PDF Vector
A fully featured PDF APIs for developers - Parse any PDF or Word document, extract structured data, and access millions of academic papers - all through simple APIs.
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Academic research search across five databases with PDF vector & multiple exports
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description: Unified Academic Search Across Major Research Databases This powerful workflow enables researchers to search multiple academic databases simultaneously, automatically deduplicate results, and export formatted bibliographies. By leveraging PDF Vector's multi-database search capabilities, researchers can save hours of manual searching and ensure comprehensive literature coverage across PubMed, ArXiv, Google Scholar, Semantic Scholar, and ERIC databases. Target Audience & Problem Solved This template is designed for: Graduate students conducting systematic literature reviews Researchers ensuring comprehensive coverage of their field Librarians helping patrons with complex searches Academic teams building shared bibliographies It solves the critical problem of fragmented academic search by providing a single interface to query all major databases, eliminating duplicate results, and standardizing output formats. Prerequisites n8n instance with PDF Vector node installed PDF Vector API credentials with search permissions Basic understanding of academic search syntax Optional: PostgreSQL for search history logging Minimum 50 API credits for comprehensive searches Step-by-Step Setup Instructions Configure PDF Vector Credentials Go to n8n Credentials section Create new PDF Vector credentials Enter your API key from pdfvector.io Test the connection to verify setup Import the Workflow Template Copy the template JSON code In n8n, click "Import Workflow" Paste the JSON and save Review all nodes for any configuration needs Customize Search Parameters Open the "Set Search Parameters" node Modify the default search query for your field Adjust the year range (default: 2020-present) Set results per source limit (default: 25) Configure Export Options Choose your preferred export formats (BibTeX, CSV, JSON) Set the output directory for files Configure file naming conventions Enable/disable specific export types Test Your Configuration Run the workflow with a sample query Check that all databases return results Verify deduplication is working correctly Confirm export files are created properly Implementation Details The workflow implements a sophisticated search pipeline: Parallel Database Queries: Searches all configured databases simultaneously for efficiency Smart Deduplication: Uses DOI matching and fuzzy title comparison to remove duplicates Relevance Scoring: Combines citation count, title relevance, and recency for ranking Format Generation: Creates properly formatted citations in multiple styles Batch Processing: Handles large result sets without memory issues Customization Guide Adding Custom Databases: javascript // In the PDF Vector search node, add to providers array: "providers": ["pubmed", "semanticscholar", "arxiv", "googlescholar", "eric", "yourcustomdb"] Modifying Relevance Algorithm: Edit the "Rank by Relevance" node to adjust scoring weights: javascript // Adjust these weights for your needs: const titleWeight = 10; // Title match importance const citationWeight = 5; // Citation count importance const recencyWeight = 10; // Recent publication bonus const fulltextWeight = 15; // Full-text availability bonus Custom Export Formats: Add new format generators in the workflow: javascript // Example: Add APA format export const apaFormat = papers.map(p => { const authors = p.authors.slice(0, 3).join(', '); return ${authors} (${p.year}). ${p.title}. ${p.journal || 'Preprint'}.; }); Advanced Filtering: Implement additional filters: Journal impact factor thresholds Open access only options Language restrictions Methodology filters for systematic reviews Search Features: Query multiple databases in parallel Advanced filtering and deduplication Citation format export (BibTeX, RIS, etc.) Relevance ranking across sources Full-text availability checking Workflow Process: Input: Search query and parameters Parallel Search: Query all databases Merge & Deduplicate: Combine results Rank: Sort by relevance/citations Enrich: Add full-text links Export: Multiple format options
By PDF VectorAutomate academic literature reviews with GPT-4 and multi-database search
Overview Conducting comprehensive literature reviews is one of the most time-consuming aspects of academic research. This workflow revolutionizes the process by automating literature search, paper analysis, and review generation across multiple academic databases. It handles both digital papers and scanned documents (PDFs, JPGs, PNGs), using OCR technology for older publications or image-based content. What You Can Do Automate searches across multiple academic databases simultaneously Analyze and rank papers by relevance, citations, and impact Generate comprehensive literature reviews with proper citations Process both digital and scanned documents with OCR Identify research gaps and emerging trends systematically Who It's For Researchers, graduate students, academic institutions, literature review teams, and academic writers who need to conduct comprehensive literature reviews efficiently while maintaining high quality and