You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
swarms/swarms/prompts/agent_prompts.py

182 lines
7.6 KiB

# Agent prompts for MCP testing and interactions
MATH_AGENT_PROMPT = """You are a specialized math agent that can perform calculations by calling external math service APIs.
Key responsibilities:
1. Understand mathematical queries and break them down into basic operations
2. Use available math tools (add, multiply, divide) appropriately
3. Provide clear explanations of calculations
4. Handle errors gracefully if operations fail
Remember to use the available MCP tools for calculations rather than doing them directly."""
FINANCE_AGENT_PROMPT = """You are a financial analysis agent with access to stock market data services.
Key responsibilities:
1. Interpret financial queries and determine required data
2. Use available stock tools to fetch market data
3. Analyze trends and provide insights
4. Format responses in a clear, professional manner
Use the available MCP tools to fetch real market data rather than making assumptions."""
def generate_agent_role_prompt(agent):
"""Generates the agent role prompt.
Args: agent (str): The type of the agent.
Returns: str: The agent role prompt.
"""
prompts = {
"Finance Agent": (
"You are a seasoned finance analyst AI assistant. Your"
" primary goal is to compose comprehensive, astute,"
" impartial, and methodically arranged financial reports"
" based on provided data and trends."
),
"Travel Agent": (
"You are a world-travelled AI tour guide assistant. Your"
" main purpose is to draft engaging, insightful,"
" unbiased, and well-structured travel reports on given"
" locations, including history, attractions, and cultural"
" insights."
),
"Academic Research Agent": (
"You are an AI academic research assistant. Your primary"
" responsibility is to create thorough, academically"
" rigorous, unbiased, and systematically organized"
" reports on a given research topic, following the"
" standards of scholarly work."
),
"Default Agent": (
"You are an AI critical thinker research assistant. Your"
" sole purpose is to write well written, critically"
" acclaimed, objective and structured reports on given"
" text."
),
}
return prompts.get(agent, "No such agent")
def generate_report_prompt(question, research_summary):
"""Generates the report prompt for the given question and research summary.
Args: question (str): The question to generate the report prompt for
research_summary (str): The research summary to generate the report prompt for
Returns: str: The report prompt for the given question and research summary
"""
return (
f'"""{research_summary}""" Using the above information,'
f' answer the following question or topic: "{question}" in a'
" detailed report -- The report should focus on the answer"
" to the question, should be well structured, informative,"
" in depth, with facts and numbers if available, a minimum"
" of 1,200 words and with markdown syntax and apa format."
" Write all source urls at the end of the report in apa"
" format"
)
def generate_search_queries_prompt(question):
"""Generates the search queries prompt for the given question.
Args: question (str): The question to generate the search queries prompt for
Returns: str: The search queries prompt for the given question
"""
return (
"Write 4 google search queries to search online that form an"
f' objective opinion from the following: "{question}"You must'
" respond with a list of strings in the following format:"
' ["query 1", "query 2", "query 3", "query 4"]'
)
def generate_resource_report_prompt(question, research_summary):
"""Generates the resource report prompt for the given question and research summary.
Args:
question (str): The question to generate the resource report prompt for.
research_summary (str): The research summary to generate the resource report prompt for.
Returns:
str: The resource report prompt for the given question and research summary.
"""
return (
f'"""{research_summary}""" Based on the above information,'
" generate a bibliography recommendation report for the"
f' following question or topic: "{question}". The report'
" should provide a detailed analysis of each recommended"
" resource, explaining how each source can contribute to"
" finding answers to the research question. Focus on the"
" relevance, reliability, and significance of each source."
" Ensure that the report is well-structured, informative,"
" in-depth, and follows Markdown syntax. Include relevant"
" facts, figures, and numbers whenever available. The report"
" should have a minimum length of 1,200 words."
)
def generate_outline_report_prompt(question, research_summary):
"""Generates the outline report prompt for the given question and research summary.
Args: question (str): The question to generate the outline report prompt for
research_summary (str): The research summary to generate the outline report prompt for
Returns: str: The outline report prompt for the given question and research summary
"""
return (
f'"""{research_summary}""" Using the above information,'
" generate an outline for a research report in Markdown"
f' syntax for the following question or topic: "{question}".'
" The outline should provide a well-structured framework for"
" the research report, including the main sections,"
" subsections, and key points to be covered. The research"
" report should be detailed, informative, in-depth, and a"
" minimum of 1,200 words. Use appropriate Markdown syntax to"
" format the outline and ensure readability."
)
def generate_concepts_prompt(question, research_summary):
"""Generates the concepts prompt for the given question.
Args: question (str): The question to generate the concepts prompt for
research_summary (str): The research summary to generate the concepts prompt for
Returns: str: The concepts prompt for the given question
"""
return (
f'"""{research_summary}""" Using the above information,'
" generate a list of 5 main concepts to learn for a research"
f' report on the following question or topic: "{question}".'
" The outline should provide a well-structured frameworkYou"
" must respond with a list of strings in the following"
' format: ["concepts 1", "concepts 2", "concepts 3",'
' "concepts 4, concepts 5"]'
)
def generate_lesson_prompt(concept):
"""
Generates the lesson prompt for the given question.
Args:
concept (str): The concept to generate the lesson prompt for.
Returns:
str: The lesson prompt for the given concept.
"""
prompt = (
f"generate a comprehensive lesson about {concept} in Markdown"
f" syntax. This should include the definitionof {concept},"
" its historical background and development, its"
" applications or uses in differentfields, and notable"
f" events or facts related to {concept}."
)
return prompt
def get_report_by_type(report_type):
report_type_mapping = {
"research_report": generate_report_prompt,
"resource_report": generate_resource_report_prompt,
"outline_report": generate_outline_report_prompt,
}
return report_type_mapping[report_type]