{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"opus","slug":"opus","name":"OPUS","type":"dataset","url":"https://opus.nlpl.eu","page_url":"https://unfragile.ai/opus","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"opus__cap_0","uri":"capability://search.retrieval.multilingual.parallel.corpus.discovery.via.searchable.index","name":"multilingual parallel corpus discovery via searchable index","description":"Provides a web-based search interface that queries a database index across 1,214 distinct parallel corpora spanning 1,005 languages, allowing users to filter by language pair and corpus type to identify relevant training data. The discovery system aggregates metadata (sentence pair counts, corpus source, release dates) from heterogeneous sources including subtitles, institutional documents, and web crawls, presenting results ranked by corpus size and relevance.","intents":["Find parallel sentence pairs for a specific language pair I need to train a translation model on","Discover which corpora contain data for low-resource language pairs","Compare available corpus sizes and sources for a given language pair to select the most suitable training data","Browse all available corpora to understand what multilingual data exists in the collection"],"best_for":["machine translation researchers evaluating data availability","NLP practitioners building translation systems for specific language pairs","linguists conducting multilingual corpus analysis","organizations assessing data coverage before committing to MT projects"],"limitations":["Language pair availability is sparse — 1,005 languages but only 1,214 corpora total means many language pairs have zero or minimal coverage","No explicit language pair availability matrix provided — users must search individually to determine if a specific pair exists","Search interface does not expose alignment confidence scores, quality metrics, or preprocessing applied to sentence pairs","Cannot filter by data quality, domain, or temporal characteristics — only by corpus name and language pair"],"requires":["Web browser with internet access","No authentication required","Knowledge of ISO 639 language codes or language names"],"input_types":["language pair (e.g., 'en-de')","corpus name or keyword","free-text search query"],"output_types":["corpus metadata (name, sentence pair count, source, release date)","language pair availability list","corpus description and composition"],"categories":["search-retrieval","data-discovery"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_1","uri":"capability://data.processing.analysis.bulk.parallel.corpus.download.with.source.specific.formatting","name":"bulk parallel corpus download with source-specific formatting","description":"Enables download of aligned sentence pairs from selected corpora in their native format, aggregating data from 102.9 billion total sentence pairs across sources like OpenSubtitles (27.2B), NLLB (22.7B), CCMatrix (17.1B), and 1,209 additional corpora. Downloads are organized hierarchically by corpus and language pair, with file formats and encoding specifications determined by the source corpus (format specifications not explicitly documented in available materials).","intents":["Download a specific corpus (e.g., OpenSubtitles v2024) for a language pair to use as training data","Bulk download multiple corpora for a language pair to create a combined training dataset","Access institutional/legal parallel data (EU documents, patents, medical texts) for domain-specific MT training","Retrieve historical corpus versions for reproducibility or comparative analysis"],"best_for":["machine translation researchers training custom models","organizations building translation systems with specific domain requirements","NLP practitioners needing large-scale parallel data for fine-tuning","academic teams conducting multilingual NLP research"],"limitations":["File format specifications not documented — unclear whether downloads are provided as parallel files, TMX, Moses format, or plain text","No API or programmatic download interface documented — appears to require manual web interface interaction","Download bandwidth and size constraints unknown — no documentation of rate limiting or maximum file sizes","Licensing terms per corpus unknown — critical gap for commercial use; no explicit attribution or usage rights documented","No built-in deduplication across overlapping corpora — users may download duplicate sentence pairs if selecting multiple sources","Sentence pairs provided without alignment confidence scores or quality metrics — no filtering options for data quality"],"requires":["Web browser with download capability","Sufficient local storage (individual corpora range from millions to billions of sentence pairs)","No authentication required for public corpora","Ability to parse downloaded format (format unknown without accessing website)"],"input_types":["corpus name","language pair","version identifier (e.g., 'OpenSubtitles v2024')"],"output_types":["aligned sentence pairs in corpus-native format","parallel text files or structured data format","metadata files with corpus information"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_10","uri":"capability://data.processing.analysis.specialized.domain.corpus.access.medical.patents.bible","name":"specialized domain corpus access (medical, patents, bible)","description":"Provides access to specialized domain-specific parallel corpora including EMEA (medical, 282.5M pairs), EuroPat (patents, 252.2M), and Bible translations (88.3M), enabling training of translation systems for specialized domains with domain-specific terminology and language patterns. These corpora are sourced from authoritative domain-specific documents and enable building translation systems for vertical