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SPARKNET / src /tools /langchain_tools.py

Initial commit: SPARKNET framework

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"""
LangChain-Compatible Tools for SPARKNET
All tools follow LangChain's tool interface for seamless integration
with LangGraph agents and workflows.
"""

from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field
from langchain_core.tools import StructuredTool, tool
from loguru import logger
import json

# PDF processing
try:
    import PyPDF2
    import fitz  # pymupdf
    PDF_AVAILABLE = True
except ImportError:
    PDF_AVAILABLE = False
    logger.warning("PDF libraries not available. Install PyPDF2 and pymupdf.")

# Document generation
try:
    from reportlab.lib.pagesizes import letter
    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
    from reportlab.lib.styles import getSampleStyleSheet
    REPORTLAB_AVAILABLE = True
except ImportError:
    REPORTLAB_AVAILABLE = False
    logger.warning("ReportLab not available. Install reportlab for PDF generation.")

# Web search and research
try:
    from duckduckgo_search import DDGS
    DDGS_AVAILABLE = True
except ImportError:
    DDGS_AVAILABLE = False
    logger.warning("DuckDuckGo search not available.")

try:
    import wikipedia
    WIKIPEDIA_AVAILABLE = True
except ImportError:
    WIKIPEDIA_AVAILABLE = False
    logger.warning("Wikipedia not available.")

try:
    import arxiv
    ARXIV_AVAILABLE = True
except ImportError:
    ARXIV_AVAILABLE = False
    logger.warning("Arxiv not available.")

# GPU monitoring
from ..utils.gpu_manager import get_gpu_manager


# ============================================================================
# Pydantic Input Schemas
# ============================================================================

class PDFExtractorInput(BaseModel):
    """Input schema for PDF extraction."""
    file_path: str = Field(..., description="Path to the PDF file")
    page_range: Optional[str] = Field(None, description="Page range (e.g., '1-5', 'all')")
    extract_metadata: bool = Field(True, description="Extract PDF metadata")


class PatentParserInput(BaseModel):
    """Input schema for patent parsing."""
    text: str = Field(..., description="Patent text to parse")
    extract_claims: bool = Field(True, description="Extract patent claims")
    extract_abstract: bool = Field(True, description="Extract abstract")
    extract_description: bool = Field(True, description="Extract description")


class WebSearchInput(BaseModel):
    """Input schema for web search."""
    query: str = Field(..., description="Search query")
    max_results: int = Field(5, description="Maximum number of results")
    region: str = Field("wt-wt", description="Search region (e.g., 'us-en', 'wt-wt')")


class WikipediaInput(BaseModel):
    """Input schema for Wikipedia lookup."""
    query: str = Field(..., description="Wikipedia search query")
    sentences: int = Field(3, description="Number of sentences to return")


class ArxivInput(BaseModel):
    """Input schema for Arxiv search."""
    query: str = Field(..., description="Search query")
    max_results: int = Field(5, description="Maximum number of results")
    sort_by: str = Field("relevance", description="Sort by: relevance, lastUpdatedDate, submittedDate")


class DocumentGeneratorInput(BaseModel):
    """Input schema for document generation."""
    output_path: str = Field(..., description="Output PDF file path")
    title: str = Field(..., description="Document title")
    content: str = Field(..., description="Document content (markdown or plain text)")
    author: Optional[str] = Field(None, description="Document author")


class GPUMonitorInput(BaseModel):
    """Input schema for GPU monitoring."""
    gpu_id: Optional[int] = Field(None, description="Specific GPU ID or None for all GPUs")


# ============================================================================
# PDF Tools
# ============================================================================

def pdf_extractor_func(file_path: str, page_range: Optional[str] = None,
                       extract_metadata: bool = True) -> str:
    """
    Extract text and metadata from PDF files.
    Supports both PyPDF2 and PyMuPDF (fitz) backends.
    Args:
        file_path: Path to PDF file
        page_range: Page range like '1-5' or 'all' (default: all)
        extract_metadata: Whether to extract metadata
    Returns:
        Extracted text and metadata as formatted string
    """
    if not PDF_AVAILABLE:
        return "Error: PDF libraries not installed. Run: pip install PyPDF2 pymupdf"

    try:
        # Open PDF with PyMuPDF (better text extraction)
        doc = fitz.open(file_path)

        # Parse page range
        if page_range and page_range.lower() != 'all':
            start, end = map(int, page_range.split('-'))
            pages = range(start - 1, min(end, len(doc)))  # 0-indexed
        else:
            pages = range(len(doc))

        # Extract text
        text_parts = []
        for page_num in pages:
            page = doc[page_num]
            text_parts.append(f"--- Page {page_num + 1} ---\n{page.get_text()}")

        extracted_text = "\n\n".join(text_parts)

