{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-vectifyai--pageindex","slug":"vectifyai--pageindex","name":"PageIndex","type":"agent","url":"https://pageindex.ai","page_url":"https://unfragile.ai/vectifyai--pageindex","categories":["rag-knowledge","documentation"],"tags":["agentic-ai","agents","ai","ai-agents","context-engineering","information-retrieval","llm","rag","reasoning","retrieval","retrieval-augmented-generation","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-vectifyai--pageindex__cap_0","uri":"capability://data.processing.analysis.hierarchical.tree.based.document.indexing.with.llm.generated.summaries","name":"hierarchical tree-based document indexing with llm-generated summaries","description":"Processes PDF and Markdown documents into recursive JSON tree structures where each node represents a document section with extracted title, page range, and LLM-generated summary. The indexing pipeline uses table-of-contents extraction and semantic section detection to build a hierarchical representation without requiring vector embeddings or manual chunking, enabling natural document structure preservation.","intents":["I need to index a large PDF document while preserving its logical structure for later reasoning-based retrieval","I want to generate summaries of document sections automatically without manually defining chunk boundaries","I need to create a searchable index that maintains page references and section hierarchy for explainability"],"best_for":["teams building RAG systems on professional/technical documents requiring domain expertise","developers needing explainable document retrieval with section-level granularity","organizations processing long documents (100+ pages) where flat chunking degrades performance"],"limitations":["Requires LLM API calls during indexing phase, adding latency proportional to document length","Table-of-contents extraction may fail on documents with non-standard structure or missing TOC","LLM-generated summaries inherit hallucination risks from the underlying model","No built-in support for documents with complex layouts (multi-column, embedded images with text)"],"requires":["Python 3.9+","API key for OpenAI, Anthropic, or compatible LLM provider","PDF processing library (PyPDF2 or similar for PDF input)","Markdown parser for Markdown document support"],"input_types":["PDF files","Markdown files","Plain text documents"],"output_types":["JSON tree structure with node_id, title, start_index, end_index, summary, and optional full_text fields"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_1","uri":"capability://planning.reasoning.llm.driven.tree.navigation.and.semantic.section.selection","name":"llm-driven tree navigation and semantic section selection","description":"Implements a retrieval phase where LLMs navigate the hierarchical tree index using a search prompt to reason about which sections are relevant, selecting nodes by node_id and fetching full text for answer generation. The system uses the tree structure as a reasoning scaffold, allowing the LLM to traverse from high-level summaries to specific sections without vector similarity approximation.","intents":["I want to retrieve relevant document sections by having an LLM reason over the document structure rather than using vector similarity","I need to find specific sections in a long document where the answer requires understanding context across multiple hierarchical levels","I want retrieval results that include page references and section titles for transparency and verification"],"best_for":["developers building agentic RAG systems where reasoning transparency is critical","teams working with professional documents (financial reports, legal contracts, technical specs) where relevance requires domain reasoning","applications requiring explainable retrieval with verifiable source citations"],"limitations":["LLM reasoning adds latency compared to vector similarity search (typically 500ms-2s per query depending on tree depth)","Performance degrades if tree depth exceeds 10-15 levels due to context window constraints","Requires careful prompt engineering to guide LLM navigation effectively","May miss relevant sections if LLM reasoning diverges from document structure logic"],"requires":["Indexed document tree from hierarchical indexing capability","LLM API access with sufficient context window (8k+ tokens recommended)","Search query or user intent as natural language input"],"input_types":["JSON tree structure (output from indexing phase)","Natural language search query","Optional metadata filters"],"output_types":["Selected node_ids with full text content","Page ranges and section titles for each retrieved section","Reasoning trace showing LLM navigation path (optional)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_10","uri":"capability://automation.workflow.configuration.system.with.model.selection.temperature.tuning.and.indexing.parameters","name":"configuration system with model selection, temperature tuning, and indexing parameters","description":"Provides a flexible configuration system that allows users to specify LLM model selection (OpenAI, Anthropic, Ollama), temperature and sampling parameters, indexing strategies, and retrieval behavior. Configuration can be set via environment variables, config files, or programmatic API, enabling customization without code changes.","intents":["I need to switch between different LLM providers