_step_by_flow_uid: Optional[str] = None
next_step_priority: float = 0.0
# The comment is extract from the source code
next_step_comment: Optional[str] = None
# The updates to the context that should be applied before the next step
context_updates: dict = field(default_factory=dict)
def compute_next_state(state: State, event: dict) -> State:
"""Computes the next state of the flow-driven system.
Currently, this is a very simplified implementation, with the following assumptions:
- All flows are singleton i.e. you can't have multiple instances of the same flow.
- Flows can be interrupted by one flow at a time.
- Flows are resumed when the interruption flow completes.
- No prioritization between flows, the first one that can decide something will be used.
Args:
state (State): The current state of the system.
event (dict): The event triggering the computation.
Returns:
State: The updated state of the system.
"""
# We don't advance flow on `StartInternalSystemAction`, but on `InternalSystemActionFinished`.
if event["type"] == "StartInternalSystemAction":
return state
# We don't need to decide any next step on context updates.
if event["type"] == "ContextUpdate":
# TODO: add support to also remove keys from the context.
# maybe with a special context key e.g. "__remove__": ["key1", "key2"]
state.context.update(event["data"])
state.context_updates = {}
state.next_step = None
return state
# Update the default context variables
# TODO: refactor this logic in a single place
if event["type"] == "UserMessage":
state.context["last_user_message"] = event["text"]
elif event["type"] == "StartUtteranceBotAction":
state.context["last_bot_message"] = event["script"]
state.context["event"] = event
state.context["config"] = state.rails_config
# Initialize the new state
new_state = State(
context=state.context,
flow_states=[],
flow_configs=state.flow_configs,
rails_config=state.rails_config,
)
# The UID of the flow that will determine the next step
new_state.next_step_by_flow_uid = None
# This is to handle an edge case in the simplified implementation
extension_flow_completed = False
# First, we try to advance the existing flows
for flow_state in state.flow_states:
flow_config = state.flow_configs[flow_state.flow_id]
# We skip processing any completed/aborted flows
if (
flow_state.status == FlowStatus.COMPLETED
or flow_state.status == FlowStatus.ABORTED
):
continue
# If the flow was interrupted, we just copy it to the new state
if flow_state.status == FlowStatus.INTERRUPTED:
new_state.flow_states.append(flow_state)
continue
# If it's not a completed flow, we have a valid head element
flow_head_element = flow_config.elements[flow_state.head]
# If the flow is not triggered by the current even type, we copy it as is
if event["type"] not in flow_config.trigger_event_types:
new_state.flow_states.append(flow_state)
# If we don't have a next step, up to this point, and the current flow is on
# an actionable item, we set it as the next step. We adjust the priority
# with 0.9 so that flows that decide on the current event have a higher priority.
_record_next_step(new_state, flow_state, flow_config, priority_modifier=0.9)
continue
# If we're at a branching point, we look at all individual heads.
matching_head = None
if flow_head_element["_type"] == "branch":
for branch_head in flow_head_element["branch_heads"]:
if _is_match(
flow_config.elements[flow_state.head + branch_head], event
):
matching_head = flow_state.head + branch_head + 1
else:
if _is_match(flow_head_element, event):
matching_head = flow_state.head + 1
if matching_head:
# The flow can advance
flow_state.head = matching_head
_slide_with_subflows(new_state, flow_state)
if flow_state.head < 0:
# If a flow finished, we mark it as completed
flow_state.status = FlowStatus.COMPLETED
if flow_config.is_extension:
extension_flow_completed = True
# we don't interrupt on executable elements or if the flow is not interruptible
elif (
_is_actionable(flow_config.elements[flow_state.head])
or not flow_config.is_interruptible
):
flow_state.status = FlowStatus.ABORTED
else:
flow_state.status = FlowStatus.INTERRUPTED
# We copy the flow to the new state
new_state.flow_states.append(flow_state)
# Next, we try to start new flows
for flow_config in state.flow_configs.values():
# We don't allow subflow to start on their own
if flow_config.is_subflow:
continue
# If the flow can't be started multiple times in parallel and
# a flow with the same id is started, we skip.
if not flow_config.allow_multiple and flow_config.id in [
fs.flow_id for fs in new_state.flow_states
]:
continue
# We try to slide first, just in case a flow starts with sliding logic
start_head = slide(new_state, flow_config, 0)
# If the first element matches the current event, we start a new flow
if _is_match(flow_config.elements[start_head], event):
flow_uid = str(uuid.uuid4())
flow_state = FlowState(
uid=flow_uid, flow_id=flow_config.id, head=start_head + 1
)
new_state.flow_states.append(flow_state)
_slide_with_subflows(new_state, flow_state)
# If there's any extension flow that has completed, we re-activate all aborted flows
if extension_flow_completed:
for flow_state in new_state.flow_states:
if flow_state.status == FlowStatus.ABORTED:
flow_state.status = FlowStatus.ACTIVE
# And potentially use them for the next decision
flow_config = state.flow_configs[flow_state.flow_id]
_record_next_step(new_state, flow_state, flow_config)
# If there are any flows that have been interrupted in this iteration, we consider
# them to be interrupted by the flow that determined the next step.
for flow_state in new_state.flow_states:
if (
flow_state.status == FlowStatus.INTERRUPTED
and flow_state.interrupted_by is None
):
flow_state.interrupted_by = new_state.next_step_by_flow_uid
# We compute the decision flow config and state
decision_flow_config = None
decision_flow_state = None
for flow_state in new_state.flow_states:
if flow_state.uid == new_state.next_step_by_flow_uid:
decision_flow_config = state.flow_configs[flow_state.flow_id]
decision_flow_state = flow_state
# If we have aborted flows, and the current flow is an extension, when we interrupt them.
# We are only interested when the extension flow actually decided, not just started.
if (
decision_flow_config
and decision_flow_config.is_extension
and decision_flow_state.head > 1
):
for flow_state in new_state.flow_states:
if (
flow_state.status == FlowStatus.ABORTED
and state.flow_configs[flow_state.flow_id].is_interruptible
):
flow_state.status = FlowStatus.INTERRUPTED
flow_state.interrupted_by = new_state.next_step_by_flow_uid
# If there are flows that were waiting on completed flows, we reactivate them
changes = True
while changes:
changes = False
for flow_state in new_state.flow_states:
if flow_state.status == FlowStatus.INTERRUPTED:
# TODO: optimize this with a dict of statuses
# If already there are no more flows to interrupt, we should resume
should_resume = flow_state.interrupted_by is None
# Check if it was waiting on a completed flow
if not should_resume:
for _flow_state in new_state.flow_states:
if _flow_state.uid == flow_state.interrupted_by:
if _flow_state.status == FlowStatus.COMPLETED:
should_resume = True
break
if should_resume:
flow_state.status = FlowStatus.ACTIVE
flow_state.interrupted_by = None
_slide_with_subflows(new_state, flow_state)
if flow_state.head < 0:
flow_state.status = FlowStatus.COMPLETED
changes = True
return new_state
def _step_to_event(step: dict) -> dict:
"""Converts a next step from a flow element into an actual event.
Args:
step (dict): The next step from a flow element.
Returns:
dict: The corresponding event.
"""
step_type = step["_type"]
if step_type == "run_action":
if step["action_name"] == "utter":
return new_event_dict(
"BotIntent",
intent=step["action_params"]["value"],
)
else:
action_name = step["action_name"]
action_params = step.get("action_params", {})
action_result_key = step.get("action_result_key")
return new_event_dict(
"StartInternalSystemAction",
action_name=action_name,
action_params=action_params,
action_result_key=action_result_key,
)
else:
raise ValueError(f"Unknown next step type: {step_type}")
def new_event_dict(event_type: str, **payload) -> Dict[str, Any]:
"""Helper to create a generic event structure."""
event: Dict[str, Any] = {
"type": event_type,
"uid": new_uid(),
"event_created_at": datetime.now(timezone.utc).isoformat(),
"source_uid": "NeMoGuardrails",
}
event = {**event, **payload}
if "Action" in event_type:
_add_modality_info(event)
_update_action_properties(event)
ensure_valid_event(event)
return event
The provided code snippet includes necessary dependencies for implementing the `compute_next_steps` function. Write a Python function `def compute_next_steps( history: List[dict], flow_configs: Dict[str, FlowConfig], rails_config: "RailsConfig", processing_log: List[dict], ) -> List[dict]` to solve the following problem:
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: List[dict]: The list of computed next steps.
Here is the function:
def compute_next_steps(
history: List[dict],
flow_configs: Dict[str, FlowConfig],
rails_config: "RailsConfig",
processing_log: List[dict],
) -> List[dict]:
"""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:
List[dict]: The list of computed next steps.
"""
state = State(
context={}, flow_states=[], flow_configs=flow_configs, rails_config=rails_config
)
# First, we process the history and apply any alterations e.g. 'hide_prev_turn'
actual_history = []
for event in history:
if event["type"] == "hide_prev_turn":
# we look up the last `UtteranceUserActionFinished` event and remove everything after
end = len(actual_history) - 1
while (
end > 0 and actual_history[end]["type"] != "UtteranceUserActionFinished"
):
end -= 1
assert actual_history[end]["type"] == "UtteranceUserActionFinished"
actual_history = actual_history[0:end]
else:
actual_history.append(event)
steps_history = []
for event in actual_history:
# We append the events to the steps history
steps_history.append(event)
state = compute_next_state(state, event)
# NOTE (Jul 24, Razvan): this is a quick fix. Will debug further.
if event["type"] == "BotIntent" and event["intent"] == "stop":
# Reset all flows
state.flow_states = []
next_steps = []
# If we have context updates after this event, we first add that.
if state.context_updates:
next_steps.append(new_event_dict("ContextUpdate", data=state.context_updates))
# If we have a next step, we make sure to convert it to proper event structure.
if state.next_step:
next_step_event = _step_to_event(state.next_step)
if next_step_event["type"] == "BotIntent" and state.next_step_comment:
# For bot intents, we use the comment as instructions
next_step_event["instructions"] = state.next_step_comment
next_steps.append(next_step_event)
# Finally, we check if there was an explicit "stop" request
if actual_history:
last_event = actual_history[-1]
if last_event["type"] == "BotIntent" and last_event["intent"] == "stop":
# In this case, we remove any next steps
next_steps = []
# If we have a next step, we record the flow that triggered it in the processing log
if next_steps and state.next_step_by_flow_uid:
for flow_state in state.flow_states:
if flow_state.uid == state.next_step_by_flow_uid:
flow_id = flow_state.flow_id
processing_log.append(
{
"type": "step",
"timestamp": time(),
"flow_id": flow_id,
"next_steps": next_steps,
}
)
return next_steps | 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: List[dict]: The list of computed next steps. |
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 provided code snippet includes necessary dependencies for implementing the `compute_context` function. Write a Python function `def compute_context(history: List[dict])` to solve the following problem:
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.
Here is the function:
def compute_context(history: List[dict]):
"""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.
"""
context = {
"last_user_message": None,
"last_bot_message": None,
}
for event in history:
if event["type"] == "ContextUpdate":
context.update(event["data"])
if event["type"] == "UserMessage":
context["last_user_message"] = event["text"]
elif event["type"] == "StartUtteranceBotAction":
context["last_bot_message"] = event["script"]
if history:
context["event"] = history[-1]
return context | 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
s = 0
in_string = False
while i < len(text) - len(word):
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"':
in_string = True
if text[i : i + len(word)] == word:
part = text[s:i].strip()
if len(part) > 0:
parts.append(part)
i += len(word)
s = i
else:
i += 1
if s < len(text):
part = text[s:].strip()
# edge case, make sure the part does not end with the actual word
if part.endswith(word):
part = part[0 : -1 * len(word)]
if len(part) > 0:
parts.append(part)
return parts
The provided code snippet includes necessary dependencies for implementing the `get_numbered_lines` function. Write a Python function `def get_numbered_lines(content: str)` to solve the following problem:
Helper to returned numbered lines. Comments and empty lines are ignored.
Here is the function:
def get_numbered_lines(content: str):
"""Helper to returned numbered lines.
Comments and empty lines are ignored.
"""
raw_lines = content.split("\n")
lines = []
i = 0
multiline_comment = False
current_comment = None
while i < len(raw_lines):
raw_line = raw_lines[i].strip()
# If we have a line comment, we record it
if raw_line.startswith("#"):
if current_comment is None:
current_comment = raw_line[1:].strip()
else:
# For line comments on consecutive lines, we gather them
current_comment += "\n" + raw_line[1:].strip()
# Get rid of empty lines and comments
if len(raw_line) == 0 or raw_line[0] == "#":
i += 1
continue
# If there is a comment at the end of the line, we first remove it
parts = word_split(raw_line, "#")
raw_line = parts[0]
if not multiline_comment and raw_line.startswith('"""'):
if raw_line == '"""' or not raw_line.endswith('"""'):
multiline_comment = True
current_comment = raw_line[3:]
else:
current_comment = raw_line[3:-3]
i += 1
continue
if multiline_comment:
if raw_line.endswith('"""'):
current_comment += "\n" + raw_line[0:-3]
multiline_comment = False
else:
current_comment += "\n" + raw_line
i += 1
continue
# Compute indentation level
ind = 0
while raw_lines[i][ind] == " ":
ind += 1
# As long as the line ends with "\", we also append the next lines
# but without the indentation.
# Also, if there's an active "operator" like "or", we also continue to the next line
text = raw_line
while i < len(raw_lines) - 1 and text[-1] == "\\" or text.endswith(" or"):
i += 1
if text[-1] == "\\":
text = text[0:-1]
if text[-1] != " ":
text = text + " "
text = text + raw_lines[i].strip()
lines.append(
{
# Get rid of any white space
"text": text,
"number": i + 1,
"indentation": ind,
"comment": current_comment,
}
)
current_comment = None
i += 1
return lines | 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 + 1:
new_parts = parts[0:max_instances]
new_parts.append(separator.join(parts[max_instances:]))
parts = new_parts
return parts
The provided code snippet includes necessary dependencies for implementing the `remove_token` function. Write a Python function `def remove_token(token: str, line: str)` to solve the following problem:
Helper to remove a token
Here is the function:
def remove_token(token: str, line: str):
"""Helper to remove a token"""
line = line.strip()
parts = split_max(line, " ", 1)
assert parts[0] == token
return parts[1].strip() if len(parts) > 1 else "" | 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 + 1:
new_parts = parts[0:max_instances]
new_parts.append(separator.join(parts[max_instances:]))
parts = new_parts
return parts
The provided code snippet includes necessary dependencies for implementing the `extract_main_token` function. Write a Python function `def extract_main_token(text: str)` to solve the following problem:
Helper to extract the main token from a line
Here is the function:
def extract_main_token(text: str):
"""Helper to extract the main token from a line"""
main_token = text.split(" ")[0]
# For else, we also want to catch the next keyword (if/when)
if main_token == "else" and text.strip() != "else":
main_token = "else " + split_max(text, " ", 1)[1].strip().split(" ")[0]
if main_token == "go":
main_token = "go " + split_max(text, " ", 1)[1].strip().split(" ")[0]
return main_token | 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.
Here is the function:
def char_split(
text: str, c: str, ignore_parenthesis=False, ignore_strings=False
) -> List[str]:
"""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.
"""
parts = []
# Edge case
if text == "":
return [""]
# The current position
i = 0
# The start of the current part
s = 0
in_string = False
parenthesis_counter = 0
while i < len(text) - 1:
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"' and not ignore_strings:
in_string = True
# Only split by character when not inside a parenthesis
if text[i] == c and parenthesis_counter == 0:
part = text[s:i].strip()
if len(part) > 0:
parts.append(part)
i += 1
s = i
else:
if text[i] in ["(", "[", "{"] and not ignore_parenthesis:
parenthesis_counter += 1
elif text[i] in [")", "]", "}"]:
parenthesis_counter -= 1
i += 1
if s < len(text):
part = text[s:].strip()
if len(part) > 0:
parts.append(part)
return parts | 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 params_tokenize(text):
"""Tokenizer specific to the params parsing."""
tokens = []
# The current position
i = 0
# The start of the current part
s = 0
in_string = False
while i < len(text):
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"':
in_string = True
if text[i] in [" ", "-", ":", ",", "="]:
token = text[s:i].strip()
if len(token) > 0:
tokens.append(token)
if text[i] != " ":
tokens.append(text[i])
i += 1
s = i
else:
i += 1
if s < len(text):
token = text[s:].strip()
if len(token) > 0:
tokens.append(token)
return tokens | 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 function:
def get_first_key(d: dict):
"""Helper to get the first key, which transpiles correctly."""
for k in d.keys():
return k | 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 + 1:
new_parts = parts[0:max_instances]
new_parts.append(separator.join(parts[max_instances:]))
parts = new_parts
return parts
def ws_tokenize(text):
"""Tokenize a text by whitespace and taking strings into account."""
return word_split(text, " ")
The provided code snippet includes necessary dependencies for implementing the `extract_topic_object` function. Write a Python function `def extract_topic_object(text: Text) -> Tuple[Text, Optional[Text]]` to solve the following problem:
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)
Here is the function:
def extract_topic_object(text: Text) -> Tuple[Text, Optional[Text]]:
"""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)
"""
if " " in text:
parts = ws_tokenize(text)
assert len(parts) == 3
assert parts[1] == "for"
return parts[0], parts[2]
elif "(" in text:
parts = split_max(text[0:-1], "(", 1)
assert len(parts) == 2
return parts[0], parts[1]
else:
return text, None | 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 + 1:
new_parts = parts[0:max_instances]
new_parts.append(separator.join(parts[max_instances:]))
parts = new_parts
return parts
The provided code snippet includes necessary dependencies for implementing the `parse_package_name` function. Write a Python function `def parse_package_name(text)` to solve the following problem:
Helper to extract a normalized package name.
Here is the function:
def parse_package_name(text):
"""Helper to extract a normalized package name."""
# get rid of quotes
package_name = text
if package_name[0] == '"' or package_name[0] == "'":
package_name = package_name[1:-1]
# Get rid of the "bot/"
if package_name[0:4] == "bot/":
package_name = split_max(package_name, "/", 1)[1]
return package_name | 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.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) hash *= -1; return hash.toString(16); }
Here is the function:
def string_hash(s):
"""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) hash *= -1;
return hash.toString(16);
}
"""
_hash = 0
if len(s) == 0:
return 0
for i in range(len(s)):
_char = ord(s[i])
_hash = ((_hash << 5) - _hash) + _char
_hash = _hash & 0xFFFFFFFF
if _hash >= (1 << 31):
_hash = -1 * (_hash - (1 << 32))
return hex(_hash)[2:] | 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) hash *= -1; return hash.toString(16); } |
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 parse_flow_elements
log = logging.getLogger(__name__)
def _extract_flow_code(file_content: str, flow_elements: List[dict]) -> Optional[str]:
"""Helper to extract the source code for a flow.
Currently, it uses a simple heuristic that copies all the lines between the minimum
and the maximum lines
"""
content_lines = file_content.split("\n")
min_line = -1
max_line = -1
for element in flow_elements:
if "_source_mapping" not in element:
continue
line_number = element["_source_mapping"]["line_number"] - 1
if min_line == -1 or line_number < min_line:
min_line = line_number
if max_line == -1 or line_number > max_line:
max_line = line_number
# If we have a range, we extract it
if min_line >= 0:
# Exclude all non-blank lines
flow_lines = [
_line
for _line in content_lines[min_line : max_line + 1]
if _line.strip() != ""
]
return textwrap.dedent("\n".join(flow_lines))
return None
def parse_coflows_to_yml_flows(
filename: str,
content: str,
include_source_mapping: bool = False,
snippets: Optional[dict] = None,
):
"""
Parses a file in .co format to a YAML flows format.
Parameters:
- filename (str): The name of the file.
- content (str): The content of the file.
- include_source_mapping (bool): Whether to include source mapping into the flow elements.
- snippets (Optional[dict]): Snippets to use when parsing the file.
Returns:
The parsed YAML flows.
Example:
```python
filename = "example.co"
content = "..."
flows = parse_coflows_to_yml_flows(filename, content, include_source_mapping=True)
```
Note:
- The function uses the ColangParser to convert .co format to YAML flows.
"""
parser = ColangParser(filename, content, include_source_mapping, snippets)
return parser.parse()
def parse_snippets_and_imports(filename: str, content: str):
"""
Parses snippets and imports from a file.
Parameters:
- filename (str): The name of the file.
- content (str): The content of the file.
Returns:
A dictionary of snippets and a list of skill names.
Example:
```python
filename = "example.co"
content = "..."
snippets, skills = parse_snippets_and_imports(filename, content)
```
Note:
- The function uses the ColangParser to extract snippets and imports.
"""
parser = ColangParser(filename, content)
return parser.parse_snippets_and_imports()
def parse_md_file(file_name, content=None):
"""Parse a Markdown file for patterns.
The content can be also passed as a parameter to skip reading it.
:param file_name: A markdown file
:param content: The content of the file.
:return: A list of patterns.