thoroughness. The Problem It Solves Manual literature reviews are extremely time-consuming and often miss relevant papers across different databases. Researchers struggle to synthesize large volumes of academic papers, track citations properly, and identify research gaps systematically. This template automates the entire process from search to synthesis, ensuring comprehensive coverage and proper citation management. Setup Instructions: Configure PDF Vector API credentials with academic search access Set up search parameters including databases and date ranges Define inclusion and exclusion criteria for paper selection Choose citation style (APA, MLA, Chicago, etc.) Configure output format preferences Set up reference management software integration if needed Define research topic and keywords for search Key Features: Simultaneous search across PubMed, arXiv, Semantic Scholar, and other databases Intelligent paper ranking based on citation count, recency, and relevance OCR support for scanned documents and older publications Automatic extraction of methodologies, findings, and limitations Citation network analysis to identify seminal works Automatic theme organization and research gap identification Multiple citation format support (APA, MLA, Chicago) Quality scoring based on journal impact factors Customization Options: Configure search parameters for specific research domains Set up automated searches for ongoing literature monitoring Integrate with reference management software (Zotero, Mendeley) Customize output format and structure Add collaborative review features for research teams Set up quality filters based on journal rankings Configure notification systems for new relevant papers Implementation Details: The workflow uses advanced algorithms to search multiple academic databases simultaneously, ranking papers by relevance and impact. It processes full-text PDFs when available and uses OCR for scanned documents. The system automatically extracts key information, organizes findings by themes, and generates structured literature reviews with proper citations and reference management. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorExtract data from documents with GPT-4, PDFVector & PostgreSQL export
Intelligent Document Processing & Data Extraction Extract structured data from unstructured documents like invoices, contracts, reports, and forms. Uses AI to identify and extract key information automatically. Pipeline Features: Process multiple document types (PDFs, Word docs) AI-powered field extraction Custom extraction templates Data validation and cleaning Export to databases or spreadsheets Workflow Steps: Document Input: Various sources supported Parse Document: Convert to structured text Extract Fields: AI identifies key data points Validate Data: Check extracted values Transform: Format for destination system Store/Export: Save to database or file Use Cases: Invoice processing automation Contract data extraction Form digitization Report mining
By PDF VectorBulk PDF to markdown conversion with Google Drive & LLM-powered parsing
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. High-Volume PDF to Markdown Conversion Convert multiple PDF documents to clean, structured Markdown format in bulk. Perfect for documentation teams, content managers, and anyone needing to process large volumes of PDFs. Key Features: Process PDFs from multiple sources (URLs, Google Drive, Dropbox) Intelligent LLM-based parsing for complex layouts Preserve formatting, tables, and structure Export to various destinations Workflow Components: Input Sources: Multiple file sources supported Batch Processing: Handle hundreds of PDFs efficiently Smart Parsing: Auto-detect when LLM parsing is needed Quality Check: Validate conversion results Export Options: Save to cloud storage or database Ideal For: Converting technical documentation Migrating legacy PDF content Building searchable knowledge bases
By PDF VectorBuild comprehensive literature reviews with GPT-4 and multi-database search
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Comprehensive Literature Review Automation Automate your literature review process by searching across multiple academic databases, parsing papers, and organizing findings into a structured review document. Features: Search multiple academic databases simultaneously (PubMed, ArXiv, Google Scholar, etc.) Parse and analyze top papers automatically Generate citation-ready summaries Export to various formats (Markdown, Word, PDF) Workflow Steps: Input: Research topic and parameters PDF Vector Search: Query multiple academic databases Filter & Rank: Select top relevant papers Parse Papers: Extract content from PDFs Synthesize: Create literature review sections Export: Generate final document Use Cases: PhD students conducting systematic reviews Researchers exploring new fields Grant writers needing background sections
By PDF VectorProcess documents with OCR, analytics & Google Drive using PDF Vector