without code changes","I want to tune LLM behavior (temperature, top-p) for indexing and retrieval","I need to configure indexing parameters like summary length or tree depth limits"],"best_for":["teams experimenting with different LLM models and configurations","developers building configurable RAG systems for different use cases","organizations needing to switch between cloud and local LLM providers"],"limitations":["Configuration complexity increases with number of tunable parameters","No built-in validation or conflict detection for incompatible configurations","Some parameters may have non-obvious interactions (e.g., temperature vs top-p)","Configuration changes require re-indexing for some parameters"],"requires":["Configuration file or environment variables","API keys for selected LLM providers"],"input_types":["Configuration file (YAML, JSON, or environment variables)","Programmatic configuration objects"],"output_types":["Validated configuration object","Configuration applied to indexing and retrieval operations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_11","uri":"capability://automation.workflow.command.line.interface.with.document.indexing.and.query.execution","name":"command-line interface with document indexing and query execution","description":"Provides a comprehensive CLI tool (run_pageindex.py) that exposes indexing and retrieval operations without requiring Python programming. The CLI supports document upload, index generation, query execution, and result formatting, enabling non-technical users and shell scripts to interact with PageIndex functionality.","intents":["I want to index documents and run queries from the command line without writing Python code","I need to integrate PageIndex into shell scripts and automation workflows","I want to quickly test PageIndex functionality without building an application"],"best_for":["non-technical users exploring PageIndex functionality","DevOps engineers integrating PageIndex into automation workflows","developers prototyping RAG systems before building full applications"],"limitations":["CLI interface may be less flexible than programmatic API for complex workflows","Limited support for streaming or real-time result processing","Output formatting options may not cover all use cases","Requires shell environment and Python installation"],"requires":["Python 3.9+ with PageIndex installed","Shell environment (bash, zsh, etc.)","LLM API key configured"],"input_types":["Command-line arguments and flags","Document files","Query strings"],"output_types":["Console output with formatted results","JSON output for programmatic consumption","Index files in JSON format"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_12","uri":"capability://planning.reasoning.reasoning.based.relevance.scoring.with.explainable.section.selection","name":"reasoning-based relevance scoring with explainable section selection","description":"Implements a relevance scoring mechanism where the LLM reasons about section relevance based on content understanding rather than statistical similarity. The system generates explicit reasoning traces showing why sections were selected, enabling users to understand and verify retrieval decisions. Scores reflect semantic relevance determined through LLM reasoning rather than embedding distance.","intents":["I need to understand why specific sections were retrieved for a query","I want retrieval results with explicit reasoning about relevance","I need to verify that retrieved sections are actually relevant, not just statistically similar"],"best_for":["applications requiring explainable AI and audit trails","teams building systems where retrieval transparency is critical","domains (legal, financial, medical) where reasoning justification is required"],"limitations":["Reasoning generation adds latency to retrieval (typically 500ms-2s per query)","LLM reasoning quality varies and may include spurious justifications","Reasoning traces can be verbose and difficult to parse programmatically","No standardized format for reasoning output across different LLM providers"],"requires":["LLM with reasoning capability (GPT-4, Claude 3, etc.)","Indexed document tree","Sufficient context window for reasoning trace generation"],"input_types":["Search query","Document tree structure"],"output_types":["Selected sections with relevance scores","Reasoning trace explaining selection decisions","Confidence indicators for each selection"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_2","uri":"capability://search.retrieval.multi.strategy.document.search.with.tree.metadata.semantic.and.description.based.retrieval","name":"multi-strategy document search with tree, metadata, semantic, and description-based retrieval","description":"Provides four distinct retrieval strategies operating on the same hierarchical index: tree-based search (LLM navigates hierarchy), metadata search (filters by page range or section title), semantic search (uses descriptions to find relevant sections), and description-based search (matches against LLM-generated summaries). Each strategy can be composed or used independently depending on query type and document characteristics.","intents":["I need to search documents using different strategies depending on whether I have a specific section name, page range, or semantic query","I want to combine multiple search approaches to improve recall and handle different query types","I need to