"""
if content is None:
file = open(file_name, "r")
content = file.read()
file.close()
sym = None
# First we extract the language
file_lang = parse_md_lang(file_name, content)
result: dict = {"patterns": [], "mappings": [], "utterances": {}}
# The supported symbol types are: "intent", "object", "utterance"
symbol_type = "intent"
symbol_params = []
symbol_context = None
symbol_meta = {}
symbol_context_meta = {}
idx = 0
lines = content.split("\n")
while idx < len(lines):
line = lines[idx].strip()
idx += 1
# Skip blank lines
if not line:
continue
if line == "### IGNORE BELOW ###":
break
if line.startswith("#") and not line.startswith("##"):
_type = line[1:].lower().strip()
if _type.startswith("intent"):
symbol_type = "intent"
elif _type.startswith("object"):
symbol_type = "object"
elif _type.startswith("utterance"):
symbol_type = "utterance"
elif _type.startswith("property") or _type.startswith("properties"):
symbol_type = "property"
elif _type.startswith("type"):
symbol_type = "type"
# Deal with intents part
if line.startswith("##") and not line.startswith("###"):
sym = line[2:].strip()
if not sym:
raise ValueError(f"sym cannot be empty at line: {idx + 1}")
symbol_type = _get_symbol_type(sym) or symbol_type
symbol_params = []
symbol_context = None
symbol_meta = {}
symbol_context_meta = {}
# TODO: remove this hack to ignore lines starting with "> "
# it was added for the quick demo
if line.startswith(">") and not line.startswith("> "):
sym = line[1:].strip()
if not sym:
raise ValueError(f"sym cannot be empty at line: {idx + 1}")
# check if we have mappings as parameters
# e.g. symbol(param1: type1, param2: type2, ...)
symbol_params = []
symbol_context = None
if "(" in sym:
sym, symbol_params = split_max(sym, "(", 1)
symbol_params = get_stripped_tokens(
symbol_params.split(")")[0].split(",")
)
# Make sure we have the type of the symbol in the name of the symbol
symbol_type = _get_symbol_type(sym) or symbol_type
sym = _get_typed_symbol_name(sym, symbol_type)
# append the mappings also
for param in symbol_params:
# It's a mapping only if it contains ":"
if ":" in param:
name, value = get_stripped_tokens(split_max(param, ":", 1))
result["mappings"].append((f"{sym}:{name}", _get_param_type(value)))
# Lines starting with "> " represent a mapping for the current symbol
# Record the mappings also
if line.startswith("> "):
parts = get_stripped_tokens(split_max(line[4:], ":", 1))
# We have a special case for the "_context" parameter, which marks the context
# of the symbol. So, we record it separately and use it further down the line.
if parts[0] == "_context":
symbol_context = parts[1]
# We also reset the symbol context meta on context change
symbol_context_meta = {}
continue
# We have another special case for "_meta_*" parameters which mark parameters
# that must be passed as meta information to the NLG and further
if parts[0].startswith("_meta_"):
var_name = parts[0][6:]
var_expr = " ".join(parts[1:])
# we put this either in the symbol meta, or symbol context meta
if symbol_context:
symbol_context_meta[var_name] = var_expr
else:
symbol_meta[var_name] = var_expr
continue
# Make sure we have the type of the symbol in the name of the symbol
sym = _get_typed_symbol_name(sym, symbol_type)
# For objects, we translate the "string" type to "kb:Object:prop|partial"
param_type = _get_param_type(parts[1])
if symbol_type == "object" and param_type in ["string", "text"]:
object_name = split_max(sym, ":", 1)[1]
param_type = f"kb:{object_name}:{parts[0]}|partial"
# TODO: figure out a cleaner way to deal with this
# For the "type:time" type, we transform it into "lookup:time"
if param_type == "type:time":
param_type = "lookup:time"
result["mappings"].append((f"{sym}:{parts[0]}", param_type))
symbol_params.append(parts[0])
elif line.startswith("-") or line.startswith("*"):
if sym is None:
raise ValueError(f"sym is none at line: {idx + 1}")
else:
kind = line[0]
pattern, params = parse_pattern(line[1:].strip())
# If we have a context for the symbol, we record it here
if symbol_context:
params["_context"] = symbol_context
# Make sure we have the type of the symbol in the name of the symbol
sym = _get_typed_symbol_name(sym, symbol_type)
# For intent, objects, properties and types, we record the pattern
if symbol_type in [
"intent",
"object",
"property",
"type",
"sym",
"lookup",
]:
# For "type" symbols, we need to make sure that the capture parameter
# (should be only one) is specified as [bla](type_name=value)
# So, we need to convert:
# - [bla](type_name) -> [bla](type_name=bla)
# - [bla](value) -> [bla](type_name=bla)
# - [bla](value=bla2) -> [bla](type_name=bla2)
#
# Also, we need to make sure we update the pattern itself
if symbol_type == "type":
symbol_name = split_max(sym, ":", 1)[1]
for k in list(params.keys()):
if (
k == "value" or k == symbol_name
) and k not in symbol_params:
value = params[k][9:]
new_k = f"{symbol_name}={value}"
params[new_k] = value
del params[k]
pattern = pattern.replace(f"{{{k}}}", f"{{{new_k}}}")
elif k.startswith("value="):
new_k = f"{symbol_name}{k[5:]}"
params[new_k] = params[k]
del params[k]
pattern = pattern.replace(f"{{{k}}}", f"{{{new_k}}}")
# if the symbol does not start with its type, we prepend it
pattern_config = dict(
lang=file_lang,
type="PATTERN" if kind == "-" else "ARG",
sym=sym,
body=pattern,
params=params,
)
result["patterns"].append(pattern_config)
# For utterances, we record them in the separate dict
elif symbol_type == "utterance":
_record_utterance(
result,
sym,
symbol_params,
symbol_context,
symbol_meta,
symbol_context_meta,
data=pattern,
)
# Here we're dealing with a YAML block
elif line.startswith("```"):
block_lines = []
# then we fetch the whole block
line = lines[idx]
idx += 1
while not line.startswith("```"):
block_lines.append(line)
line = lines[idx]
idx += 1
# we also skip the last ``` line
idx += 1
# at this point we need to parse the yaml block
d = yaml.safe_load("\n".join(block_lines))
# If we don't have an active symbol, we skip
# (maybe we're dealing with the `lang` tag)
if not sym:
continue
sym = _get_typed_symbol_name(sym, symbol_type)
# Currently we only support the YAML block for utterances
if symbol_type == "utterance":
_record_utterance(
result,
sym,
symbol_params,
symbol_context,
symbol_meta,
symbol_context_meta,
data=d,
)
else:
raise Exception(f"YAML blocks for symbol {sym} not supported.")
return result
def parse_flow_elements(items):
"""Parses the flow elements from CoYML format to CIL format."""
# Extract
elements = _extract_elements(items)
# And resolve goto's
elements = _resolve_gotos(elements)
# Finally, we proces the ellipsis syntax
elements = _process_ellipsis(elements)
return elements
The provided code snippet includes necessary dependencies for implementing the `parse_colang_file` function. Write a Python function `def parse_colang_file(filename: str, content: str, include_source_mapping: bool = True)` to solve the following problem:
Parse the content of a .co file into the CoYML format.
Here is the function:
def parse_colang_file(filename: str, content: str, include_source_mapping: bool = True):
"""Parse the content of a .co file into the CoYML format."""
snippets, imports = parse_snippets_and_imports(filename, content)
result = parse_coflows_to_yml_flows(
filename,
content,
snippets=snippets,
include_source_mapping=include_source_mapping,
)
flows = []
for flow_id, items in result["flows"].items():
elements = parse_flow_elements(items)
source_code = _extract_flow_code(content, elements)
flows.append({"id": flow_id, "elements": elements, "source_code": source_code})
user_messages = {}
bot_messages = {}
if result.get("markdown"):
log.debug(f"Found markdown content in {filename}")
md_result = parse_md_file(filename, content=result["markdown"])
# Record the user messages
# The `patterns` result from Markdown parsing contains patterns of the form
# {'lang': 'en', 'type': 'PATTERN', 'sym': 'intent:express|greeting', 'body': 'hi', 'params': {}}
# We need to convert these to the CoYML format.
for pattern in md_result["patterns"]:
sym = pattern["sym"]
# Ignore non-intent symbols
if not sym.startswith("intent:"):
continue
# The "|" is an old convention made by the parser, we roll back.
intent = sym[7:].replace("|", " ")
if intent not in user_messages:
user_messages[intent] = []
user_messages[intent].append(pattern["body"])
# For the bot messages, we just copy them from the `utterances` dict.
# The elements have the structure {"text": ..., "_context": ...}
for intent, utterances in md_result["utterances"].items():
if intent not in bot_messages:
bot_messages[intent] = []
if not isinstance(utterances, list):
utterances = [utterances]
for utterance in utterances:
bot_messages[intent].append(utterance["text"])
data = {
"user_messages": user_messages,
"bot_messages": bot_messages,
"flows": flows,
}
return data | 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:
"""
# if there is any property that stars with ":" we transform it to "_"
for _k in list(d.keys()):
if _k[0] == ":":
d["_" + _k[1:]] = d[_k]
del d[_k]
d_type = list(d.keys())[0]
d_value = d[d_type]
d_params = {}
# if the value of the first key is a string, we see if there are any parameters,
# but we skip for elements where it doesn't make sense
if d_type not in ["set", "if", "while"]:
if isinstance(d_value, str) and "(" in d_value:
d_value = _extract_inline_params(d_value, d_params)
elif isinstance(d_value, list):
new_d_value = []
for v in d_value:
if isinstance(v, str) and "(" in v:
v = _extract_inline_params(v, d_params)
new_d_value.append(v)
d_value = new_d_value
if d_type in ["user", "intent", "you"]:
# remove <<IS NOT NONE>> parameters
is_not_none_params = []
for k in list(d_params.keys()):
if d_params[k] == "<<IS NOT NONE>>":
# we get rid of "$" if it exists
del d_params[k]
if k[0] == "$":
k = k[1:]
is_not_none_params.append(k)
elif k[0] == "$":
# If a parameters name starts with "$" we remove it
d_params[k[1:]] = d_params[k]
del d_params[k]
element = {
"_type": "UserIntent",
# We replace all spaces in intent names with "|"
"intent_name": d_value,
"intent_params": {
# exclude the initial key and any meta properties
# 1) **{k: _to_value(v) for k, v in d.items() if k != d_type and k[0] != "_"},
# 2) **d_params
},
# Meta properties i.e. starting with "_" are added top level
# 3) **{k: _to_value(v) for k, v in d.items() if k[0] == "_"}
}
# 1)
for k in d.keys():
if k != d_type and k[0] != "_":
element["intent_params"][k] = _to_value(d[k])
# 2)
for k in d_params.keys():
element["intent_params"][k] = d_params[k]
# 3)
for k in d.keys():
if k[0] == "_":
element[k] = _to_value(d[k])
if is_not_none_params:
_pp = []
for p in is_not_none_params:
_pp.append(f"$intent_params.{p if p[0] != '$' else p[1:]} is not None")
element["_match"] = " and ".join(_pp)
elif d_type in ["UtteranceUserActionFinished"]:
element = {
"_type": "UtteranceUserActionFinished",
"final_transcript": d_value,
}
elif d_type in ["StartUtteranceBotAction"]:
element = {
"_type": "StartUtteranceBotAction",
"content": d_value,
}
elif d_type in ["bot", "utter", "ask", "bot_ask"]:
element = {
"_type": "run_action",
"action_name": "utter",
"action_params": {
"value": d_value,
# 1) **{k: _to_value(v) for k, v in d.items() if k != d_type and k != "_source_mapping"},
# 2) **d_params
},
}
# 1)
for k in d.keys():
if k != d_type and k != "_source_mapping":
element["action_params"][k] = _to_value(d[k])
# 2)
for k in d_params.keys():
element["action_params"][k] = d_params[k]
elif d_type in ["run", "action", "execute"]:
# if we have an "=" that means we're also dealing with an assignment
action_name = d_value
action_result_key = None
# We extract positional parameters as "$1", "$2", etc.
# It is a bit hackish, but we use the <<IS NOT NOT>> marker to figure out the params
idx = 1
positional_params = {}
for k in list(d_params.keys()):
if d_params[k] == "<<IS NOT NONE>>":
positional_params[f"${idx}"] = k
idx += 1
del d_params[k]
for k in positional_params.keys():
d_params[k] = positional_params[k]
if "=" in action_name:
action_result_key, action_name = get_stripped_tokens(
split_max(d_value, "=", 1)
)
# if action_result starts with a $, which is recommended for clarity, we remove
if action_result_key[0] == "$":
action_result_key = action_result_key[1:]
element = {
"_type": "run_action",
"action_name": action_name,
"action_params": {
# 1) **{k: _to_value(v) for k, v in d.items() if k != d_type and k != "_source_mapping"},
# 2) **d_params
},
# The context key where the result should be stored, if any
"action_result_key": action_result_key,
}
# 1)
for k in d.keys():
if k != d_type and k != "_source_mapping":
element["action_params"][k] = _to_value(d[k])
# 2)
for k in d_params.keys():
element["action_params"][k] = d_params[k]
elif d_type in ["check"]:
element = {"_type": "check", "expression": d_value}
elif d_type in ["pass", "continue"]:
element = {"_type": "continue"}
elif d_type in ["stop", "abort"]:
element = {"_type": "stop"}
elif d_type in ["break"]:
element = {"_type": "break"}
elif d_type in ["return"]:
element = {"_type": "jump", "_next": "-1", "_absolute": True}
# Include the return values information
if "_return_values" in d:
element["_return_values"] = d["_return_values"]
elif d_type in ["if"]:
element = {
"_type": "if",
"expression": d_value,
"then": d["then"],
"else": d["else"] if "else" in d else [],
}
elif d_type in ["while"]:
element = {"_type": "while", "expression": d_value, "do": d["do"]}
elif d_type in ["set"]:
key, expression = get_stripped_tokens(split_max(d_value, "=", 1))
# if the key starts with a $, which is recommended for clarity, then
# we remove it
if key[0] == "$":
key = key[1:]
element = {
"_type": "set",
"key": key,
"expression": expression,
}
elif d_type in ["checkpoint", "label"]:
element = {"_type": "label", "name": d_value}
# Propagate the value also
if "value" in d:
element["value"] = d["value"]
elif d_type in ["goto"]:
element = {"_type": "goto", "label": d_value}
elif d_type in ["meta"]:
element = {"_type": "meta", "meta": d_value}
elif d_type in ["event"]:
element = {
"_type": d_value,
# 1) **{k: _to_value(v) for k, v in d.items() if k != d_type and k != "_source_mapping"},
# 2) **d_params
}
# 1)
for k in d.keys():
if k != d_type and k != "_source_mapping":
element[k] = _to_value(d[k])
# 2)
for k in d_params.keys():
element[k] = d_params[k]
elif d_type in ["flow", "call", "activate"]:
# We transform <<IS NOT NONE>> into positional parameters
i = 0
new_params = {}
for k in list(d_params.keys()):
if d_params[k] == "<<IS NOT NONE>>":
new_params[f"${i}"] = k
else:
new_params[k] = d_params[k]
i += 1
element = {
"_type": "flow",
"flow_name": d_value,
# The parameters are not used for now, but we pass them anyway
"flow_parameters": {
# 1) **{k: _to_value(v) for k, v in d.items() if k != d_type and k != "_source_mapping"
# and k != "_return_vars"},
# 2) **new_params
},
"return_vars": d["_return_vars"] if "_return_vars" in d else [],
}
# 1)
for k in d.keys():
if k != d_type and k != "_source_mapping" and k != "_return_vars":
element["flow_parameters"][k] = _to_value(d[k])
# 2)
for k in new_params.keys():
element["flow_parameters"][k] = _to_value(new_params[k])
# Element for inferring that when something happened, then something else also happened
elif d_type in ["infer", "add", "new", "post"]:
# currently we support only one infer
# TODO: add support for more
infer_event = d_value
if isinstance(infer_event, list):
infer_event = infer_event[0]
# element = {
# "_type": "infer",
# "event": _dict_to_element(infer_event)
# }
element = {
"_type": "run_action",
"action_name": "create_event",
"action_params": {
"event": {
# 1)
# k: v for k, v in _dict_to_element(infer_event).items()
# if k != "_source_mapping"
}
},
}
# 1)
dd = _dict_to_element(infer_event)
for k in dd.keys():
if k != "_source_mapping":
element["action_params"]["event"][k] = dd[k]
# For `any` element types, we first extract the elements and they will be later
# included in the main flow
elif d_type in ["any", "or"]:
element = {
"_type": "any",
"count": len(d_value),
"elements": [
# 1) _dict_to_element(_d) for _d in d_value
],
}
# 1)
for _d in d_value:
element["elements"].append(_dict_to_element(_d))
else:
raise Exception(f"Unknown dict format for: {json.dumps(d)}")
# Add the source mapping information if available
if "_source_mapping" in d:
element["_source_mapping"] = d["_source_mapping"]
return element
The provided code snippet includes necessary dependencies for implementing the `get_events` function. Write a Python function `def get_events(events_data: List)` to solve the following problem:
Helper to convert a list of events data to 'full events
Here is the function:
def get_events(events_data: List):
"""Helper to convert a list of events data to 'full events'"""
events = []
for event in events_data:
# Just a normalization
if "type" in event:
event["_type"] = event["type"]
del event["type"]
# if it's a dict, but without a "_type" that means it's a shorthand dict
if "_type" not in event:
event = _dict_to_element(event)
events.append(event)
return events | 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,
key_generator: KeyGenerator = None,
cache_store: CacheStore = None,
store_config: dict = None,
):
self._key_generator = key_generator
self._cache_store = cache_store
self._store_config = store_config or {}
def from_dict(cls, d: Dict[str, str]):
key_generator = KeyGenerator.from_name(d.get("key_generator"))()
store_config = d.get("store_config")
cache_store = CacheStore.from_name(d.get("store"))(**store_config)
return cls(key_generator=key_generator, cache_store=cache_store)
def from_config(cls, config: EmbeddingsCacheConfig):
# config is of type EmbeddingSearchProvider
return cls.from_dict(config.to_dict())
def get_config(self):
return EmbeddingsCacheConfig(
key_generator=self._key_generator.name,
store=self._cache_store.name,
store_config=self._store_config,
)
def get(self, texts):
raise NotImplementedError
def _(self, text: str):
key = self._key_generator.generate_key(text)
log.info(f"Fetching key {key} for text '{text[:20]}...' from cache")
result = self._cache_store.get(key)
return result
def _(self, texts: list):
cached = {}
for text in texts:
result = self.get(text)
if result is not None:
cached[text] = result
if len(cached) != len(texts):
log.info(f"Cache hit rate: {len(cached) / len(texts)}")
return cached
def set(self, texts):
raise NotImplementedError
def _(self, text: str, value: List[float]):
key = self._key_generator.generate_key(text)
log.info(f"Cache miss for text '{text}'. Storing key {key} in cache.")
self._cache_store.set(key, value)
def _(self, texts: list, values: List[List[float]]):
for text, value in zip(texts, values):
self.set(text, value)
def clear(self):
self._cache_store.clear()
The provided code snippet includes necessary dependencies for implementing the `cache_embeddings` function. Write a Python function `def cache_embeddings(func)` to solve the following problem:
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_embeddings` method of a subclass of `EmbeddingsIndex` that accepts a list of strings and returns a list of lists of floats. Args: func (Callable[[Any, List[str]], Awaitable[List[List[float]]]]): The method to decorate. The first argument should be `self`. Returns: Callable[[Any, List[str]], Awaitable[List[List[float]]]]: The decorated method. Example: class MyClass: @property def cache_config(self): return EmbeddingsCacheConfig() @cache_embeddings async def get_embeddings(self, texts: List[str]) -> List[List[float]]: # implementation here pass
Here is the function:
def cache_embeddings(func):
"""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_embeddings` method of a subclass of `EmbeddingsIndex` that accepts a list of strings and returns a list of lists of floats.
Args:
func (Callable[[Any, List[str]], Awaitable[List[List[float]]]]): The method to decorate. The first argument should be `self`.
Returns:
Callable[[Any, List[str]], Awaitable[List[List[float]]]]: The decorated method.
Example:
class MyClass:
@property
def cache_config(self):
return EmbeddingsCacheConfig()
@cache_embeddings
async def get_embeddings(self, texts: List[str]) -> List[List[float]]:
# implementation here
pass
"""
@functools.wraps(func)
async def wrapper_decorator(self, texts):
results = []
embeddings_cache = EmbeddingsCache.from_config(self.cache_config)
if not self.cache_config.enabled:
# if cache is not enabled compute embeddings for the whole input
return await func(self, texts)
cached_texts = {}
uncached_texts = []
cached_texts = embeddings_cache.get(texts)
uncached_texts = [text for text in texts if text not in cached_texts]
# Only call func for uncached texts
if uncached_texts:
uncached_results = await func(self, uncached_texts)
embeddings_cache.set(uncached_texts, uncached_results)
cached_texts.update(embeddings_cache.get(uncached_texts))
# Reorder results to match the order of the input texts,
results = [cached_texts.get(text) for text in texts]
return results
return wrapper_decorator | 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_embeddings` method of a subclass of `EmbeddingsIndex` that accepts a list of strings and returns a list of lists of floats. Args: func (Callable[[Any, List[str]], Awaitable[List[List[float]]]]): The method to decorate. The first argument should be `self`. Returns: Callable[[Any, List[str]], Awaitable[List[List[float]]]]: The decorated method. Example: class MyClass: @property def cache_config(self): return EmbeddingsCacheConfig() @cache_embeddings async def get_embeddings(self, texts: List[str]) -> List[List[float]]: # implementation here pass |
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
self.original_params = {}
def __enter__(self):
# Here we can access and modify the global language model parameters.
self.original_params = {}
for param, value in self.altered_params.items():
if hasattr(self.llm, param):
self.original_params[param] = getattr(self.llm, param)
setattr(self.llm, param, value)
# TODO: Fix the cases where self.llm.model_kwargs is not iterable
# https://github.com/NVIDIA/NeMo-Guardrails/issues/92.
# elif param in getattr(self.llm, "model_kwargs", {}):
# self.original_params[param] = self.llm.model_kwargs[param]
# self.llm.model_kwargs[param] = value
else:
log.warning(
"Parameter %s does not exist for %s",
param,
self.llm.__class__.__name__,
)
def __exit__(self, type, value, traceback):
# Restore original parameters when exiting the context
for param, value in self.original_params.items():
if hasattr(self.llm, param):
setattr(self.llm, param, value)
elif hasattr(self.llm, "model_kwargs") and param in getattr(
self.llm, "model_kwargs", {}
):
self.llm.model_kwargs[param] = value
_param_managers: Dict[Type[BaseLanguageModel], Type[LLMParams]] = {}
The provided code snippet includes necessary dependencies for implementing the `register_param_manager` function. Write a Python function `def register_param_manager(llm_type: Type[BaseLanguageModel], manager: Type[LLMParams])` to solve the following problem:
Register a parameter manager.
Here is the function:
def register_param_manager(llm_type: Type[BaseLanguageModel], manager: Type[LLMParams]):
"""Register a parameter manager."""
_param_managers[llm_type] = manager | 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 a Python function `def user_intent_parser(s: str)` to solve the following problem:
Parses the user intent.
Here is the function:
def user_intent_parser(s: str):
"""Parses the user intent."""
return _replace_prefix(s.strip(), "User intent: ", " ") | 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 Python function `def bot_intent_parser(s: str)` to solve the following problem:
Parses the bot intent.
Here is the function:
def bot_intent_parser(s: str):
"""Parses the bot intent."""
return _replace_prefix(s.strip(), "Bot intent: ", "bot ") | 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 a Python function `def bot_message_parser(s: str)` to solve the following problem:
Parses the bot messages.
Here is the function:
def bot_message_parser(s: str):
"""Parses the bot messages."""
return _replace_prefix(s.strip(), "Bot message: ", " ") | 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 Python function `def verbose_v1_parser(s: str)` to solve the following problem:
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"
Here is the function:
def verbose_v1_parser(s: str):
"""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"
"""
lines = s.split("\n")
prefixes = [
("User message: ", "user "),
("Bot message: ", " "),
("User intent: ", " "),
("Bot intent: ", "bot "),
]
for i in range(len(lines)):
# Some LLMs generate a space at the beginning of the first line
lines[i] = lines[i].strip()
for prefix, repl in prefixes:
# Also allow prefixes to be in lower-case
lines[i] = _replace_prefix(lines[i], prefix, repl)
lines[i] = _replace_prefix(lines[i], prefix.lower(), repl)
return "\n".join(lines) | 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."""
task: str = Field(description="The id of the task associated with this prompt.")
content: Optional[str] = Field(
default=None, description="The content of the prompt, if it's a string."
)
messages: Optional[List[Union[MessageTemplate, str]]] = Field(
default=None,
description="The list of messages included in the prompt. Used for chat models.",
)
models: Optional[List[str]] = Field(
default=None,
description="If specified, the prompt will be used only for the given LLM engines/models. "
"The format is a list of strings with the format: <engine> or <engine>/<model>.",
)
output_parser: Optional[str] = Field(
default=None,
description="The name of the output parser to use for this prompt.",
)
max_length: Optional[int] = Field(
default=16000,
description="The maximum length of the prompt in number of characters.",
)
mode: Optional[str] = Field(
default=_default_config["prompting_mode"],
description="Corresponds to the `prompting_mode` for which this prompt is fetched. Default is 'standard'.",
)
stop: Optional[List[str]] = Field(
default=None,
description="If specified, will be configure stop tokens for models that support this.",
)
def check_fields(cls, values):
if not values.get("content") and not values.get("messages"):
raise ValidationError("One of `content` or `messages` must be provided.")
if values.get("content") and values.get("messages"):
raise ValidationError(
"Only one of `content` or `messages` must be provided."