Overview Organizations dealing with high-volume document processing face challenges in efficiently handling diverse document types while maintaining quality and tracking performance metrics. This enterprise-grade workflow provides a scalable solution for batch processing documents including PDFs, scanned documents, and images (JPG, PNG) with comprehensive analytics, error handling, and quality assurance. What You Can Do Process thousands of documents in parallel batches efficiently Monitor performance metrics and success rates in real-time Handle diverse document formats with automatic format detection Generate comprehensive analytics dashboards and reports Implement automated quality assurance and error handling Who It's For Large organizations, document processing centers, digital transformation teams, enterprise IT departments, and businesses that need to process thousands of documents reliably with detailed performance tracking and analytics. The Problem It Solves High-volume document processing without proper monitoring leads to bottlenecks, quality issues, and inefficient resource usage. Organizations struggle to track processing success rates, identify problematic document types, and optimize their workflows. This template provides enterprise-grade batch processing with comprehensive analytics and automated quality assurance. Setup Instructions: Configure Google Drive credentials for document folder access Install the PDF Vector community node from the n8n marketplace Configure PDF Vector API credentials with appropriate rate limits Set up batch processing parameters (batch size, retry logic) Configure quality thresholds and validation rules Set up analytics dashboard and reporting preferences Configure error handling and notification systems Key Features: Parallel batch processing for maximum throughput Support for mixed document formats (PDFs, Word docs, images) OCR processing for handwritten and scanned documents Comprehensive analytics dashboard with success rates and performance metrics Automatic document prioritization based on size and complexity Intelligent error handling with automatic retry logic Quality assurance checks and validation Real-time processing monitoring and alerts Customization Options: Configure custom document categories and processing rules Set up specific extraction templates for different document types Implement automated workflows for documents that fail quality checks Configure credit usage optimization to minimize costs Set up custom analytics and reporting dashboards Add integration with existing document management systems Configure automated notifications for processing completion or errors Implementation Details: The workflow uses intelligent batching to process documents efficiently while monitoring performance metrics in real-time. It automatically handles different document formats, applies OCR when needed, and provides detailed analytics to help organizations optimize their document processing operations. The system includes sophisticated error recovery and quality assurance mechanisms. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorExtract & store invoice data with PDF vector, Google Drive & database
Overview Transform your accounts payable department with this enterprise-grade invoice processing solution. This workflow automates the entire invoice lifecycle - from document ingestion through payment processing. It handles invoices from multiple sources (Google Drive, email attachments, API submissions), extracts data using AI, validates against purchase orders, routes for appropriate approvals based on amount thresholds, and integrates seamlessly with your ERP system. The solution includes vendor master data management, duplicate invoice detection, real-time spend analytics, and complete audit trails for compliance. What You Can Do This comprehensive workflow creates an intelligent invoice processing pipeline that monitors multiple input channels (Google Drive, email, webhooks) for new invoices and automatically extracts data from PDFs, images, and scanned documents using AI. It validates vendor information against your master database, matches invoices to purchase orders, and detects discrepancies. The workflow implements multi-level approval routing based on invoice amount and department, prevents duplicate payments through intelligent matching algorithms, and integrates with QuickBooks, SAP, or other ERP systems. Additionally, it generates real-time dashboards showing processing metrics and cash flow insights while sending automated reminders for pending approvals. Who It's For Perfect for medium to large businesses, accounting departments, and financial service providers processing more than 100 invoices monthly across multiple vendors. Ideal for organizations that need to enforce approval hierarchies and spending limits, require integration with existing ERP/accounting systems, want to reduce processing time from days to minutes, need audit trails and compliance reporting, and seek to eliminate manual data entry errors and duplicate payments. The Problem It Solves Manual invoice processing creates significant operational challenges including data entry errors (3-5% error rate), processing delays (8-10 days per invoice), duplicate payments (0.1-0.5% of invoices), approval bottlenecks causing late fees, lack of visibility into pending invoices and cash commitments, and compliance issues from missing audit trails. This workflow reduces processing time by 80%, eliminates data entry errors, prevents duplicate payments, and provides complete visibility into your payables process. Setup Instructions Google Drive Setup: Create dedicated folders for invoice intake and configure access permissions PDF Vector Configuration: Set up API credentials with appropriate rate limits for your volume Database Setup: Deploy the