        # Extract metadata
        result = f"PDF: {file_path}\n"
        result += f"Total Pages: {len(doc)}\n"
        result += f"Extracted Pages: {len(pages)}\n\n"

        if extract_metadata:
            metadata = doc.metadata
            result += "Metadata:\n"
            for key, value in metadata.items():
                if value:
                    result += f"  {key}: {value}\n"
            result += "\n"

        result += "=" * 80 + "\n"
        result += "EXTRACTED TEXT:\n"
        result += "=" * 80 + "\n"
        result += extracted_text

        doc.close()

        logger.info(f"Extracted {len(pages)} pages from {file_path}")
        return result

    except Exception as e:
        logger.error(f"PDF extraction failed: {e}")
        return f"Error extracting PDF: {str(e)}"


def patent_parser_func(text: str, extract_claims: bool = True,
                       extract_abstract: bool = True, extract_description: bool = True) -> str:
    """
    Parse patent document structure and extract key sections.
    Uses heuristics to identify: abstract, claims, description, drawings.
    Args:
        text: Patent text (from PDF or plain text)
        extract_claims: Extract patent claims
        extract_abstract: Extract abstract
        extract_description: Extract detailed description
    Returns:
        Structured patent information as JSON string
    """
    try:
        result = {
            "abstract": "",
            "claims": [],
            "description": "",
            "metadata": {}
        }

        lines = text.split('\n')
        current_section = None

        # Simple heuristic-based parser
        for i, line in enumerate(lines):
            line_lower = line.lower().strip()

            # Detect sections
            if 'abstract' in line_lower and len(line_lower) < 50:
                current_section = 'abstract'
                continue
            elif 'claim' in line_lower and len(line_lower) < 50:
                current_section = 'claims'
                continue
            elif 'description' in line_lower or 'detailed description' in line_lower:
                if len(line_lower) < 100:
                    current_section = 'description'
                    continue
            elif 'drawing' in line_lower or 'figure' in line_lower:
                if len(line_lower) < 50:
                    current_section = 'drawings'
                    continue

            # Extract content based on section
            if current_section == 'abstract' and extract_abstract:
                if line.strip():
                    result['abstract'] += line + "\n"
            elif current_section == 'claims' and extract_claims:
                if line.strip() and (line.strip()[0].isdigit() or 'wherein' in line_lower):
                    result['claims'].append(line.strip())
            elif current_section == 'description' and extract_description:
                if line.strip():
                    result['description'] += line + "\n"

        # Extract patent number if present
        for line in lines[:20]:  # Check first 20 lines
            if 'patent' in line.lower() and any(char.isdigit() for char in line):
                result['metadata']['patent_number'] = line.strip()
                break

        # Format output
        output = "PATENT ANALYSIS\n"
        output += "=" * 80 + "\n\n"

        if result['abstract']:
            output += "ABSTRACT:\n"
            output += result['abstract'].strip()[:500]  # Limit length
            output += "\n\n"

        if result['claims']:
            output += f"CLAIMS ({len(result['claims'])} found):\n"
            for i, claim in enumerate(result['claims'][:10], 1):  # First 10 claims
                output += f"\n{i}. {claim}\n"
            output += "\n"

        if result['description']:
            output += "DESCRIPTION (excerpt):\n"
            output += result['description'].strip()[:1000]  # First 1000 chars
            output += "\n\n"

        output += "=" * 80 + "\n"
        output += f"JSON OUTPUT:\n{json.dumps(result, indent=2)}"

        logger.info(f"Parsed patent: {len(result['claims'])} claims extracted")
        return output

    except Exception as e:
        logger.error(f"Patent parsing failed: {e}")
        return f"Error parsing patent: {str(e)}"


# ============================================================================
# Web Search & Research Tools
# ============================================================================

def web_search_func(query: str, max_results: int = 5, region: str = "wt-wt") -> str:
    """
    Search the web using DuckDuckGo.
    Returns top results with title, snippet, and URL.
    Args:
        query: Search query
        max_results: Maximum number of results
        region: Search region code
    Returns:
        Formatted search results
    """
    if not DDGS_AVAILABLE:
        return "Error: DuckDuckGo search not installed. Run: pip install duckduckgo-search"

    try:
        ddgs = DDGS()
        results = list(ddgs.text(query, region=region, max_results=max_results))

        if not results:
            return f"No results found for: {query}"

        output = f"WEB SEARCH RESULTS: {query}\n"
        output += "=" * 80 + "\n\n"

        for i, result in enumerate(results, 1):
            output += f"{i}. {result.get('title', 'No title')}\n"
            output += f"   {result.get('body', 'No description')}\n"
            output += f"   URL: {result.get('href', 'No URL')}\n\n"

        logger.info(f"Web search completed: {len(results)} results for '{query}'")
        return output

    except Exception as e:
        logger.error(f"Web search failed: {e}")
        return f"Error performing web search: {str(e)}"


def wikipedia_func(query: str, sentences: int = 3) -> str:
    """
    Search Wikipedia and return summary.
    Args:
        query: Wikipedia search query
        sentences: Number of sentences to return
    Returns:
        Wikipedia summary
    """
    if not WIKIPEDIA_AVAILABLE:
        return "Error: Wikipedia not installed. Run: pip install wikipedia"

    try:
        # Search for page
        search_results = wikipedia.search(query)

        if not search_results:
            return f"No Wikipedia page found for: {query}"