)
return values
The provided code snippet includes necessary dependencies for implementing the `_load_prompts` function. Write a Python function `def _load_prompts() -> List[TaskPrompt]` to solve the following problem:
Load the predefined prompts from the `prompts` directory.
Here is the function:
def _load_prompts() -> List[TaskPrompt]:
"""Load the predefined prompts from the `prompts` directory."""
# List of directory containing prompts
prompts_dirs = [os.path.join(CURRENT_DIR, "prompts")]
# Fetch prompt directory from env var this should be either abs path or relative to cwd
prompts_dir = os.getenv("PROMPTS_DIR", None)
if prompts_dir and os.path.exists(prompts_dir):
prompts_dirs.append(prompts_dir)
prompts = []
for path in prompts_dirs:
for root, dirs, files in os.walk(path):
for filename in files:
if filename.endswith(".yml") or filename.endswith(".yaml"):
with open(
os.path.join(root, filename), encoding="utf-8"
) as prompts_file:
prompts.extend(yaml.safe_load(prompts_file.read())["prompts"])
return [TaskPrompt(**prompt) for prompt in prompts] | 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 the given task.
We intentionally update the matching model at equal score, to take the last one,
basically allowing to override a prompt for a specific model.
"""
matching_prompt = None
matching_score = 0
for prompt in prompts:
if prompt.task != task_name:
continue
_score = 0
# If no model is specified, we are dealing with a general prompt, and it has the
# lowest score.
if not prompt.models:
_score = 0.2
else:
for _model in prompt.models:
# If we have an exact match, the score is 1.
if _model == model:
_score = 1
break
# If we match just the provider, the score is 0.5.
elif model.startswith(_model + "/"):
_score = 0.5
break
# If we match just the model, the score is 0.8.
elif model.endswith("/" + _model):
_score = 0.8
break
if prompt.mode != prompting_mode:
# Penalize matching score for being in an incorrect mode.
# This way, if a prompt with the correct mode (say "compact") is found, it will be preferred over a prompt with another mode (say "standard").
if prompt.mode == "standard":
# why 0.5? why not <0.2? To give preference to matching model or provider over matching mode.
# This way, standard mode with matching provider at gets a score of 0.5 * 0.5 = 0.25
# (> 0.2 for a matching mode but without a matching provider or model).
_score *= 0.5
else:
continue # if it's the mode doesn't match AND it's not standard too, discard this match
if _score >= matching_score:
matching_score = _score
matching_prompt = prompt
if matching_prompt:
return matching_prompt
raise ValueError(f"Could not find prompt for task {task_name} and model {model}")
class Task(Enum):
"""The various tasks that can be performed by the LLM."""
# Core LLM tasks
GENERAL = "general"
GENERATE_USER_INTENT = "generate_user_intent"
GENERATE_NEXT_STEPS = "generate_next_steps"
GENERATE_BOT_MESSAGE = "generate_bot_message"
GENERATE_INTENT_STEPS_MESSAGE = "generate_intent_steps_message"
GENERATE_VALUE = "generate_value"
GENERATE_VALUE_FROM_INSTRUCTION = "generate_value_from_instruction"
GENERATE_USER_INTENT_FROM_USER_ACTION = "generate_user_intent_from_user_action"
GENERATE_FLOW_FROM_INSTRUCTIONS = "generate_flow_from_instructions"
GENERATE_FLOW_FROM_NAME = "generate_flow_from_name"
GENERATE_FLOW_CONTINUATION = "generate_flow_continuation"
# Tasks for various rails
SELF_CHECK_INPUT = "self_check_input"
SELF_CHECK_OUTPUT = "self_check_output"
LLAMA_GUARD_CHECK_INPUT = "llama_guard_check_input"
LLAMA_GUARD_CHECK_OUTPUT = "llama_guard_check_output"
SELF_CHECK_FACTS = "fact_checking"
CHECK_HALLUCINATION = "check_hallucination"
class TaskPrompt(BaseModel):
"""Configuration for prompts that will be used for a specific task."""
task: str = Field(description="The id of the task associated with this prompt.")
content: Optional[str] = Field(
default=None, description="The content of the prompt, if it's a string."
)
messages: Optional[List[Union[MessageTemplate, str]]] = Field(
default=None,
description="The list of messages included in the prompt. Used for chat models.",
)
models: Optional[List[str]] = Field(
default=None,
description="If specified, the prompt will be used only for the given LLM engines/models. "
"The format is a list of strings with the format: <engine> or <engine>/<model>.",
)
output_parser: Optional[str] = Field(
default=None,
description="The name of the output parser to use for this prompt.",
)
max_length: Optional[int] = Field(
default=16000,
description="The maximum length of the prompt in number of characters.",
)
mode: Optional[str] = Field(
default=_default_config["prompting_mode"],
description="Corresponds to the `prompting_mode` for which this prompt is fetched. Default is 'standard'.",
)
stop: Optional[List[str]] = Field(
default=None,
description="If specified, will be configure stop tokens for models that support this.",
)
def check_fields(cls, values):
if not values.get("content") and not values.get("messages"):
raise ValidationError("One of `content` or `messages` must be provided.")
if values.get("content") and values.get("messages"):
raise ValidationError(
"Only one of `content` or `messages` must be provided."
)
return values
class RailsConfig(BaseModel):
"""Configuration object for the models and the rails.
TODO: add typed config for user_messages, bot_messages, and flows.
"""
models: List[Model] = Field(
description="The list of models used by the rails configuration."
)
user_messages: Dict[str, List[str]] = Field(
default_factory=dict,
description="The list of user messages that should be used for the rails.",
)
bot_messages: Dict[str, List[str]] = Field(
default_factory=dict,
description="The list of bot messages that should be used for the rails.",
)
flows: List[Union[Dict, Flow]] = Field(
default_factory=list,
description="The list of flows that should be used for the rails.",
)
instructions: Optional[List[Instruction]] = Field(
default=[Instruction.parse_obj(obj) for obj in _default_config["instructions"]],
description="List of instructions in natural language that the LLM should use.",
)
docs: Optional[List[Document]] = Field(
default=None,
description="List of documents that should be used for question answering.",
)
actions_server_url: Optional[str] = Field(
default=None,
description="The URL of the actions server that should be used for the rails.",
) # consider as conflict
sample_conversation: Optional[str] = Field(
default=_default_config["sample_conversation"],
description="The sample conversation that should be used inside the prompts.",
)
prompts: Optional[List[TaskPrompt]] = Field(
default=None,
description="The prompts that should be used for the various LLM tasks.",
)
prompting_mode: Optional[str] = Field(
default=_default_config["prompting_mode"],
description="Allows choosing between different prompting strategies.",
)
config_path: Optional[str] = Field(
default=None, description="The path from which the configuration was loaded."
)
import_paths: Optional[List[str]] = Field(
default_factory=list,
description="A list of additional paths from which configuration elements (colang flows, .yml files, actions)"
" should be loaded.",
)
# Some tasks need to be as deterministic as possible. The lowest possible temperature
# will be used for those tasks. Models like dolly don't allow for a temperature of 0.0,
# for example, in which case a custom one can be set.
lowest_temperature: Optional[float] = Field(
default=0.0,
description="The lowest temperature that should be used for the LLM.",
)
# This should only be enabled for highly capable LLMs i.e. gpt-3.5-turbo-instruct or similar.
enable_multi_step_generation: Optional[bool] = Field(
default=False,
description="Whether to enable multi-step generation for the LLM.",
)
colang_version: str = Field(default="1.0", description="The Colang version to use.")
custom_data: Dict = Field(
default_factory=dict,
description="Any custom configuration data that might be needed.",
)
knowledge_base: KnowledgeBaseConfig = Field(
default_factory=KnowledgeBaseConfig,
description="Configuration for the built-in knowledge base support.",
)
core: CoreConfig = Field(
default_factory=CoreConfig,
description="Configuration for core internal mechanics.",
)
rails: Rails = Field(
default_factory=Rails,
description="Configuration for the various rails (input, output, etc.).",
)
streaming: bool = Field(
default=False,
description="Whether this configuration should use streaming mode or not.",
)
passthrough: bool = Field(
default=False,
description="Weather the original prompt should pass through the guardrails configuration as is. "
"This means it will not be altered in any way. ",
)
def check_prompt_exist_for_self_check_rails(cls, values):
rails = values.get("rails", {})
enabled_input_rails = rails.get("input", {}).get("flows", [])
enabled_output_rails = rails.get("output", {}).get("flows", [])
provided_task_prompts = [
prompt.get("task") for prompt in values.get("prompts", [])
]
# Input moderation prompt verification
if (
"self check input" in enabled_input_rails
and "self_check_input" not in provided_task_prompts
):
raise ValueError("You must provide a `self_check_input` prompt template.")
if (
"llama guard check input" in enabled_input_rails
and "llama_guard_check_input" not in provided_task_prompts
):
raise ValueError(
"You must provide a `llama_guard_check_input` prompt template."
)
# Output moderation prompt verification
if (
"self check output" in enabled_output_rails
and "self_check_output" not in provided_task_prompts
):
raise ValueError("You must provide a `self_check_output` prompt template.")
if (
"llama guard check output" in enabled_output_rails
and "llama_guard_check_output" not in provided_task_prompts
):
raise ValueError(
"You must provide a `llama_guard_check_output` prompt template."
)
if (
"self check facts" in enabled_output_rails
and "self_check_facts" not in provided_task_prompts
):
raise ValueError("You must provide a `self_check_facts` prompt template.")
return values
raw_llm_call_action: Optional[str] = Field(
default="raw llm call",
description="The name of the action that would execute the original raw LLM call. ",
)
def from_path(
config_path: str,
):
"""Loads a configuration from a given path.
Supports loading a from a single file, or from a directory.
"""
# If the config path is a file, we load the YAML content.
# Otherwise, if it's a folder, we iterate through all files.
if config_path.endswith(".yaml") or config_path.endswith(".yml"):
with open(config_path) as f:
raw_config = yaml.safe_load(f.read())
elif os.path.isdir(config_path):
raw_config, colang_files = _load_path(config_path)
# If we have import paths, we also need to load them.
if raw_config.get("import_paths"):
_load_imported_paths(raw_config, colang_files)
# Parse the colang files after we know the colang version
colang_version = raw_config.get("colang_version", "1.0")
# To allow overriding of elements from imported paths, we need to process
# these in reverse order.
for file, full_path in reversed(colang_files):
with open(full_path, "r", encoding="utf-8") as f:
_raw_config = parse_colang_file(
file, content=f.read(), version=colang_version
)
_join_config(raw_config, _raw_config)
else:
raise ValueError(f"Invalid config path {config_path}.")
# If there are no instructions, we use the default ones.
if len(raw_config.get("instructions", [])) == 0:
raw_config["instructions"] = _default_config["instructions"]
raw_config["config_path"] = config_path
return RailsConfig.parse_object(raw_config)
def from_content(
colang_content: Optional[str] = None,
yaml_content: Optional[str] = None,
config: Optional[dict] = None,
):
"""Loads a configuration from the provided colang/YAML content/config dict."""
raw_config = {}
if config:
_join_config(raw_config, config)
if yaml_content:
_join_config(raw_config, yaml.safe_load(yaml_content))
# If we have import paths, we also need to load them.
colang_files = []
if raw_config.get("import_paths"):
_load_imported_paths(raw_config, colang_files)
# Parse the colang files after we know the colang version
colang_version = raw_config.get("colang_version", "1.0")
# To allow overriding of elements from imported paths, we need to process
# these in reverse order.
for file, full_path in reversed(colang_files):
with open(full_path, "r", encoding="utf-8") as f:
_raw_config = parse_colang_file(
file, content=f.read(), version=colang_version
)
_join_config(raw_config, _raw_config)
# Finally, parse the content colang.
if colang_content:
_join_config(
raw_config,
parse_colang_file(
"main.co",
content=colang_content,
version=colang_version,
),
)
# If there are no instructions, we use the default ones.
if len(raw_config.get("instructions", [])) == 0:
raw_config["instructions"] = _default_config["instructions"]
return RailsConfig.parse_object(raw_config)
def parse_object(cls, obj):
"""Parses a configuration object from a given dictionary."""
# If we have flows, we need to process them further from CoYML to CIL, but only for
# version 1.0.
if obj.get("colang_version", "1.0") == "1.0":
for flow_data in obj.get("flows", []):
# If the first element in the flow does not have a "_type", we need to convert
if flow_data.get("elements") and not flow_data["elements"][0].get(
"_type"
):
flow_data["elements"] = parse_flow_elements(flow_data["elements"])
return RailsConfig.parse_obj(obj)
def streaming_supported(self):
"""Whether the current config supports streaming or not.
Currently, we don't support streaming if there are output rails.
"""
if len(self.rails.output.flows) > 0:
return False
return True
def __add__(self, other):
"""Adds two RailsConfig objects."""
return _join_rails_configs(self, other)
The provided code snippet includes necessary dependencies for implementing the `get_prompt` function. Write a Python function `def get_prompt(config: RailsConfig, task: Union[str, Task]) -> TaskPrompt` to solve the following problem:
Return the prompt for the given task.
Here is the function:
def get_prompt(config: RailsConfig, task: Union[str, Task]) -> TaskPrompt:
"""Return the prompt for the given task."""
# Currently, we use the main model for all tasks
# TODO: add support to use different models for different tasks
# Fetch current task parameters like name, models to use, and the prompting mode
task_name = str(task.value) if isinstance(task, Task) else task
task_model = "unknown"
if config.models:
task_model = config.models[0].engine
if config.models[0].model:
task_model += "/" + config.models[0].model
task_prompting_mode = "standard"
if config.prompting_mode:
# if exists in config, overwrite, else, default to "standard"
task_prompting_mode = config.prompting_mode
prompts = _prompts + (config.prompts or [])
prompt = _get_prompt(task_name, task_model, task_prompting_mode, prompts)
if prompt:
return prompt
else:
raise ValueError(f"No prompt found for task: {task}") | 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 of user messages and bot responses in colang format.
user "Hi, how are you today?"
express greeting
bot express greeting
"Greetings! I am the official NVIDIA Benefits Ambassador AI bot and I'm here to assist you."
user "What can you help me with?"
ask capabilities
bot inform capabilities
"As an AI, I can provide you with a wide range of services, such as ..."
"""
history = ""
if not events:
return history
# We try to automatically detect if we have a Colang 1.0 or a 2.x history
# TODO: Think about more robust approach?
colang_version = "1.0"
for event in events:
if isinstance(event, InternalEvent):
event = {"type": event.name, **event.arguments}
if event["type"] in InternalEvents.ALL:
colang_version = "2.x"
if colang_version == "1.0":
# We compute the index of the last bot message. We need it so that we include
# the bot message instruction only for the last one.
last_bot_intent_idx = len(events) - 1
while last_bot_intent_idx >= 0:
if events[last_bot_intent_idx]["type"] == "BotIntent":
break
last_bot_intent_idx -= 1
for idx, event in enumerate(events):
if event["type"] == "UserMessage" and include_texts:
history += f'user "{event["text"]}"\n'
elif event["type"] == "UserIntent":
if include_texts:
history += f' {event["intent"]}\n'
else:
history += f'user {event["intent"]}\n'
elif event["type"] == "BotIntent":
# If we have instructions, we add them before the bot message.
# But we only do that for the last bot message.
if "instructions" in event and idx == last_bot_intent_idx:
history += f"# {event['instructions']}\n"
history += f'bot {event["intent"]}\n'
elif event["type"] == "StartUtteranceBotAction" and include_texts:
history += f' "{event["script"]}"\n'
# We skip system actions from this log
elif event["type"] == "StartInternalSystemAction" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
history += f'execute {event["action_name"]}\n'
elif event["type"] == "InternalSystemActionFinished" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
# We make sure the return value is a string with no new lines
return_value = str(event["return_value"]).replace("\n", " ")
history += f"# The result was {return_value}\n"
elif event["type"] == "mask_prev_user_message":
utterance_to_replace = get_last_user_utterance(events[:idx])
# We replace the last user utterance that led to jailbreak rail trigger with a placeholder text
split_history = history.rsplit(utterance_to_replace, 1)
placeholder_text = "<<<This text is hidden because the assistant should not talk about this.>>>"
history = placeholder_text.join(split_history)
elif colang_version == "2.x":
new_history: List[str] = []
# Structure the user/bot intent/action events
action_group: List[InternalEvent] = []
current_intent: Optional[str] = None
for event in events:
if not isinstance(event, InternalEvent):
continue
if (
event.name == InternalEvents.BOT_ACTION_LOG
or event.name == InternalEvents.USER_ACTION_LOG
):
if len(action_group) > 0 and (
current_intent is None
or current_intent != event.arguments["intent_flow_id"]
):
new_history.append(events_to_dialog_history(action_group))
new_history.append("")
action_group.clear()
action_group.append(event)
current_intent = event.arguments["intent_flow_id"]
elif (
event.name == InternalEvents.BOT_INTENT_LOG
or event.name == InternalEvents.USER_INTENT_LOG
):
if event.arguments["flow_id"] == current_intent:
# Found parent of current group
if event.name == InternalEvents.BOT_INTENT_LOG:
new_history.append(events_to_dialog_history([event]))
new_history.append(events_to_dialog_history(action_group))
elif event.arguments["flow_id"] is not None:
new_history.append(events_to_dialog_history(action_group))
new_history.append(events_to_dialog_history([event]))
new_history.append("")
else:
# New unrelated intent
if action_group:
new_history.append(events_to_dialog_history(action_group))
new_history.append("")
new_history.append(events_to_dialog_history([event]))
new_history.append("")
# Start a new group
action_group.clear()
current_intent = None
if action_group:
new_history.append(events_to_dialog_history(action_group))
history = "\n".join(new_history).rstrip("\n")
return history
The provided code snippet includes necessary dependencies for implementing the `colang` function. Write a Python function `def colang(events: List[dict]) -> str` to solve the following problem:
Filter that turns an array of events into a colang history.
Here is the function:
def colang(events: List[dict]) -> str:
"""Filter that turns an array of events into a colang history."""
return get_colang_history(events) | 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 of user messages and bot responses in colang format.
user "Hi, how are you today?"
express greeting
bot express greeting
"Greetings! I am the official NVIDIA Benefits Ambassador AI bot and I'm here to assist you."
user "What can you help me with?"
ask capabilities
bot inform capabilities
"As an AI, I can provide you with a wide range of services, such as ..."
"""
history = ""
if not events:
return history
# We try to automatically detect if we have a Colang 1.0 or a 2.x history
# TODO: Think about more robust approach?
colang_version = "1.0"
for event in events:
if isinstance(event, InternalEvent):
event = {"type": event.name, **event.arguments}
if event["type"] in InternalEvents.ALL:
colang_version = "2.x"
if colang_version == "1.0":
# We compute the index of the last bot message. We need it so that we include
# the bot message instruction only for the last one.
last_bot_intent_idx = len(events) - 1
while last_bot_intent_idx >= 0:
if events[last_bot_intent_idx]["type"] == "BotIntent":
break
last_bot_intent_idx -= 1
for idx, event in enumerate(events):
if event["type"] == "UserMessage" and include_texts:
history += f'user "{event["text"]}"\n'
elif event["type"] == "UserIntent":
if include_texts:
history += f' {event["intent"]}\n'
else:
history += f'user {event["intent"]}\n'
elif event["type"] == "BotIntent":
# If we have instructions, we add them before the bot message.
# But we only do that for the last bot message.
if "instructions" in event and idx == last_bot_intent_idx:
history += f"# {event['instructions']}\n"
history += f'bot {event["intent"]}\n'
elif event["type"] == "StartUtteranceBotAction" and include_texts:
history += f' "{event["script"]}"\n'
# We skip system actions from this log
elif event["type"] == "StartInternalSystemAction" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
history += f'execute {event["action_name"]}\n'
elif event["type"] == "InternalSystemActionFinished" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
# We make sure the return value is a string with no new lines
return_value = str(event["return_value"]).replace("\n", " ")
history += f"# The result was {return_value}\n"
elif event["type"] == "mask_prev_user_message":
utterance_to_replace = get_last_user_utterance(events[:idx])
# We replace the last user utterance that led to jailbreak rail trigger with a placeholder text
split_history = history.rsplit(utterance_to_replace, 1)
placeholder_text = "<<<This text is hidden because the assistant should not talk about this.>>>"
history = placeholder_text.join(split_history)
elif colang_version == "2.x":
new_history: List[str] = []
# Structure the user/bot intent/action events
action_group: List[InternalEvent] = []
current_intent: Optional[str] = None
for event in events:
if not isinstance(event, InternalEvent):
continue
if (
event.name == InternalEvents.BOT_ACTION_LOG
or event.name == InternalEvents.USER_ACTION_LOG
):
if len(action_group) > 0 and (
current_intent is None
or current_intent != event.arguments["intent_flow_id"]
):
new_history.append(events_to_dialog_history(action_group))
new_history.append("")
action_group.clear()
action_group.append(event)
current_intent = event.arguments["intent_flow_id"]
elif (
event.name == InternalEvents.BOT_INTENT_LOG
or event.name == InternalEvents.USER_INTENT_LOG
):
if event.arguments["flow_id"] == current_intent:
# Found parent of current group
if event.name == InternalEvents.BOT_INTENT_LOG:
new_history.append(events_to_dialog_history([event]))
new_history.append(events_to_dialog_history(action_group))
elif event.arguments["flow_id"] is not None:
new_history.append(events_to_dialog_history(action_group))
new_history.append(events_to_dialog_history([event]))
new_history.append("")
else:
# New unrelated intent
if action_group:
new_history.append(events_to_dialog_history(action_group))
new_history.append("")
new_history.append(events_to_dialog_history([event]))
new_history.append("")
# Start a new group
action_group.clear()
current_intent = None
if action_group:
new_history.append(events_to_dialog_history(action_group))
history = "\n".join(new_history).rstrip("\n")
return history
def remove_action_intent_identifiers(lines: List[str]) -> List[str]:
"""Removes the action/intent identifiers."""
return [
s.replace("bot intent: ", "")
.replace("bot action: ", "")
.replace("user intent: ", "")
.replace("user action: ", "")
for s in lines
]
The provided code snippet includes necessary dependencies for implementing the `colang_without_identifiers` function. Write a Python function `def colang_without_identifiers(events: List[dict]) -> str` to solve the following problem:
Filter that turns an array of events into a colang history.
Here is the function:
def colang_without_identifiers(events: List[dict]) -> str:
"""Filter that turns an array of events into a colang history."""
return remove_action_intent_identifiers([get_colang_history(events)])[0] | 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: str) -> List[dict]` to solve the following problem:
Filter that given a history in colang format, returns all messages.
Here is the function:
def to_messages(colang_history: str) -> List[dict]:
"""Filter that given a history in colang format, returns all messages."""
messages = []
# For now, we use a simple heuristic. The line `user "xxx"` gets translated to
# a message from the user, and the rest gets translated to messages from the assistant.
lines = colang_history.split("\n")
bot_lines = []
for i, line in enumerate(lines):
if line.startswith('user "'):
# If we have bot lines in the buffer, we first add a bot message.
if bot_lines:
messages.append({"type": "assistant", "content": "\n".join(bot_lines)})
bot_lines = []
messages.append({"type": "user", "content": line[6:-1]})
elif line.strip() == "":
# On empty lines, we also reset the bot buffer.
if bot_lines:
messages.append({"type": "assistant", "content": "\n".join(bot_lines)})
bot_lines = []
else:
if i > 0 and lines[i - 1].startswith('user "'):
line = "User intent: " + line.strip()
elif line.startswith("user "):
line = "User intent: " + line[5:].strip()
elif line.startswith("bot "):
line = "Bot intent: " + line[4:].strip()
elif line.startswith(' "'):
line = "Bot message: " + line[2:].strip()
bot_lines.append(line)
# Check if there is a last message from the bot.
if bot_lines:
messages.append({"type": "bot", "content": "\n".join(bot_lines)})
return messages | 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: str) -> str` to solve the following problem:
Filter that given a history in colang format, returns a verbose version of the history.