markets.","intents":["Access medical domain parallel data for building healthcare translation systems","Obtain patent translation data for technical and legal translation","Train translation models on religious or biblical language","Build specialized translation systems for vertical markets with domain-specific terminology"],"best_for":["organizations building medical or healthcare translation systems","patent offices and legal firms needing technical translation data","religious organizations and publishers requiring biblical translation","practitioners building specialized MT systems for vertical markets"],"limitations":["Specialized domain corpora are small relative to general-domain data — EMEA (282.5M) and EuroPat (252.2M) are <0.3% of OPUS collection each","Limited domain coverage — only medical, patents, and Bible represented; no legal, technical, financial, or other specialized domains","No documentation of preprocessing, terminology extraction, or domain-specific quality filtering","Licensing terms for specialized corpora unknown — may have restrictions on commercial use or attribution requirements","No metadata on terminology coverage, domain-specific language patterns, or suitability for specific use cases"],"requires":["Access to OPUS interface","Understanding that specialized domain corpora are small and may need to be combined with general-domain data","Domain expertise to evaluate suitability for specific applications"],"input_types":["corpus name (e.g., 'EMEA', 'EuroPat', 'Bible')","language pair"],"output_types":["specialized domain parallel sentence pairs","domain-specific aligned data with terminology","corpus metadata including source and size"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_2","uri":"capability://data.processing.analysis.domain.specific.parallel.corpus.selection.and.filtering","name":"domain-specific parallel corpus selection and filtering","description":"Enables users to identify and download parallel corpora organized by domain and source type, including subtitle-based data (OpenSubtitles, TED talks), institutional/legal documents (EU Europarl, JRC-Acquis, DGT), web-crawled general-domain data (CCMatrix, ParaCrawl, WikiMatrix), and specialized corpora (medical EMEA, patents EuroPat, Bible translations). The collection exposes corpus composition metadata allowing users to understand source characteristics and select data matching their domain requirements.","intents":["Find medical or legal domain-specific parallel data for specialized translation models","Select subtitle-based corpora for conversational or informal language translation training","Identify web-crawled general-domain data for broad-coverage translation systems","Combine domain-specific corpora (e.g., medical + legal) to create multi-domain training datasets"],"best_for":["organizations building domain-specific translation systems (medical, legal, technical)","researchers studying domain adaptation in machine translation","practitioners needing conversational data (subtitles) vs formal institutional data","teams creating specialized MT models for vertical markets"],"limitations":["Domain metadata is implicit in corpus names and sources — no explicit domain tagging or filtering interface documented","No quality metrics by domain — cannot assess whether medical or legal data meets accuracy standards","Uneven domain coverage — top 3 corpora (OpenSubtitles, NLLB, CCMatrix) represent 65.17% of data; specialized domains have minimal representation","No ability to filter by temporal characteristics, formality level, or other linguistic properties beyond domain","Domain-specific corpora may have licensing restrictions not documented in OPUS metadata"],"requires":["Understanding of corpus source types and their domain characteristics","Knowledge of which corpora correspond to desired domains (not explicitly labeled)","Web browser access to OPUS interface"],"input_types":["domain name or corpus type (e.g., 'medical', 'legal', 'subtitles')","language pair","corpus name"],"output_types":["domain-specific parallel corpus files","corpus metadata including source and composition","sentence pair counts per domain"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_3","uri":"capability://data.processing.analysis.multilingual.corpus.composition.analysis.and.statistics","name":"multilingual corpus composition analysis and statistics","description":"Exposes corpus-level metadata including total sentence pair counts, percentage of collection, source type, and release dates, enabling users to understand the composition and scale of available parallel data. Provides aggregate statistics showing that top 10 corpora account for ~93.5% of total data, with detailed breakdowns for major sources (OpenSubtitles 27.2B/26.47%, NLLB 22.7B/22.09%, CCMatrix 17.1B/16.61%, ParaCrawl 4.6B/4.50%).","intents":["Understand the composition and distribution of available parallel data across language pairs","Assess data imbalance — determine whether a language pair has sufficient coverage for training","Compare corpus sizes to select the largest available data source for a language pair","Analyze the long tail of corpora to understand coverage for low-resource languages"],"best_for":["machine translation researchers evaluating data availability and imbalance","practitioners assessing feasibility of training models for specific language pairs","organizations planning data acquisition strategies based on existing coverage","linguists studying language representation in parallel corpora"],"limitations":["Statistics are aggregate only — no per-language-pair breakdowns provided (e.g., how much data exists for en-ja vs en-de)","No quality-weighted statistics — sentence pair counts treat all pairs equally regardless of alignment quality or noise","Historical