        # Get first result
        page = wikipedia.page(search_results[0], auto_suggest=False)

        # Get summary
        summary = wikipedia.summary(search_results[0], sentences=sentences, auto_suggest=False)

        output = f"WIKIPEDIA: {page.title}\n"
        output += "=" * 80 + "\n\n"
        output += summary + "\n\n"
        output += f"URL: {page.url}\n"
        output += f"Categories: {', '.join(page.categories[:5])}\n"

        logger.info(f"Wikipedia lookup completed: {page.title}")
        return output

    except wikipedia.exceptions.DisambiguationError as e:
        options = ', '.join(e.options[:5])
        return f"Disambiguation needed for '{query}'. Options: {options}"
    except wikipedia.exceptions.PageError:
        return f"No Wikipedia page found for: {query}"
    except Exception as e:
        logger.error(f"Wikipedia lookup failed: {e}")
        return f"Error: {str(e)}"


def arxiv_func(query: str, max_results: int = 5, sort_by: str = "relevance") -> str:
    """
    Search Arxiv for academic papers.
    Args:
        query: Search query
        max_results: Maximum number of results
        sort_by: Sort by relevance, lastUpdatedDate, or submittedDate
    Returns:
        Formatted Arxiv results
    """
    if not ARXIV_AVAILABLE:
        return "Error: Arxiv not installed. Run: pip install arxiv"

    try:
        # Map sort_by to arxiv.SortCriterion
        sort_map = {
            "relevance": arxiv.SortCriterion.Relevance,
            "lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate,
            "submittedDate": arxiv.SortCriterion.SubmittedDate,
        }
        sort_criterion = sort_map.get(sort_by, arxiv.SortCriterion.Relevance)

        # Search Arxiv
        search = arxiv.Search(
            query=query,
            max_results=max_results,
            sort_by=sort_criterion
        )

        results = list(search.results())

        if not results:
            return f"No Arxiv papers found for: {query}"

        output = f"ARXIV SEARCH: {query}\n"
        output += "=" * 80 + "\n\n"

        for i, paper in enumerate(results, 1):
            output += f"{i}. {paper.title}\n"
            output += f"   Authors: {', '.join(str(author) for author in paper.authors[:3])}\n"
            output += f"   Published: {paper.published.strftime('%Y-%m-%d')}\n"
            output += f"   Summary: {paper.summary[:200]}...\n"
            output += f"   PDF: {paper.pdf_url}\n"
            output += f"   Categories: {', '.join(paper.categories)}\n\n"

        logger.info(f"Arxiv search completed: {len(results)} papers for '{query}'")
        return output

    except Exception as e:
        logger.error(f"Arxiv search failed: {e}")
        return f"Error searching Arxiv: {str(e)}"


# ============================================================================
# Document Generation
# ============================================================================

def document_generator_func(output_path: str, title: str, content: str,
                           author: Optional[str] = None) -> str:
    """
    Generate PDF document from text content.
    Supports basic formatting and styling.
    Args:
        output_path: Output PDF file path
        title: Document title
        content: Document content (plain text or simple markdown)
        author: Optional author name
    Returns:
        Success message with file path
    """
    if not REPORTLAB_AVAILABLE:
        return "Error: ReportLab not installed. Run: pip install reportlab"

    try:
        # Create PDF
        doc = SimpleDocTemplate(output_path, pagesize=letter)
        styles = getSampleStyleSheet()
        story = []

        # Title
        title_style = styles['Title']
        story.append(Paragraph(title, title_style))
        story.append(Spacer(1, 12))

        # Author
        if author:
            author_style = styles['Normal']
            story.append(Paragraph(f"By: {author}", author_style))
            story.append(Spacer(1, 12))

        # Content (split into paragraphs)
        paragraphs = content.split('\n\n')
        for para in paragraphs:
            if para.strip():
                # Simple markdown-like processing
                if para.strip().startswith('#'):
                    # Heading
                    heading_text = para.strip().lstrip('#').strip()
                    story.append(Paragraph(heading_text, styles['Heading2']))
                else:
                    # Regular paragraph
                    story.append(Paragraph(para.strip(), styles['Normal']))
                story.append(Spacer(1, 6))

        # Build PDF
        doc.build(story)

        logger.info(f"Generated PDF: {output_path}")
        return f"Successfully generated PDF: {output_path}\nTitle: {title}\nPages: {len(paragraphs)}"

    except Exception as e:
        logger.error(f"PDF generation failed: {e}")
        return f"Error generating PDF: {str(e)}"


# ============================================================================
# GPU Monitoring (converted from existing tool)
# ============================================================================

def gpu_monitor_func(gpu_id: Optional[int] = None) -> str:
    """
    Monitor GPU status, memory usage, and utilization.
    Args:
        gpu_id: Specific GPU ID or None for all GPUs
    Returns:
        Formatted GPU status information
    """
    try:
        gpu_manager = get_gpu_manager()

        if gpu_id is not None:
            # Monitor specific GPU
            info = gpu_manager.get_gpu_info(gpu_id)

            if "error" in info:
                return f"Error: {info['error']}"

            output = f"GPU {info['gpu_id']}: {info['name']}\n"
            output += f"Memory: {info['memory_used'] / 1024**3:.2f} GB / {info['memory_total'] / 1024**3:.2f} GB "
            output += f"({info['memory_percent']:.1f}% used)\n"
            output += f"Free Memory: {info['memory_free'] / 1024**3:.2f} GB\n"
            output += f"GPU Utilization: {info['gpu_utilization']}%\n"
            output += f"Temperature: {info['temperature']}°C\n"

            return output
        else:
            # Monitor all GPUs
            return gpu_manager.monitor()

    except Exception as e:
        logger.error(f"GPU monitoring error: {e}")
        return f"Error monitoring GPU: {str(e)}"