Here is the function:
def verbose_v1(colang_history: str) -> str:
"""Filter that given a history in colang format, returns a verbose version of the history."""
lines = colang_history.split("\n")
for i, line in enumerate(lines):
if line.startswith('user "'):
lines[i] = 'User message: "' + line[6:]
elif (
line.startswith(" ")
and i > 0
and lines[i - 1].startswith("User message: ")
):
lines[i] = "User intent: " + line.strip()
elif line.startswith("user "):
lines[i] = "User intent: " + line[5:].strip()
elif line.startswith("bot "):
lines[i] = "Bot intent: " + line[4:]
elif line.startswith(' "'):
lines[i] = "Bot message: " + line[2:]
return "\n".join(lines) | 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_sequence(events: List[dict]) -> str` to solve the following problem:
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. ```
Here is the function:
def user_assistant_sequence(events: List[dict]) -> str:
"""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.
```
"""
history_items = []
for event in events:
if event["type"] == "UserMessage":
history_items.append("User: " + event["text"])
elif event["type"] == "StartUtteranceBotAction":
history_items.append("Assistant: " + event["script"])
return "\n".join(history_items) | 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_messages(colang_history: str)` to solve the following problem:
Filters that given a history in colang format, removes all texts.
Here is the function:
def remove_text_messages(colang_history: str):
"""Filters that given a history in colang format, removes all texts."""
# Get rid of messages from the user
colang_history = re.sub(r'user "[^\n]+"\n {2}', "user ", colang_history)
# Get rid of one line user messages
colang_history = re.sub(r"^\s*user [^\n]+\n\n", "", colang_history)
# Get rid of bot messages
colang_history = re.sub(r'bot ([^\n]+)\n {2}"[\s\S]*?"', r"bot \1", colang_history)
return colang_history | 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: str, n: int) -> str` to solve the following problem:
Returns the first n turns from a given colang history.
Here is the function:
def first_turns(colang_history: str, n: int) -> str:
"""Returns the first n turns from a given colang history."""
lines = colang_history.split("\n")
turn_count = 0
i = 0
while i < len(lines):
if lines[i].startswith('user "') or lines[i].startswith("user action: "):
turn_count += 1
if turn_count == n + 1:
break
i += 1
return "\n".join(lines[0:i]) | 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: str, n: int) -> str` to solve the following problem:
Returns the last n turns from a given colang history.
Here is the function:
def last_turns(colang_history: str, n: int) -> str:
"""Returns the last n turns from a given colang history."""
lines = colang_history.split("\n")
turn_count = 0
i = len(lines) - 1
while i > 0:
if lines[i].startswith('user "') or lines[i].startswith("user action: "):
turn_count += 1
if turn_count == n:
break
i -= 1
return "\n".join(lines[i:]) | 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) -> str` to solve the following problem:
Indents the provided text with the provided number of spaces.
Here is the function:
def indent(text: str, n_spaces: int) -> str:
"""Indents the provided text with the provided number of spaces."""
return textwrap.indent(text, " " * n_spaces) | 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_assistant_sequence_nemollm(events: List[dict]) -> str` to solve the following problem:
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. ```
Here is the function:
def user_assistant_sequence_nemollm(events: List[dict]) -> str:
"""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.
```
"""
history_items = []
for event in events:
if event["type"] == "UserMessage":
history_items.append("<extra_id_1>User\n" + event["text"])
elif event["type"] == "StartUtteranceBotAction":
history_items.append("<extra_id_1>Assistant\n" + event["script"])
return "\n".join(history_items) | 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().startswith("#"):
i -= 1
return lines[i]
The provided code snippet includes necessary dependencies for implementing the `to_messages_nemollm` function. Write a Python function `def to_messages_nemollm(colang_history: str) -> str` to solve the following problem:
Filter that given a history in colang format, returns a messages string in the chat format used by NeMo LLM models.
Here is the function:
def to_messages_nemollm(colang_history: str) -> str:
"""Filter that given a history in colang format, returns a messages string
in the chat format used by NeMo LLM models."""
messages = []
# For now, we use a simple heuristic. The line `user "xxx"` gets translated to
# a message from the user, and the rest gets translated to messages from the assistant.
lines = colang_history.split("\n")
bot_lines = []
for i, line in enumerate(lines):
if line.startswith('user "'):
# If we have bot lines in the buffer, we first add a bot message.
if bot_lines:
messages.append({"type": "assistant", "content": "\n".join(bot_lines)})
bot_lines = []
messages.append({"type": "user", "content": line[6:-1]})
elif line.strip() == "":
# On empty lines, we also reset the bot buffer.
if bot_lines:
messages.append({"type": "assistant", "content": "\n".join(bot_lines)})
bot_lines = []
else:
if i > 0 and _previous_line(lines, i).startswith('user "'):
if not line.strip().startswith("#"):
line = "User intent: " + line.strip()
elif line.startswith("user "):
line = "User intent: " + line[5:].strip()
elif line.startswith("bot "):
line = "Bot intent: " + line[4:].strip()
elif line.startswith(' "'):
line = "Bot message: " + line[2:].strip()
bot_lines.append(line)
# Check if there is a last message from the bot.
if bot_lines:
messages.append({"type": "bot", "content": "\n".join(bot_lines)})
messages_string = ""
for m in messages:
if m["type"] == "assistant" or m["type"] == "bot":
messages_string += "<extra_id_1>Assistant\n" + m["content"] + "\n"
elif m["type"] == "user":
messages_string += "<extra_id_1>User\n" + m["content"] + "\n"
return messages_string | 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_events(conversation: List) -> List[dict]` to solve the following problem:
Filter that given a conversation, returns a list of events.
Here is the function:
def conversation_to_events(conversation: List) -> List[dict]:
"""Filter that given a conversation, returns a list of events."""
events = []
for turn in conversation:
if "user" in turn:
events.append(
{
"type": "UtteranceUserActionFinished",
"final_transcript": turn["user"],
}
)
if "user_intent" in turn:
events.append(
{
"type": "UserIntent",
"intent": turn["user_intent"],
}
)
if "bot" in turn:
events.append(
{
"type": "StartUtteranceBotAction",
"script": turn["bot"],
}
)
if "bot_intent" in turn:
events.append(
{
"type": "BotIntent",
"intent": turn["bot_intent"],
}
)
return events | 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 import HuggingFacePipeline
from langchain.schema.output import GenerationChunk
from langchain_community import llms
from nemoguardrails.rails.llm.config import Model
from .nemollm import NeMoLLM
from .trtllm.llm import TRTLLM
log = logging.getLogger(__name__)
_providers: Dict[str, Type[BaseLanguageModel]] = {"nemollm": NeMoLLM, "trt_llm": TRTLLM}
async def _acall(self, *args, **kwargs):
"""Hackish way to add async support to LLM providers that don't have it.
We just call the sync version of the function.
"""
# TODO: run this in a thread pool!
return self._call(*args, **kwargs)
The provided code snippet includes necessary dependencies for implementing the `discover_langchain_providers` function. Write a Python function `def discover_langchain_providers()` to solve the following problem:
Automatically discover all LLM providers from LangChain.
Here is the function:
def discover_langchain_providers():
"""Automatically discover all LLM providers from LangChain."""
# To deal with deprecated stuff and avoid warnings, we compose the type_to_cls_dict here
if hasattr(llms, "get_type_to_cls_dict"):
type_to_cls_dict = {
k: v()
for k, v in llms.get_type_to_cls_dict().items()
# Exclude deprecated ones
if k not in ["mlflow-chat", "databricks-chat"]
}
else:
type_to_cls_dict = llms.type_to_cls_dict
_providers.update(type_to_cls_dict)
# We make sure we have OpenAI from the right package.
if "openai" in _providers:
try:
from langchain_openai import OpenAI
del _providers["openai"]
_providers["openai"] = OpenAI
except ImportError:
# If the `langchain_openai` package is not installed, the warning
# will come from langchain.
pass
# We also do some monkey patching to make sure that all LLM providers have async support
for provider_cls in _providers.values():
# If the "_acall" method is not defined, we add it.
if issubclass(provider_cls, LLM) and "_acall" not in provider_cls.__dict__:
log.debug("Adding async support to %s", provider_cls.__name__)
provider_cls._acall = _acall | 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 import HuggingFacePipeline
from langchain.schema.output import GenerationChunk
from langchain_community import llms
from nemoguardrails.rails.llm.config import Model
from .nemollm import NeMoLLM
from .trtllm.llm import TRTLLM
_providers: Dict[str, Type[BaseLanguageModel]] = {"nemollm": NeMoLLM, "trt_llm": TRTLLM}
The provided code snippet includes necessary dependencies for implementing the `register_llm_provider` function. Write a Python function `def register_llm_provider(name: str, provider_cls: Type[BaseLanguageModel])` to solve the following problem:
Register an additional LLM provider.
Here is the function:
def register_llm_provider(name: str, provider_cls: Type[BaseLanguageModel]):
"""Register an additional LLM provider."""
_providers[name] = provider_cls | 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 import HuggingFacePipeline
from langchain.schema.output import GenerationChunk
from langchain_community import llms
from nemoguardrails.rails.llm.config import Model
from .nemollm import NeMoLLM
from .trtllm.llm import TRTLLM
_providers: Dict[str, Type[BaseLanguageModel]] = {"nemollm": NeMoLLM, "trt_llm": TRTLLM}
class Model(BaseModel):
"""Configuration of a model used by the rails engine.
Typically, the main model is configured e.g.:
{
"type": "main",
"engine": "openai",
"model": "gpt-3.5-turbo-instruct"
}
"""
type: str
engine: str
model: Optional[str] = Field(
default=None,
description="The name of the model. If not specified, it should be specified through the parameters attribute.",
)
parameters: Dict[str, Any] = Field(default_factory=dict)
def get_llm_provider(model_config: Model) -> Type[BaseLanguageModel]:
if model_config.engine not in _providers:
raise RuntimeError(f"Could not find LLM provider '{model_config.engine}'")
# For OpenAI, we use a different provider depending on whether it's a chat model or not
if (
model_config.engine == "openai"
and ("gpt-3.5" in model_config.model or "gpt-4" in model_config.model)
and "instruct" not in model_config.model
):
try:
from langchain_openai.chat_models import ChatOpenAI
return ChatOpenAI
except ImportError:
raise ImportError(
"Could not import langchain_openai, please install it with "
"`pip install langchain-openai`."
)
elif model_config.engine == "azure" and (
"gpt-3.5" in model_config.model or "gpt-4" in model_config.model
):
try:
from langchain_openai.chat_models import AzureChatOpenAI
return AzureChatOpenAI
except ImportError:
raise ImportError(
"Could not import langchain_openai, please install it with "
"`pip install langchain-openai`."
)
else:
return _providers[model_config.engine] | 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 import HuggingFacePipeline
from langchain.schema.output import GenerationChunk
from langchain_community import llms
from nemoguardrails.rails.llm.config import Model
from .nemollm import NeMoLLM
from .trtllm.llm import TRTLLM
_providers: Dict[str, Type[BaseLanguageModel]] = {"nemollm": NeMoLLM, "trt_llm": TRTLLM}
The provided code snippet includes necessary dependencies for implementing the `get_llm_provider_names` function. Write a Python function `def get_llm_provider_names() -> List[str]` to solve the following problem:
Returns the list of supported LLM providers.
Here is the function:
def get_llm_provider_names() -> List[str]:
"""Returns the list of supported LLM providers."""
return list(sorted(list(_providers.keys()))) | 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 import verbose
from nemoguardrails.logging.verbose import Styles, set_verbose_llm_calls
from nemoguardrails.streaming import StreamingHandler
from nemoguardrails.utils import new_event_dict
async def _run_chat_v1_0(
config_path: Optional[str] = None,
verbose: bool = False,
streaming: bool = False,
server_url: Optional[str] = None,
config_id: Optional[str] = None,
):
"""Asynchronously 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.
streaming (bool): Whether to enable streaming mode. Defaults to False.
server_url (Optional[str]): The URL of the chat server. Defaults to None.
config_id (Optional[str]): The configuration ID. Defaults to None.
"""
if config_path is None and server_url is None:
raise RuntimeError(
"At least one of `config_path` or `server-url` must be provided."
)
if not server_url:
rails_config = RailsConfig.from_path(config_path)
rails_app = LLMRails(rails_config, verbose=verbose)
if streaming and not rails_config.streaming_supported:
print(
f"WARNING: The config `{config_path}` does not support streaming. "
"Falling back to normal mode."
)
streaming = False
else:
rails_app = None
history = []
# And go into the default listening loop.
while True:
print("")
user_message = input("> ")
history.append({"role": "user", "content": user_message})
if not server_url:
# If we have streaming from a locally loaded config, we initialize the handler.
if streaming and not server_url and rails_app.main_llm_supports_streaming:
streaming_handler = StreamingHandler(enable_print=True)
else:
streaming_handler = None
bot_message = await rails_app.generate_async(
messages=history, streaming_handler=streaming_handler
)
if not streaming or not rails_app.main_llm_supports_streaming:
# We print bot messages in green.
print(Styles.GREEN + f"{bot_message['content']}" + Styles.RESET_ALL)
else:
data = {
"config_id": config_id,
"messages": history,
"stream": streaming,
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{server_url}/v1/chat/completions",
json=data,
) as response:
# If the response is streaming, we show each chunk as it comes
if response.headers.get("Transfer-Encoding") == "chunked":
bot_message_text = ""
async for chunk in response.content.iter_any():
chunk = chunk.decode("utf-8")
print(Styles.GREEN + f"{chunk}" + Styles.RESET_ALL, end="")
bot_message_text += chunk
print("")
bot_message = {"role": "assistant", "content": bot_message_text}
else:
result = await response.json()
bot_message = result["messages"][0]
# We print bot messages in green.
print(
Styles.GREEN
+ f"{bot_message['content']}"
+ Styles.RESET_ALL
)
history.append(bot_message)
async def _run_chat_v2_x(rails_app: LLMRails):
"""Simple chat loop for v2.x using the stateful events API."""
state = None
waiting_user_input = False
running_timer_tasks: Dict[str, asyncio.Task] = {}
input_events: List[dict] = []
output_events: List[dict] = []
output_state = None
session: PromptSession = PromptSession()
# Start an asynchronous timer
async def _start_timer(timer_name: str, delay_seconds: float, action_uid: str):
nonlocal input_events
# print(
# Styles.GREY + f"timer (start): {timer_name}/{action_uid}" + Styles.RESET_ALL
# )
await asyncio.sleep(delay_seconds)
# print(
# Styles.GREY
# + f"timer (finished): {timer_name}/{action_uid}"
# + Styles.RESET_ALL
# )
input_events.append(
new_event_dict(
"TimerBotActionFinished",
action_uid=action_uid,
is_success=True,
timer_name=timer_name,
)
)
running_timer_tasks.pop(action_uid)
if waiting_user_input:
await _process_input_events()
def _process_output():
"""Helper to process the output events."""
nonlocal output_events, output_state, input_events, state
# We detect any "StartUtteranceBotAction" events, show the message, and
# generate the corresponding Finished events as new input events.
for event in output_events:
# Add all output events also to input events
input_events.append(event)
if event["type"] == "StartUtteranceBotAction":
# We print bot messages in green.
if not verbose.verbose_mode_enabled:
print(Styles.GREEN + f"\n{event['script']}\n" + Styles.RESET_ALL)
else:
print(
Styles.BLACK
+ Styles.GREEN_BACKGROUND
+ f"bot utterance: {event['script']}"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"UtteranceBotActionStarted",
action_uid=event["action_uid"],
)
)
input_events.append(
new_event_dict(
"UtteranceBotActionFinished",
action_uid=event["action_uid"],
is_success=True,
final_script=event["script"],
)
)
elif event["type"] == "StartGestureBotAction":
# We print gesture messages in green.
if not verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.BLUE_BACKGROUND
+ f"Gesture: {event['gesture']}"
+ Styles.RESET_ALL
)
else:
print(
Styles.BLACK
+ Styles.BLUE_BACKGROUND
+ f"bot gesture: {event['gesture']}"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"GestureBotActionStarted",
action_uid=event["action_uid"],
)
)
input_events.append(
new_event_dict(
"GestureBotActionFinished",
action_uid=event["action_uid"],
is_success=True,
)
)
elif event["type"] == "StartPostureBotAction":
# We print posture messages in green.
if not verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.BLUE_BACKGROUND
+ f"Posture: {event['posture']}."
+ Styles.RESET_ALL
)
else:
print(
Styles.BLACK
+ Styles.BLUE_BACKGROUND
+ f"bot posture (start): (posture={event['posture']}, action_uid={event['action_uid']}))"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"PostureBotActionStarted",
action_uid=event["action_uid"],
)
)
elif event["type"] == "StopPostureBotAction":
if verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.BLUE_BACKGROUND
+ f"bot posture (stop): (action_uid={event['action_uid']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"PostureBotActionFinished",
action_uid=event["action_uid"],
is_success=True,
)
)
elif event["type"] == "StartVisualInformationSceneAction":
# We print scene messages in green.
if not verbose.verbose_mode_enabled:
options = extract_scene_text_content(event["content"])
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"Scene information: {event['title']}{options}"
+ Styles.RESET_ALL
)
else:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene information (start): (title={event['title']}, action_uid={event['action_uid']}, content={event['content']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualInformationSceneActionStarted",
action_uid=event["action_uid"],
)
)
elif event["type"] == "StopVisualInformationSceneAction":
if verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene information (stop): (action_uid={event['action_uid']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualInformationSceneActionFinished",
action_uid=event["action_uid"],
is_success=True,
)
)
elif event["type"] == "StartVisualFormSceneAction":
# We print scene messages in green.
if not verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"Scene form: {event['prompt']}"
+ Styles.RESET_ALL
)
else:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene form (start): (prompt={event['prompt']}, action_uid={event['action_uid']}, inputs={event['inputs']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualFormSceneActionStarted",
action_uid=event["action_uid"],
)
)
elif event["type"] == "StopVisualFormSceneAction":
if verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene form (stop): (action_uid={event['action_uid']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualFormSceneActionFinished",
action_uid=event["action_uid"],
is_success=True,
)
)
elif event["type"] == "StartVisualChoiceSceneAction":
# We print scene messages in green.
if not verbose.verbose_mode_enabled:
options = extract_scene_text_content(event["options"])
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"Scene choice: {event['prompt']}{options}"
+ Styles.RESET_ALL
)
else:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene choice (start): (prompt={event['prompt']}, action_uid={event['action_uid']}, options={event['options']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualChoiceSceneActionStarted",
action_uid=event["action_uid"],
)
)
elif event["type"] == "StopVisualChoiceSceneAction":
if verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.MAGENTA_BACKGROUND
+ f"scene choice (stop): (action_uid={event['action_uid']})"
+ Styles.RESET_ALL
)
input_events.append(
new_event_dict(
"VisualChoiceSceneActionFinished",
action_uid=event["action_uid"],
is_success=True,
)
)
elif event["type"] == "StartTimerBotAction":
if verbose.verbose_mode_enabled:
print(
Styles.BLACK
+ Styles.GREY
+ f"timer (start): {event['timer_name']} {event['duration']}"
+ Styles.RESET_ALL
)
action_uid = event["action_uid"]
timer = _start_timer(event["timer_name"], event["duration"], action_uid)
# Manage timer tasks
if action_uid not in running_timer_tasks:
task = asyncio.create_task(timer)
running_timer_tasks.update({action_uid: task})
input_events.append(
new_event_dict(
"TimerBotActionStarted",
action_uid=event["action_uid"],
)
)
elif event["type"] == "StopTimerBotAction":
if verbose.verbose_mode_enabled:
print(
Styles.GREY
+ f"timer (stop): {event['action_uid']}"
+ Styles.RESET_ALL
)
action_uid = event["action_uid"]
if action_uid in running_timer_tasks:
running_timer_tasks[action_uid].cancel()
running_timer_tasks.pop(action_uid)
elif event["type"] == "TimerBotActionFinished":
if verbose.verbose_mode_enabled:
print(
Styles.GREY
+ f"timer (finished): {event['action_uid']}"
+ Styles.RESET_ALL
)
action_uid = event["action_uid"]
if action_uid in running_timer_tasks:
running_timer_tasks[action_uid].cancel()
running_timer_tasks.pop(action_uid)
elif event["type"] == "LocalAsyncCounter":
if verbose.verbose_mode_enabled:
print(Styles.GREY + f"Event: {event}" + Styles.RESET_ALL)
else:
if not verbose.verbose_mode_enabled:
print(f"Event: {event['type']}")
else:
print(f"Event: {event['type']}: {event}")
# TODO: deserialize the output state
# state = State.from_dict(output_state)
# Simulate serialization for testing
# data = pickle.dumps(output_state)
# output_state = pickle.loads(data)
state = output_state
async def _check_local_async_actions():
nonlocal output_events, output_state, input_events, check_task
while True:
# We only run the check when we wait for user input, but not the first time.
if not waiting_user_input or first_time:
await asyncio.sleep(0.1)
continue
if len(input_events) == 0:
input_events = [new_event_dict("CheckLocalAsync")]
output_events, output_state = await rails_app.process_events_async(
input_events, state
)
input_events = []
# Process output_events and potentially generate new input_events
_process_output()
if (
len(output_events) == 1
and output_events[0]["type"] == "LocalAsyncCounter"
and output_events[0]["counter"] == 0
):
# If there are no pending actions, we stop
check_task.cancel()
check_task = None
return
output_events.clear()
await asyncio.sleep(0.2)
async def _process_input_events():
nonlocal first_time, output_events, output_state, input_events, check_task
while input_events or first_time:
output_events, output_state = await rails_app.process_events_async(
input_events, state
)
input_events = []
_process_output()
# If we don't have a check task, we start it
if check_task is None:
check_task = asyncio.create_task(_check_local_async_actions())
first_time = False
# Start the task for checking async actions
check_task = asyncio.create_task(_check_local_async_actions())
# And go into the default listening loop.
first_time = True
with patch_stdout(raw=True):
while True:
if first_time:
input_events = []
else:
waiting_user_input = True
user_message: str = await session.prompt_async("> ")
waiting_user_input = False
if user_message == "":
input_events = [
{
"type": "CheckLocalAsync",
}
]
elif user_message.startswith("/"):
# Non-UtteranceBotAction actions
event_input = user_message.lstrip("/")
event = parse_events_inputs(event_input)
if event is None:
print(
Styles.RED
+ f"Invalid event: {event_input}"
+ Styles.RESET_ALL
)
else:
input_events = [event]
else:
input_events = [
{
"type": "UtteranceUserActionFinished",
"final_transcript": user_message,
}
]
await _process_input_events()
def set_verbose_llm_calls(verbose: bool):
"""Configure the verbose LLM calls mode."""
global verbose_llm_calls
verbose_llm_calls = verbose
The provided code snippet includes necessary dependencies for implementing the `run_chat` function. Write a Python function `def run_chat( config_path: Optional[str] = None, verbose: bool = False, verbose_llm_calls: bool = False, streaming: bool = False, server_url: Optional[str] = None, config_id: Optional[str] = None, )` to solve the following problem:
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 enable streaming mode. Defaults to False. server_url (Optional[str]): The URL of the chat server. Defaults to None. config_id (Optional[str]): The configuration ID. Defaults to None.