statistics not provided — cannot track how corpus composition has changed over time","Some corpora marked 'Not specified' in size (Ubuntu, DocHPLT, liv4ever, komi) — incomplete metadata","No information on deduplication or overlap between corpora — actual unique sentence pairs may be lower than reported totals"],"requires":["Access to OPUS website or documentation","Ability to interpret corpus metadata and percentages","No technical prerequisites"],"input_types":["corpus name or language pair"],"output_types":["sentence pair count","percentage of total collection","source type and release date","corpus composition statistics"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_4","uri":"capability://data.processing.analysis.version.tracked.corpus.releases.with.historical.access","name":"version-tracked corpus releases with historical access","description":"Maintains version history for major corpora with explicit release dates, enabling users to access specific versions for reproducibility and comparative analysis. Tracks releases including OpenSubtitles v2024 (released 2025-02-14), HPLT and MultiHPLT v2 (released 2025-01-25), and historical versions back to 2017, allowing researchers to reproduce results with the same data version used in prior work.","intents":["Download a specific corpus version to reproduce prior research results","Compare translation model performance across different corpus versions","Track how corpus composition has evolved over time","Access historical data for longitudinal studies or temporal analysis"],"best_for":["machine translation researchers requiring reproducibility","practitioners validating published results with original data versions","teams conducting comparative studies across corpus versions","organizations tracking data quality improvements over time"],"limitations":["Version history only documented for major corpora (OpenSubtitles, HPLT, MultiHPLT) — unclear which other corpora maintain version tracking","No changelog or documentation of differences between versions — users cannot determine what changed between releases","Update frequency unknown — no SLA or schedule for when new versions are released","Some corpora marked 'Not specified' in version or release date — incomplete version metadata","No automated version management or dependency tracking — users must manually specify versions in training pipelines"],"requires":["Knowledge of desired corpus version number or release date","Access to OPUS website or documentation listing available versions","No technical prerequisites for download"],"input_types":["corpus name","version identifier (e.g., 'v2024')","release date"],"output_types":["versioned corpus files","release metadata (date, version number)","corpus composition for that version"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_5","uri":"capability://data.processing.analysis.low.resource.and.rare.language.pair.data.aggregation","name":"low-resource and rare language pair data aggregation","description":"Aggregates parallel data for 1,005 languages including low-resource and endangered languages, though with highly uneven coverage. Provides access to specialized multilingual corpora (MultiHPLT 2.7B pairs, MultiParaCrawl 2.8B, MultiCCAligned 2.4B) designed to cover broader language sets, alongside language-specific corpora for rare pairs. However, the long tail of 1,200+ corpora with minimal coverage means many language pairs have severely limited data.","intents":["Find parallel data for low-resource or endangered language pairs","Identify multilingual corpora that cover multiple language pairs simultaneously","Assess data availability for rare language combinations","Build translation systems for underrepresented languages using available OPUS data"],"best_for":["organizations working on low-resource language translation","researchers studying zero-shot or few-shot translation","linguists documenting endangered languages","NGOs and governments building translation systems for underserved languages"],"limitations":["Highly uneven language coverage — 1,005 languages but only 1,214 corpora total suggests most language pairs have zero or minimal data","No explicit language pair availability matrix — users must search individually to determine if a rare pair exists","Top 3 corpora (OpenSubtitles, NLLB, CCMatrix) represent 65.17% of data; specialized language pairs likely concentrated in long tail of small corpora","No documentation of which languages are covered by multilingual corpora (MultiHPLT, MultiParaCrawl, MultiCCAligned)","Data quality for low-resource pairs unknown — no quality filtering or confidence scores provided"],"requires":["Knowledge of ISO 639 language codes for rare languages","Willingness to work with potentially small datasets","Understanding that many rare language pairs may have zero coverage"],"input_types":["language pair (including rare/endangered language codes)","language name"],"output_types":["parallel corpus for rare language pair (if available)","metadata on corpus size and source","multilingual corpus files covering multiple language pairs"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_6","uri":"capability://data.processing.analysis.institutional.and.legal.document.parallel.corpus.access","name":"institutional and legal document parallel corpus access","description":"Provides access to large-scale institutional and legal parallel corpora sourced from EU documents and similar official sources, including Europarl (217.4M pairs), JRC-Acquis (215.9M), DGT (1.2B), and similar sources. These corpora contain formal, high-quality aligned sentence pairs from official multilingual documents, suitable for training translation systems on institutional and legal language.","intents":["Access high-quality formal language parallel