{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_chat-with-docs","slug":"chat-with-docs","name":"Chat with Docs","type":"product","url":"https://chatwithdocs.co","page_url":"https://unfragile.ai/chat-with-docs","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_chat-with-docs__cap_0","uri":"capability://memory.knowledge.document.to.vector.embedding.and.indexing","name":"document-to-vector-embedding-and-indexing","description":"Converts uploaded PDF and document files into dense vector embeddings using transformer-based models, then indexes them in a vector database for semantic similarity search. The system chunks documents into semantically coherent segments, embeds each chunk, and stores metadata (page numbers, section headers) alongside vectors to enable fast retrieval during query time. This approach enables natural language queries to match relevant document sections without keyword matching.","intents":["I need to upload a 200-page PDF and query it conversationally without manually searching","I want to find all sections discussing a concept across multiple documents using natural language","I need to extract specific information from dense technical documents without reading them cover-to-cover"],"best_for":["research professionals analyzing academic papers and reports","legal analysts reviewing contracts and regulatory documents","business analysts extracting insights from market research PDFs"],"limitations":["Chunking strategy may lose context at chunk boundaries, reducing accuracy for questions spanning multiple sections","Vector embedding quality depends on model choice; generic models may underperform on domain-specific jargon (medical, legal, technical)","No built-in support for structured data extraction from tables or forms—treats all content as unstructured text","Indexing latency scales with document size; very large PDFs (500+ pages) may take 30+ seconds to process"],"requires":["PDF or document file in supported format (PDF, DOCX, TXT)","Active internet connection for embedding API calls","Document size under platform's upload limit (likely 50-100MB based on typical SaaS constraints)"],"input_types":["PDF","DOCX","TXT","potentially other document formats"],"output_types":["indexed vector embeddings","metadata-enriched chunk store","retrieval-ready document representation"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_1","uri":"capability://text.generation.language.conversational.rag.query.engine","name":"conversational-rag-query-engine","description":"Implements a retrieval-augmented generation (RAG) pipeline that retrieves relevant document chunks from the vector index based on user queries, then passes those chunks as context to a large language model to generate conversational answers. The system maintains conversation history to enable multi-turn dialogue where follow-up questions can reference previous context. Retrieval is performed via semantic similarity scoring, with top-k chunks selected and ranked before being fed to the LLM.","intents":["I want to ask follow-up questions about document content in natural conversation style","I need the AI to synthesize information from multiple relevant sections into a coherent answer","I want to understand not just what information exists, but why it matters in context"],"best_for":["researchers conducting exploratory analysis of unfamiliar documents","students studying complex materials who need clarification and synthesis","professionals needing quick answers without reading entire documents"],"limitations":["RAG quality depends on retrieval accuracy; irrelevant chunks in context can cause hallucinations or incorrect answers","Conversation history is maintained in-session only; no persistent multi-session memory across different chat instances","LLM response latency adds 2-5 seconds per query due to API calls; not suitable for real-time interactive use cases","No explicit fact-checking or citation verification; answers may confidently state incorrect information if retrieval fails","Limited to documents uploaded in current session; cannot cross-reference multiple independent document sets"],"requires":["At least one document indexed and embedded in the vector store","Active API connection to LLM provider (OpenAI, Anthropic, or similar)","Sufficient token budget for both retrieval context and generation"],"input_types":["natural language query (text)","conversation history (implicit)"],"output_types":["natural language response (text)","potentially source citations or chunk references"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_2","uri":"capability://search.retrieval.multi.document.semantic.search","name":"multi-document-semantic-search","description":"Enables users to upload and index multiple documents simultaneously, then perform semantic searches across the entire corpus to find relevant information regardless of which source document contains it. The system maintains separate vector indices per document while allowing unified cross-document queries, with results ranked by relevance and tagged with source document metadata. This allows researchers to treat multiple PDFs as a single searchable knowledge base.","intents":["I have 10 research papers and need to find all mentions of a specific methodology across all of them","I want to compare how different documents address the same topic","I need to identify contradictions or agreements between multiple sources on a question"],"best_for":["literature review researchers comparing multiple academic sources","legal professionals reviewing multiple contracts or regulatory documents","analysts synthesizing insights from multiple market research reports"],"limitations":["No built-in deduplication; if multiple documents contain identical or near-identical content, results may show redundant chunks","Cross-document synthesis requires manual review; the system retrieves relevant chunks but doesn't automatically identify contradictions or synthesize conflicting information","Scaling to very large document sets (100+ documents) may degrade search performance or increase latency","No document relationship mapping; cannot infer that Document A references Document B or track citation chains"],"requires":["Multiple documents uploaded to the same workspace or project","Sufficient storage quota for indexing all documents","Consistent metadata tagging for documents to enable source filtering"],"input_types":["multiple PDF/DOCX/TXT files"],"output_types":["ranked list of relevant chunks with source document attribution","cross-document relevance scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_3","uri":"capability://text.generation.language.natural.language.document.querying","name":"natural-language-document-querying","description":"Accepts free-form natural language questions about document content and returns conversational answers without requiring users to learn query syntax or