Here is the function:
def run_chat(
config_path: Optional[str] = None,
verbose: bool = False,
verbose_llm_calls: bool = False,
streaming: bool = False,
server_url: Optional[str] = None,
config_id: Optional[str] = None,
):
"""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 enable streaming mode. Defaults to False.
server_url (Optional[str]): The URL of the chat server. Defaults to None.
config_id (Optional[str]): The configuration ID. Defaults to None.
"""
# If the `--verbose-llm-calls` mode is used, we activate the verbose mode.
# This means that the user doesn't have to use both options at the same time.
verbose = verbose or verbose_llm_calls
rails_config = RailsConfig.from_path(config_path)
rails_app = LLMRails(rails_config, verbose=verbose)
set_verbose_llm_calls(verbose_llm_calls)
if verbose and not verbose_llm_calls:
print(
"NOTE: use the `--verbose-llm-calls` option to include the LLM prompts "
"and completions in the log.\n"
)
print("Starting the chat (Press Ctrl + C twice to quit) ...")
if rails_config.colang_version == "1.0":
asyncio.run(
_run_chat_v1_0(
config_path=config_path,
verbose=verbose,
streaming=streaming,
server_url=server_url,
config_id=config_id,
)
)
elif rails_config.colang_version == "2.x":
asyncio.run(_run_chat_v2_x(rails_app))
else:
raise Exception(f"Invalid colang version: {rails_config.colang_version}") | 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 enable streaming mode. Defaults to False. server_url (Optional[str]): The URL of the chat server. Defaults to None. config_id (Optional[str]): The configuration ID. Defaults to None. |
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 = True | 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 sync call is made from an async loop. Returns True if a sync call is made from an async loop.
Here is the function:
def check_sync_call_from_async_loop():
"""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.
"""
if hasattr(asyncio, "_nest_patched"):
return False
if nest_asyncio_patch_applied:
return False
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if loop and loop.is_running():
return True
return False | 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.
events (Optional[List[dict]]): The events to be added to the stream.
context_updates (Optional[dict]): Updates made to the context by this action.
"""
# The value returned by the action
return_value: Optional[Any] = None
# The events that should be added to the stream
events: Optional[List[dict]] = None
# The updates made to the context by this action
context_updates: Optional[dict] = field(default_factory=dict)
def new_event_dict(event_type: str, **payload) -> Dict[str, Any]:
"""Helper to create a generic event structure."""
event: Dict[str, Any] = {
"type": event_type,
"uid": new_uid(),
"event_created_at": datetime.now(timezone.utc).isoformat(),
"source_uid": "NeMoGuardrails",
}
event = {**event, **payload}
if "Action" in event_type:
_add_modality_info(event)
_update_action_properties(event)
ensure_valid_event(event)
return event
The provided code snippet includes necessary dependencies for implementing the `create_event` function. Write a Python function `async def create_event( event: dict, context: Optional[dict] = None, )` to solve the following problem:
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.
Here is the function:
async def create_event(
event: dict,
context: Optional[dict] = None,
):
"""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.
"""
event_dict = new_event_dict(
event["_type"], **{k: v for k, v in event.items() if k != "_type"}
)
# We add basic support for referring variables as values
for k, v in event_dict.items():
if isinstance(v, str) and v[0] == "$":
event_dict[k] = context.get(v[1:])
return ActionResult(events=[event_dict]) | 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")
API_URL_BASE = f"https://api.wolframalpha.com/v2/result?appid={APP_ID}"
class ActionResult:
"""Data class representing the result of an action.
Attributes:
return_value (Optional[Any]): The value returned by the action.
events (Optional[List[dict]]): The events to be added to the stream.
context_updates (Optional[dict]): Updates made to the context by this action.
"""
# The value returned by the action
return_value: Optional[Any] = None
# The events that should be added to the stream
events: Optional[List[dict]] = None
# The updates made to the context by this action
context_updates: Optional[dict] = field(default_factory=dict)
def new_event_dict(event_type: str, **payload) -> Dict[str, Any]:
"""Helper to create a generic event structure."""
event: Dict[str, Any] = {
"type": event_type,
"uid": new_uid(),
"event_created_at": datetime.now(timezone.utc).isoformat(),
"source_uid": "NeMoGuardrails",
}
event = {**event, **payload}
if "Action" in event_type:
_add_modality_info(event)
_update_action_properties(event)
ensure_valid_event(event)
return event
The provided code snippet includes necessary dependencies for implementing the `wolfram_alpha_request` function. Write a Python function `async def wolfram_alpha_request( query: Optional[str] = None, context: Optional[dict] = None )` to solve the following problem:
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 to Wolfram Alpha.
Here is the function:
async def wolfram_alpha_request(
query: Optional[str] = None, context: Optional[dict] = None
):
"""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 to Wolfram Alpha.
"""
# If we don't have an explicit query, we take the last user message
if query is None and context is not None:
query = context.get("last_user_message") or "2+3"
if query is None:
raise Exception("No query was provided to Wolfram Alpha.")
if APP_ID is None:
return ActionResult(
return_value=False,
events=[
new_event_dict(
"BotIntent", intent="inform wolfram alpha app id not set"
),
new_event_dict(
"StartUtteranceBotAction",
script="Wolfram Alpha app ID is not set. Please set the WOLFRAM_ALPHA_APP_ID environment variable.",
),
new_event_dict("BotIntent", intent="stop"),
],
)
url = API_URL_BASE + "&" + parse.urlencode({"i": query})
log.info(f"Wolfram Alpha: executing request for: {query}")
async with aiohttp.ClientSession() as session:
async with session.get(url) as resp:
if resp.status != 200:
log.info(f"Wolfram Alpha request failed : {query}")
return ActionResult(
return_value=False,
events=[
new_event_dict(
"BotIntent", intent="inform wolfram alpha not working"
),
new_event_dict(
"StartUtteranceBotAction",
script="Apologies, but I cannot answer this question at this time. I am having trouble getting the answer from Wolfram Alpha.",
),
new_event_dict("BotIntent", intent="stop"),
],
)
result = await resp.text()
log.info(f"Wolfram Alpha: the result was {result}.")
return result | 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 to Wolfram Alpha. |
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, )` to solve the following problem:
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.
Here is the 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.
"""
def decorator(fn_or_cls):
"""Inner decorator function to add metadata to the action.
Args:
fn_or_cls: The function or class being decorated.
"""
fn_or_cls.action_meta = {
"name": name or fn_or_cls.__name__,
"is_system_action": is_system_action,
"execute_async": execute_async,
}
return fn_or_cls
return decorator | 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,
get_first_nonempty_line,
get_initial_actions,
get_last_user_utterance_event_v2_x,
llm_call,
remove_action_intent_identifiers,
)
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.lang.utils import new_uuid
from nemoguardrails.colang.v2_x.runtime.flows import (
ActionEvent,
InternalEvent,
LlmResponseError,
)
from nemoguardrails.colang.v2_x.runtime.statemachine import (
Event,
InternalEvents,
State,
find_all_active_event_matchers,
get_element_from_head,
get_event_from_element,
)
from nemoguardrails.embeddings.index import EmbeddingsIndex, IndexItem
from nemoguardrails.llm.filters import colang
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.types import Task
The provided code snippet includes necessary dependencies for implementing the `_remove_leading_empty_lines` function. Write a Python function `def _remove_leading_empty_lines(s: str) -> str` to solve the following problem:
Remove the leading empty lines if they exist. A line is considered empty if it has only white spaces.
Here is the function:
def _remove_leading_empty_lines(s: str) -> str:
"""Remove the leading empty lines if they exist.
A line is considered empty if it has only white spaces.
"""
lines = s.split("\n")
while lines and lines[0].strip() == "":
lines = lines[1:]
return "\n".join(lines) | 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[str] = (), **validation_args)` to solve the following problem:
A generic decorator that can be used by any action (class method or function) for input validation. Supported validation choices are: length and quote.
Here is the function:
def validate_input(attribute: str, validators: List[str] = (), **validation_args):
"""A generic decorator that can be used by any action (class method or function) for input validation.
Supported validation choices are: length and quote.
"""
def _validate_input(f):
def wrapper(*args, **kwargs):
obj = None
if attribute in kwargs:
attribute_value = kwargs.get(attribute)
else:
obj = args[0]
attribute_value = getattr(obj, attribute)
if not attribute_value:
raise ValueError(f"Attribute {attribute} is empty.")
if "length" in validators:
max_len = (
validation_args["max_len"]
if "max_len" in validation_args
else MAX_LEN
)
if len(attribute_value) > max_len:
raise ValueError(f"Attribute {attribute} is too long.")
if "quote" in validators:
if obj:
setattr(obj, attribute, quote(attribute_value))
elif attribute in kwargs:
kwargs[attribute] = quote(attribute_value)
return f(*args, **kwargs)
return wrapper
def decorator(obj):
if isinstance(obj, type):
if hasattr(obj, "run") and callable(getattr(obj, "run")):
setattr(obj, "run", _validate_input(getattr(obj, "run")))
return obj
else:
return _validate_input(obj)
return decorator | 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)
match = pattern.search(resp)
if match:
return True
return False
def contains_secrets(resp):
"""Validate if response have any of the key present
Refer https://github.com/Yelp/detect-secrets for detection process
response is string of format
AWSKeyDetector : False
ArtifactoryDetector : False
"""
try:
import detect_secrets
except ModuleNotFoundError:
raise ValueError(
"Could not import detect_secrets. Please install using `pip install detect-secrets`"
)
with detect_secrets.settings.default_settings():
res = detect_secrets.scan_adhoc_string(resp)
for secret_type in res.split("\n"):
if "True" in secret_type:
return True
return False
The provided code snippet includes necessary dependencies for implementing the `validate_response` function. Write a Python function `def validate_response(validators: List[str] = [], **validation_args)` to solve the following problem:
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
Here is the function:
def validate_response(validators: List[str] = [], **validation_args):
"""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
"""
def _validate_response(f):
def wrapper(*args, **kwargs):
def filter_ip(resp: str):
"""Filter out IP addresses from the response."""
ip_regex = re.compile(r"\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b")
return re.sub(ip_regex, "", resp)
response_value = f(*args, **kwargs)
if "length" in validators and len(response_value) > MAX_LEN:
raise ValueError(f"Response Attribute {response_value} is too long.")
if "ip_filter" in validators:
if isinstance(response_value, str):
response_value = filter_ip(response_value)
elif isinstance(response_value, dict):
for key, value in response_value:
response_value[key] = filter_ip(value)
if "is_default_resp" in validators:
if _is_default_resp(response_value):
raise ValueError("Default Response received from action")
if "filter_secrets" in validators:
if contains_secrets(json.dumps(response_value)):
raise ValueError("The response contains sensitive data.")
return response_value
return wrapper
def decorator(obj):
if isinstance(obj, type):
if hasattr(obj, "run") and callable(getattr(obj, "run")):
setattr(obj, "run", _validate_response(getattr(obj, "run")))
return obj
else:
return _validate_response(obj)
return decorator | 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.
events (Optional[List[dict]]): The events to be added to the stream.
context_updates (Optional[dict]): Updates made to the context by this action.
"""
# The value returned by the action
return_value: Optional[Any] = None
# The events that should be added to the stream
events: Optional[List[dict]] = None
# The updates made to the context by this action
context_updates: Optional[dict] = field(default_factory=dict)
class KnowledgeBase:
"""
Basic implementation of a knowledge base.
This class represents a knowledge base that can store and index documents for efficient retrieval.
It utilizes an embedding search provider to build and search an index for relevant information.
Parameters:
- documents (List[str]): A list of documents to initialize the knowledge base.
- config (KnowledgeBaseConfig): Configuration for the knowledge base.
- get_embedding_search_provider_instance (Callable[[Optional[EmbeddingSearchProvider]], EmbeddingsIndex]):
A callable function to get an instance of the embedding search provider.
Methods:
- init(): Initializes the knowledge base by splitting documents into topic chunks.
- build(): Builds the knowledge base index, utilizing the configured embedding search provider.
- search_relevant_chunks(text: str, max_results: int = 3): Searches the index for the most relevant chunks.
Attributes:
- documents (List[str]): The list of documents provided during initialization.
- chunks (List[dict]): A list of topic chunks extracted from the documents.
- index (EmbeddingsIndex): The knowledge base index used for searching.
- config (KnowledgeBaseConfig): Configuration for the knowledge base.
Example:
```python
# Creating a KnowledgeBase instance
kb = KnowledgeBase(documents=["Document 1", "Document 2"], config=my_config, get_embedding_search_provider_instance=my_provider)
# Initializing and building the knowledge base
kb.init()
await kb.build()
# Searching for relevant chunks
results = await kb.search_relevant_chunks("query text", max_results=5)
```
Note:
- The knowledge base supports markdown format documents.
- The index is built using an embedding search provider, and the result is cached for future use.
"""
def __init__(
self,
documents: List[str],
config: KnowledgeBaseConfig,
get_embedding_search_provider_instance: Callable[
[Optional[EmbeddingSearchProvider]], EmbeddingsIndex
],
):
self.documents = documents
self.chunks = []
self.index = None
self.config = config
self._get_embeddings_search_instance = get_embedding_search_provider_instance
def init(self):
"""Initialize the knowledge base.
The initial data is loaded from the `$kb_docs` context key. The key is populated when
the model is loaded. Currently, only markdown format is supported.
"""
if not self.documents:
return
# Start splitting every doc into topic chunks
for doc in self.documents:
chunks = split_markdown_in_topic_chunks(doc)
self.chunks.extend(chunks)
async def build(self):
"""Builds the knowledge base index."""
t0 = time()
index_items = []
all_text_items = []
for chunk in self.chunks:
text = f"# {chunk['title']}\n\n{chunk['body'].strip()}"
all_text_items.append(text)
index_items.append(IndexItem(text=text, meta=chunk))
# Stop if there are no items
if not index_items:
return
# We compute the md5
# As part of the hash, we also include the embedding engine and the model
# to prevent the cache being used incorrectly when the embedding model changes.
hash_prefix = self.config.embedding_search_provider.parameters.get(
"embedding_engine", ""
) + self.config.embedding_search_provider.parameters.get("embedding_model", "")
md5_hash = hashlib.md5(
(hash_prefix + "".join(all_text_items)).encode("utf-8")
).hexdigest()
cache_file = os.path.join(CACHE_FOLDER, f"{md5_hash}.ann")
embedding_size_file = os.path.join(CACHE_FOLDER, f"{md5_hash}.esize")
# If we have already computed this before, we use it
if (
self.config.embedding_search_provider.name == "default"
and os.path.exists(cache_file)
and os.path.exists(embedding_size_file)
):
from annoy import AnnoyIndex
from nemoguardrails.embeddings.basic import BasicEmbeddingsIndex
log.info(cache_file)
self.index = cast(
BasicEmbeddingsIndex,
self._get_embeddings_search_instance(
self.config.embedding_search_provider
),
)
with open(embedding_size_file, "r") as f:
embedding_size = int(f.read())
ann_index = AnnoyIndex(embedding_size, "angular")
ann_index.load(cache_file)
self.index.embeddings_index = ann_index
await self.index.add_items(index_items)
else:
self.index = self._get_embeddings_search_instance(
self.config.embedding_search_provider
)
await self.index.add_items(index_items)
await self.index.build()
# For the default Embedding Search provider, which uses annoy, we also
# persist the index after it's computed.
if self.config.embedding_search_provider.name == "default":
from nemoguardrails.embeddings.basic import BasicEmbeddingsIndex
# We also save the file for future use
os.makedirs(CACHE_FOLDER, exist_ok=True)
basic_index = cast(BasicEmbeddingsIndex, self.index)
basic_index.embeddings_index.save(cache_file)
# And, explicitly save the size as we need it when we reload
with open(embedding_size_file, "w") as f:
f.write(str(basic_index.embedding_size))
log.info(f"Building the Knowledge Base index took {time() - t0} seconds.")
async def search_relevant_chunks(self, text, max_results: int = 3):
"""Search the index for the most relevant chunks."""
if self.index is None:
return []
results = await self.index.search(text, max_results=max_results)
# Return the chunks directly
return [result.meta for result in results]
The provided code snippet includes necessary dependencies for implementing the `retrieve_relevant_chunks` function. Write a Python function `async def retrieve_relevant_chunks( context: Optional[dict] = None, kb: Optional[KnowledgeBase] = None, )` to solve the following problem:
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 relevant chunks with context updates: - "relevant_chunks" -- the relevant chunks as a single string, - "relevant_chunks_sep" -- the relevant chunks as a list of strings before concatenation, - "retrieved_for" -- the user message that the chunks were retrieved for. Note: This action retrieves relevant chunks from the KnowledgeBase based on the user's last message and updates the context with the information. Example: ``` result = await retrieve_relevant_chunks(context=my_context, kb=my_knowledge_base) print(result.return_value) # Relevant chunks as a string print(result.context_updates) # Updated context with relevant chunks ```
Here is the function:
async def retrieve_relevant_chunks(
context: Optional[dict] = None,
kb: Optional[KnowledgeBase] = None,
):
"""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 relevant chunks with context updates:
- "relevant_chunks" -- the relevant chunks as a single string,
- "relevant_chunks_sep" -- the relevant chunks as a list of strings before concatenation,
- "retrieved_for" -- the user message that the chunks were retrieved for.
Note:
This action retrieves relevant chunks from the KnowledgeBase based on the user's last message
and updates the context with the information.
Example:
```
result = await retrieve_relevant_chunks(context=my_context, kb=my_knowledge_base)
print(result.return_value) # Relevant chunks as a string
print(result.context_updates) # Updated context with relevant chunks
```
"""
user_message = context.get("last_user_message")
context_updates = {}
if user_message and kb:
# Are these needed two needed?
context_updates["relevant_chunks"] = ""
context_updates["relevant_chunks_sep"] = []
context_updates["retrieved_for"] = user_message
chunks = [
chunk["body"] for chunk in await kb.search_relevant_chunks(user_message)
]
context_updates["relevant_chunks"] = "\n".join(chunks)
context_updates["relevant_chunks_sep"] = chunks
else:
# No KB is set up, we keep the existing relevant_chunks if we have them.
context_updates["relevant_chunks"] = context.get("relevant_chunks", "") + "\n"
context_updates["relevant_chunks_sep"] = context.get("relevant_chunks_sep", [])
context_updates["retrieved_for"] = None
return ActionResult(
return_value=context_updates["relevant_chunks"],
context_updates=context_updates,
) | 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 relevant chunks with context updates: - "relevant_chunks" -- the relevant chunks as a single string, - "relevant_chunks_sep" -- the relevant chunks as a list of strings before concatenation, - "retrieved_for" -- the user message that the chunks were retrieved for. Note: This action retrieves relevant chunks from the KnowledgeBase based on the user's last message and updates the context with the information. Example: ``` result = await retrieve_relevant_chunks(context=my_context, kb=my_knowledge_base) print(result.return_value) # Relevant chunks as a string print(result.context_updates) # Updated context with relevant chunks ``` |
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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
class Flow(Element):
"""Element that represents a flow."""
name: str = ""
parameters: List[FlowParamDef] = field(default_factory=list)
return_members: List[FlowReturnMemberDef] = field(default_factory=list)
elements: List[Element] = field(default_factory=list)
source_code: Optional[str] = None
file_info: Dict[str, Any] = field(default_factory=dict)
_type: str = "flow"
The provided code snippet includes necessary dependencies for implementing the `flow_to_colang` function. Write a Python function `def flow_to_colang(flow: Union[dict, Flow]) -> str` to solve the following problem:
Converts a flow to colang format. Example flow: ``` - user: ask capabilities - bot: inform capabilities ``` to colang: ``` user ask capabilities bot inform capabilities ```
Here is the function:
def flow_to_colang(flow: Union[dict, Flow]) -> str:
"""Converts a flow to colang format.
Example flow:
```
- user: ask capabilities
- bot: inform capabilities
```
to colang:
```
user ask capabilities
bot inform capabilities
```
"""
# TODO: use the source code lines if available.
colang_flow = ""
if isinstance(flow, Flow):
# TODO: generate the flow code from the flow.elements array
pass
else:
for element in flow["elements"]:
if "_type" not in element:
raise Exception("bla")
if element["_type"] == "UserIntent":
colang_flow += f'user {element["intent_name"]}\n'
elif element["_type"] == "run_action" and element["action_name"] == "utter":
colang_flow += f'bot {element["action_params"]["value"]}\n'
return colang_flow | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_retrieved_relevant_chunks` function. Write a Python function `def get_retrieved_relevant_chunks(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the retrieved chunks for current user utterance from the events.
Here is the function:
def get_retrieved_relevant_chunks(events: List[dict]) -> Optional[dict]:
"""Returns the retrieved chunks for current user utterance from the events."""
for event in reversed(events):
if event["type"] == "UserMessage":
break
if event["type"] == "ContextUpdate" and "relevant_chunks" in event.get(
"data", {}
):
return event["data"]["relevant_chunks"]
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_last_user_utterance_event` function. Write a Python function `def get_last_user_utterance_event(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the last user utterance from the events.
Here is the function:
def get_last_user_utterance_event(events: List[dict]) -> Optional[dict]:
"""Returns the last user utterance from the events."""
for event in reversed(events):
if isinstance(event, dict) and event["type"] == "UserMessage":
return event
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_last_user_utterance_event_v2_x` function. Write a Python function `def get_last_user_utterance_event_v2_x(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the last user utterance from the events.
Here is the function:
def get_last_user_utterance_event_v2_x(events: List[dict]) -> Optional[dict]:
"""Returns the last user utterance from the events."""
for event in reversed(events):
if isinstance(event, dict) and event["type"] == "UtteranceUserActionFinished":
return event
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_last_user_intent_event` function. Write a Python function `def get_last_user_intent_event(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the last user intent from the events.
Here is the function:
def get_last_user_intent_event(events: List[dict]) -> Optional[dict]:
"""Returns the last user intent from the events."""
for event in reversed(events):
if event["type"] == "UserIntent":
return event
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_last_bot_intent_event` function. Write a Python function `def get_last_bot_intent_event(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the last user intent from the events.
Here is the function:
def get_last_bot_intent_event(events: List[dict]) -> Optional[dict]:
"""Returns the last user intent from the events."""
for event in reversed(events):
if event["type"] == "BotIntent":
return event
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_last_bot_utterance_event` function. Write a Python function `def get_last_bot_utterance_event(events: List[dict]) -> Optional[dict]` to solve the following problem:
Returns the last bot utterance from the events.
Here is the function:
def get_last_bot_utterance_event(events: List[dict]) -> Optional[dict]:
"""Returns the last bot utterance from the events."""
for event in reversed(events):
if event["type"] == "StartUtteranceBotAction":
return event
return None | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `remove_text_messages_from_history` function. Write a Python function `def remove_text_messages_from_history(history: str) -> str` to solve the following problem:
Helper that given a history in colang format, removes all texts.
Here is the function:
def remove_text_messages_from_history(history: str) -> str:
"""Helper that given a history in colang format, removes all texts."""