data from EU and institutional sources","Train translation models on legal and regulatory language","Build translation systems for official documents and institutional communications","Obtain aligned data from authoritative multilingual sources with consistent quality"],"best_for":["organizations building translation systems for legal, regulatory, or institutional documents","government agencies and international organizations needing official translation data","practitioners requiring formal, high-quality parallel data","teams working on domain-specific MT for institutional language"],"limitations":["Institutional corpora are heavily weighted toward EU languages — limited coverage for non-EU language pairs","DGT corpus (1.2B pairs) dominates institutional data, potentially creating bias toward EU institutional language","No documentation of preprocessing, normalization, or quality filtering applied to institutional documents","Licensing terms for institutional data unknown — may have restrictions on commercial use or attribution requirements","No metadata on document types, time periods, or regulatory domains covered by institutional corpora"],"requires":["Access to OPUS interface","Understanding that institutional data is formal and may not suit informal translation tasks","Potential need to handle EU-centric language bias"],"input_types":["corpus name (e.g., 'Europarl', 'JRC-Acquis', 'DGT')","language pair"],"output_types":["aligned sentence pairs from institutional documents","formal language parallel data","corpus metadata including source and size"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_7","uri":"capability://data.processing.analysis.web.crawled.general.domain.parallel.corpus.aggregation","name":"web-crawled general-domain parallel corpus aggregation","description":"Aggregates large-scale web-crawled general-domain parallel corpora including CCMatrix (17.1B pairs, 16.61% of collection), ParaCrawl (4.6B pairs, 4.50%), and WikiMatrix (933.6M pairs), providing broad-coverage training data sourced from web documents and Wikipedia. These corpora enable training of general-purpose translation systems covering diverse topics and language styles extracted from web sources.","intents":["Access large-scale general-domain parallel data for broad-coverage translation models","Train translation systems on diverse web-sourced content and topics","Obtain Wikipedia-based parallel data for encyclopedic language translation","Build general-purpose MT systems using web-crawled data at scale"],"best_for":["organizations building general-purpose translation systems","practitioners needing large-scale diverse training data","teams training broad-coverage MT models for multiple domains","researchers studying web-scale parallel corpora"],"limitations":["Web-crawled data quality is variable — no explicit quality filtering or noise metrics provided","No documentation of deduplication across CCMatrix, ParaCrawl, and WikiMatrix — potential overlap and duplicate sentence pairs","Preprocessing and normalization applied to web-crawled data unknown","No filtering by topic, domain, or content type — data is heterogeneous and unstructured","Alignment quality for web-crawled pairs unknown — no confidence scores or quality metrics provided","CCMatrix and ParaCrawl together represent 21.11% of collection, but composition and overlap unknown"],"requires":["Tolerance for variable data quality typical of web-crawled sources","Ability to handle diverse topics and language styles","Sufficient storage for large corpus downloads (CCMatrix alone is 17.1B pairs)"],"input_types":["corpus name (e.g., 'CCMatrix', 'ParaCrawl', 'WikiMatrix')","language pair"],"output_types":["web-crawled parallel sentence pairs","general-domain aligned data","corpus metadata including source and size"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_8","uri":"capability://data.processing.analysis.subtitle.based.conversational.language.parallel.corpus.access","name":"subtitle-based conversational language parallel corpus access","description":"Provides access to large-scale subtitle-based parallel corpora including OpenSubtitles (27.2B pairs, 26.47% of collection), TED2020 (153.1M), and NeuLab-TedTalks (79.7M), sourced from movie and TV subtitles and transcribed talks. These corpora contain conversational, informal language suitable for training translation systems on spoken language, dialogue, and informal registers.","intents":["Access large-scale conversational language parallel data from subtitles","Train translation models on informal, spoken language and dialogue","Build translation systems for movies, TV, and video content","Obtain TED talk transcripts for formal conversational language translation"],"best_for":["organizations building translation systems for video, movies, and entertainment content","practitioners needing conversational and informal language data","teams training MT models for spoken language and dialogue","researchers studying informal language translation"],"limitations":["OpenSubtitles v2024 is the largest single corpus in OPUS (27.2B pairs) — dominates subtitle-based data and may create bias toward subtitle language","Subtitle data quality is variable — subtitles often contain OCR errors, abbreviations, and informal language","No documentation of preprocessing or quality filtering applied to subtitle data","Licensing terms for OpenSubtitles and other subtitle corpora unknown — may have restrictions on commercial use","Temporal bias — subtitle data reflects language from specific time periods and media sources","No filtering by content type, formality level, or language register"],"requires":["Tolerance for informal language, abbreviations, and potential OCR errors in subtitle data","Understanding that subtitle language may not suit