search across multiple documents simultaneously using consistent retrieval logic"],"best_for":["teams building flexible search interfaces that adapt to different query types","applications processing heterogeneous document collections with varying structure","developers implementing multi-document search where different documents benefit from different retrieval strategies"],"limitations":["Metadata search requires well-formed titles and page ranges in the index","Semantic search depends on quality of LLM-generated descriptions, which may be incomplete","Description-based search may miss relevant sections if summaries are too brief or abstract","No automatic strategy selection — requires explicit configuration or user guidance"],"requires":["Indexed document tree with summaries and metadata","For semantic/description search: LLM API access","Query specification indicating which strategy to use"],"input_types":["JSON tree structure","Search query (natural language, metadata filters, or section descriptions)","Strategy selection parameter"],"output_types":["Ranked list of relevant nodes with full text","Metadata (page ranges, section titles, summaries)","Strategy-specific confidence scores or reasoning"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_3","uri":"capability://image.visual.vision.based.document.processing.with.image.to.text.extraction","name":"vision-based document processing with image-to-text extraction","description":"Extends the indexing pipeline to process documents containing images, diagrams, and visual elements by using vision LLMs to extract text and semantic content from images. The extracted visual content is integrated into the tree structure alongside text-based sections, enabling comprehensive indexing of documents with mixed media content.","intents":["I need to index documents with embedded images, diagrams, and charts without losing information from visual content","I want to make visual elements searchable and retrievable through the same tree-based interface as text","I need to process technical documents with schematics, flowcharts, or visual specifications"],"best_for":["teams processing technical documentation with diagrams and schematics","applications handling financial reports with charts and tables","developers building RAG systems for scientific or engineering documents with visual content"],"limitations":["Vision LLM processing adds significant latency (2-5s per image depending on model)","Requires separate vision model API access (e.g., GPT-4V, Claude Vision)","Vision extraction quality varies by image type and resolution","Complex diagrams may require manual annotation for accurate semantic understanding","Increases indexing costs proportional to number of images in document"],"requires":["Vision-capable LLM API (GPT-4V, Claude 3 Vision, or equivalent)","Base document indexing capability","Image extraction and preprocessing pipeline"],"input_types":["PDF or Markdown documents containing embedded images","Image files with page context"],"output_types":["JSON tree with image content integrated as text summaries","Image metadata (position, size, extracted text, semantic description)","Cross-references between text sections and related images"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_4","uri":"capability://tool.use.integration.agentic.rag.integration.with.openai.agents.sdk.and.tool.use.orchestration","name":"agentic rag integration with openai agents sdk and tool-use orchestration","description":"Provides native integration with OpenAI Agents SDK and other agentic frameworks, exposing PageIndex retrieval as a callable tool that agents can invoke during reasoning loops. The integration enables agents to autonomously decide when to retrieve document sections, compose multi-step queries, and iteratively refine retrieval based on intermediate results.","intents":["I want to build an AI agent that can autonomously retrieve relevant document sections as part of its reasoning process","I need agents to compose complex multi-step queries that require iterative retrieval and reasoning","I want to integrate document retrieval into agentic workflows without manual orchestration"],"best_for":["teams building autonomous agents that reason over document collections","developers implementing complex research or analysis workflows requiring iterative retrieval","applications where agents need to make decisions about what documents to consult"],"limitations":["Agent reasoning adds latency and cost due to multiple LLM calls per query","Agents may retrieve irrelevant sections if reasoning diverges from document structure","Requires careful prompt engineering to guide agent retrieval behavior","Limited to OpenAI Agents SDK and compatible frameworks (Anthropic, etc.)","No built-in mechanisms for agent hallucination detection or retrieval validation"],"requires":["OpenAI Agents SDK or compatible agentic framework","PageIndex indexed document tree","OpenAI API key with agents model access","Tool schema definition for retrieval function"],"input_types":["Agent task or user query","Indexed document tree","Tool schema specification"],"output_types":["Agent reasoning trace with retrieval decisions","Retrieved document sections used in agent reasoning","Final agent response with source citations"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.implementation.for.standardized.tool.integration","name":"model context protocol (mcp) server