# ============================================================================
# Create LangChain Tools
# ============================================================================

# Use StructuredTool for tools with Pydantic input schemas
pdf_extractor_tool = StructuredTool.from_function(
    func=pdf_extractor_func,
    name="pdf_extractor",
    description=(
        "Extract text and metadata from PDF files. "
        "Useful for analyzing patent documents, research papers, and legal documents. "
        "Supports page range selection and metadata extraction."
    ),
    args_schema=PDFExtractorInput,
    return_direct=False,
)

patent_parser_tool = StructuredTool.from_function(
    func=patent_parser_func,
    name="patent_parser",
    description=(
        "Parse patent document structure and extract key sections: abstract, claims, description. "
        "Useful for analyzing patent documents and identifying key innovations."
    ),
    args_schema=PatentParserInput,
    return_direct=False,
)

web_search_tool = StructuredTool.from_function(
    func=web_search_func,
    name="web_search",
    description=(
        "Search the web using DuckDuckGo. Returns top results with titles, snippets, and URLs. "
        "Useful for market research, competitor analysis, and finding relevant information."
    ),
    args_schema=WebSearchInput,
    return_direct=False,
)

wikipedia_tool = StructuredTool.from_function(
    func=wikipedia_func,
    name="wikipedia",
    description=(
        "Search Wikipedia and get article summaries. "
        "Useful for background information on technologies, companies, and concepts."
    ),
    args_schema=WikipediaInput,
    return_direct=False,
)

arxiv_tool = StructuredTool.from_function(
    func=arxiv_func,
    name="arxiv_search",
    description=(
        "Search Arxiv for academic papers and preprints. "
        "Useful for finding relevant research, state-of-the-art methods, and technical background."
    ),
    args_schema=ArxivInput,
    return_direct=False,
)

document_generator_tool = StructuredTool.from_function(
    func=document_generator_func,
    name="document_generator",
    description=(
        "Generate PDF documents from text content. "
        "Useful for creating reports, briefs, and documentation."
    ),
    args_schema=DocumentGeneratorInput,
    return_direct=False,
)

gpu_monitor_tool = StructuredTool.from_function(
    func=gpu_monitor_func,
    name="gpu_monitor",
    description=(
        "Monitor GPU status including memory usage, utilization, and temperature. "
        "Useful for checking GPU availability before running models."
    ),
    args_schema=GPUMonitorInput,
    return_direct=False,
)


# ============================================================================
# Tool Registry for VISTA Scenarios
# ============================================================================

class VISTAToolRegistry:
    """
    Registry of tools organized by VISTA scenario.
    Enables scenario-specific tool selection for optimal performance.
    """

    SCENARIO_TOOLS = {
        "patent_wakeup": [
            pdf_extractor_tool,
            patent_parser_tool,
            web_search_tool,
            wikipedia_tool,
            arxiv_tool,
            document_generator_tool,
        ],
        "agreement_safety": [
            pdf_extractor_tool,
            web_search_tool,
            document_generator_tool,
        ],
        "partner_matching": [
            web_search_tool,
            wikipedia_tool,
            arxiv_tool,
        ],
        "general": [
            pdf_extractor_tool,
            patent_parser_tool,
            web_search_tool,
            wikipedia_tool,
            arxiv_tool,
            document_generator_tool,
            gpu_monitor_tool,
        ],
    }

    @classmethod
    def get_tools(cls, scenario: str = "general") -> List[StructuredTool]:
        """
        Get tools for a specific VISTA scenario.
        Args:
            scenario: VISTA scenario type
        Returns:
            List of LangChain tools
        """
        tools = cls.SCENARIO_TOOLS.get(scenario, cls.SCENARIO_TOOLS["general"])
        logger.info(f"Retrieved {len(tools)} tools for scenario: {scenario}")
        return tools

    @classmethod
    def get_all_tools(cls) -> List[StructuredTool]:
        """Get all available tools."""
        return cls.SCENARIO_TOOLS["general"]

    @classmethod
    def list_scenarios(cls) -> List[str]:
        """List available scenarios."""
        return list(cls.SCENARIO_TOOLS.keys())


# ============================================================================
# Convenience Functions
# ============================================================================

def get_vista_tools(scenario: str = "general") -> List[StructuredTool]:
    """
    Get LangChain tools for a VISTA scenario.
    Args:
        scenario: Scenario name (patent_wakeup, agreement_safety, partner_matching, general)
    Returns:
        List of LangChain StructuredTool instances
    """
    return VISTAToolRegistry.get_tools(scenario)


def get_all_tools() -> List[StructuredTool]:
    """Get all available LangChain tools."""
    return VISTAToolRegistry.get_all_tools()


# Export all tools
__all__ = [
    "pdf_extractor_tool",
    "patent_parser_tool",
    "web_search_tool",
    "wikipedia_tool",
    "arxiv_tool",
    "document_generator_tool",
    "gpu_monitor_tool",
    "VISTAToolRegistry",
    "get_vista_tools",
    "get_all_tools",
]

	"""
	LangChain-Compatible Tools for SPARKNET
	All tools follow LangChain's tool interface for seamless integration
	with LangGraph agents and workflows.
	"""

	from typing import Optional, List, Dict, Any
	from pydantic import BaseModel, Field
	from langchain_core.tools import StructuredTool, tool
	from loguru import logger
	import json

	# PDF processing
	try:
	import PyPDF2
	import fitz # pymupdf
	PDF_AVAILABLE = True
	except ImportError:
	PDF_AVAILABLE = False
	logger.warning("PDF libraries not available. Install PyPDF2 and pymupdf.")