document structure. The system interprets user intent from natural language, translates it into semantic search queries, retrieves relevant context, and generates human-readable responses. This eliminates the friction of traditional search interfaces (Ctrl+F, keyword search, boolean operators) and makes document exploration accessible to non-technical users.","intents":["I want to ask 'What are the main findings?' without knowing document structure or keywords","I need to ask complex questions like 'How does this compare to the previous year?' without manual searching","I want to explore documents conversationally without learning a search syntax"],"best_for":["non-technical users (students, business professionals) who avoid traditional search tools","researchers conducting exploratory analysis where query intent evolves iteratively","professionals with limited time who need quick answers over thorough document review"],"limitations":["Ambiguous or vague natural language queries may be misinterpreted, leading to irrelevant results","Complex multi-part questions may be partially answered or require rephrasing","No support for advanced search operators (date ranges, exact phrase matching, boolean logic) that power users might expect","Query interpretation relies on LLM understanding; domain-specific jargon or acronyms may be misunderstood","No query history or saved searches; users cannot reuse or refine previous queries"],"requires":["Indexed document corpus","LLM with instruction-following capability","User familiarity with conversational English (language-dependent)"],"input_types":["natural language text query"],"output_types":["natural language response (text)","optional source citations"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_4","uri":"capability://data.processing.analysis.document.upload.and.processing.pipeline","name":"document-upload-and-processing-pipeline","description":"Provides a user-facing interface for uploading documents (PDFs, DOCX, TXT) and automatically processes them through a pipeline: file validation, text extraction, chunking, embedding, and indexing. The system handles document parsing (extracting text from PDFs, handling formatting), splitting content into semantically coherent chunks, and storing metadata (filename, upload date, page numbers). Processing is asynchronous, allowing users to continue working while documents are indexed in the background.","intents":["I want to upload a PDF and start querying it within seconds without manual preprocessing","I need to batch-upload multiple documents and have them all indexed automatically","I want to replace a document with a newer version without losing conversation history"],"best_for":["users who want zero-friction document onboarding","teams managing document libraries with frequent updates","researchers working with diverse document formats and sources"],"limitations":["PDF parsing quality varies by PDF type (scanned images vs. text-based); scanned PDFs may require OCR, adding latency","No support for complex document structures (multi-column layouts, embedded tables, forms); content may be extracted in incorrect order","File size limits (typically 50-100MB) prevent uploading very large documents or archives","No version control; uploading a new version of a document overwrites the previous index without preserving history","Chunking strategy is fixed; users cannot customize chunk size or overlap for domain-specific optimization"],"requires":["Document file in supported format (PDF, DOCX, TXT)","File size within platform limits","Sufficient storage quota in user account"],"input_types":["PDF","DOCX","TXT","potentially other formats"],"output_types":["indexed document representation","processing status/progress indicator"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_5","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation-history-and-context-management","description":"Maintains a persistent conversation history within a chat session, allowing users to ask follow-up questions that reference previous context without re-specifying document scope or repeating information. The system stores previous queries and responses, injects relevant history into LLM prompts to enable contextual understanding, and allows users to reference earlier points in conversation. This creates a stateful dialogue experience rather than isolated, independent queries.","intents":["I want to ask a follow-up question that builds on my previous query without restating context","I need the AI to remember what we discussed earlier in the conversation","I want to explore a topic iteratively, refining my questions based on previous answers"],"best_for":["researchers conducting exploratory analysis with evolving questions","students learning from documents through iterative questioning","professionals conducting in-depth analysis requiring multiple related queries"],"limitations":["Conversation history is session-scoped; closing the chat loses all history (no persistent cross-session memory)","Long conversation histories increase LLM token usage and latency; very long conversations may hit token limits","No explicit conversation branching; users cannot explore alternative lines of questioning from a previous point","History is not searchable; users cannot retrieve a specific earlier question or answer without scrolling","No conversation export or sharing; history cannot be saved or shared with collaborators"],"requires":["Active chat session","Sufficient LLM token budget to include history in prompts"],"input_types":["natural language query (text)","implicit conversation history"],"output_types":["natural language response (text)","updated conversation history"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_6","uri":"capability://memory.knowledge.source.attribution.and.citation.tracking","name":"source-attribution-and-citation-tracking","description":"Tracks which document chunks were used to generate each response and provides source attribution, allowing users to verify answers by reviewing original document content. The system tags retrieved chunks with metadata (source document, page number, section) and optionally displays citations or links to source material in responses. This enables transparency and allows users to fact-check AI-generated answers against original sources.","intents":["I want to verify that the AI's answer is actually supported by the document","I need to cite the original source when using information from the AI's response","I want to see which document sections the AI used to answer my question"],"best_for":["academic researchers and students who need to cite sources","legal professionals verifying that answers are grounded in contract language","professionals building reports that require source documentation"],"limitations":["Citation accuracy depends on retrieval quality; if wrong chunks are retrieved, citations will be