provided schema for vendor master and invoice tracking tables Email Integration: Configure IMAP credentials for invoice email monitoring (optional) ERP Connection: Set up API access to your accounting system (QuickBooks, SAP, etc.) Approval Rules: Define approval thresholds and routing rules in the configuration node Notification Setup: Configure Slack/email for approval notifications and alerts Key Features Multi-Channel Invoice Ingestion: Automatically collect invoices from Google Drive, email attachments, and API uploads Advanced OCR and AI Extraction: Process any invoice format including handwritten notes and poor quality scans Vendor Master Integration: Validate and enrich vendor data, maintaining a clean vendor database 3-Way Matching: Automatically match invoices to purchase orders and goods receipts Dynamic Approval Routing: Route based on amount, department, vendor, or custom rules Duplicate Detection: Prevent duplicate payments using fuzzy matching algorithms Real-Time Analytics: Track KPIs like processing time, approval delays, and early payment discounts Exception Handling: Intelligent routing of problematic invoices for manual review Audit Trail: Complete tracking of all actions, approvals, and system modifications Payment Scheduling: Optimize payment timing to capture discounts and manage cash flow Customization Options This workflow can be customized to add industry-specific extraction fields, implement GL coding rules based on vendor or amount, create department-specific approval workflows, add currency conversion for international invoices, integrate with additional systems (banks, expense management), configure custom dashboards and reporting, set up vendor portals for invoice status inquiries, and implement machine learning for automatic GL coding suggestions. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorExtract clinical data from medical documents with PDF vector & HIPAA compliance
Overview Healthcare organizations face significant challenges in digitizing and processing medical records while maintaining strict HIPAA compliance. This workflow provides a secure, automated solution for extracting clinical data from various medical documents including discharge summaries, lab reports, clinical notes, prescription records, and scanned medical images (JPG, PNG). What You Can Do Extract clinical data from medical documents while maintaining HIPAA compliance Process handwritten notes and scanned medical images with OCR Automatically identify and protect PHI (Protected Health Information) Generate structured data from various medical document formats Maintain audit trails for regulatory compliance Who It's For Healthcare providers, medical billing companies, clinical research organizations, health information exchanges, and medical practice administrators who need to digitize and extract data from medical records while maintaining HIPAA compliance. The Problem It Solves Manual medical record processing is time-consuming, error-prone, and creates compliance risks. Healthcare organizations struggle to extract structured data from handwritten notes, scanned documents, and various medical forms while protecting PHI. This template automates the extraction process while maintaining the highest security standards for Protected Health Information. Setup Instructions: Configure Google Drive credentials with proper medical record access controls Install the PDF Vector community node from the n8n marketplace Configure PDF Vector API credentials with HIPAA-compliant settings Set up secure database storage with encryption at rest Define PHI handling rules and extraction parameters Configure audit logging for regulatory compliance Set up integration with your Electronic Health Record (EHR) system Key Features: Secure retrieval of medical documents from Google Drive HIPAA-compliant processing with automatic PHI masking OCR support for handwritten notes and scanned medical images Automatic extraction of diagnoses with ICD-10 code validation Medication list processing with dosage and frequency information Lab results extraction with reference ranges and flagging Vital signs capture and normalization Complete audit trail for regulatory compliance Integration-ready format for EHR systems Customization Options: Define institution-specific medical terminology and abbreviations Configure automated alerts for critical lab values or abnormal results Set up custom extraction fields for specialized medical forms Implement medication interaction warnings and contraindication checks Add support for multiple languages and international medical coding systems Configure integration with specific EHR platforms (Epic, Cerner, etc.) Set up automated quality assurance checks and validation rules Implementation Details: The workflow uses advanced AI with medical domain knowledge to understand clinical terminology and extract relevant information while automatically identifying and protecting PHI. It processes various document formats including handwritten prescriptions, lab reports, discharge summaries, and clinical notes. The system maintains strict security protocols with encryption at rest and in transit, ensuring full HIPAA compliance throughout the processing pipeline. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorAutomated academic paper monitoring with PDF vector, GPT-3.5, & Slack alerts