	# Document generation
	try:
	from reportlab.lib.pagesizes import letter
	from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
	from reportlab.lib.styles import getSampleStyleSheet
	REPORTLAB_AVAILABLE = True
	except ImportError:
	REPORTLAB_AVAILABLE = False
	logger.warning("ReportLab not available. Install reportlab for PDF generation.")

	# Web search and research
	try:
	from duckduckgo_search import DDGS
	DDGS_AVAILABLE = True
	except ImportError:
	DDGS_AVAILABLE = False
	logger.warning("DuckDuckGo search not available.")

	try:
	import wikipedia
	WIKIPEDIA_AVAILABLE = True
	except ImportError:
	WIKIPEDIA_AVAILABLE = False
	logger.warning("Wikipedia not available.")

	try:
	import arxiv
	ARXIV_AVAILABLE = True
	except ImportError:
	ARXIV_AVAILABLE = False
	logger.warning("Arxiv not available.")

	# GPU monitoring
	from ..utils.gpu_manager import get_gpu_manager


	# ============================================================================
	# Pydantic Input Schemas
	# ============================================================================

	class PDFExtractorInput(BaseModel):
	"""Input schema for PDF extraction."""
	file_path: str = Field(..., description="Path to the PDF file")
	page_range: Optional[str] = Field(None, description="Page range (e.g., '1-5', 'all')")
	extract_metadata: bool = Field(True, description="Extract PDF metadata")


	class PatentParserInput(BaseModel):
	"""Input schema for patent parsing."""
	text: str = Field(..., description="Patent text to parse")
	extract_claims: bool = Field(True, description="Extract patent claims")
	extract_abstract: bool = Field(True, description="Extract abstract")
	extract_description: bool = Field(True, description="Extract description")


	class WebSearchInput(BaseModel):
	"""Input schema for web search."""
	query: str = Field(..., description="Search query")
	max_results: int = Field(5, description="Maximum number of results")
	region: str = Field("wt-wt", description="Search region (e.g., 'us-en', 'wt-wt')")


	class WikipediaInput(BaseModel):
	"""Input schema for Wikipedia lookup."""
	query: str = Field(..., description="Wikipedia search query")
	sentences: int = Field(3, description="Number of sentences to return")


	class ArxivInput(BaseModel):
	"""Input schema for Arxiv search."""
	query: str = Field(..., description="Search query")
	max_results: int = Field(5, description="Maximum number of results")
	sort_by: str = Field("relevance", description="Sort by: relevance, lastUpdatedDate, submittedDate")


	class DocumentGeneratorInput(BaseModel):
	"""Input schema for document generation."""
	output_path: str = Field(..., description="Output PDF file path")
	title: str = Field(..., description="Document title")
	content: str = Field(..., description="Document content (markdown or plain text)")
	author: Optional[str] = Field(None, description="Document author")


	class GPUMonitorInput(BaseModel):
	"""Input schema for GPU monitoring."""
	gpu_id: Optional[int] = Field(None, description="Specific GPU ID or None for all GPUs")


	# ============================================================================
	# PDF Tools
	# ============================================================================

	def pdf_extractor_func(file_path: str, page_range: Optional[str] = None,
	extract_metadata: bool = True) -> str:
	"""
	Extract text and metadata from PDF files.
	Supports both PyPDF2 and PyMuPDF (fitz) backends.

	Args:
	file_path: Path to PDF file
	page_range: Page range like '1-5' or 'all' (default: all)
	extract_metadata: Whether to extract metadata

	Returns:
	Extracted text and metadata as formatted string
	"""
	if not PDF_AVAILABLE:
	return "Error: PDF libraries not installed. Run: pip install PyPDF2 pymupdf"

	try:
	# Open PDF with PyMuPDF (better text extraction)
	doc = fitz.open(file_path)

	# Parse page range
	if page_range and page_range.lower() != 'all':
	start, end = map(int, page_range.split('-'))
	pages = range(start - 1, min(end, len(doc))) # 0-indexed
	else:
	pages = range(len(doc))

	# Extract text
	text_parts = []
	for page_num in pages:
	page = doc[page_num]
	text_parts.append(f"--- Page {page_num + 1} ---\n{page.get_text()}")

	extracted_text = "\n\n".join(text_parts)

	# Extract metadata
	result = f"PDF: {file_path}\n"
	result += f"Total Pages: {len(doc)}\n"
	result += f"Extracted Pages: {len(pages)}\n\n"

	if extract_metadata:
	metadata = doc.metadata
	result += "Metadata:\n"
	for key, value in metadata.items():
	if value:
	result += f" {key}: {value}\n"
	result += "\n"

	result += "=" * 80 + "\n"
	result += "EXTRACTED TEXT:\n"
	result += "=" * 80 + "\n"
	result += extracted_text

	doc.close()

	logger.info(f"Extracted {len(pages)} pages from {file_path}")
	return result

	except Exception as e:
	logger.error(f"PDF extraction failed: {e}")
	return f"Error extracting PDF: {str(e)}"


	def patent_parser_func(text: str, extract_claims: bool = True,
	extract_abstract: bool = True, extract_description: bool = True) -> str:
	"""
	Parse patent document structure and extract key sections.
	Uses heuristics to identify: abstract, claims, description, drawings.

