{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_doclime","slug":"doclime","name":"Doclime","type":"product","url":"https://doclime.com","page_url":"https://unfragile.ai/doclime","categories":["research-search"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_doclime__cap_0","uri":"capability://search.retrieval.semantic.search.across.document.collections","name":"semantic-search-across-document-collections","description":"Performs vector-based semantic search over uploaded PDF documents and academic papers by converting natural language queries into embeddings and matching them against indexed document embeddings. Uses dense retrieval (likely transformer-based embeddings like BERT or specialized academic models) rather than keyword/BM25 matching, enabling the system to understand research intent and find conceptually related papers even when keyword overlap is minimal. The indexing pipeline processes PDFs on upload, extracting text and generating embeddings that are stored in a vector database for fast approximate nearest neighbor retrieval.","intents":["Find relevant academic papers across a large collection without manually reading abstracts or using keyword matching","Discover papers on related topics that use different terminology but address the same research questions","Quickly narrow down a literature review by semantic relevance rather than citation counts or publication date"],"best_for":["Graduate students and PhD candidates conducting systematic literature reviews with 100+ papers","Independent researchers without institutional access to premium academic databases","Teams building research tools that need semantic search as a core feature"],"limitations":["Freemium tier likely caps document uploads (estimated <100 PDFs) and queries per month, restricting large-scale literature reviews","Embedding quality depends on the underlying model; specialized academic embeddings may outperform general-purpose models for domain-specific queries","No visibility into indexing latency — processing large PDFs may take seconds to minutes before they become searchable","Search results depend on the relevance of the training corpus for the embedding model; niche research areas may have lower quality matches"],"requires":["PDF files with extractable text (scanned PDFs without OCR will not index properly)","Active internet connection for query processing","Freemium account or paid subscription"],"input_types":["natural language query (text)","PDF documents (binary)"],"output_types":["ranked list of documents with relevance scores","document metadata (title, authors, publication date)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_1","uri":"capability://text.generation.language.direct.pdf.query.and.extraction","name":"direct-pdf-query-and-extraction","description":"Enables users to ask natural language questions about specific PDF documents and receive extracted answers without manual reading. The system likely uses a retrieval-augmented generation (RAG) pipeline: when a user queries a document, the system retrieves relevant text chunks from the PDF using semantic similarity, then passes those chunks to an LLM to generate a contextual answer. This combines document chunking (splitting PDFs into overlapping sections), embedding-based retrieval, and LLM inference to provide document-specific answers with source citations.","intents":["Extract key findings or methodology details from a research paper without reading the entire document","Get answers to specific questions about a paper's content (e.g., 'What was the sample size?' or 'What are the limitations?')","Quickly summarize or compare findings across multiple uploaded papers"],"best_for":["Researchers conducting rapid literature reviews who need to extract specific information from dozens of papers","Students writing literature review sections who need to synthesize findings across multiple sources","Non-technical users who want to interact with academic papers conversationally"],"limitations":["RAG quality depends on chunk size and retrieval accuracy; poorly chunked PDFs may miss context or return irrelevant sections","LLM hallucination risk — the system may generate plausible-sounding answers that are not supported by the document text","Freemium tier likely limits queries per document or total queries per month","No built-in fact-checking or citation verification; users must validate extracted information against the source PDF","Works best on well-structured PDFs with clear text extraction; scanned images or complex layouts may degrade accuracy"],"requires":["PDF file with extractable text content","Active internet connection","Freemium account or paid subscription"],"input_types":["natural language question (text)","PDF document (binary)"],"output_types":["natural language answer (text)","source citations with page numbers (optional)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_2","uri":"capability://text.generation.language.multi.document.synthesis.and.comparison","name":"multi-document-synthesis-and-comparison","description":"Allows users to query across multiple uploaded PDFs simultaneously to synthesize findings, identify contradictions, or compare methodologies across papers. The system likely uses a hierarchical RAG approach: retrieving relevant chunks from each document based on the query, then using an LLM to synthesize or compare the retrieved information. This requires managing context across multiple documents, deduplicating similar findings, and generating comparative summaries that highlight agreements and disagreements across sources.","intents":["Compare methodologies or findings across multiple research papers to identify best practices or conflicting results","Synthesize findings from 10+ papers into a cohesive literature review summary without manual reading","Identify gaps in the literature by comparing what different papers address and what they omit"],"best_for":["PhD students and postdocs writing comprehensive literature reviews or meta-analyses","Researchers conducting systematic reviews who need to extract and compare data across many papers","Teams building research synthesis tools that aggregate findings across multiple sources"],"limitations":["Synthesis quality degrades with document count; comparing 50+ papers may exceed LLM context windows or produce generic summaries","No built-in deduplication of similar findings across papers; the system may report the same finding multiple times","Freemium tier likely caps the number of documents that can be queried simultaneously (estimated <10 documents per query)","Synthesis may miss nuanced differences in methodology or context that distinguish superficially similar findings","No structured output format for comparative data; results are free-form text rather than tables or matrices"],"requires":["Multiple PDF files (minimum 2, likely capped at 10-50 on freemium tier)","Active internet connection","Freemium account or paid subscription"],"input_types":["natural language query (text)","multiple PDF documents (binary)"],"output_types":["synthesized summary (text)","comparative analysis (text)","list of agreements/disagreements (text)"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_3","uri":"capability://data.processing.analysis.pdf.text.extraction.and.indexing","name":"pdf-text-extraction-and-indexing","description":"Processes uploaded PDF files to extract text content and prepare it for semantic search and querying. The system handles PDF parsing (converting binary PDF format to text), text cleaning (removing headers, footers, page numbers), and chunking (splitting text into overlapping segments for retrieval). The extracted and chunked text is then embedded using a transformer-based embedding model and stored in a vector database for fast retrieval. This pipeline must handle diverse PDF formats, including scanned documents (via OCR if supported) and complex layouts.","intents":["Upload a research paper and make it immediately searchable and queryable","Batch upload multiple PDFs to build a personal research library","Ensure PDFs are properly indexed so semantic search returns relevant results"],"best_for":["Researchers building personal knowledge bases from downloaded papers","Teams managing document collections that need to be searchable","Users who want to avoid manual tagging or categorization of documents"],"limitations":["Scanned PDFs without OCR support will not be indexed; OCR capability is unknown and may require paid tier","Complex layouts (multi-column, tables, figures) may be extracted incorrectly, degrading search quality","Indexing latency is unknown; large PDFs may take minutes to process before becoming searchable","No support for non-PDF formats (e.g., Word documents, HTML); users must convert to PDF first","Freemium tier likely caps total storage or number of documents (estimated <100 PDFs)"],"requires":["PDF file with extractable text or OCR support (if scanned)","Active internet connection","Freemium account or paid subscription"],"input_types":["PDF document (binary)"],"output_types":["indexed document metadata (title, authors, page count)","vector embeddings (stored internally)","searchable document record"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_4","uri":"capability://search.retrieval.research.intent.aware.query.expansion","name":"research-intent-aware-query-expansion","description":"Automatically expands or reformulates user queries to improve semantic search results by understanding research intent. When a user enters a query like 'machine learning for medical diagnosis', the system may expand it to include related terms like 'deep learning', 'clinical decision support', 'diagnostic AI', and 'neural networks for healthcare' before performing retrieval. This likely uses query expansion techniques such as synonym injection, semantic paraphrasing via LLMs, or learned query reformulation models. The expanded queries are then used to retrieve more relevant documents from the vector database.","intents":["Find papers on a research topic even when using different terminology than the papers themselves","Improve search recall by automatically including related concepts and synonyms","Reduce the need for multiple search iterations to find comprehensive results"],"best_for":["Researchers new to a field who may not know the standard terminology","Interdisciplinary researchers bridging multiple fields with different vocabularies","Users conducting broad literature reviews who want comprehensive coverage"],"limitations":["Query expansion may introduce noise or retrieve off-topic papers if expansion is too aggressive","Expansion quality depends on the underlying model; generic models may not understand domain-specific terminology","No user control over expansion strategy; users cannot disable expansion or customize it for their needs","Expansion adds latency to search queries (estimated +100-500ms per query)","May not work well for very specific or niche research topics with limited training data"],"requires":["Natural language query (text)","Active internet connection","Freemium account or paid subscription"],"input_types":["natural language query (text)"],"output_types":["expanded query terms (internal, not shown to user)","ranked list of documents (final output)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_5","uri":"capability://automation.workflow.freemium.tier.document.and.query.limits","name":"freemium-tier-document-and-query-limits","description":"Implements usage-based access controls on the freemium tier, capping the number of documents users can upload, queries they can perform, and API calls they can make. This is a business model enforcement mechanism that limits free users to a subset of platform capabilities (estimated <100 documents, <50 queries/month) while offering unlimited access on paid tiers. The system tracks usage per user account and enforces limits at the API level, returning rate-limit errors when users exceed their quota.","intents":["Allow researchers to try the platform with a small document collection before committing to a paid plan","Prevent free tier abuse by limiting resource consumption","Encourage conversion to paid tiers for power users with large document collections"],"best_for":["Individual researchers and students evaluating the platform","Teams deciding whether to adopt Doclime for their research workflow","Doclime's business model (freemium monetization)"],"limitations":["Freemium tier is likely too restrictive for serious literature reviews (estimated <100 documents, <50 queries/month)","No clear communication of limits in the editorial summary; users may hit limits unexpectedly","Limits may frustrate power users and drive them to competitors with more generous free tiers","No rollover of unused queries; monthly quota resets regardless of usage pattern"],"requires":["Freemium account (no payment required)","Active internet connection"],"input_types":["user account and usage tracking (internal)"],"output_types":["usage statistics (shown to user)","rate-limit errors when quota