formal or technical translation tasks","Awareness of potential licensing restrictions on subtitle data"],"input_types":["corpus name (e.g., 'OpenSubtitles', 'TED2020', 'NeuLab-TedTalks')","language pair"],"output_types":["subtitle-based parallel sentence pairs","conversational language aligned data","corpus metadata including source and size"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__cap_9","uri":"capability://data.processing.analysis.nllb.multilingual.machine.translation.training.data.access","name":"nllb multilingual machine translation training data access","description":"Provides access to NLLB (No Language Left Behind) corpus containing 22.7B aligned sentence pairs (22.09% of OPUS collection), a large-scale multilingual dataset created by Meta for training translation models covering 200+ languages. The NLLB corpus represents a significant component of OPUS and enables training of multilingual translation systems with broad language coverage.","intents":["Access the NLLB multilingual training dataset for building translation models","Train translation systems covering 200+ languages using NLLB data","Obtain large-scale multilingual parallel data from a single curated source","Build multilingual MT systems using NLLB as a primary training corpus"],"best_for":["organizations building multilingual translation systems","practitioners training models for 200+ languages simultaneously","teams creating translation systems aligned with Meta's NLLB initiative","researchers studying multilingual machine translation"],"limitations":["NLLB corpus composition and source languages unknown — no documentation of which languages are covered or their relative proportions","No documentation of preprocessing, quality filtering, or alignment methodology used in NLLB","NLLB represents 22.09% of OPUS collection — significant but not dominant; users may need to combine with other corpora","Licensing terms for NLLB data unknown — may have restrictions on commercial use or attribution requirements","No information on how NLLB overlaps with other multilingual corpora (MultiHPLT, MultiParaCrawl, MultiCCAligned)"],"requires":["Access to OPUS interface","Understanding that NLLB is a curated multilingual dataset with specific design choices","Potential need to combine NLLB with other corpora for comprehensive language coverage"],"input_types":["corpus name ('NLLB')","language pair"],"output_types":["NLLB multilingual parallel sentence pairs","aligned data for 200+ languages","corpus metadata including size and composition"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opus__headline","uri":"capability://data.processing.analysis.parallel.corpus.dataset.for.machine.translation.research","name":"parallel corpus dataset for machine translation research","description":"OPUS is an extensive open parallel corpus dataset containing billions of aligned sentences across hundreds of language pairs, ideal for machine translation and NLP research.","intents":["best parallel corpus for translation","parallel dataset for NLP research","open dataset for machine translation","corpus for multilingual sentence alignment","large-scale dataset for language modeling"],"best_for":["machine translation researchers","NLP developers"],"limitations":["data quality variability","no real-time updates"],"requires":["technical expertise in data handling"],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Web browser with internet access","No authentication required","Knowledge of ISO 639 language codes or language names","Web browser with download capability","Sufficient local storage (individual corpora range from millions to billions of sentence pairs)","No authentication required for public corpora","Ability to parse downloaded format (format unknown without accessing website)","Access to OPUS interface","Understanding that specialized domain corpora are small and may need to be combined with general-domain data","Domain expertise to evaluate suitability for specific applications"],"failure_modes":["Language pair availability is sparse — 1,005 languages but only 1,214 corpora total means many language pairs have zero or minimal coverage","No explicit language pair availability matrix provided — users must search individually to determine if a specific pair exists","Search interface does not expose alignment confidence scores, quality metrics, or preprocessing applied to sentence pairs","Cannot filter by data quality, domain, or temporal characteristics — only by corpus name and language pair","File format specifications not documented — unclear whether downloads are provided as parallel files, TMX, Moses format, or plain text","No API or programmatic download interface documented — appears to require manual web interface interaction","Download bandwidth and size constraints unknown — no documentation of rate limiting or maximum file sizes","Licensing terms per corpus unknown — critical gap for commercial use; no explicit attribution or usage rights documented","No built-in deduplication across overlapping corpora — users may download duplicate sentence pairs if selecting multiple sources","Sentence pairs provided without alignment confidence scores or quality metrics — no filtering options for data quality","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.059Z","last_scraped_at":null,"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=opus","compare_url":"https://unfragile.ai/compare?artifact=opus"}},"signature":"jai5D8ELOFbPMSm8zIJzLxwHEo9I8YVlzjHg6pNYRPEmNV3yNW1Vnt486q6WkGxftEJ5wOcpQi9J5mwWX6xsCQ==","signedAt":"2026-06-20T03:43:39.280Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/opus","artifact":"https://unfragile.ai/opus","verify":"https://unfragile.ai/api/v1/verify?slug=opus","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"}}