from praisonaiagents.tools import search_arxiv
from praisonaiagents.tools import get_arxiv_paper
from praisonaiagents.tools import get_papers_by_author
from praisonaiagents.tools import get_papers_by_category

# Basic search
papers = search_arxiv("quantum computing")

# Advanced search
papers = search_arxiv(
    query="quantum computing",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending",
    include_fields=["title", "authors", "summary"]
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00123",
#         "title": "Quantum Computing: A Survey",
#         "authors": ["John Doe", "Jane Smith"],
#         "summary": "This paper reviews...",
#         "published": "2024-01-01T00:00:00",
#         "updated": "2024-01-02T00:00:00"
#     },
#     ...
# ]

# Get paper by ID
paper = get_arxiv_paper("2401.00123")

# Get specific fields
paper = get_arxiv_paper(
    paper_id="2401.00123",
    include_fields=["title", "authors", "pdf_url"]
)

# Returns: Dict[str, Any]
# Example output:
# {
#     "arxiv_id": "2401.00123",
#     "title": "Quantum Computing: A Survey",
#     "authors": ["John Doe", "Jane Smith"],
#     "pdf_url": "https://arxiv.org/pdf/2401.00123",
#     "abstract_url": "https://arxiv.org/abs/2401.00123",
#     "published": "2024-01-01T00:00:00",
#     "updated": "2024-01-02T00:00:00"
# }

# Get author's papers
papers = get_papers_by_author("Yoshua Bengio")

# Recent papers first
papers = get_papers_by_author(
    author="Yoshua Bengio",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending"
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00124",
#         "title": "Deep Learning Advances",
#         "authors": ["Yoshua Bengio", "Other Author"],
#         "published": "2024-01-01T00:00:00"
#     },
#     ...
# ]

# Get papers in category
papers = get_papers_by_category("cs.AI")

# Recent machine learning papers
papers = get_papers_by_category(
    category="cs.LG",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending"
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00125",
#         "title": "New ML Algorithm",
#         "categories": ["cs.LG", "cs.AI"],
#         "published": "2024-01-01T00:00:00"
#     },
#     ...
# ]

# Search multiple related topics
topics = ["machine learning", "neural networks", "deep learning"]
all_papers = []
for topic in topics:
    papers = search_arxiv(
        topic,
        max_results=5,
        sort_by="relevance"
    )
    all_papers.extend(papers)

# Get recent papers by multiple authors
authors = ["Yoshua Bengio", "Geoffrey Hinton", "Yann LeCun"]
author_papers = {}
for author in authors:
    papers = get_papers_by_author(
        author,
        max_results=3,
        sort_by="submittedDate"
    )
    author_papers[author] = papers

# Monitor new papers in AI categories
categories = ["cs.AI", "cs.LG", "cs.CL"]
new_papers = {}
for category in categories:
    papers = get_papers_by_category(
        category,
        max_results=5,
        sort_by="submittedDate"
    )
    new_papers[category] = papers

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category

# Create research agent
research_agent = Agent(
    name="PaperSearcher",
    role="Scientific Literature Specialist",
    goal="Find relevant scientific papers on specified topics.",
    backstory="Expert in academic research and literature analysis.",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
    self_reflect=False
)

# Define research task
research_task = Task(
    description="Search for papers on 'transformer models in NLP' from the last year.",
    expected_output="List of relevant papers with abstracts and key findings.",
    agent=research_agent,
    name="nlp_research"
)

# Run agent
agents = PraisonAIAgents(
    agents=[research_agent],
    tasks=[research_task],
    process="sequential"
)
agents.start()

# Create research agent
research_agent = Agent(
    name="Researcher",
    role="Literature Specialist",
    goal="Gather comprehensive scientific literature.",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
    self_reflect=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Research Analyst",
    goal="Analyze and synthesize research findings.",
    backstory="Expert in scientific literature analysis.",
    self_reflect=False
)

# Define tasks
research_task = Task(
    description="Search for papers on quantum computing applications in cryptography.",
    agent=research_agent,
    name="quantum_research"
)

analysis_task = Task(
    description="Analyze the papers and identify key trends and breakthroughs.",
    agent=analysis_agent,
    name="research_analysis"
)

# Run agents
agents = PraisonAIAgents(
    agents=[research_agent, analysis_agent],
    tasks=[research_task, analysis_task],
    process="sequential"
)
agents.start()

# Literature search agent
searcher = Agent(
    name="Searcher",
    role="Literature Specialist",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
)

# Review agent
reviewer = Agent(
    name="Reviewer",
    role="Research Reviewer"
)

# Define tasks
search_task = Task(
    description="Find papers on AI ethics",
    agent=searcher
)

review_task = Task(
    description="Review and summarize findings",
    agent=reviewer
)

# Run workflow
agents = PraisonAIAgents(
    agents=[searcher, reviewer],
    tasks=[search_task, review_task]
)