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Automated Academic Paper Monitoring Stay updated with the latest research in your field. This bot monitors multiple academic databases for new papers matching your interests and sends personalized alerts. Bot Features: Monitor keywords across multiple databases Filter by authors, journals, or institutions Daily/weekly digest emails Slack notifications for high-impact papers Automatic paper summarization Workflow Components: Schedule: Run daily/weekly checks Search: Query latest papers across databases Filter: Apply custom criteria Summarize: Generate paper summaries Notify: Send alerts via email/Slack Archive: Store papers for future reference Perfect For: Research groups tracking their field PhD students monitoring specific topics Labs following competitor publications
By PDF VectorEnterprise contract lifecycle management with AI risk analysis
Overview Transform your contract management process with this enterprise-grade workflow that handles the complete contract lifecycle - from initial intake through execution, monitoring, and renewal. This comprehensive solution combines AI-powered contract analysis with automated risk scoring, clause comparison, obligation tracking, and proactive alerts. It integrates with multiple data sources including email, SharePoint, contract CLM systems, and creates a centralized contract intelligence hub that prevents revenue leakage, ensures compliance, and accelerates deal velocity. What You Can Do This advanced workflow orchestrates a complete contract management ecosystem that monitors multiple channels (email, Google Drive, SharePoint, APIs) for new contracts and amendments. It extracts and analyzes over 50 contract data points using AI, performs multi-dimensional risk assessment across legal, financial, and operational factors, compares clauses against your approved template library, tracks all obligations and key dates with automated reminders, integrates with Salesforce/CRM for deal alignment, routes contracts through dynamic approval workflows based on risk scores, generates executive dashboards with contract analytics, and maintains a searchable repository with version control. The system handles complex scenarios including multi-party agreements, framework contracts with statements of work, international contracts requiring jurisdiction analysis, and M&A due diligence requiring bulk contract review. Who It's For Designed for enterprise legal operations teams managing thousands of contracts annually, procurement departments negotiating complex vendor agreements, contract managers overseeing multi-million dollar portfolios, compliance teams ensuring regulatory adherence across jurisdictions, sales operations needing faster contract turnaround, and C-suite executives requiring contract intelligence for strategic decisions. Essential for organizations in regulated industries (healthcare, finance, government) and companies undergoing digital transformation of their legal operations. The Problem It Solves Manual contract management creates massive operational risks and inefficiencies. Organizations typically have contracts scattered across emails, shared drives, and filing cabinets with no central visibility. This leads to missed renewal deadlines costing 5-10% of contract value, unauthorized contract variations creating compliance risks, obligation failures resulting in penalties and damaged relationships, and inability to leverage favorable terms across similar contracts. Studies show that inefficient contract management costs organizations up to 9% of annual revenue. This workflow creates a single source of truth for all contracts, automates tracking and compliance, and provides predictive insights to prevent issues before they occur. Setup Instructions Multi-Channel Integration: Configure connectors for email (Office 365/Gmail), Google Drive, SharePoint, and contract management systems PDF Vector Setup: Install PDF Vector node and configure API with enterprise rate limits Database Configuration: Set up PostgreSQL/MySQL for contract repository with proper indexing Template Library: Upload your standard contract templates and approved clause library Risk Framework: Configure risk scoring matrix for your industry (legal, financial, operational risks) Approval Matrix: Define approval routing based on contract value, type, and risk score CRM Integration: Connect to Salesforce/HubSpot for opportunity and account alignment Notification Setup: Configure Slack/Teams channels and email distribution lists Dashboard Creation: Set up Tableau/PowerBI connectors for executive reporting Security Configuration: Enable encryption, audit logging, and role-based access controls Key Features Intelligent Intake System: Monitor email attachments, shared folders, CRM uploads, and API submissions Advanced AI Extraction: Extract 50+ data points including nested obligations and conditional terms Multi-Dimensional Risk Scoring: Analyze legal, financial, operational, and reputational risks Clause Library Comparison: Compare against approved templates and flag deviations Obligation Management: Track deliverables, milestones, and SLAs with automated alerts Dynamic Approval Routing: Route based on AI risk score, contract value, and deviation analysis Version Control & Redlining: Track all changes and maintain complete audit trail Salesforce Integration: Sync contract data with opportunities and accounts Predictive Analytics: