{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_corpora","slug":"corpora","name":"Corpora","type":"product","url":"https://www.askcorpora.com","page_url":"https://unfragile.ai/corpora","categories":["research-search"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_corpora__cap_0","uri":"capability://data.processing.analysis.natural.language.data.querying.with.conversational.interface","name":"natural language data querying with conversational interface","description":"Converts natural language questions into structured database queries through a conversational AI layer that interprets user intent and translates it to SQL or equivalent query syntax. The system maintains conversation context across multiple turns, allowing users to refine queries iteratively without re-specifying the full data context. This approach abstracts away query language complexity while preserving the ability to explore data through multi-turn dialogue.","intents":["I want to ask questions about my dataset in plain English without learning SQL","I need to explore data interactively and refine queries based on results","I want non-technical team members to be able to query our database directly"],"best_for":["Business analysts and researchers without SQL expertise","Teams democratizing data access across non-technical stakeholders","Organizations reducing dependency on data engineers for ad-hoc queries"],"limitations":["Accuracy depends on training data quality and schema clarity — ambiguous column names or complex relationships may produce incorrect queries","Context window limitations may degrade performance on very long conversation histories (typically 10-20+ turns)","Complex multi-table joins or window functions may not be reliably generated from natural language"],"requires":["Connected data source (database, CSV, or API endpoint)","Schema metadata or data dictionary for the AI to reference","Internet connection for cloud-based inference"],"input_types":["natural language text (questions, follow-ups)","structured schema metadata"],"output_types":["query results (tabular data)","natural language explanations of results","visualization-ready structured data"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_1","uri":"capability://tool.use.integration.custom.bot.builder.with.no.code.configuration","name":"custom bot builder with no-code configuration","description":"Provides a visual interface to define custom conversational agents without requiring prompt engineering or code. Users configure bot behavior through form-based settings (system instructions, knowledge sources, response constraints) and the platform generates the underlying prompt templates and routing logic. This approach democratizes bot creation by abstracting prompt engineering complexity while maintaining customization through structured configuration rather than free-form text editing.","intents":["I want to create a domain-specific chatbot for my data without writing prompts","I need to configure bot personality, guardrails, and knowledge sources through a UI","I want to iterate on bot behavior without technical expertise in prompt design"],"best_for":["Non-technical domain experts building specialized bots","Product teams prototyping conversational interfaces rapidly","Organizations standardizing bot creation across teams without prompt engineering bottlenecks"],"limitations":["No-code approach limits advanced customization — complex reasoning patterns or multi-step orchestration may require fallback to API-based configuration","Predefined configuration templates may not cover all use cases, forcing users to choose closest approximation","Difficult to version control or audit bot configuration changes without explicit export/import mechanisms"],"requires":["Corpora account with bot creation permissions","Knowledge source or data source to bind to the bot","Web browser with JavaScript enabled"],"input_types":["form inputs (system instructions, constraints, knowledge sources)","uploaded documents or connected data sources"],"output_types":["configured bot instance (API endpoint or embedded widget)","bot configuration metadata (exportable format unknown)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_2","uri":"capability://data.processing.analysis.analytics.and.insights.generation.from.conversational.interactions","name":"analytics and insights generation from conversational interactions","description":"Automatically extracts patterns, trends, and actionable insights from conversation logs and query results through statistical analysis and LLM-based summarization. The system tracks which questions are asked most frequently, identifies data exploration patterns, and generates natural language summaries of key findings. This capability transforms raw interaction data into business intelligence without requiring manual analysis.","intents":["I want to understand what questions users are asking about our data most frequently","I need to identify trends or patterns in how teams are exploring our datasets","I want automated summaries of key insights from conversation logs"],"best_for":["Data governance teams monitoring data usage and access patterns","Product managers understanding user behavior with conversational interfaces","Researchers analyzing how stakeholders interact with datasets"],"limitations":["Insight quality depends on conversation volume — low-traffic bots may produce statistically insignificant patterns","LLM-based summarization may hallucinate or over-generalize from limited data samples","No built-in privacy controls for sensitive conversation content in analytics — may expose PII or proprietary queries in summaries"],"requires":["Minimum conversation history (threshold unknown, likely 50+ interactions)","Opt-in analytics tracking enabled on bot instances"],"input_types":["conversation logs (questions, results, user interactions)","query execution metadata (timing, data volume, result counts)"],"output_types":["dashboard visualizations (query frequency, user segments)","natural language insight summaries","trend reports (exportable format unknown)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_3","uri":"capability://data.processing.analysis.multi.source.data.integration.and.schema.mapping","name":"multi-source data integration and schema mapping","description":"Connects to multiple data sources (databases, APIs, CSV uploads, cloud storage) and automatically infers or accepts schema definitions to enable unified querying across heterogeneous data. The system maintains a unified schema layer that maps source-specific field names and types to a canonical representation, allowing