# Get rid of messages from the user
history = re.sub(r'user "[^\n]+"\n {2}', "user ", history)
# Get rid of one line user messages
history = re.sub(r"^\s*user [^\n]+\n\n", "", history)
# Get rid of bot messages
history = re.sub(r'bot ([^\n]+)\n {2}"[\s\S]*?"', r"bot \1", history)
return history | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_first_nonempty_line` function. Write a Python function `def get_first_nonempty_line(s: str) -> Optional[str]` to solve the following problem:
Helper that returns the first non-empty line from a string
Here is the function:
def get_first_nonempty_line(s: str) -> Optional[str]:
"""Helper that returns the first non-empty line from a string"""
if not s:
return None
first_nonempty_line = None
lines = [line.strip() for line in s.split("\n")]
for line in lines:
if len(line) > 0:
first_nonempty_line = line
break
return first_nonempty_line | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_top_k_nonempty_lines` function. Write a Python function `def get_top_k_nonempty_lines(s: str, k: int = 1) -> Optional[List[str]]` to solve the following problem:
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.
Here is the function:
def get_top_k_nonempty_lines(s: str, k: int = 1) -> Optional[List[str]]:
"""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."""
if not s:
return None
lines = [line.strip() for line in s.split("\n")]
# Ignore line comments and empty lines
lines = [line for line in lines if len(line) > 0 and line[0] != "#"]
return lines[:k] | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `get_initial_actions` function. Write a Python function `def get_initial_actions(strings: List[str]) -> List[str]` to solve the following problem:
Returns the first action before an empty line.
Here is the function:
def get_initial_actions(strings: List[str]) -> List[str]:
"""Returns the first action before an empty line."""
previous_strings = []
for string in strings:
if string == "":
break
previous_strings.append(string)
return previous_strings | 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 AIMessage, HumanMessage, SystemMessage
from nemoguardrails.colang.v2_x.lang.colang_ast import Flow
from nemoguardrails.colang.v2_x.runtime.flows import InternalEvent, InternalEvents
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.logging.callbacks import logging_callbacks
from nemoguardrails.logging.explain import LLMCallInfo
The provided code snippet includes necessary dependencies for implementing the `escape_flow_name` function. Write a Python function `def escape_flow_name(name: str) -> str` to solve the following problem:
Escape invalid keywords in flow names.
Here is the function:
def escape_flow_name(name: str) -> str:
"""Escape invalid keywords in flow names."""
# TODO: We need to figure out how we can distinguish from valid flow parameters
result = (
name.replace(" and ", "_and_")
.replace(" or ", "_or_")
.replace(" as ", "_as_")
.replace(" not ", "_not_")
.replace(" is ", "_is_")
.replace(" in ", "_in_")
.replace("(", "")
.replace(")", "")
.replace("'", "")
.replace('"', "")
.replace("-", "_")
)
return re.sub(r"\b\d+\b", lambda match: f"_{match.group()}_", result) | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
app = FastAPI(
title="Guardrails Server API",
description=api_description,
version="0.1.0",
license_info={"name": "Apache License, Version 2.0"},
)
app.rails_config_path = os.path.normpath(
os.path.join(os.path.dirname(__file__), "..", "..", "examples", "bots")
)
app.disable_chat_ui = False
app.auto_reload = False
app.stop_signal = False
app.single_config_mode = False
app.single_config_id = None
The provided code snippet includes necessary dependencies for implementing the `get_rails_configs` function. Write a Python function `async def get_rails_configs()` to solve the following problem:
Returns the list of available rails configurations.
Here is the function:
async def get_rails_configs():
"""Returns the list of available rails configurations."""
# In single-config mode, we return a single config.
if app.single_config_mode:
# And we use the name of the root folder as the id of the config.
return [{"id": app.single_config_id}]
# We extract all folder names as config names
config_ids = [
f
for f in os.listdir(app.rails_config_path)
if os.path.isdir(os.path.join(app.rails_config_path, f))
and f[0] != "."
and f[0] != "_"
# We filter out all the configs for which there is no `config.yml` file.
and (
os.path.exists(os.path.join(app.rails_config_path, f, "config.yml"))
or os.path.exists(os.path.join(app.rails_config_path, f, "config.yaml"))
)
]
return [{"id": config_id} for config_id in config_ids] | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
log = logging.getLogger(__name__)
registered_loggers = []
api_request_headers = contextvars.ContextVar("headers")
datastore: Optional[DataStore] = None
class RequestBody(BaseModel):
config_id: Optional[str] = Field(
default=None, description="The id of the configuration to be used."
)
config_ids: Optional[List[str]] = Field(
default=None,
description="The list of configuration ids to be used. "
"If set, the configurations will be combined.",
)
thread_id: Optional[str] = Field(
default=None,
description="The id of an existing thread to which the messages should be added.",
)
messages: List[dict] = Field(
default=None, description="The list of messages in the current conversation."
)
context: Optional[dict] = Field(
default=None,
description="Additional context data to be added to the conversation.",
)
stream: Optional[bool] = Field(
default=False,
description="If set, partial message deltas will be sent, like in ChatGPT. "
"Tokens will be sent as data-only server-sent events as they become "
"available, with the stream terminated by a data: [DONE] message.",
)
options: Optional[GenerationOptions] = Field(
default=None, description="Additional options for controlling the generation."
)
def check_if_set(cls, v, values, **kwargs):
if v is not None and values.get("config_id") is not None:
raise ValueError("Only one of config_id or config_ids should be specified")
if v is None and values.get("config_id") is None:
raise ValueError("Either config_id or config_ids must be specified")
return v
def _get_rails(config_ids: List[str]) -> LLMRails:
"""Returns the rails instance for the given config id."""
# If we have a single config id, we just use it as the key
configs_cache_key = _generate_cache_key(config_ids)
if configs_cache_key in llm_rails_instances:
return llm_rails_instances[configs_cache_key]
# In single-config mode, we only load the main config directory
if app.single_config_mode:
if config_ids != [app.single_config_id]:
raise ValueError(f"Invalid configuration ids: {config_ids}")
# We set this to an empty string so tha when joined with the root path, we
# get the same thing.
config_ids = [""]
full_llm_rails_config = None
for config_id in config_ids:
full_path = os.path.normpath(os.path.join(app.rails_config_path, config_id))
if not full_path.startswith(app.rails_config_path):
raise Exception("Not allowed.")
rails_config = RailsConfig.from_path(full_path)
if not full_llm_rails_config:
full_llm_rails_config = rails_config
else:
full_llm_rails_config = full_llm_rails_config + rails_config
llm_rails = LLMRails(config=full_llm_rails_config, verbose=True)
llm_rails_instances[configs_cache_key] = llm_rails
# If we have a cache for the events, we restore it
llm_rails.events_history_cache = llm_rails_events_history_cache.get(
configs_cache_key, {}
)
return llm_rails
"/v1/chat/completions",
response_model=ResponseBody,
response_model_exclude_none=True,
class GenerationResponse(BaseModel):
# TODO: add typing for the list of messages
response: Union[str, List[dict]] = Field(
description="The list of the generated messages."
)
llm_output: Optional[dict] = Field(
default=None, description="Contains any additional output coming from the LLM."
)
output_data: Optional[dict] = Field(
default=None,
description="The output data, i.e. a dict with the values corresponding to the `output_vars`.",
)
log: Optional[GenerationLog] = Field(
default=None, description="Additional logging information."
)
class StreamingHandler(AsyncCallbackHandler, AsyncIterator):
"""Streaming async handler.
Implements the LangChain AsyncCallbackHandler, so it can be notified of new tokens.
It also implements the AsyncIterator interface, so it can be used directly to stream
back the response.
"""
def __init__(self, enable_print: bool = False, enable_buffer: bool = False):
# A unique id for the stream handler
self.uid = new_uuid()
# The queue where the chunks are gathered for when the handler also acts as an AsyncIterator
self.queue = asyncio.Queue()
self.streaming_finished_event = asyncio.Event()
# When printing is enabled, the handler will print the processed chunks in green.
self.enable_print = enable_print
# When buffering is enabled, the chunks will gather in a buffer.
self.enable_buffer = enable_buffer
# The prefix/suffix that should be removed
self.prefix = None
self.suffix = None
# The current chunk which needs to be checked for prefix/suffix matching
self.current_chunk = ""
# The current buffer, until we start the processing.
self.buffer = ""
# The full completion
self.completion = ""
# Weather we're interested in the top k non-empty lines
self.k = 0
self.top_k_nonempty_lines_event = asyncio.Event()
# If set, the chunk will be piped to the specified handler rather than added to
# the queue or printed
self.pipe_to = None
self.first_token = True
# The stop chunks
self.stop = []
def set_pattern(self, prefix: Optional[str] = None, suffix: Optional[str] = None):
"""Sets the patter that is expected.
If a prefix or a suffix are specified, they will be removed from the output.
"""
self.prefix = prefix
self.suffix = suffix
def set_pipe_to(self, another_handler):
self.pipe_to = another_handler
async def wait(self):
"""Waits until the stream finishes and returns the full completion."""
await self.streaming_finished_event.wait()
return self.completion
async def wait_top_k_nonempty_lines(self, k: int):
"""Waits for top k non-empty lines from the LLM.
When k lines have been received (and k+1 has been started) it will return
and remove them from the buffer
"""
self.k = k
await self.top_k_nonempty_lines_event.wait()
lines = self.buffer.split("\n")
top_k_lines = []
i = 0
for i in range(len(lines)):
line = lines[i].strip()
if len(line) > 0 and line[0] != "#":
top_k_lines.append(lines[i])
if len(top_k_lines) == k:
break
self.buffer = "\n".join(lines[i + 1 :])
return "\n".join(top_k_lines)
async def enable_buffering(self):
self.enable_buffer = True
self.buffer = ""
async def disable_buffering(self):
"""When we disable the buffer, we process the buffer as a chunk."""
self.enable_buffer = False
await self.push_chunk(self.buffer)
self.buffer = ""
async def __anext__(self):
element = None
try:
element = await self.queue.get()
except RuntimeError as ex:
if "Event loop is closed" not in str(ex):
raise ex
if element is None or element == "":
raise StopAsyncIteration
else:
return element
async def _process(self, chunk: str):
"""Process a chunk of text.
If we're in buffering mode, we just record it.
If we need to pipe it to another streaming handler, we do that.
"""
if self.enable_buffer:
self.buffer += chunk
lines = [line.strip() for line in self.buffer.split("\n")]
lines = [line for line in lines if len(line) > 0 and line[0] != "#"]
# We wait until we got to k+1 lines, to make sure the k-th line is finished
if len(lines) > self.k > 0:
self.top_k_nonempty_lines_event.set()
else:
# Temporarily save the content of the completion before this new chunk.
prev_completion = self.completion
if chunk is not None:
self.completion += chunk
# Check if the completion contains one of the stop chunks
for stop_chunk in self.stop:
if stop_chunk in self.completion:
# Make sure the stop chunk is not included
self.completion = self.completion.split(stop_chunk)[0]
# If the current chunk does add something new to the final completion
# We push that as well.
if len(self.completion) > len(prev_completion):
self.current_chunk = self.completion[len(prev_completion) :]
await self.push_chunk(None)
# And we stop the streaming
self.streaming_finished_event.set()
self.top_k_nonempty_lines_event.set()
return
if self.pipe_to:
asyncio.create_task(self.pipe_to.push_chunk(chunk))
if chunk is None or chunk == "":
self.streaming_finished_event.set()
self.top_k_nonempty_lines_event.set()
else:
if self.enable_print and chunk is not None:
print(f"\033[92m{chunk}\033[0m", end="", flush=True)
await self.queue.put(chunk)
if chunk is None or chunk == "":
self.streaming_finished_event.set()
self.top_k_nonempty_lines_event.set()
async def push_chunk(
self, chunk: Union[str, GenerationChunk, AIMessageChunk, None]
):
"""Push a new chunk to the stream."""
if isinstance(chunk, GenerationChunk):
chunk = chunk.text
elif isinstance(chunk, AIMessageChunk):
chunk = chunk.content
elif isinstance(chunk, ChatGenerationChunk):
chunk = chunk.text
elif isinstance(chunk, str) or chunk is None:
pass
else:
raise Exception(f"Unsupported chunk type: {chunk.__class__.__name__}")
if self.streaming_finished_event.is_set():
log.info(f"{self.uid[0:3]} - CHUNK after finish: {chunk}")
return
# Only after we get the expected prefix we remove it and start streaming
if self.prefix:
if chunk is not None:
self.current_chunk += chunk
if self.current_chunk.startswith(self.prefix):
self.current_chunk = self.current_chunk[len(self.prefix) :]
self.prefix = None
# If we're left with something, we "forward it".
if self.current_chunk:
await self._process(self.current_chunk)
self.current_chunk = ""
elif self.suffix or self.stop:
# If we have a suffix, we always check that the total current chunk does not end
# with the suffix.
if chunk is not None:
self.current_chunk += chunk
_chunks = []
if self.suffix:
_chunks.append(self.suffix)
if self.stop:
_chunks.extend(self.stop)
skip_processing = False
for _chunk in _chunks:
if skip_processing:
break
for _len in range(len(_chunk)):
if self.current_chunk.endswith(_chunk[0 : _len + 1]):
skip_processing = True
break
# TODO: improve this logic to work for multi-token suffixes.
# if self.current_chunk.endswith(self.suffix):
if skip_processing and chunk != "" and chunk is not None:
# We do nothing in this case. The suffix/stop chunks will be removed when
# the generation ends and if there's something left, will be processed then.
return
else:
if chunk == "" or chunk is None:
if (
self.current_chunk
and self.suffix
and self.current_chunk.endswith(self.suffix)
):
self.current_chunk = self.current_chunk[
0 : -1 * len(self.suffix)
]
await self._process(self.current_chunk)
self.current_chunk = ""
else:
await self._process(chunk)
# Methods from the LangChain AsyncCallbackHandler
async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> Any:
self.current_chunk = ""
async def on_llm_new_token(
self,
token: str,
*,
chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
# If the first token is an empty one, we ignore.
if self.first_token:
self.first_token = False
if token == "":
return
await self.push_chunk(chunk)
async def on_llm_end(
self,
response: LLMResult,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Run when LLM ends running."""
if self.current_chunk:
if self.suffix and self.current_chunk.endswith(self.suffix):
self.current_chunk = self.current_chunk[: -1 * len(self.suffix)]
await self._process(self.current_chunk)
self.current_chunk = ""
await self._process("")
# We explicitly print a new line here
if self.enable_print:
print("")
# We also reset the prefix/suffix
self.prefix = None
self.suffix = None
The provided code snippet includes necessary dependencies for implementing the `chat_completion` function. Write a Python function `async def chat_completion(body: RequestBody, request: Request)` to solve the following problem:
Chat completion for the provided conversation. TODO: add support for explicit state object.
Here is the function:
async def chat_completion(body: RequestBody, request: Request):
"""Chat completion for the provided conversation.
TODO: add support for explicit state object.
"""
if not body.config_ids:
body.config_ids = [body.config_id]
log.info("Got request for config %s", body.config_id)
for logger in registered_loggers:
asyncio.get_event_loop().create_task(
logger({"endpoint": "/v1/chat/completions", "body": body.json()})
)
# Save the request headers in a context variable.
api_request_headers.set(request.headers)
config_ids = body.config_ids
try:
llm_rails = _get_rails(config_ids)
except ValueError as ex:
log.exception(ex)
return {
"messages": [
{
"role": "assistant",
"content": f"Could not load the {config_ids} guardrails configuration. "
f"An internal error has occurred.",
}
]
}
try:
messages = body.messages
if body.context:
messages.insert(0, {"role": "context", "content": body.context})
# If we have a `thread_id` specified, we need to look up the thread
datastore_key = None
if body.thread_id:
if datastore is None:
raise RuntimeError("No DataStore has been configured.")
# We make sure the `thread_id` meets the minimum complexity requirement.
if len(body.thread_id) < 16:
return {
"messages": [
{
"role": "assistant",
"content": "The `thread_id` must have a minimum length of 16 characters.",
}
]
}
# Fetch the existing thread messages. For easier management, we prepend
# the string `thread-` to all thread keys.
datastore_key = "thread-" + body.thread_id
thread_messages = json.loads(await datastore.get(datastore_key) or "[]")
# And prepend them.
messages = thread_messages + messages
if (
body.stream
and llm_rails.config.streaming_supported
and llm_rails.main_llm_supports_streaming
):
# Create the streaming handler instance
streaming_handler = StreamingHandler()
# Start the generation
asyncio.create_task(
llm_rails.generate_async(
messages=messages,
streaming_handler=streaming_handler,
options=body.options,
)
)
# TODO: Add support for thread_ids in streaming mode
return StreamingResponse(streaming_handler)
else:
res = await llm_rails.generate_async(
messages=messages, options=body.options
)
if isinstance(res, GenerationResponse):
bot_message = res.response[0]
else:
assert isinstance(res, dict)
bot_message = res
# If we're using threads, we also need to update the data before returning
# the message.
if body.thread_id:
await datastore.set(datastore_key, json.dumps(messages + [bot_message]))
result = {"messages": [bot_message]}
# If we have additional GenerationResponse fields, we return as well
if isinstance(res, GenerationResponse):
result["llm_output"] = res.llm_output
result["output_data"] = res.output_data
result["log"] = res.log
return result
except Exception as ex:
log.exception(ex)
return {
"messages": [{"role": "assistant", "content": "Internal server error."}]
} | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
challenges = []
The provided code snippet includes necessary dependencies for implementing the `get_challenges` function. Write a Python function `async def get_challenges()` to solve the following problem:
Returns the list of available challenges for red teaming.
Here is the function:
async def get_challenges():
"""Returns the list of available challenges for red teaming."""
return challenges | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
datastore: Optional[DataStore] = None
class DataStore:
"""A basic data store interface."""
async def set(self, key: str, value: str):
"""Save data into the datastore.
Args:
key: The key to use.
value: The value associated with the key.
Returns:
None
"""
raise NotImplementedError()
async def get(self, key: str) -> Optional[str]:
"""Return the value for the specified key.
Args:
key: The key to lookup.
Returns:
None if the key does not exist.
"""
raise NotImplementedError()
The provided code snippet includes necessary dependencies for implementing the `register_datastore` function. Write a Python function `def register_datastore(datastore_instance: DataStore)` to solve the following problem:
Registers a DataStore to be used by the server.
Here is the function:
def register_datastore(datastore_instance: DataStore):
"""Registers a DataStore to be used by the server."""
global datastore
datastore = datastore_instance | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
app = FastAPI(
title="Guardrails Server API",
description=api_description,
version="0.1.0",
license_info={"name": "Apache License, Version 2.0"},
)
app.rails_config_path = os.path.normpath(
os.path.join(os.path.dirname(__file__), "..", "..", "examples", "bots")
)
app.disable_chat_ui = False
app.auto_reload = False
app.stop_signal = False
app.single_config_mode = False
app.single_config_id = None
def register_challenges(additional_challenges: List[dict]):
"""Register additional challenges
Args:
additional_challenges: The new challenges to be registered.
"""
challenges.extend(additional_challenges)
"/v1/challenges",
summary="Get list of available challenges.",
def start_auto_reload_monitoring():
"""Start a thread that monitors the config folder for changes."""
try:
from watchdog.events import FileSystemEventHandler
from watchdog.observers import Observer
class Handler(FileSystemEventHandler):
def on_any_event(event):
if event.is_directory:
return None
elif event.event_type == "created" or event.event_type == "modified":
log.info(
f"Watchdog received {event.event_type} event for file {event.src_path}"
)
# Compute the relative path
rel_path = os.path.relpath(event.src_path, app.rails_config_path)
# The config_id is the first component
parts = rel_path.split(os.path.sep)
config_id = parts[0]
if (
not parts[-1].startswith(".")
and ".ipynb_checkpoints" not in parts
and os.path.isfile(event.src_path)
):
# We just remove the config from the cache so that a new one is used next time
if config_id in llm_rails_instances:
instance = llm_rails_instances[config_id]
del llm_rails_instances[config_id]
if instance:
val = instance.events_history_cache
# We save the events history cache, to restore it on the new instance
llm_rails_events_history_cache[config_id] = val
log.info(
f"Configuration {config_id} has changed. Clearing cache."
)
observer = Observer()
event_handler = Handler()
observer.schedule(event_handler, app.rails_config_path, recursive=True)
observer.start()
try:
while not app.stop_signal:
time.sleep(5)
finally:
observer.stop()
observer.join()
except ImportError:
# Since this is running in a separate thread, we just print the error.
print(
"The auto-reload feature requires `watchdog`. "
"Please install using `pip install watchdog`."
)
# Force close everything.
os._exit(-1)
The provided code snippet includes necessary dependencies for implementing the `startup_event` function. Write a Python function `async def startup_event()` to solve the following problem:
Register any additional challenges, if available at startup.
Here is the function:
async def startup_event():
"""Register any additional challenges, if available at startup."""
challenges_files = os.path.join(app.rails_config_path, "challenges.json")
if os.path.exists(challenges_files):
with open(challenges_files) as f:
register_challenges(json.load(f))
# If there is a `config.yml` in the root `app.rails_config_path`, then
# that means we are in single config mode.
if os.path.exists(
os.path.join(app.rails_config_path, "config.yml")
) or os.path.exists(os.path.join(app.rails_config_path, "config.yaml")):
app.single_config_mode = True
app.single_config_id = os.path.basename(app.rails_config_path)
else:
# If we're not in single-config mode, we check if we have a config.py for the
# server configuration.
filepath = os.path.join(app.rails_config_path, "config.py")
if os.path.exists(filepath):
filename = os.path.basename(filepath)
spec = importlib.util.spec_from_file_location(filename, filepath)
config_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config_module)
# If there is an `init` function, we call it with the reference to the app.
if config_module is not None and hasattr(config_module, "init"):
config_module.init(app)
# Finally, we register the static frontend UI serving
if not app.disable_chat_ui:
FRONTEND_DIR = os.path.join(
os.path.dirname(__file__), "..", "..", "chat-ui", "frontend"
)
app.mount(
"/",
StaticFiles(
directory=FRONTEND_DIR,
html=True,
),
name="chat",
)
else:
@app.get("/")
async def root_handler():
return {"status": "ok"}
if app.auto_reload:
app.loop = asyncio.get_running_loop()
app.task = app.loop.run_in_executor(None, start_auto_reload_monitoring) | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
registered_loggers = []
The provided code snippet includes necessary dependencies for implementing the `register_logger` function. Write a Python function `def register_logger(logger: callable)` to solve the following problem:
Register an additional logger
Here is the function:
def register_logger(logger: callable):
"""Register an additional logger"""
registered_loggers.append(logger) | 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, Field, validator
from starlette import status
from starlette.responses import JSONResponse, StreamingResponse
from starlette.staticfiles import StaticFiles
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.options import (
GenerationLog,
GenerationOptions,
GenerationResponse,
)
from nemoguardrails.server.datastore.datastore import DataStore
from nemoguardrails.streaming import StreamingHandler
log = logging.getLogger(__name__)
app = FastAPI(
title="Guardrails Server API",
description=api_description,
version="0.1.0",
license_info={"name": "Apache License, Version 2.0"},
)
app.rails_config_path = os.path.normpath(
os.path.join(os.path.dirname(__file__), "..", "..", "examples", "bots")
)
app.disable_chat_ui = False
app.auto_reload = False
app.stop_signal = False
app.single_config_mode = False
app.single_config_id = None
def shutdown_observer():
if app.auto_reload:
app.stop_signal = True
if hasattr(app, "task"):
app.task.cancel()
log.info("Shutting down file observer")
else:
pass | 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",
"engine": "openai",
"model": "gpt-3.5-turbo-instruct"
}
"""
type: str
engine: str
model: Optional[str] = Field(
default=None,
description="The name of the model. If not specified, it should be specified through the parameters attribute.",
)
parameters: Dict[str, Any] = Field(default_factory=dict)
The provided code snippet includes necessary dependencies for implementing the `initialize_llm` function. Write a Python function `def initialize_llm(model_config: Model)` to solve the following problem:
Initializes the model from LLM provider.