	Args:
	text: Patent text (from PDF or plain text)
	extract_claims: Extract patent claims
	extract_abstract: Extract abstract
	extract_description: Extract detailed description

	Returns:
	Structured patent information as JSON string
	"""
	try:
	result = {
	"abstract": "",
	"claims": [],
	"description": "",
	"metadata": {}
	}

	lines = text.split('\n')
	current_section = None

	# Simple heuristic-based parser
	for i, line in enumerate(lines):
	line_lower = line.lower().strip()

	# Detect sections
	if 'abstract' in line_lower and len(line_lower) < 50:
	current_section = 'abstract'
	continue
	elif 'claim' in line_lower and len(line_lower) < 50:
	current_section = 'claims'
	continue
	elif 'description' in line_lower or 'detailed description' in line_lower:
	if len(line_lower) < 100:
	current_section = 'description'
	continue
	elif 'drawing' in line_lower or 'figure' in line_lower:
	if len(line_lower) < 50:
	current_section = 'drawings'
	continue

	# Extract content based on section
	if current_section == 'abstract' and extract_abstract:
	if line.strip():
	result['abstract'] += line + "\n"
	elif current_section == 'claims' and extract_claims:
	if line.strip() and (line.strip()[0].isdigit() or 'wherein' in line_lower):
	result['claims'].append(line.strip())
	elif current_section == 'description' and extract_description:
	if line.strip():
	result['description'] += line + "\n"

	# Extract patent number if present
	for line in lines[:20]: # Check first 20 lines
	if 'patent' in line.lower() and any(char.isdigit() for char in line):
	result['metadata']['patent_number'] = line.strip()
	break

	# Format output
	output = "PATENT ANALYSIS\n"
	output += "=" * 80 + "\n\n"

	if result['abstract']:
	output += "ABSTRACT:\n"
	output += result['abstract'].strip()[:500] # Limit length
	output += "\n\n"

	if result['claims']:
	output += f"CLAIMS ({len(result['claims'])} found):\n"
	for i, claim in enumerate(result['claims'][:10], 1): # First 10 claims
	output += f"\n{i}. {claim}\n"
	output += "\n"

	if result['description']:
	output += "DESCRIPTION (excerpt):\n"
	output += result['description'].strip()[:1000] # First 1000 chars
	output += "\n\n"

	output += "=" * 80 + "\n"
	output += f"JSON OUTPUT:\n{json.dumps(result, indent=2)}"

	logger.info(f"Parsed patent: {len(result['claims'])} claims extracted")
	return output

	except Exception as e:
	logger.error(f"Patent parsing failed: {e}")
	return f"Error parsing patent: {str(e)}"


	# ============================================================================
	# Web Search & Research Tools
	# ============================================================================

	def web_search_func(query: str, max_results: int = 5, region: str = "wt-wt") -> str:
	"""
	Search the web using DuckDuckGo.
	Returns top results with title, snippet, and URL.

	Args:
	query: Search query
	max_results: Maximum number of results
	region: Search region code

	Returns:
	Formatted search results
	"""
	if not DDGS_AVAILABLE:
	return "Error: DuckDuckGo search not installed. Run: pip install duckduckgo-search"

	try:
	ddgs = DDGS()
	results = list(ddgs.text(query, region=region, max_results=max_results))

	if not results:
	return f"No results found for: {query}"

	output = f"WEB SEARCH RESULTS: {query}\n"
	output += "=" * 80 + "\n\n"

	for i, result in enumerate(results, 1):
	output += f"{i}. {result.get('title', 'No title')}\n"
	output += f" {result.get('body', 'No description')}\n"
	output += f" URL: {result.get('href', 'No URL')}\n\n"

	logger.info(f"Web search completed: {len(results)} results for '{query}'")
	return output

	except Exception as e:
	logger.error(f"Web search failed: {e}")
	return f"Error performing web search: {str(e)}"


	def wikipedia_func(query: str, sentences: int = 3) -> str:
	"""
	Search Wikipedia and return summary.

	Args:
	query: Wikipedia search query
	sentences: Number of sentences to return

	Returns:
	Wikipedia summary
	"""
	if not WIKIPEDIA_AVAILABLE:
	return "Error: Wikipedia not installed. Run: pip install wikipedia"

	try:
	# Search for page
	search_results = wikipedia.search(query)

	if not search_results:
	return f"No Wikipedia page found for: {query}"

	# Get first result
	page = wikipedia.page(search_results[0], auto_suggest=False)

	# Get summary
	summary = wikipedia.summary(search_results[0], sentences=sentences, auto_suggest=False)

	output = f"WIKIPEDIA: {page.title}\n"
	output += "=" * 80 + "\n\n"
	output += summary + "\n\n"
	output += f"URL: {page.url}\n"
	output += f"Categories: {', '.join(page.categories[:5])}\n"

	logger.info(f"Wikipedia lookup completed: {page.title}")
	return output

	except wikipedia.exceptions.DisambiguationError as e:
	options = ', '.join(e.options[:5])
	return f"Disambiguation needed for '{query}'. Options: {options}"
	except wikipedia.exceptions.PageError:
	return f"No Wikipedia page found for: {query}"
	except Exception as e:
	logger.error(f"Wikipedia lookup failed: {e}")
	return f"Error: {str(e)}"


	def arxiv_func(query: str, max_results: int = 5, sort_by: str = "relevance") -> str:
	"""
	Search Arxiv for academic papers.