conversational queries to transparently span multiple sources. This abstraction enables users to query across silos without understanding underlying data structure differences.","intents":["I want to query data from multiple databases or APIs as if they were a single source","I need to map fields across different data sources with different naming conventions","I want to enable cross-source analytics without building custom ETL pipelines"],"best_for":["Organizations with data spread across multiple systems (CRM, data warehouse, APIs)","Research teams combining datasets from different sources","Teams avoiding custom ETL development for exploratory analysis"],"limitations":["Schema inference may be inaccurate for complex or nested data structures — manual schema definition often required","Cross-source joins may be slow or impossible if sources don't support efficient federation","Data consistency issues (stale caches, eventual consistency) not explicitly handled — results may reflect different temporal snapshots"],"requires":["Connection credentials for each data source (API keys, database credentials, etc.)","Network access from Corpora infrastructure to source systems","Schema metadata or ability to auto-discover schema from sources"],"input_types":["data source connection parameters","schema definitions (auto-inferred or manually specified)","field mapping rules (source field → canonical field)"],"output_types":["unified query results (merged from multiple sources)","schema metadata (canonical representation)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_4","uri":"capability://memory.knowledge.conversational.context.and.memory.management.across.sessions","name":"conversational context and memory management across sessions","description":"Maintains conversation state and user context across multiple sessions, allowing bots to remember previous interactions, user preferences, and data exploration history. The system stores conversation metadata and relevant context in a session store (likely vector embeddings for semantic recall) and retrieves relevant prior context when answering new questions. This enables multi-session conversations where users can reference previous findings or continue exploratory analysis without re-establishing context.","intents":["I want my bot to remember what I asked last week and build on those findings","I need the bot to maintain user preferences and personalize responses across sessions","I want to reference previous query results in new questions without re-running them"],"best_for":["Long-running research projects requiring continuity across sessions","Teams with recurring data exploration workflows","Users building complex analyses incrementally over time"],"limitations":["Context retrieval may be lossy — semantic similarity matching may miss relevant prior context if phrased differently","Memory grows unbounded without explicit pruning — very long conversation histories may degrade retrieval performance","No explicit control over what context is retained — users cannot selectively forget or archive old conversations"],"requires":["User authentication to associate sessions with identity","Session storage backend (cloud-based, likely with retention limits)","Embedding model for semantic context retrieval"],"input_types":["conversation turns (questions, results, user feedback)","session metadata (timestamps, user ID, bot ID)"],"output_types":["retrieved context (relevant prior conversations)","session summaries (exportable format unknown)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_5","uri":"capability://data.processing.analysis.response.formatting.and.visualization.generation","name":"response formatting and visualization generation","description":"Automatically formats query results and generates appropriate visualizations (charts, tables, summaries) based on result type and user context. The system infers visualization type from data shape (time series → line chart, categorical distribution → bar chart) and generates visualization specifications (Vega-Lite, Plotly, or similar) that can be rendered in the UI or exported. This capability makes data exploration more intuitive by presenting results in the most appropriate visual form without user configuration.","intents":["I want query results automatically formatted as charts instead of raw tables","I need the bot to choose the best visualization for the data type","I want to export visualizations for presentations or reports"],"best_for":["Non-technical users who benefit from visual data exploration","Teams creating reports or presentations from query results","Dashboarding use cases where automatic visualization saves configuration time"],"limitations":["Automatic visualization selection may be suboptimal for domain-specific use cases — users may need manual override","Complex multi-dimensional data may not have a clear optimal visualization","Export formats may be limited (PNG, SVG, JSON) — no native PowerPoint or Google Slides integration"],"requires":["Query results in structured format (tabular or time-series)","Visualization rendering engine (browser-based, likely D3.js or Vega-Lite)"],"input_types":["structured query results (rows, columns, data types)","user context (previous visualizations, preferences)"],"output_types":["visualization specifications (Vega-Lite JSON or similar)","rendered visualizations (HTML, PNG, SVG)","formatted text summaries"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_6","uri":"capability://memory.knowledge.knowledge.source.binding.and.document.based.context.injection","name":"knowledge source binding and document-based context injection","description":"Allows users to upload or link documents, knowledge bases, or external sources that the bot uses as context for answering questions. The system ingests these sources, creates embeddings, and retrieves relevant passages during query execution to ground responses in provided knowledge. This enables bots to answer questions about specific datasets, documentation, or domain knowledge without requiring users to manually specify context in each query.","intents":["I want my bot to answer questions based on our internal documentation or knowledge base","I need the bot to cite sources when answering questions","I want to create a domain-specific bot trained on our proprietary data"],"best_for":["Organizations creating internal knowledge assistants","Teams building domain-specific bots with proprietary knowledge","Support teams automating FAQ responses with source attribution"],"limitations":["Retrieval quality depends on embedding model and chunking strategy — long documents may lose