implementation for standardized tool integration","description":"Implements PageIndex as an MCP server, exposing document indexing and retrieval capabilities through the standardized MCP protocol. This enables integration with any MCP-compatible client (Claude Desktop, IDEs, other LLM applications) without custom integration code, providing a vendor-neutral interface to PageIndex functionality.","intents":["I want to use PageIndex retrieval in Claude Desktop or other MCP-compatible applications without custom integration","I need to expose PageIndex as a standard tool that multiple LLM clients can access","I want to build document retrieval capabilities that work across different LLM platforms"],"best_for":["teams building tools that need to work across multiple LLM platforms","developers integrating PageIndex into Claude Desktop or other MCP clients","organizations standardizing on MCP for LLM tool integration"],"limitations":["MCP protocol overhead adds latency compared to direct API calls","Limited to MCP-compatible clients (not all LLM platforms support MCP yet)","Requires MCP server deployment and management","No built-in authentication beyond MCP protocol mechanisms"],"requires":["MCP server implementation (provided by PageIndex)","MCP-compatible client (Claude Desktop, compatible IDE, etc.)","Document index in PageIndex format"],"input_types":["MCP tool call requests with retrieval parameters","Document index specification"],"output_types":["MCP tool response with retrieved sections","Structured metadata about retrieved content"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_6","uri":"capability://tool.use.integration.cloud.api.based.retrieval.with.managed.indexing.and.query.execution","name":"cloud api-based retrieval with managed indexing and query execution","description":"Provides a cloud-hosted API service that manages document indexing and retrieval without requiring local deployment. Users submit documents to the cloud service, which handles indexing, storage, and query execution, returning results via REST API. The cloud service abstracts infrastructure management while maintaining the reasoning-based retrieval approach.","intents":["I want to use PageIndex retrieval without managing local infrastructure or deployment","I need a managed service that handles document indexing and storage","I want to integrate PageIndex into applications via simple REST API calls"],"best_for":["teams without infrastructure expertise or resources for self-hosted deployment","applications requiring quick integration without DevOps overhead","organizations preferring managed services over self-hosted solutions"],"limitations":["Cloud API introduces network latency compared to local retrieval","Requires internet connectivity for all operations","Data is stored on PageIndex cloud infrastructure (privacy/compliance considerations)","API rate limits and quota management required","Pricing model based on API usage (indexing and query costs)"],"requires":["PageIndex cloud API account and API key","Internet connectivity","Document files to index"],"input_types":["PDF, Markdown, or text documents via API upload","Search queries via REST API"],"output_types":["JSON response with retrieved sections and metadata","Index status and management information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_7","uri":"capability://tool.use.integration.self.hosted.pageindexclient.with.local.document.processing.and.retrieval","name":"self-hosted pageindexclient with local document processing and retrieval","description":"Provides a Python client library (PageIndexClient) for self-hosted deployment, enabling local document indexing and retrieval without cloud dependencies. The client handles the complete indexing pipeline locally, storing indices as JSON files, and supports both programmatic and CLI-based usage for integration into local applications and workflows.","intents":["I want to run PageIndex locally without sending documents to cloud services","I need to integrate PageIndex indexing and retrieval into Python applications","I want to manage document indices locally with full control over storage and processing"],"best_for":["teams with privacy or compliance requirements preventing cloud document storage","developers building Python applications with embedded document retrieval","organizations with infrastructure to manage local deployment"],"limitations":["Requires local LLM API access (OpenAI, Anthropic, etc.) for indexing and retrieval","Indexing latency depends on local compute resources and LLM API response times","Requires manual index management and storage","No built-in scaling or load balancing for high-volume indexing","Operator responsible for dependency management and Python environment setup"],"requires":["Python 3.9+","LLM API key (OpenAI, Anthropic, or compatible provider)","PDF/Markdown processing libraries","Local file system for index storage"],"input_types":["Local PDF, Markdown, or text files","Python function calls or CLI commands"],"output_types":["JSON index files stored locally","Retrieved sections as Python objects or JSON"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_8","uri":"capability://data.processing.analysis.pdf.processing.with.table.of.contents.extraction.and.page.range.tracking","name":"pdf processing with table-of-contents extraction and page-range tracking","description":"Implements specialized PDF processing that extracts