	Args:
	query: Search query
	max_results: Maximum number of results
	sort_by: Sort by relevance, lastUpdatedDate, or submittedDate

	Returns:
	Formatted Arxiv results
	"""
	if not ARXIV_AVAILABLE:
	return "Error: Arxiv not installed. Run: pip install arxiv"

	try:
	# Map sort_by to arxiv.SortCriterion
	sort_map = {
	"relevance": arxiv.SortCriterion.Relevance,
	"lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate,
	"submittedDate": arxiv.SortCriterion.SubmittedDate,
	}
	sort_criterion = sort_map.get(sort_by, arxiv.SortCriterion.Relevance)

	# Search Arxiv
	search = arxiv.Search(
	query=query,
	max_results=max_results,
	sort_by=sort_criterion
	)

	results = list(search.results())

	if not results:
	return f"No Arxiv papers found for: {query}"

	output = f"ARXIV SEARCH: {query}\n"
	output += "=" * 80 + "\n\n"

	for i, paper in enumerate(results, 1):
	output += f"{i}. {paper.title}\n"
	output += f" Authors: {', '.join(str(author) for author in paper.authors[:3])}\n"
	output += f" Published: {paper.published.strftime('%Y-%m-%d')}\n"
	output += f" Summary: {paper.summary[:200]}...\n"
	output += f" PDF: {paper.pdf_url}\n"
	output += f" Categories: {', '.join(paper.categories)}\n\n"

	logger.info(f"Arxiv search completed: {len(results)} papers for '{query}'")
	return output

	except Exception as e:
	logger.error(f"Arxiv search failed: {e}")
	return f"Error searching Arxiv: {str(e)}"


	# ============================================================================
	# Document Generation
	# ============================================================================

	def document_generator_func(output_path: str, title: str, content: str,
	author: Optional[str] = None) -> str:
	"""
	Generate PDF document from text content.
	Supports basic formatting and styling.

	Args:
	output_path: Output PDF file path
	title: Document title
	content: Document content (plain text or simple markdown)
	author: Optional author name

	Returns:
	Success message with file path
	"""
	if not REPORTLAB_AVAILABLE:
	return "Error: ReportLab not installed. Run: pip install reportlab"

	try:
	# Create PDF
	doc = SimpleDocTemplate(output_path, pagesize=letter)
	styles = getSampleStyleSheet()
	story = []

	# Title
	title_style = styles['Title']
	story.append(Paragraph(title, title_style))
	story.append(Spacer(1, 12))

	# Author
	if author:
	author_style = styles['Normal']
	story.append(Paragraph(f"By: {author}", author_style))
	story.append(Spacer(1, 12))

	# Content (split into paragraphs)
	paragraphs = content.split('\n\n')
	for para in paragraphs:
	if para.strip():
	# Simple markdown-like processing
	if para.strip().startswith('#'):
	# Heading
	heading_text = para.strip().lstrip('#').strip()
	story.append(Paragraph(heading_text, styles['Heading2']))
	else:
	# Regular paragraph
	story.append(Paragraph(para.strip(), styles['Normal']))
	story.append(Spacer(1, 6))

	# Build PDF
	doc.build(story)

	logger.info(f"Generated PDF: {output_path}")
	return f"Successfully generated PDF: {output_path}\nTitle: {title}\nPages: {len(paragraphs)}"

	except Exception as e:
	logger.error(f"PDF generation failed: {e}")
	return f"Error generating PDF: {str(e)}"


	# ============================================================================
	# GPU Monitoring (converted from existing tool)
	# ============================================================================

	def gpu_monitor_func(gpu_id: Optional[int] = None) -> str:
	"""
	Monitor GPU status, memory usage, and utilization.

	Args:
	gpu_id: Specific GPU ID or None for all GPUs

	Returns:
	Formatted GPU status information
	"""
	try:
	gpu_manager = get_gpu_manager()

	if gpu_id is not None:
	# Monitor specific GPU
	info = gpu_manager.get_gpu_info(gpu_id)

	if "error" in info:
	return f"Error: {info['error']}"

	output = f"GPU {info['gpu_id']}: {info['name']}\n"
	output += f"Memory: {info['memory_used'] / 10243:.2f} GB / {info['memory_total'] / 10243:.2f} GB "
	output += f"({info['memory_percent']:.1f}% used)\n"
	output += f"Free Memory: {info['memory_free'] / 1024**3:.2f} GB\n"
	output += f"GPU Utilization: {info['gpu_utilization']}%\n"
	output += f"Temperature: {info['temperature']}°C\n"

	return output
	else:
	# Monitor all GPUs
	return gpu_manager.monitor()

	except Exception as e:
	logger.error(f"GPU monitoring error: {e}")
	return f"Error monitoring GPU: {str(e)}"


