"""
This module contains the tools catalog for the Vectara Agentic.
"""
from typing import Optional
from pydantic import Field
import requests
from .types import LLMRole
from .utils import get_llm
req_session = requests.Session()
get_headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.5",
"Accept-Encoding": "gzip, deflate",
"Connection": "keep-alive",
}
#
# Standard Tools
#
[docs]
def summarize_text(
text: str = Field(description="the original text."),
expertise: str = Field(
description="the expertise to apply to the summarization.",
),
) -> str:
"""
This is a helper tool.
Use this tool to summarize text using a given expertise
with no more than summary_max_length characters.
Args:
text (str): The original text.
expertise (str): The expertise to apply to the summarization.
Returns:
str: The summarized text.
"""
if not isinstance(expertise, str):
return "Please provide a valid string for expertise."
if not isinstance(text, str):
return "Please provide a valid string for text."
expertise = "general" if len(expertise) < 3 else expertise.lower()
prompt = f"As an expert in {expertise}, summarize the provided text"
prompt += " into a concise summary."
prompt += f"\noriginal text: {text}\nsummary:"
llm = get_llm(LLMRole.TOOL)
response = llm.complete(prompt)
return response.text
[docs]
def rephrase_text(
text: str = Field(description="the original text."),
instructions: str = Field(
description="the specific instructions for how to rephrase the text."
),
) -> str:
"""
This is a helper tool.
Use this tool to rephrase the text according to the provided instructions.
For example, instructions could be "as a 5 year old would say it."
Args:
text (str): The original text.
instructions (str): The specific instructions for how to rephrase the text.
Returns:
str: The rephrased text.
"""
prompt = f"""
Rephrase the provided text according to the following instructions: {instructions}.
If the input is Markdown, keep the output in Markdown as well.
original text: {text}
rephrased text:
"""
llm = get_llm(LLMRole.TOOL)
response = llm.complete(prompt)
return response.text
[docs]
def critique_text(
text: str = Field(description="the original text."),
role: Optional[str] = Field(
None, description="the role of the person providing critique."
),
point_of_view: Optional[str] = Field(
None, description="the point of view with which to provide critique."
),
) -> str:
"""
This is a helper tool.
Critique the text from the specified point of view.
Args:
text (str): The original text.
role (str): The role of the person providing critique.
point_of_view (str): The point of view with which to provide critique.
Returns:
str: The critique of the text.
"""
if role:
prompt = f"As a {role}, critique the provided text from the point of view of {point_of_view}."
else:
prompt = (
f"Critique the provided text from the point of view of {point_of_view}."
)
prompt += "Structure the critique as bullet points.\n"
prompt += f"Original text: {text}\nCritique:"
llm = get_llm(LLMRole.TOOL)
response = llm.complete(prompt)
return response.text
#
# Guardrails tools
#
[docs]
def guardrails_no_politics(text: str = Field(description="the original text.")) -> str:
"""
A guardrails tool.
Given the input text, rephrases the text to ensure that the response avoids any specific political content.
Args:
text (str): The original text.
Returns:
str: The rephrased text.
"""
return rephrase_text(text, "avoid any specific political content.")
[docs]
def guardrails_be_polite(text: str = Field(description="the original text.")) -> str:
"""
A guardrails tool.
Given the input text, rephrases the text to ensure that the response is polite.
Args:
text (str): The original text.
Returns:
str: The rephrased text.
"""
return rephrase_text(text, "Ensure the response is super polite.")