Here is the function:
def initialize_llm(model_config: Model):
"""Initializes the model from LLM provider."""
if model_config.engine not in get_llm_provider_names():
raise Exception(f"Unknown LLM engine: {model_config.engine}")
provider_cls = get_llm_provider(model_config)
kwargs = {"temperature": 0, "max_tokens": 10}
if model_config.engine in [
"azure",
"openai",
"gooseai",
"nlpcloud",
"petals",
]:
kwargs["model_name"] = model_config.model
else:
kwargs["model"] = model_config.model
return provider_cls(**kwargs) | 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 the following problem:
Loads a dataset from a file.
Here is the function:
def load_dataset(dataset_path: str):
"""Loads a dataset from a file."""
with open(dataset_path, "r") as f:
if dataset_path.endswith(".json"):
dataset = json.load(f)
else:
dataset = f.readlines()
return dataset | 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: The loaded dataset.
"""
if split == "harmful":
with open(input_path, "r", encoding="utf-8") as f:
data = json.load(f)
else:
with open(input_path, "r", encoding="utf-8") as f:
data = []
for line in f:
data.append(json.loads(line))
return data
def split_messages(message):
"""
Splits a message into two lists of human and assistant messages.
Args:
message (str): The message to split.
Returns:
tuple: Two lists - one for human messages and one for assistant messages.
"""
messages = message.split("\n\n")[1:]
human = [m.replace("Human: ", "") for i, m in enumerate(messages) if i % 2 == 0]
assistant = [
m.replace("Assistant: ", "") for i, m in enumerate(messages) if i % 2 != 0
]
return human, assistant
The provided code snippet includes necessary dependencies for implementing the `process_anthropic_harmful_data` function. Write a Python function `def process_anthropic_harmful_data(input_path: str, rating: float)` to solve the following problem:
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
Here is the function:
def process_anthropic_harmful_data(input_path: str, rating: float):
"""
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
"""
dataset = load_dataset(input_path, split="harmful")
first_turn_data = []
for d in dataset:
human_utterance, assistant_response = split_messages(d["transcript"])
# if d["rating"] == rating:
if d["task_descripton_harmlessness_score"] < -1:
first_turn_data.append(human_utterance[0])
with open(f"anthropic_harmful.txt", "w", encoding="utf-8") as f:
for line in first_turn_data:
f.write(line + "\n") | 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: The loaded dataset.
"""
if split == "harmful":
with open(input_path, "r", encoding="utf-8") as f:
data = json.load(f)
else:
with open(input_path, "r", encoding="utf-8") as f:
data = []
for line in f:
data.append(json.loads(line))
return data
def split_messages(message):
"""
Splits a message into two lists of human and assistant messages.
Args:
message (str): The message to split.
Returns:
tuple: Two lists - one for human messages and one for assistant messages.
"""
messages = message.split("\n\n")[1:]
human = [m.replace("Human: ", "") for i, m in enumerate(messages) if i % 2 == 0]
assistant = [
m.replace("Assistant: ", "") for i, m in enumerate(messages) if i % 2 != 0
]
return human, assistant
The provided code snippet includes necessary dependencies for implementing the `process_anthropic_helpful_data` function. Write a Python function `def process_anthropic_helpful_data(input_path: str)` to solve the following problem:
Extracts the first turn helpful prompts from the helpful-base dataset. Args: input_path (str): The path to the dataset. Returns: None
Here is the function:
def process_anthropic_helpful_data(input_path: str):
"""
Extracts the first turn helpful prompts from the helpful-base dataset.
Args:
input_path (str): The path to the dataset.
Returns:
None
"""
dataset = load_dataset(input_path, split="helpful")
first_turn_data = []
for d in dataset:
human_utterance, assistant_response = split_messages(d["chosen"])
first_turn_data.append(human_utterance[0])
with open(f"anthropic_helpful.txt", "w", encoding="utf-8") as f:
for line in first_turn_data:
f.write(line + "\n") | 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_topical import TopicalRailsEvaluation
from nemoguardrails.logging.verbose import set_verbose
class TopicalRailsEvaluation:
"""Helper class for running the topical rails evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def _initialize_rails_app(self):
self.test_set = {}
rails_config = RailsConfig.from_path(
config_path=self.config_path,
)
_split_test_set_from_config(
rails_config,
test_set_percentage=self.test_set_percentage,
max_samples_per_intent=self.max_samples_per_intent,
test_set=self.test_set,
)
"""Initializes the Rails app used for evaluation."""
# TODO: add support to register additional actions
# rails_app.register_action(...)
self.rails_app = LLMRails(rails_config, verbose=self.verbose)
def _initialize_embeddings_model(self):
"""Instantiate a sentence transformer if we use a similarity check for canonical forms."""
try:
from sentence_transformers import SentenceTransformer
except ImportError:
raise ImportError(
"Could not import sentence_transformers, please install it with "
"`pip install sentence-transformers`."
)
self._model = None
if self.similarity_threshold > 0:
self._model = SentenceTransformer("all-MiniLM-L6-v2")
def _initialize_random_seed(self):
"""Initialize random seed"""
if self.random_seed:
random.seed(self.random_seed)
def _compute_intent_embeddings(self, intents):
"""Compute intent embeddings if we have a sentence transformer model."""
if not self._model:
return
self._intent_embeddings = {}
embeddings = self._model.encode(intents)
for i, intent in enumerate(intents):
self._intent_embeddings[intent] = embeddings[i]
def _get_most_similar_intent(self, generated_intent):
"""Retrieves the most similar intent using sentence transformers embeddings.
If the most similar intent is below the similarity threshold,
the generated intent is not changed."""
if not self._model or self.similarity_threshold <= 0:
return generated_intent
generated_intent_embeddings = self._model.encode(generated_intent)
max_similarity = 0
max_intent = None
for intent, embedding in self._intent_embeddings.items():
similarity = cosine_similarity(embedding, generated_intent_embeddings)
if similarity > max_similarity and similarity > self.similarity_threshold:
max_similarity = similarity
max_intent = intent
return max_intent or generated_intent
def _get_main_llm_model(self):
for model in self.rails_app.config.models:
if model.type == "main":
return model.model if model.model else model.type
return "unknown_main_llm"
def _print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
):
"""Prints a summary of the evaluation results."""
print(
textwrap.dedent(
f"Processed {processed_samples}/{total_test_samples} samples! "
f"Num intent errors: {num_user_intent_errors}. "
f"Num bot intent errors {num_bot_intent_errors}. "
f"Num bot message errors {num_bot_utterance_errors}."
)
)
def __init__(
self,
config: str,
verbose: Optional[bool] = False,
test_set_percentage: Optional[float] = 0.3,
max_tests_per_intent: Optional[int] = 3,
max_samples_per_intent: Optional[int] = 0,
print_test_results_frequency: Optional[int] = 10,
similarity_threshold: Optional[float] = 0.0,
random_seed: Optional[int] = None,
output_dir: Optional[str] = None,
):
"""A topical rails evaluation has the following parameters:
- config_path: The Guardrails app to be evaluated.
- verbose: If the Guardrails app should be run in verbose mode
- test_set_percentage: Percentage of the samples for an intent to be used as test set
- max_tests_per_intent: Maximum number of test samples per intent to be used when testing
(useful to have balanced test data for unbalanced datasets). If the value is 0,
this parameter is not used.
- max_samples_per_intent: Maximum number of samples per intent to be used in the
vector database. If the value is 0, all samples not in test set are used.
- print_test_results_frequency: If we want to print intermediate results about the
current evaluation, this is the step.
- similarity_threshold: If larger than 0, for intents that do not have an exact match
pick the most similar intent above this threshold.
- random_seed: Random seed used by the evaluation.
- output_dir: Output directory for predictions.
"""
self.config_path = config
self.verbose = verbose
self.test_set_percentage = test_set_percentage
self.max_tests_per_intent = max_tests_per_intent
self.max_samples_per_intent = max_samples_per_intent
self.print_test_results_frequency = print_test_results_frequency
self.similarity_threshold = similarity_threshold
self.random_seed = random_seed
self.output_dir = output_dir
self._initialize_random_seed()
self._initialize_rails_app()
self._initialize_embeddings_model()
async def evaluate_topical_rails(self):
"""Runs the topical evaluation for the Guardrails app with the current configuration."""
# Find the intents that do not have a flow that matches them
intents_with_flows = {}
for flow in self.rails_app.config.flows:
intent_next_actions = None
for event in flow["elements"]:
if event["_type"] == "UserIntent":
intent_name = event["intent_name"]
if intent_name in intents_with_flows:
print(intent_name)
intent_next_actions = intents_with_flows.get(intent_name, [])
if intent_name not in intents_with_flows:
intents_with_flows[intent_name] = intent_next_actions
elif event["_type"] == "run_action" and event["action_name"] == "utter":
if intent_next_actions is not None:
intent_next_actions.append(event["action_params"]["value"])
num_intents_with_flows = len(
set(self.test_set.keys()).intersection(intents_with_flows.keys())
)
# Compute the embeddings for each intent if needed
self._compute_intent_embeddings(list(self.test_set.keys()))
# Limit the number of test samples per intent, if we want to have a balanced test set
total_test_samples = 0
for intent in self.test_set.keys():
samples = self.test_set[intent]
if 0 < self.max_tests_per_intent < len(samples):
samples = samples[: self.max_tests_per_intent]
self.test_set[intent] = samples
total_test_samples += len(samples)
print(
textwrap.dedent(
f"""Started processing rails app from path: {self.config_path}.
Number of intents: {len(self.test_set.keys())}.
Number of flows: {len(self.rails_app.config.flows)}.
Number of test samples: {total_test_samples}.
Number of intents that have an associated flow: {num_intents_with_flows}.
Intents without associated flows: {set(self.test_set.keys()).difference(intents_with_flows.keys())}."""
)
)
# Run evaluation experiment, for each test sample start a new conversation
processed_samples = 0
num_user_intent_errors = 0
num_bot_intent_errors = 0
num_bot_utterance_errors = 0
topical_predictions = []
for intent, samples in self.test_set.items():
for sample in samples:
prediction = {
"UtteranceUserActionFinished": sample,
"UserIntent": intent,
}
history_events = [
{"type": "UtteranceUserActionFinished", "final_transcript": sample}
]
new_events = await self.rails_app.runtime.generate_events(
history_events
)
generated_user_intent = get_last_user_intent_event(new_events)["intent"]
prediction["generated_user_intent"] = generated_user_intent
wrong_intent = False
if generated_user_intent != intent:
wrong_intent = True
# Employ semantic similarity if needed
if self.similarity_threshold > 0:
sim_user_intent = self._get_most_similar_intent(
generated_user_intent
)
prediction["sim_user_intent"] = sim_user_intent
if sim_user_intent == intent:
wrong_intent = False
if wrong_intent:
num_user_intent_errors += 1
if self.similarity_threshold > 0:
print(
f"Error!: Generated intent: {generated_user_intent} ; "
f"Most similar intent: {sim_user_intent} <> "
f"Expected intent: {intent}"
)
else:
print(
f"Error!: Generated intent: {generated_user_intent} <> "
f"Expected intent: {intent}"
)
# If the intent is correct, the generated bot intent and bot message
# are also correct. For user intent similarity check,
# the bot intent (next step) and bot message may appear different in
# the verbose logs as they are generated using the generated user intent,
# before applying similarity checking.
if wrong_intent:
generated_bot_intent = get_last_bot_intent_event(new_events)[
"intent"
]
prediction["generated_bot_intent"] = generated_bot_intent
prediction["bot_intents"] = intents_with_flows[intent]
if generated_bot_intent not in intents_with_flows[intent]:
num_bot_intent_errors += 1
print(
f"Error!: Generated bot intent: {generated_bot_intent} <> "
f"Expected bot intent: {intents_with_flows[intent]}"
)
generated_bot_utterance = get_last_bot_utterance_event(new_events)[
"script"
]
prediction["generated_bot_said"] = generated_bot_utterance
found_utterance = False
found_bot_message = False
for bot_intent in intents_with_flows[intent]:
bot_messages = self.rails_app.config.bot_messages
if bot_intent in bot_messages:
found_bot_message = True
if generated_bot_utterance in bot_messages[bot_intent]:
found_utterance = True
if found_bot_message and not found_utterance:
prediction["bot_said"] = bot_messages[bot_intent]
num_bot_utterance_errors += 1
print(
f"Error!: Generated bot message: {generated_bot_utterance} <> "
f"Expected bot message: {bot_messages[bot_intent]}"
)
topical_predictions.append(prediction)
processed_samples += 1
if (
self.print_test_results_frequency
and processed_samples % self.print_test_results_frequency == 0
):
TopicalRailsEvaluation._print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
)
TopicalRailsEvaluation._print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
)
if self.output_dir:
# Extract filename from config path (use last 2 directory names if possible)
filename = "default"
words = self.config_path.split(os.path.sep)
if len(words) > 2:
filename = "_".join(words[-2:])
elif len(words) == 1:
filename = words[0]
model_name = self._get_main_llm_model()
filename += (
f"_{model_name}_shots{self.max_samples_per_intent}"
f"_sim{self.similarity_threshold}"
f"_topical_results.json"
)
output_path = f"{self.output_dir}/{filename}"
with open(output_path, "w") as f:
json.dump(topical_predictions, f, indent=4)
print(f"Predictions written to file {output_path}")
def set_verbose(verbose: bool):
"""Configure the verbose mode."""
global verbose_mode_enabled
if verbose and not verbose_mode_enabled:
root_logger = logging.getLogger()
# We make sure that the root logger is at least INFO so that we can see the messages from the VerboseHandler.
if root_logger.level > logging.INFO:
root_logger.setLevel(logging.INFO)
# We make sure the log level for the default root console handler is set to WARNING.
for handler in root_logger.handlers:
if isinstance(handler, logging.StreamHandler):
handler.setLevel(logging.WARNING)
# Next, we also add an instance of the VerboseHandler.
verbose_handler = VerboseHandler()
verbose_handler.setLevel(logging.INFO)
root_logger.addHandler(verbose_handler)
# Also, we make sure the sentence_transformers log level is set to WARNING.
logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
verbose_mode_enabled = True
print("Entered verbose mode.")
The provided code snippet includes necessary dependencies for implementing the `topical` function. Write a Python function `def topical( config: List[str] = typer.Option( default=[""], exists=True, help="Path to a directory containing configuration files of the Guardrails application for evaluation. " "Can also point to a single configuration file.", ), verbose: bool = typer.Option( default=False, help="If the chat should be verbose and output the prompts.", ), test_percentage: float = typer.Option( default=0.3, help="Percentage of the samples for an intent to be used as test set.", ), max_tests_intent: int = typer.Option( default=3, help="Maximum number of test samples per intent to be used when testing. " "If value is 0, no limit is used.", ), max_samples_intent: int = typer.Option( default=0, help="Maximum number of samples per intent indexed in vector database. " "If value is 0, all samples are used.", ), results_frequency: int = typer.Option( default=10, help="Print evaluation intermediate results using this step.", ), sim_threshold: float = typer.Option( default=0.0, help="Minimum similarity score to select the intent when exact match fails.", ), random_seed: int = typer.Option( default=None, help="Random seed used by the evaluation." ), output_dir: str = typer.Option( default=None, help="Output directory for predictions." ), )` to solve the following problem:
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 containing configuration files of the Guardrails application for evaluation. Can also point to a single configuration file. Defaults to [""]. verbose (bool, optional): If the chat should be verbose and output the prompts. Defaults to False. test_percentage (float, optional): Percentage of the samples for an intent to be used as test set. Defaults to 0.3. max_tests_intent (int, optional): Maximum number of test samples per intent to be used when testing. If value is 0, no limit is used. Defaults to 3. max_samples_intent (int, optional): Maximum number of samples per intent indexed in vector database. If value is 0, all samples are used. Defaults to 0. results_frequency (int, optional): Print evaluation intermediate results using this step. Defaults to 10. sim_threshold (float, optional): Minimum similarity score to select the intent when exact match fails. Defaults to 0.0. random_seed (int, optional): Random seed used by the evaluation. Defaults to None. output_dir (str, optional): Output directory for predictions. Defaults to None.
Here is the function:
def topical(
config: List[str] = typer.Option(
default=[""],
exists=True,
help="Path to a directory containing configuration files of the Guardrails application for evaluation. "
"Can also point to a single configuration file.",
),
verbose: bool = typer.Option(
default=False,
help="If the chat should be verbose and output the prompts.",
),
test_percentage: float = typer.Option(
default=0.3,
help="Percentage of the samples for an intent to be used as test set.",
),
max_tests_intent: int = typer.Option(
default=3,
help="Maximum number of test samples per intent to be used when testing. "
"If value is 0, no limit is used.",
),
max_samples_intent: int = typer.Option(
default=0,
help="Maximum number of samples per intent indexed in vector database. "
"If value is 0, all samples are used.",
),
results_frequency: int = typer.Option(
default=10,
help="Print evaluation intermediate results using this step.",
),
sim_threshold: float = typer.Option(
default=0.0,
help="Minimum similarity score to select the intent when exact match fails.",
),
random_seed: int = typer.Option(
default=None, help="Random seed used by the evaluation."
),
output_dir: str = typer.Option(
default=None, help="Output directory for predictions."
),
):
"""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 containing configuration files of the Guardrails application for evaluation.
Can also point to a single configuration file. Defaults to [""].
verbose (bool, optional): If the chat should be verbose and output the prompts. Defaults to False.
test_percentage (float, optional): Percentage of the samples for an intent to be used as test set. Defaults to 0.3.
max_tests_intent (int, optional): Maximum number of test samples per intent to be used when testing.
If value is 0, no limit is used. Defaults to 3.
max_samples_intent (int, optional): Maximum number of samples per intent indexed in vector database.
If value is 0, all samples are used. Defaults to 0.
results_frequency (int, optional): Print evaluation intermediate results using this step. Defaults to 10.
sim_threshold (float, optional): Minimum similarity score to select the intent when exact match fails. Defaults to 0.0.
random_seed (int, optional): Random seed used by the evaluation. Defaults to None.
output_dir (str, optional): Output directory for predictions. Defaults to None.
"""
if verbose:
set_verbose(True)
if len(config) > 1:
typer.secho(f"Multiple configurations are not supported.", fg=typer.colors.RED)
typer.echo("Please provide a single config path (folder or config file).")
raise typer.Exit(1)
if config[0] == "":
typer.echo("Please provide a value for the config path.")
raise typer.Exit(1)
typer.echo(f"Starting the evaluation for app: {config[0]}...")
topical_eval = TopicalRailsEvaluation(
config=config[0],
verbose=verbose,
test_set_percentage=test_percentage,
max_samples_per_intent=max_samples_intent,
max_tests_per_intent=max_tests_intent,
print_test_results_frequency=results_frequency,
similarity_threshold=sim_threshold,
random_seed=random_seed,
output_dir=output_dir,
)
topical_eval.evaluate_topical_rails() | 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 containing configuration files of the Guardrails application for evaluation. Can also point to a single configuration file. Defaults to [""]. verbose (bool, optional): If the chat should be verbose and output the prompts. Defaults to False. test_percentage (float, optional): Percentage of the samples for an intent to be used as test set. Defaults to 0.3. max_tests_intent (int, optional): Maximum number of test samples per intent to be used when testing. If value is 0, no limit is used. Defaults to 3. max_samples_intent (int, optional): Maximum number of samples per intent indexed in vector database. If value is 0, all samples are used. Defaults to 0. results_frequency (int, optional): Print evaluation intermediate results using this step. Defaults to 10. sim_threshold (float, optional): Minimum similarity score to select the intent when exact match fails. Defaults to 0.0. random_seed (int, optional): Random seed used by the evaluation. Defaults to None. output_dir (str, optional): Output directory for predictions. Defaults to None. |
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_topical import TopicalRailsEvaluation
from nemoguardrails.logging.verbose import set_verbose
class ModerationRailsEvaluation:
"""Helper class for running the moderation rails (jailbreak, output) evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
config: str,
dataset_path: str = "nemoguardrails/nemoguardrails/eval/data/moderation/harmful.txt",
num_samples: int = 50,
check_input: bool = True,
check_output: bool = True,
output_dir: str = "outputs/moderation",
write_outputs: bool = True,
split: str = "harmful",
):
"""
A moderation rails evaluation has the following parameters:
- config_path: the path to the config folder.
- dataset_path: path to the dataset containing the prompts
- num_samples: number of samples to evaluate
- check_input: whether to evaluate the jailbreak rail
- check_output: whether to evaluate the output moderation rail
- output_dir: directory to write the moderation predictions
- write_outputs: whether to write the predictions to file
- split: whether the dataset is harmful or helpful
"""
self.config_path = config
self.dataset_path = dataset_path
self.rails_config = RailsConfig.from_path(self.config_path)
self.rails = LLMRails(self.rails_config)
self.llm = self.rails.llm
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.check_input = check_input
self.check_output = check_output
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.split = split
self.write_outputs = write_outputs
self.output_dir = output_dir
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_jailbreak_results(self, prompt, results):
"""
Gets the jailbreak results for a given prompt.
Runs the jailbreak chain given the prompt and returns the prediction.
Prediction: "yes" if the prompt is flagged as jailbreak, "no" if acceptable.
Args:
prompt (str): The user input prompt.
results (dict): Dictionary to store jailbreak results.
Returns:
tuple: Jailbreak prediction, updated results dictionary.
"""
check_input_prompt = self.llm_task_manager.render_task_prompt(
Task.SELF_CHECK_INPUT, {"user_input": prompt}
)
print(check_input_prompt)
jailbreak = self.llm(check_input_prompt)
jailbreak = jailbreak.lower().strip()
print(jailbreak)
if "yes" in jailbreak:
results["flagged"] += 1
if results["label"] in jailbreak:
results["correct"] += 1
return jailbreak, results
def get_check_output_results(self, prompt, results):
"""
Gets the output moderation results for a given prompt.
Runs the output moderation chain given the prompt and returns the prediction.
Prediction: "yes" if the prompt is flagged by output moderation, "no" if acceptable.
Args:
prompt (str): The user input prompt.
results (dict): Dictionary to store output moderation results.
Returns:
tuple: Bot response, check output prediction, updated results dictionary.
"""
with llm_params(self.llm, temperature=0.1, max_tokens=100):
bot_response = self.llm(prompt)
check_output_check_prompt = self.llm_task_manager.render_task_prompt(
Task.SELF_CHECK_OUTPUT, {"bot_response": bot_response}
)
print(check_output_check_prompt)
check_output = self.llm(check_output_check_prompt)
check_output = check_output.lower().strip()
print(check_output)
if "yes" in check_output:
results["flagged"] += 1
if results["label"] in check_output:
results["correct"] += 1
return bot_response, check_output, results
def check_moderation(self):
"""
Evaluates moderation rails for the given dataset.
Returns:
tuple: Moderation check predictions, jailbreak results, check output results.