	# ============================================================================
	# Create LangChain Tools
	# ============================================================================

	# Use StructuredTool for tools with Pydantic input schemas
	pdf_extractor_tool = StructuredTool.from_function(
	func=pdf_extractor_func,
	name="pdf_extractor",
	description=(
	"Extract text and metadata from PDF files. "
	"Useful for analyzing patent documents, research papers, and legal documents. "
	"Supports page range selection and metadata extraction."
	),
	args_schema=PDFExtractorInput,
	return_direct=False,
	)

	patent_parser_tool = StructuredTool.from_function(
	func=patent_parser_func,
	name="patent_parser",
	description=(
	"Parse patent document structure and extract key sections: abstract, claims, description. "
	"Useful for analyzing patent documents and identifying key innovations."
	),
	args_schema=PatentParserInput,
	return_direct=False,
	)

	web_search_tool = StructuredTool.from_function(
	func=web_search_func,
	name="web_search",
	description=(
	"Search the web using DuckDuckGo. Returns top results with titles, snippets, and URLs. "
	"Useful for market research, competitor analysis, and finding relevant information."
	),
	args_schema=WebSearchInput,
	return_direct=False,
	)

	wikipedia_tool = StructuredTool.from_function(
	func=wikipedia_func,
	name="wikipedia",
	description=(
	"Search Wikipedia and get article summaries. "
	"Useful for background information on technologies, companies, and concepts."
	),
	args_schema=WikipediaInput,
	return_direct=False,
	)

	arxiv_tool = StructuredTool.from_function(
	func=arxiv_func,
	name="arxiv_search",
	description=(
	"Search Arxiv for academic papers and preprints. "
	"Useful for finding relevant research, state-of-the-art methods, and technical background."
	),
	args_schema=ArxivInput,
	return_direct=False,
	)

	document_generator_tool = StructuredTool.from_function(
	func=document_generator_func,
	name="document_generator",
	description=(
	"Generate PDF documents from text content. "
	"Useful for creating reports, briefs, and documentation."
	),
	args_schema=DocumentGeneratorInput,
	return_direct=False,
	)

	gpu_monitor_tool = StructuredTool.from_function(
	func=gpu_monitor_func,
	name="gpu_monitor",
	description=(
	"Monitor GPU status including memory usage, utilization, and temperature. "
	"Useful for checking GPU availability before running models."
	),
	args_schema=GPUMonitorInput,
	return_direct=False,
	)


	# ============================================================================
	# Tool Registry for VISTA Scenarios
	# ============================================================================

	class VISTAToolRegistry:
	"""
	Registry of tools organized by VISTA scenario.
	Enables scenario-specific tool selection for optimal performance.
	"""

	SCENARIO_TOOLS = {
	"patent_wakeup": [
	pdf_extractor_tool,
	patent_parser_tool,
	web_search_tool,
	wikipedia_tool,
	arxiv_tool,
	document_generator_tool,
	],
	"agreement_safety": [
	pdf_extractor_tool,
	web_search_tool,
	document_generator_tool,
	],
	"partner_matching": [
	web_search_tool,
	wikipedia_tool,
	arxiv_tool,
	],
	"general": [
	pdf_extractor_tool,
	patent_parser_tool,
	web_search_tool,
	wikipedia_tool,
	arxiv_tool,
	document_generator_tool,
	gpu_monitor_tool,
	],
	}

	@classmethod
	def get_tools(cls, scenario: str = "general") -> List[StructuredTool]:
	"""
	Get tools for a specific VISTA scenario.

	Args:
	scenario: VISTA scenario type

	Returns:
	List of LangChain tools
	"""
	tools = cls.SCENARIO_TOOLS.get(scenario, cls.SCENARIO_TOOLS["general"])
	logger.info(f"Retrieved {len(tools)} tools for scenario: {scenario}")
	return tools

	@classmethod
	def get_all_tools(cls) -> List[StructuredTool]:
	"""Get all available tools."""
	return cls.SCENARIO_TOOLS["general"]

	@classmethod
	def list_scenarios(cls) -> List[str]:
	"""List available scenarios."""
	return list(cls.SCENARIO_TOOLS.keys())


	# ============================================================================
	# Convenience Functions
	# ============================================================================

	def get_vista_tools(scenario: str = "general") -> List[StructuredTool]:
	"""
	Get LangChain tools for a VISTA scenario.

	Args:
	scenario: Scenario name (patent_wakeup, agreement_safety, partner_matching, general)

	Returns:
	List of LangChain StructuredTool instances
	"""
	return VISTAToolRegistry.get_tools(scenario)


	def get_all_tools() -> List[StructuredTool]:
	"""Get all available LangChain tools."""
	return VISTAToolRegistry.get_all_tools()


	# Export all tools
	__all__ = [
	"pdf_extractor_tool",
	"patent_parser_tool",
	"web_search_tool",
	"wikipedia_tool",
	"arxiv_tool",
	"document_generator_tool",
	"gpu_monitor_tool",
	"VISTAToolRegistry",
	"get_vista_tools",
	"get_all_tools",
	]