Forecast renewal likelihood and negotiation outcomes Bulk Processing: Handle M&A due diligence with parallel processing of hundreds of contracts Multi-Language Support: Process contracts in 15+ languages with automatic translation Executive Dashboards: Real-time visibility into contract portfolio and risk exposure Customization Options Implement industry-specific modules for healthcare (BAAs, DPAs), financial services (ISDAs, loan agreements), technology (SaaS, licensing), or government contracting. Add AI models trained on your historical contracts for better extraction accuracy. Create custom risk factors for emerging regulations like AI governance or ESG compliance. Build integration with specific CLM systems (Ironclad, Docusign CLM, Icertis). Implement advanced analytics including contract similarity scoring, win-rate analysis by clause variations, and automatic playbook generation. Add blockchain integration for smart contract execution and configure automated contract assembly for standard agreements. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorParse and score resumes with PDF Vector AI
Overview HR departments and recruiters spend countless hours manually reviewing resumes, often missing qualified candidates due to time constraints. This workflow automates the entire resume screening process by extracting structured data from resumes in any format (PDF, Word documents, or even photographed/scanned resume images), calculating experience scores, and creating comprehensive candidate profiles ready for your ATS system. What You Can Do This workflow automatically retrieves resumes from Google Drive and uses AI to extract all relevant candidate information including personal details, work experience with dates, education, skills, and certifications. It intelligently handles various resume formats including PDFs, Word documents, and even scanned or photographed resumes using OCR. The workflow calculates total years of experience, tracks skill-specific experience, generates proficiency scores for each skill, and provides an AI-powered assessment of candidate strengths and suitability for different roles. Who It's For Perfect for HR departments processing high volumes of applications, recruitment agencies managing multiple clients, talent acquisition teams seeking to improve candidate quality, and hiring managers who want data-driven insights for decision making. Ideal for organizations that need to maintain consistent evaluation standards across different reviewers and want to reduce time-to-hire while improving candidate match quality. The Problem It Solves Manual resume screening is inefficient and inconsistent. Different reviewers may evaluate the same resume differently, leading to missed opportunities and bias. This workflow standardizes the extraction process, automatically calculates years of experience for each skill, and provides objective scoring metrics to help identify the best candidates faster while reducing human bias in the initial screening process. Setup Instructions Configure Google Drive credentials in n8n Install the PDF Vector community node from the n8n marketplace Configure your PDF Vector API credentials Set up your preferred data storage (database or spreadsheet) Customize the skill categories for your industry Configure the scoring algorithm based on your requirements Connect to your existing ATS system if needed Key Features Automatic Resume Retrieval: Pull resumes from Google Drive folders automatically Universal Format Support: Process PDFs, Word documents, and photographed resumes OCR Capabilities: Extract text from scanned or photographed documents Experience Calculation: Automatically compute total and skill-specific experience Proficiency Scoring: Generate objective skill proficiency ratings AI Assessment: Get intelligent insights on candidate fit and strengths Multi-Language Support: Handle resumes in various languages ATS Integration: Output structured data compatible with major ATS systems Customization Options Define custom skill categories relevant to your industry, adjust scoring weights for different experience types, add specific extraction fields for your organization, implement keyword matching for job requirements, set up automated candidate ranking systems, create role-specific evaluation criteria, and integrate with LinkedIn or other professional networks for enhanced candidate insights. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
By PDF VectorBuild academic citation networks with PDF Vector API for Gephi visualization
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Build Citation Networks from Research Papers Automatically build and visualize citation networks by fetching papers and their references. Discover influential works and research trends in any field. Workflow Features: Start with seed papers (DOIs, PubMed IDs, etc.) Fetch cited and citing papers recursively Build network graph data Export to visualization tools (Gephi, Cytoscape) Identify key papers and research clusters Process Flow: Input: Seed paper identifiers Fetch Papers: Get paper details and references Expand Network: Fetch cited papers (configurable depth) Build Graph: Create nodes and edges Analyze: Calculate metrics (centrality, clusters) Export: Generate visualization-ready data Applications: Research trend analysis Finding seminal papers in a field Grant proposal background research
By PDF Vector