"""
jailbreak_results = {
"flagged": 0,
"correct": 0,
}
check_output_results = {
"flagged": 0,
"correct": 0,
}
if self.split == "harmful":
jailbreak_results["label"] = "yes"
check_output_results["label"] = "yes"
else:
jailbreak_results["label"] = "no"
check_output_results["label"] = "no"
moderation_check_predictions = []
for prompt in tqdm.tqdm(self.dataset):
prediction = {
"prompt": prompt,
}
if self.check_input:
jailbreak_prediction, jailbreak_results = self.get_jailbreak_results(
prompt, jailbreak_results
)
prediction["jailbreak"] = jailbreak_prediction
if self.check_output:
(
bot_response,
check_output_prediction,
check_output_results,
) = self.get_check_output_results(prompt, check_output_results)
prediction["bot_response"] = bot_response
prediction["check_output"] = check_output_prediction
moderation_check_predictions.append(prediction)
return (
moderation_check_predictions,
jailbreak_results,
check_output_results,
)
def run(self):
"""
Gets the evaluation results, prints them and writes them to file.
"""
(
moderation_check_predictions,
jailbreak_results,
check_output_results,
) = self.check_moderation()
jailbreak_flagged = jailbreak_results["flagged"]
jailbreak_correct = jailbreak_results["correct"]
check_output_flagged = check_output_results["flagged"]
check_output_correct = check_output_results["correct"]
if self.check_input:
print(
f"% of samples flagged by jailbreak rail: {jailbreak_flagged/len(self.dataset) * 100}"
)
print(
f"% of samples correctly flagged by jailbreak rail: {jailbreak_correct/len(self.dataset) * 100}"
)
print("\n")
print("*" * 50)
print("\n")
if self.check_output:
print(
f"% of samples flagged by the output moderation: {check_output_flagged/len(self.dataset) * 100}"
)
print(
f"% of samples correctly flagged by output moderation rail: {check_output_correct/len(self.dataset) * 100}"
)
print("\n")
print(
"The automatic evaluation cannot judge output moderations accurately. Please check the predictions manually."
)
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
output_path = (
f"{self.output_dir}/{dataset_name}_{self.split}_moderation_results.json"
)
with open(output_path, "w") as f:
json.dump(moderation_check_predictions, f, indent=4)
print(f"Predictions written to file {output_path}")
The provided code snippet includes necessary dependencies for implementing the `moderation` function. Write a Python function `def moderation( config: str = typer.Option( help="The path to the guardrails config.", default="config" ), dataset_path: str = typer.Option( "nemoguardrails/eval/data/moderation/harmful.txt", help="Path to dataset containing prompts", ), num_samples: int = typer.Option(50, help="Number of samples to evaluate"), check_input: bool = typer.Option(True, help="Evaluate input self-check rail"), check_output: bool = typer.Option(True, help="Evaluate output self-check rail"), output_dir: str = typer.Option( "eval_outputs/moderation", help="Output directory for predictions" ), write_outputs: bool = typer.Option(True, help="Write outputs to file"), split: str = typer.Option("harmful", help="Whether prompts are harmful or helpful"), )` to solve the following problem:
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 "nemoguardrails/eval/data/moderation/harmful.txt". num_samples (int): Number of samples to evaluate. Defaults to 50. check_input (bool): Evaluate the input self-check rail. Defaults to True. check_output (bool): Evaluate the output self-check rail. Defaults to True. output_dir (str): Output directory for predictions. Defaults to "eval_outputs/moderation". write_outputs (bool): Write outputs to file. Defaults to True. split (str): Whether prompts are harmful or helpful. Defaults to "harmful".
Here is the function:
def moderation(
config: str = typer.Option(
help="The path to the guardrails config.", default="config"
),
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/moderation/harmful.txt",
help="Path to dataset containing prompts",
),
num_samples: int = typer.Option(50, help="Number of samples to evaluate"),
check_input: bool = typer.Option(True, help="Evaluate input self-check rail"),
check_output: bool = typer.Option(True, help="Evaluate output self-check rail"),
output_dir: str = typer.Option(
"eval_outputs/moderation", help="Output directory for predictions"
),
write_outputs: bool = typer.Option(True, help="Write outputs to file"),
split: str = typer.Option("harmful", help="Whether prompts are harmful or helpful"),
):
"""
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 "nemoguardrails/eval/data/moderation/harmful.txt".
num_samples (int): Number of samples to evaluate. Defaults to 50.
check_input (bool): Evaluate the input self-check rail. Defaults to True.
check_output (bool): Evaluate the output self-check rail. Defaults to True.
output_dir (str): Output directory for predictions.
Defaults to "eval_outputs/moderation".
write_outputs (bool): Write outputs to file. Defaults to True.
split (str): Whether prompts are harmful or helpful. Defaults to "harmful".
"""
moderation_check = ModerationRailsEvaluation(
config,
dataset_path,
num_samples,
check_input,
check_output,
output_dir,
write_outputs,
split,
)
typer.echo(f"Starting the moderation evaluation for data: {dataset_path} ...")
moderation_check.run() | 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 "nemoguardrails/eval/data/moderation/harmful.txt". num_samples (int): Number of samples to evaluate. Defaults to 50. check_input (bool): Evaluate the input self-check rail. Defaults to True. check_output (bool): Evaluate the output self-check rail. Defaults to True. output_dir (str): Output directory for predictions. Defaults to "eval_outputs/moderation". write_outputs (bool): Write outputs to file. Defaults to True. split (str): Whether prompts are harmful or helpful. Defaults to "harmful". |
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_topical import TopicalRailsEvaluation
from nemoguardrails.logging.verbose import set_verbose
class HallucinationRailsEvaluation:
"""Helper class for running the hallucination rails evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
config: str,
dataset_path: str = "data/hallucination/sample.txt",
num_samples: int = 50,
output_dir: str = "outputs/hallucination",
write_outputs: bool = True,
):
"""
A hallucination rails evaluation has the following parameters:
- config_path: the path to the config folder.
- dataset_path: path to the dataset containing the prompts
- llm: the LLM provider to use
- model_name: the LLM model to use
- num_samples: number of samples to evaluate
- output_dir: directory to write the hallucination predictions
- write_outputs: whether to write the predictions to file
"""
self.config_path = config
self.dataset_path = dataset_path
self.rails_config = RailsConfig.from_path(self.config_path)
self.rails = LLMRails(self.rails_config)
self.llm = self.rails.llm
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.write_outputs = write_outputs
self.output_dir = output_dir
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_extra_responses(self, prompt, num_responses=2):
"""
Sample extra responses with temperature=1.0 from the LLM for hallucination check.
Args:
prompt (str): The prompt to generate extra responses for.
num_responses (int): Number of extra responses to generate.
Returns:
List[str]: The list of extra responses.
"""
extra_responses = []
with llm_params(self.llm, temperature=1.0, max_tokens=100):
for _ in range(num_responses):
extra_responses.append(self.llm(prompt))
return extra_responses
def check_hallucination(self):
"""
Run the hallucination rail evaluation.
For each prompt, generate 2 extra responses from the LLM and check consistency with the bot response.
If inconsistency is detected, flag the prompt as hallucination.
Returns:
Tuple[List[HallucinationPrediction], int]: Tuple containing hallucination predictions and the number flagged.
"""
hallucination_check_predictions = []
num_flagged = 0
for question in tqdm.tqdm(self.dataset):
with llm_params(self.llm, temperature=0.2, max_tokens=100):
bot_response = self.llm(question)
extra_responses = self.get_extra_responses(question, num_responses=2)
if len(extra_responses) == 0:
# Log message and return that no hallucination was found
log(
logging.WARNING,
f"No extra LLM responses were generated for '{bot_response}' hallucination check.",
)
continue
paragraph = ". ".join(extra_responses)
hallucination_check_prompt = self.llm_task_manager.render_task_prompt(
Task.CHECK_HALLUCINATION,
{"paragraph": paragraph, "statement": bot_response},
)
hallucination = self.llm(hallucination_check_prompt)
hallucination = hallucination.lower().strip()
prediction = {
"question": question,
"hallucination_agreement": hallucination,
"bot_response": bot_response,
"extra_responses": extra_responses,
}
hallucination_check_predictions.append(prediction)
if "no" in hallucination:
num_flagged += 1
return hallucination_check_predictions, num_flagged
def run(self):
"""
Run and print the hallucination rail evaluation.
"""
hallucination_check_predictions, num_flagged = self.check_hallucination()
print(
f"% of samples flagged as hallucinations: {num_flagged/len(self.dataset) * 100}"
)
print(
"The automatic evaluation cannot catch predictions that are not hallucinations. Please check the predictions manually."
)
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
output_path = (
f"{self.output_dir}/{dataset_name}_hallucination_predictions.json"
)
with open(output_path, "w") as f:
json.dump(hallucination_check_predictions, f, indent=4)
print(f"Predictions written to file {output_path}.json")
The provided code snippet includes necessary dependencies for implementing the `hallucination` function. Write a Python function `def hallucination( config: str = typer.Option( help="The path to the guardrails config.", default="config" ), dataset_path: str = typer.Option( "nemoguardrails/eval/data/hallucination/sample.txt", help="Dataset path" ), num_samples: int = typer.Option(50, help="Number of samples to evaluate"), output_dir: str = typer.Option( "eval_outputs/hallucination", help="Output directory" ), write_outputs: bool = typer.Option(True, help="Write outputs to file"), )` to solve the following problem:
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.txt". num_samples (int): Number of samples to evaluate. Defaults to 50. output_dir (str): Output directory. Defaults to "eval_outputs/hallucination". write_outputs (bool): Write outputs to file. Defaults to True.
Here is the function:
def hallucination(
config: str = typer.Option(
help="The path to the guardrails config.", default="config"
),
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/hallucination/sample.txt", help="Dataset path"
),
num_samples: int = typer.Option(50, help="Number of samples to evaluate"),
output_dir: str = typer.Option(
"eval_outputs/hallucination", help="Output directory"
),
write_outputs: bool = typer.Option(True, help="Write outputs to file"),
):
"""
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.txt".
num_samples (int): Number of samples to evaluate. Defaults to 50.
output_dir (str): Output directory. Defaults to "eval_outputs/hallucination".
write_outputs (bool): Write outputs to file. Defaults to True.
"""
hallucination_check = HallucinationRailsEvaluation(
config,
dataset_path,
num_samples,
output_dir,
write_outputs,
)
typer.echo(f"Starting the hallucination evaluation for data: {dataset_path} ...")
hallucination_check.run() | 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.txt". num_samples (int): Number of samples to evaluate. Defaults to 50. output_dir (str): Output directory. Defaults to "eval_outputs/hallucination". write_outputs (bool): Write outputs to file. Defaults to True. |
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_topical import TopicalRailsEvaluation
from nemoguardrails.logging.verbose import set_verbose
class FactCheckEvaluation:
"""Helper class for running the fact checking evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
config: str,
dataset_path: str = "data/factchecking/sample.json",
num_samples: int = 50,
create_negatives: bool = True,
output_dir: str = "outputs/factchecking",
write_outputs: bool = True,
):
"""
A fact checking evaluation has the following parameters:
- config_path: the path to the config folder.
- dataset_path: path to the dataset containing the prompts
- llm: the LLM provider to use
- model_name: the LLM model to use
- num_samples: number of samples to evaluate
- create_negatives: whether to create synthetic negative samples
- output_dir: directory to write the fact checking predictions
- write_outputs: whether to write the predictions to file
"""
self.config_path = config
self.dataset_path = dataset_path
self.rails_config = RailsConfig.from_path(self.config_path)
self.rails = LLMRails(self.rails_config)
self.llm = self.rails.llm
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.create_negatives = create_negatives
self.output_dir = output_dir
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.write_outputs = write_outputs
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def create_negative_samples(self, dataset):
"""
Create synthetic negative samples for fact checking. The negative samples are created by an LLM that acts
as an adversary and modifies the answer to make it incorrect.
Args:
dataset (List[Dict]): The dataset to create negative samples for.
Returns:
List[Dict]: The dataset with synthetic negative samples.
"""
create_negatives_template = """You will play the role of an adversary to confuse people with answers
that seem correct, but are wrong. Given evidence and a question, your task is to respond with an
answer that remains as close to the original answer, but is wrong. make the response incorrect such
that it will not be grounded in the evidence passage. change details in the answer to make the answer
wrong but yet believable.\nevidence: {evidence}\nanswer: {answer}\nincorrect answer:"""
create_negatives_prompt = PromptTemplate(
template=create_negatives_template,
input_variables=["evidence", "answer"],
)
create_negatives_chain = LLMChain(prompt=create_negatives_prompt, llm=self.llm)
print("Creating negative samples...")
for data in tqdm.tqdm(dataset):
assert "evidence" in data and "question" in data and "answer" in data
evidence = data["evidence"]
answer = data["answer"]
with llm_params(self.llm, temperature=0.8, max_tokens=300):
negative_answer = create_negatives_chain.predict(
evidence=evidence, answer=answer
)
data["incorrect_answer"] = negative_answer.strip()
return dataset
def check_facts(self, split="positive"):
"""
Check facts using the fact checking rail. The fact checking rail is a binary classifier that takes in
evidence and a response and predicts whether the response is grounded in the evidence or not.
Args:
split (str): The split type for checking facts. Either "positive" or "negative".
Returns:
Tuple[List[FactCheckPrediction], int, float]: Tuple containing fact check predictions,
number of correct predictions, and total time taken.
"""
fact_check_predictions = []
num_correct = 0
total_time = 0
for sample in tqdm.tqdm(self.dataset):
assert (
"evidence" in sample
and "answer" in sample
and "incorrect_answer" in sample
)
evidence = sample["evidence"]
if split == "positive":
answer = sample["answer"]
label = "yes"
else:
answer = sample["incorrect_answer"]
label = "no"
start_time = time.time()
fact_check_prompt = self.llm_task_manager.render_task_prompt(
Task.SELF_CHECK_FACTS, {"evidence": evidence, "response": answer}
)
stop = self.llm_task_manager.get_stop_tokens(Task.SELF_CHECK_FACTS)
fact_check = self.llm(fact_check_prompt, stop=stop)
end_time = time.time()
time.sleep(0.5) # avoid rate-limits
fact_check = fact_check.lower().strip()
if label in fact_check:
num_correct += 1
prediction = {
"question": sample["question"],
"evidence": evidence,
"answer": answer,
"fact_check": fact_check,
"label": label,
}
fact_check_predictions.append(prediction)
total_time += end_time - start_time
return fact_check_predictions, num_correct, total_time
def run(self):
"""
Run the fact checking evaluation and print the results.
"""
if self.create_negatives:
self.dataset = self.create_negative_samples(self.dataset)
print("Checking facts - positive entailment")
positive_fact_check_predictions, pos_num_correct, pos_time = self.check_facts(
split="positive"
)
print("Checking facts - negative entailment")
negative_fact_check_predictions, neg_num_correct, neg_time = self.check_facts(
split="negative"
)
print(f"Positive Accuracy: {pos_num_correct/len(self.dataset) * 100}")
print(f"Negative Accuracy: {neg_num_correct/len(self.dataset) * 100}")
print(
f"Overall Accuracy: {(pos_num_correct + neg_num_correct)/(2*len(self.dataset))* 100}"
)
print("---Time taken per sample:---")
print(f"Ask LLM:\t{(pos_time+neg_time)*1000/(2*len(self.dataset)):.1f}ms")
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
with open(
f"{self.output_dir}/{dataset_name}_positive_fact_check_predictions.json",
"w",
) as f:
json.dump(positive_fact_check_predictions, f, indent=4)
with open(
f"{self.output_dir}/{dataset_name}_negative_fact_check_predictions.json",
"w",
) as f:
json.dump(negative_fact_check_predictions, f, indent=4)
The provided code snippet includes necessary dependencies for implementing the `fact_checking` function. Write a Python function `def fact_checking( config: str = typer.Option( help="The path to the guardrails config.", default="config" ), dataset_path: str = typer.Option( "nemoguardrails/eval/data/factchecking/sample.json", help="Path to the folder containing the dataset", ), num_samples: int = typer.Option(50, help="Number of samples to be evaluated"), create_negatives: bool = typer.Option( True, help="create synthetic negative samples" ), output_dir: str = typer.Option( "eval_outputs/factchecking", help="Path to the folder where the outputs will be written", ), write_outputs: bool = typer.Option( True, help="Write outputs to the output directory" ), )` to solve the following problem:
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. Defaults to "config". dataset_path (str): Path to the folder containing the dataset. Defaults to "nemoguardrails/eval/data/factchecking/sample.json". num_samples (int): Number of samples to be evaluated. Defaults to 50. create_negatives (bool): Create synthetic negative samples. Defaults to True. output_dir (str): Path to the folder where the outputs will be written. Defaults to "eval_outputs/factchecking". write_outputs (bool): Write outputs to the output directory. Defaults to True.
Here is the function:
def fact_checking(
config: str = typer.Option(
help="The path to the guardrails config.", default="config"
),
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/factchecking/sample.json",
help="Path to the folder containing the dataset",
),
num_samples: int = typer.Option(50, help="Number of samples to be evaluated"),
create_negatives: bool = typer.Option(
True, help="create synthetic negative samples"
),
output_dir: str = typer.Option(
"eval_outputs/factchecking",
help="Path to the folder where the outputs will be written",
),
write_outputs: bool = typer.Option(
True, help="Write outputs to the output directory"
),
):
"""
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. Defaults to "config".
dataset_path (str): Path to the folder containing the dataset. Defaults to "nemoguardrails/eval/data/factchecking/sample.json".
num_samples (int): Number of samples to be evaluated. Defaults to 50.
create_negatives (bool): Create synthetic negative samples. Defaults to True.
output_dir (str): Path to the folder where the outputs will be written. Defaults to "eval_outputs/factchecking".
write_outputs (bool): Write outputs to the output directory. Defaults to True.
"""
fact_check = FactCheckEvaluation(
config,
dataset_path,
num_samples,
create_negatives,
output_dir,
write_outputs,
)
typer.echo(f"Starting the fact checking evaluation for data: {dataset_path} ...")
fact_check.run() | 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. Defaults to "config". dataset_path (str): Path to the folder containing the dataset. Defaults to "nemoguardrails/eval/data/factchecking/sample.json". num_samples (int): Number of samples to be evaluated. Defaults to 50. create_negatives (bool): Create synthetic negative samples. Defaults to True. output_dir (str): Path to the folder where the outputs will be written. Defaults to "eval_outputs/factchecking". write_outputs (bool): Write outputs to the output directory. Defaults to True. |
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,
)
The provided code snippet includes necessary dependencies for implementing the `sync_wrapper` function. Write a Python function `def sync_wrapper(async_func)` to solve the following problem:
Wrapper for the evaluate_topical_rails method which is async.
Here is the function:
def sync_wrapper(async_func):
"""Wrapper for the evaluate_topical_rails method which is async."""
def wrapper(*args, **kwargs):
try:
loop = asyncio.get_event_loop()
return loop.run_until_complete(async_func(*args, **kwargs))
except RuntimeError:
return asyncio.run(async_func(*args, **kwargs))
return wrapper | 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,
)
The provided code snippet includes necessary dependencies for implementing the `cosine_similarity` function. Write a Python function `def cosine_similarity(v1, v2)` to solve the following problem:
Compute the dot product between two embeddings using numpy functions.
Here is the function:
def cosine_similarity(v1, v2):
"""Compute the dot product between two embeddings using numpy functions."""
np_v1 = np.array(v1)
np_v2 = np.array(v2)
return np.dot(np_v1, np_v2) / (np.linalg.norm(np_v1) * np.linalg.norm(np_v2)) | 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,
)
The provided code snippet includes necessary dependencies for implementing the `_split_test_set_from_config` function. Write a Python function `def _split_test_set_from_config( config: RailsConfig, test_set_percentage: float, test_set: Dict[str, List], max_samples_per_intent: int, random_seed: Optional[int] = None, )` to solve the following problem:
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.
Here is the function:
def _split_test_set_from_config(
config: RailsConfig,
test_set_percentage: float,
test_set: Dict[str, List],
max_samples_per_intent: int,
random_seed: Optional[int] = None,
):
"""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.
"""
if config.user_messages and test_set_percentage > 0:
for intent, samples in config.user_messages.items():
# We need at least 2 samples to create a test split
if len(samples) > 1:
if random_seed:
random.Random(random_seed).shuffle(samples)
else:
random.shuffle(samples)
num_test_elements = int(len(samples) * test_set_percentage)
test_set[intent] = samples[:num_test_elements]
config.user_messages[intent] = samples[num_test_elements:]
# Limit the number of samples per intent if specified
if 0 < max_samples_per_intent < len(config.user_messages[intent]):
config.user_messages[intent] = config.user_messages[intent][
:max_samples_per_intent
] | 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)],
check=True,
)
except subprocess.CalledProcessError as e:
print(f"An error occurred while running nbdoc_build: {e}")
return False
return True
def rename_md_to_readme(start_dir):
generated = set()
for path in Path(start_dir).rglob("*.md"):
if path.name == "README.md":
# if path.exists() and not path.absolute() in generated:
# path.unlink()
continue
# Skip processing the root directory
if path.parent.name == "getting_started":
continue
# Generate the new file name, assuming the path as a directory with README.md
readme_path = path.parent / "README.md"
# # Skip if README.md already exists
if readme_path.exists():
print(f"{readme_path} already exists, deleting.")
readme_path.unlink()
# Rename the file
path.rename(readme_path)
print(f"Renamed {path} to {readme_path}")
generated.add(readme_path.absolute())
print(f"Adding {readme_path.absolute()}")
# We do some additional post-processing
_remove_code_blocks_with_text(readme_path.absolute(), "# Init:")
_remove_code_blocks_with_text(
readme_path.absolute(), "# Hide from documentation page."
)
_remove_code_blocks_with_text(
readme_path.absolute(),
"huggingface/tokenizers: The current process just got forked",
)
_remove_code_blocks_with_text(readme_path.absolute(), "Writing config/")
_remove_code_blocks_with_text(readme_path.absolute(), "Appending to config/")
_remove_specific_text(
readme_path.absolute(),
'<CodeOutputBlock lang="bash">\n\n\n\n</CodeOutputBlock>',
)
_fix_prefix_and_type_in_code_blocks(readme_path.absolute())
_post_process(readme_path.absolute())
The provided code snippet includes necessary dependencies for implementing the `convert` function. Write a Python function `def convert(folder: str)` to solve the following problem:
Convert a Jupyter notebook in the provided folder to .md. It creates a README.md file next to the Jupyter notebook.
Here is the function:
def convert(folder: str):
"""Convert a Jupyter notebook in the provided folder to .md.
It creates a README.md file next to the Jupyter notebook.
"""
print(f"Processing {folder}...")
notebooks = [f for f in os.listdir(folder) if f.endswith(".ipynb")]
if len(notebooks) == 0:
raise RuntimeError(f"No .ipynb file found in {folder}.")
elif len(notebooks) > 1:
raise RuntimeError(f"Found {len(notebooks)} in {folder}: {notebooks}.")
print(f"Found notebook: {notebooks[0]}")
if run_nbdoc_build(folder, True):
# Rename .md files if nbdev_build was successful
rename_md_to_readme(folder)
subprocess.run(["git", "add", "."])
subprocess.run(["pre-commit", "run", "--all-files"])
else:
print("nbdoc_build command failed. Exiting without renaming .md files.") | 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_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
with torch.no_grad(), torch.autocast("cuda"):
depth_map = depth_estimator(image).predicted_depth
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(1024, 1024),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image | 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)
edges = cv2.Canny(image, t1, t2)
return Image.fromarray(edges, "L") | null |
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