misleading","No automatic fact-checking; citations prove that content was retrieved, not that it's correct or relevant","Page number citations may be inaccurate if document parsing failed or if PDFs have non-standard page numbering","No support for partial citations (e.g., citing a specific sentence within a chunk); citations are chunk-level only","Users must manually verify citations; no automated link-checking or validation"],"requires":["Document metadata (filename, page numbers) preserved during indexing","Retrieval system that tracks chunk provenance"],"input_types":["indexed documents with metadata"],"output_types":["response with source citations","links or references to source chunks"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_7","uri":"capability://automation.workflow.document.workspace.and.organization","name":"document-workspace-and-organization","description":"Provides a workspace or project structure for organizing multiple documents, conversations, and related metadata. Users can create separate workspaces for different projects, organize documents into folders or collections, and manage access or sharing settings. Each workspace maintains its own document index and conversation history, allowing users to compartmentalize knowledge bases by topic, project, or team.","intents":["I want to organize documents for different research projects separately","I need to keep conversations about Project A separate from Project B","I want to share a specific set of documents with a colleague without exposing other documents"],"best_for":["teams managing multiple projects with different document sets","researchers with diverse research interests wanting to organize by topic","organizations needing document access control and project isolation"],"limitations":["No cross-workspace search; users must switch workspaces to search different document sets","Sharing is likely limited to workspace-level; no fine-grained document-level access control","No collaboration features (real-time co-editing, comments, version control) within workspaces","Workspace switching may require page reload or navigation, adding friction to multi-project workflows","No workspace templates or bulk operations; creating new workspaces requires manual setup"],"requires":["User account with workspace creation permissions","Storage quota sufficient for multiple document sets"],"input_types":["workspace configuration (name, description)","document assignments to workspaces"],"output_types":["organized workspace structure","workspace-scoped search and conversation contexts"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chat-with-docs__cap_8","uri":"capability://data.processing.analysis.document.metadata.extraction.and.tagging","name":"document-metadata-extraction-and-tagging","description":"Automatically extracts or allows manual entry of document metadata (title, author, date, tags, category) during upload, then uses this metadata to enhance search, filtering, and organization. The system may use OCR or document parsing to extract metadata from document headers, or provide a form for users to manually specify metadata. Metadata is indexed alongside document content, enabling filtered searches (e.g., 'documents from 2023') and faceted navigation.","intents":["I want to filter search results by document date or author","I need to tag documents by topic or project for better organization","I want to see metadata like publication date or source when reviewing search results"],"best_for":["researchers managing large document collections with diverse sources","teams needing to organize documents by metadata (date, author, category)","professionals conducting literature reviews where source credibility matters"],"limitations":["Automatic metadata extraction is unreliable; many documents lack structured metadata in headers","Manual metadata entry adds friction to document upload; users may skip or enter inconsistent values","No standardized metadata schema; different documents may have different metadata fields","Metadata is not validated; users can enter arbitrary values, making filtering unreliable","No bulk metadata editing; updating metadata for multiple documents requires manual effort"],"requires":["Document metadata provided during upload (manually or extracted)","Metadata indexing in search system"],"input_types":["document metadata (title, author, date, tags, category)"],"output_types":["indexed metadata","filtered search results","metadata-enhanced document listings"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["PDF or document file in supported format (PDF, DOCX, TXT)","Active internet connection for embedding API calls","Document size under platform's upload limit (likely 50-100MB based on typical SaaS constraints)","At least one document indexed and embedded in the vector store","Active API connection to LLM provider (OpenAI, Anthropic, or similar)","Sufficient token budget for both retrieval context and generation","Multiple documents uploaded to the same workspace or project","Sufficient storage quota for indexing all documents","Consistent metadata tagging for documents to enable source filtering","Indexed document corpus"],"failure_modes":["Chunking strategy may lose context at chunk boundaries, reducing accuracy for questions spanning multiple sections","Vector embedding quality depends on model choice; generic models may underperform on domain-specific jargon (medical, legal, technical)","No built-in support for structured data extraction from tables or forms—treats all content as unstructured text","Indexing latency scales with document size; very large PDFs (500+ pages) may take 30+ seconds to process","RAG quality depends on retrieval accuracy; irrelevant chunks in context can cause hallucinations or incorrect answers","Conversation history is maintained in-session only; no persistent multi-session memory across different chat instances","LLM response latency adds 2-5 seconds per query due to API calls; not suitable for real-time interactive use cases","No explicit fact-checking or citation verification; answers may confidently state incorrect information if retrieval fails","Limited to documents uploaded in current session; cannot cross-reference multiple independent document sets","No built-in deduplication; if multiple documents contain identical or near-identical content, results may show redundant chunks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=chat-with-docs","compare_url":"https://unfragile.ai/compare?artifact=chat-with-docs"}},"signature":"X+xH2rZfytUI5fRHyvFsFWFpB7sY36ZjPBSVpPEZB0r7BwKi8CNERGDzjPi+ApqqSubLG8wJXhr5Rcg1RaYOBw==","signedAt":"2026-06-20T18:38:14.883Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chat-with-docs","artifact":"https://unfragile.ai/chat-with-docs","verify":"https://unfragile.ai/api/v1/verify?slug=chat-with-docs","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"}}