table-of-contents structure, identifies section boundaries, and tracks page ranges for each section. The processor uses PDF metadata and text analysis to reconstruct document hierarchy, enabling accurate mapping between tree nodes and source pages without requiring manual annotation.","intents":["I need to automatically extract document structure from PDFs without manual table-of-contents definition","I want to maintain accurate page references for each section in the indexed tree","I need to handle PDFs with complex structures (multiple TOCs, appendices, etc.)"],"best_for":["teams processing large PDF collections with consistent structure","applications requiring page-level accuracy for source attribution","developers building RAG systems on professional documents (reports, specifications, manuals)"],"limitations":["TOC extraction fails on PDFs without explicit table-of-contents","Page range tracking may be inaccurate for PDFs with complex layouts or embedded documents","Requires well-formed PDF structure (some scanned PDFs may not extract cleanly)","No support for PDFs with non-standard page numbering (Roman numerals, custom schemes)","Image-heavy PDFs may have incomplete text extraction"],"requires":["PDF file with extractable text and structure","PDF processing library (PyPDF2, pdfplumber, or equivalent)","Optional: OCR capability for scanned PDFs"],"input_types":["PDF files (text-based or scanned with OCR)"],"output_types":["Extracted table-of-contents structure","Section boundaries with page ranges","Full text content with page references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vectifyai--pageindex__cap_9","uri":"capability://data.processing.analysis.markdown.document.processing.with.heading.based.hierarchy.extraction","name":"markdown document processing with heading-based hierarchy extraction","description":"Implements specialized Markdown processing that uses heading hierarchy (H1, H2, H3, etc.) to automatically construct the tree structure. The processor parses Markdown syntax to identify sections, extract titles, and preserve document hierarchy without requiring external metadata or manual structure definition.","intents":["I need to index Markdown documentation while preserving heading hierarchy","I want to automatically extract document structure from Markdown without manual annotation","I need to process technical documentation written in Markdown format"],"best_for":["teams managing Markdown-based documentation (wikis, technical docs, README files)","developers building RAG systems on code documentation and guides","organizations using Markdown for knowledge bases and internal documentation"],"limitations":["Requires well-formed Markdown with consistent heading structure","Fails on Markdown with inconsistent or missing heading hierarchy","No support for Markdown extensions or custom syntax","Heading-based structure may not match logical document organization","Cannot extract page ranges (Markdown is not paginated)"],"requires":["Markdown file with heading-based structure","Markdown parser (Python markdown library or equivalent)"],"input_types":["Markdown files (.md)"],"output_types":["Hierarchical tree structure based on heading levels","Section content grouped by heading hierarchy","Metadata about heading levels and nesting"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for OpenAI, Anthropic, or compatible LLM provider","PDF processing library (PyPDF2 or similar for PDF input)","Markdown parser for Markdown document support","Indexed document tree from hierarchical indexing capability","LLM API access with sufficient context window (8k+ tokens recommended)","Search query or user intent as natural language input","Configuration file or environment variables","API keys for selected LLM providers","Python 3.9+ with PageIndex installed"],"failure_modes":["Requires LLM API calls during indexing phase, adding latency proportional to document length","Table-of-contents extraction may fail on documents with non-standard structure or missing TOC","LLM-generated summaries inherit hallucination risks from the underlying model","No built-in support for documents with complex layouts (multi-column, embedded images with text)","LLM reasoning adds latency compared to vector similarity search (typically 500ms-2s per query depending on tree depth)","Performance degrades if tree depth exceeds 10-15 levels due to context window constraints","Requires careful prompt engineering to guide LLM navigation effectively","May miss relevant sections if LLM reasoning diverges from document structure logic","Configuration complexity increases with number of tunable parameters","No built-in validation or conflict detection for incompatible configurations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7549121248276284,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-04-28T07:23:49Z"},"community":{"stars":26074,"forks":2245,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vectifyai--pageindex","compare_url":"https://unfragile.ai/compare?artifact=vectifyai--pageindex"}},"signature":"jJ16LziPDPBJSUuYc3duiepjoFbxzbWUme9vgxOVOT7cf9NJDwzqIURzEEOBeNpdnXrz1Tt9COOLQ1N9o2tLBg==","signedAt":"2026-06-20T15:18:31.479Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vectifyai--pageindex","artifact":"https://unfragile.ai/vectifyai--pageindex","verify":"https://unfragile.ai/api/v1/verify?slug=vectifyai--pageindex","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}