context across chunk boundaries","No built-in deduplication or conflict resolution if knowledge sources contain contradictory information","Updates to knowledge sources may require re-indexing with unclear latency (likely minutes to hours)"],"requires":["Documents in supported formats (PDF, TXT, Markdown, or URL)","Embedding model for semantic indexing (likely OpenAI or similar)","Vector storage backend for retrieval (likely Pinecone, Weaviate, or similar)"],"input_types":["documents (PDF, TXT, Markdown, HTML)","URLs to external knowledge sources","structured data (JSON, CSV) as knowledge"],"output_types":["indexed embeddings (stored in vector DB)","retrieved passages (with source attribution)","augmented responses (grounded in knowledge sources)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_7","uri":"capability://data.processing.analysis.query.result.caching.and.performance.optimization","name":"query result caching and performance optimization","description":"Caches frequently executed queries and their results to reduce latency and computational cost for repeated or similar queries. The system uses semantic similarity matching to identify when new queries are equivalent to cached results and returns cached data when appropriate. This optimization is transparent to users and improves performance for exploratory workflows where users often refine similar queries iteratively.","intents":["I want repeated queries to return instantly from cache","I need to reduce API costs by avoiding redundant query execution","I want faster response times for common data exploration patterns"],"best_for":["Teams with high query volume and repetitive exploration patterns","Cost-sensitive organizations using expensive data sources","Interactive exploration workflows where latency matters"],"limitations":["Cache invalidation strategy unclear — stale data may be returned if underlying source updates","Semantic similarity matching may be too aggressive or conservative, causing cache misses or incorrect hits","Cache storage costs and retention policies not documented — unclear how long results are cached"],"requires":["Query execution history (to identify patterns)","Cache storage backend (likely Redis or similar)","Embedding model for semantic similarity matching"],"input_types":["natural language queries","query execution metadata"],"output_types":["cached or fresh query results","cache hit/miss metadata (likely not exposed to users)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_corpora__cap_8","uri":"capability://safety.moderation.guardrails.and.response.safety.constraints","name":"guardrails and response safety constraints","description":"Implements configurable constraints on bot responses to prevent hallucinations, enforce data access policies, and ensure responses stay within defined boundaries. The system can restrict responses to knowledge sources only (preventing hallucinations), enforce data masking for sensitive fields, and validate responses against user-defined rules before returning them. This capability enables safe deployment of bots in regulated environments or with sensitive data.","intents":["I want to prevent my bot from making up data or hallucinating","I need to enforce data access policies (e.g., users only see their own data)","I want to ensure responses comply with regulatory requirements"],"best_for":["Organizations handling regulated data (healthcare, finance, PII)","Teams deploying bots in high-stakes environments","Compliance-focused organizations requiring audit trails"],"limitations":["Guardrails may be overly restrictive, causing bots to refuse valid requests","No built-in audit logging or compliance reporting — organizations must implement their own","Enforcement mechanism unclear — guardrails may be advisory rather than hard constraints"],"requires":["Guardrail rule definitions (format unknown, likely JSON or DSL)","Data access policies or role-based access control (RBAC) configuration","Response validation logic (custom code or predefined rules)"],"input_types":["guardrail rules (constraints, policies, validation logic)","user context (roles, permissions, data access levels)"],"output_types":["validated responses (or rejection if guardrails violated)","audit logs (if implemented)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Connected data source (database, CSV, or API endpoint)","Schema metadata or data dictionary for the AI to reference","Internet connection for cloud-based inference","Corpora account with bot creation permissions","Knowledge source or data source to bind to the bot","Web browser with JavaScript enabled","Minimum conversation history (threshold unknown, likely 50+ interactions)","Opt-in analytics tracking enabled on bot instances","Connection credentials for each data source (API keys, database credentials, etc.)","Network access from Corpora infrastructure to source systems"],"failure_modes":["Accuracy depends on training data quality and schema clarity — ambiguous column names or complex relationships may produce incorrect queries","Context window limitations may degrade performance on very long conversation histories (typically 10-20+ turns)","Complex multi-table joins or window functions may not be reliably generated from natural language","No-code approach limits advanced customization — complex reasoning patterns or multi-step orchestration may require fallback to API-based configuration","Predefined configuration templates may not cover all use cases, forcing users to choose closest approximation","Difficult to version control or audit bot configuration changes without explicit export/import mechanisms","Insight quality depends on conversation volume — low-traffic bots may produce statistically insignificant patterns","LLM-based summarization may hallucinate or over-generalize from limited data samples","No built-in privacy controls for sensitive conversation content in analytics — may expose PII or proprietary queries in summaries","Schema inference may be inaccurate for complex or nested data structures — manual schema definition often required","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":"2026-05-24T12:16:30.282Z","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=corpora","compare_url":"https://unfragile.ai/compare?artifact=corpora"}},"signature":"pBIg62vJSn7JMj/saRscDLKoBtz/ucYXrIwoH3m4OeHyfdRraBXKbwLLhXVAhlqaY5AYcHXabNmMOtEKT49UDQ==","signedAt":"2026-06-20T04:51:56.699Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/corpora","artifact":"https://unfragile.ai/corpora","verify":"https://unfragile.ai/api/v1/verify?slug=corpora","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"}}