exceeded"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_6","uri":"capability://data.processing.analysis.academic.paper.metadata.extraction","name":"academic-paper-metadata-extraction","description":"Automatically extracts structured metadata from uploaded PDFs, including title, authors, publication date, abstract, and keywords. This likely uses a combination of PDF header parsing (extracting text from the first page) and NLP-based named entity recognition (NER) to identify author names and publication dates. The extracted metadata is stored alongside the document embeddings and used for filtering search results, displaying document information, and organizing the user's document library. This enables users to see paper details without opening the full PDF.","intents":["Quickly view paper metadata (title, authors, date) without opening the PDF","Filter search results by publication date, author, or other metadata","Organize and categorize documents in a personal research library"],"best_for":["Researchers managing large document collections who need to organize and filter papers","Users building citation databases or reference managers","Teams aggregating research metadata for analysis or reporting"],"limitations":["Metadata extraction accuracy depends on PDF structure; poorly formatted papers may have missing or incorrect metadata","NER-based author extraction may fail for non-English names or unusual formatting","No manual editing of extracted metadata; users cannot correct errors","Abstract extraction may be incomplete if the PDF uses non-standard formatting","Keywords are extracted from the PDF but may not be standardized or normalized"],"requires":["PDF file with extractable text and structured header","Active internet connection","Freemium account or paid subscription"],"input_types":["PDF document (binary)"],"output_types":["structured metadata (JSON or similar)","title, authors, publication date, abstract, keywords (text fields)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doclime__cap_7","uri":"capability://memory.knowledge.vector.database.backed.semantic.indexing","name":"vector-database-backed-semantic-indexing","description":"Uses a vector database (likely Pinecone, Weaviate, or Milvus) to store and retrieve document embeddings at scale. When a user uploads a PDF, the system chunks the text, generates embeddings for each chunk using a transformer model, and stores the embeddings in the vector database with metadata (document ID, chunk index, text preview). During search, the user's query is embedded using the same model, and approximate nearest neighbor (ANN) search is performed to retrieve the most similar chunks. This architecture enables fast semantic search even with thousands of documents and millions of chunks.","intents":["Perform semantic search across large document collections (100+ documents) with sub-second latency","Scale the platform to support many concurrent users without degrading search performance","Enable real-time indexing of new documents without rebuilding the entire index"],"best_for":["Doclime's backend infrastructure (required for all semantic search and RAG capabilities)","Teams building semantic search features that need to scale to large document collections","Researchers with large personal document libraries (100+ papers)"],"limitations":["Vector database costs scale with document volume and query volume; this may limit freemium tier generosity","ANN search introduces approximation error; results may miss some relevant documents compared to exact nearest neighbor search","Embedding quality is fixed at indexing time; updating embeddings requires re-indexing all documents","Vector databases have limited filtering capabilities; complex metadata filters may require post-processing","No built-in versioning or audit trail for document updates"],"requires":["Vector database service (Pinecone, Weaviate, Milvus, or similar)","Embedding model (likely BERT, Sentence-Transformers, or OpenAI embeddings)","Active internet connection"],"input_types":["document embeddings (vectors)","query embeddings (vectors)","metadata (JSON)"],"output_types":["ranked list of similar chunks with scores","document metadata and preview text"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["PDF files with extractable text (scanned PDFs without OCR will not index properly)","Active internet connection for query processing","Freemium account or paid subscription","PDF file with extractable text content","Active internet connection","Multiple PDF files (minimum 2, likely capped at 10-50 on freemium tier)","PDF file with extractable text or OCR support (if scanned)","Natural language query (text)","Freemium account (no payment required)","PDF file with extractable text and structured header"],"failure_modes":["Freemium tier likely caps document uploads (estimated <100 PDFs) and queries per month, restricting large-scale literature reviews","Embedding quality depends on the underlying model; specialized academic embeddings may outperform general-purpose models for domain-specific queries","No visibility into indexing latency — processing large PDFs may take seconds to minutes before they become searchable","Search results depend on the relevance of the training corpus for the embedding model; niche research areas may have lower quality matches","RAG quality depends on chunk size and retrieval accuracy; poorly chunked PDFs may miss context or return irrelevant sections","LLM hallucination risk — the system may generate plausible-sounding answers that are not supported by the document text","Freemium tier likely limits queries per document or total queries per month","No built-in fact-checking or citation verification; users must validate extracted information against the source PDF","Works best on well-structured PDFs with clear text extraction; scanned images or complex layouts may degrade accuracy","Synthesis quality degrades with document count; comparing 50+ papers may exceed LLM context windows or produce generic summaries","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":"2026-05-24T12:16:30.283Z","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=doclime","compare_url":"https://unfragile.ai/compare?artifact=doclime"}},"signature":"+bbT6E7jkVuMLoYPVdHF0KWMtTVh9uzYnHNBOl0onFe+QYFYPk+jte7d3FjKKKwDzms3pGgWTPdmH1U9XBFjBg==","signedAt":"2026-06-20T18:15:00.250Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/doclime","artifact":"https://unfragile.ai/doclime","verify":"https://unfragile.ai/api/v1/verify?slug=doclime","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"}}