{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ai-dashboard-template","slug":"ai-dashboard-template","name":"AI Dashboard Template","type":"template","url":"https://github.com/vercel/ai-sdk-preview-internal-knowledge-base","page_url":"https://unfragile.ai/ai-dashboard-template","categories":["documentation","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ai-dashboard-template__cap_0","uri":"capability://data.processing.analysis.document.ingestion.and.vectorization.pipeline","name":"document-ingestion-and-vectorization-pipeline","description":"Accepts uploaded documents (PDF, TXT, Markdown) and automatically chunks them into semantic segments, then embeds each chunk using Vercel AI SDK's embedding models (supporting OpenAI, Anthropic, or local models). The pipeline stores vectors in a vector database (likely Pinecone or similar) with metadata linking back to source documents, enabling semantic search without manual preprocessing.","intents":["I need to upload a knowledge base of internal docs and make them searchable without writing ETL code","I want to ingest multiple document formats and have them automatically chunked and embedded","I need to build a RAG system where documents are indexed once and queried many times"],"best_for":["teams building internal knowledge bases with minimal DevOps overhead","companies migrating from keyword search to semantic search","developers prototyping RAG applications without managing vector infrastructure"],"limitations":["chunking strategy is fixed (likely token-based or fixed-size) — no support for custom semantic chunking logic","vector database connection requires external service credentials — no local-only option","document preprocessing happens synchronously — large batches may timeout in serverless environments","no built-in deduplication — uploading the same document twice creates duplicate embeddings"],"requires":["Node.js 18+ or serverless runtime compatible with Vercel","API key for embedding model provider (OpenAI, Anthropic, or self-hosted)","Vector database account and connection string (Pinecone, Weaviate, or similar)","File storage (Vercel Blob, S3, or similar) for original documents"],"input_types":["PDF files","plain text (.txt)","Markdown (.md)","potentially JSON or CSV"],"output_types":["vector embeddings (float arrays)","document metadata (title, source, chunk index)","indexed records in vector database"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_1","uri":"capability://search.retrieval.semantic.search.with.relevance.ranking","name":"semantic-search-with-relevance-ranking","description":"Converts user search queries into embeddings using the same model as document ingestion, then performs vector similarity search against the indexed corpus. Returns ranked results ordered by cosine similarity score, with optional filtering by document metadata (source, date, category). Implements re-ranking via cross-encoder or LLM-based relevance scoring to improve result quality beyond raw vector similarity.","intents":["I want users to search my knowledge base with natural language, not keywords","I need search results ranked by relevance, not just vector similarity","I want to filter search results by document source or metadata"],"best_for":["internal knowledge base applications where semantic understanding matters","teams building Q&A systems over proprietary documentation","organizations replacing Elasticsearch with semantic search"],"limitations":["vector similarity alone can return semantically similar but contextually irrelevant results — requires re-ranking for production quality","cold-start problem: new documents need time to be indexed before appearing in search","query embedding latency adds 100-300ms per search depending on model provider","no support for hybrid search (combining keyword + semantic) — purely vector-based"],"requires":["indexed vector database with at least 1000 documents for meaningful results","same embedding model used for both documents and queries","optional: LLM API key if implementing re-ranking"],"input_types":["natural language query (string)","optional metadata filters (JSON object)"],"output_types":["ranked list of document chunks with similarity scores","source document metadata","highlighted excerpts"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_10","uri":"capability://data.processing.analysis.feedback.loop.for.rag.quality.improvement","name":"feedback-loop-for-rag-quality-improvement","description":"Collects user feedback on search results and chat responses (thumbs up/down, explicit ratings, corrections). Analyzes feedback to identify low-quality results, hallucinations, and missing documents. Provides recommendations for improving RAG quality (e.g., re-chunking documents, adjusting similarity thresholds, adding new documents). Supports A/B testing of different RAG configurations.","intents":["I want to measure and improve the quality of my RAG system over time","I need to identify which documents are causing poor search results","I want to A/B test different embedding models or prompts"],"best_for":["teams iterating on RAG quality in production","organizations with high standards for answer accuracy","developers optimizing RAG systems based on real user data"],"limitations":["feedback collection is optional — users may not provide ratings, limiting data quality","feedback analysis requires manual interpretation — no automated root cause analysis","A/B testing adds complexity — requires splitting traffic and managing multiple RAG configurations","feedback loop is slow — improvements may take weeks to show impact"],"requires":["feedback storage (database or analytics platform)","optional: A/B testing framework (LaunchDarkly, Statsig, or custom)","optional: analytics/BI tool for feedback analysis"],"input_types":["user feedback (rating, comment, correction)","search result metadata (query, results, user action)","chat response metadata (query, response, user rating)"],"output_types":["feedback aggregation (average rating, common complaints)","quality metrics (accuracy, relevance, hallucination rate)","improvement recommendations","A/B test results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_11","uri":"capability://automation.workflow.knowledge.base.freshness.and.update.notifications","name":"knowledge-base-freshness-and-update-notifications","description":"Tracks when documents were last updated and notifies administrators when documents exceed a configurable age threshold (e.g., 'notify if any document is older than 6 months'). Supports scheduled re-indexing of documents and tracks which documents have been updated since the last index. Provides a dashboard view of document freshness and allows marking documents as 'verified' or 'outdated'.","intents":["identify outdated documents in the knowledge base that need updating","ensure the knowledge base stays current by tracking document age","schedule periodic re-indexing of documents to refresh embeddings"],"best_for":["organizations with rapidly changing documentation (e.g., product teams, support teams)","teams needing to maintain knowledge base quality over time"],"limitations":["no automatic document updates — freshness tracking is informational only","no version control for documents — can't compare old vs new versions","notifications are not intelligent (simple age-based thresholds, no content change detection)","no automatic re-indexing; administrators must manually trigger updates","no integration with source document systems (e.g., Confluence, Google Docs) for automatic syncing"],"requires":["document metadata with creation and update timestamps","database to store freshness metadata","optional notification system (email, Slack, etc.)"],"input_types":["document metadata (title, last_updated timestamp)","freshness threshold configuration (days)"],"output_types":["freshness status per document (fresh, stale, outdated)","notifications for stale documents","dashboard view of knowledge base freshness"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_2","uri":"capability://text.generation.language.streaming.rag.chat.interface","name":"streaming-rag-chat-interface","description":"Implements a conversational interface where user messages trigger a retrieval-augmented generation (RAG) pipeline: (1) embed the user query, (2) retrieve relevant documents from the vector database, (3) construct a prompt with retrieved context, (4) stream the LLM response token-by-token to the client. Uses Vercel AI SDK's streaming primitives to handle backpressure and connection management, enabling real-time chat without buffering entire responses.","intents":["I want users to ask questions about my knowledge base and get cited answers","I need a chat interface that streams responses in real-time instead of waiting for full completion","I want to show users which documents were used to answer their question"],"best_for":["teams building internal Q&A chatbots","companies deploying knowledge base assistants to employees","developers prototyping RAG applications with streaming UX"],"limitations":["context window is fixed — cannot include more than ~4000 tokens of retrieved documents without truncation","no multi-turn conversation memory — each query is independent (requires session state management for context)","streaming adds complexity to error handling — partial responses may be sent before failure detection","LLM hallucination is not mitigated — model can generate plausible-sounding answers not grounded in retrieved documents"],"requires":["LLM API key (OpenAI, Anthropic, or compatible provider)","vector database with indexed documents","client-side support for Server-Sent Events (SSE) or WebSocket streaming","Node.js 18+ for streaming response handling"],"input_types":["user message (string)","optional conversation history (array of messages)","optional system prompt override"],"output_types":["streamed text response (token-by-token)","source document citations (metadata)","usage metrics (tokens, latency)"],"categories":["text-generation-language","memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_3","uri":"capability://automation.workflow.admin.dashboard.for.corpus.management","name":"admin-dashboard-for-corpus-management","description":"Provides a web UI for administrators to view indexed documents, monitor embedding status, delete or re-index documents, and adjust search parameters (e.g., similarity threshold, chunk size). Built with React/Next.js, it connects to backend APIs that manage the vector database and document storage. Includes analytics on search queries, user engagement, and document coverage.","intents":["I need to see what documents are in my knowledge base and their indexing status","I want to remove outdated documents or re-index them with new parameters","I need to monitor which documents are being searched and how often"],"best_for":["knowledge base administrators managing document lifecycle","teams needing visibility into RAG system performance","organizations with compliance requirements for document tracking"],"limitations":["no bulk operations — documents must be deleted/re-indexed individually or via API","analytics are basic — no advanced filtering or custom report generation","no version control for documents — re-indexing overwrites previous embeddings without history","access control is basic — no fine-grained permissions (all admins have full access)"],"requires":["authentication mechanism (OAuth, API key, or session-based)","backend API endpoints for document CRUD operations","vector database with admin-level access","React 18+ or compatible frontend framework"],"input_types":["document metadata (title, source, category)","search parameter adjustments (threshold, chunk size)","bulk operations (delete, re-index)"],"output_types":["document list with status (indexed, pending, failed)","search analytics (query count, top queries, user engagement)","indexing logs and error messages"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_4","uri":"capability://memory.knowledge.multi.model.embedding.abstraction","name":"multi-model-embedding-abstraction","description":"Provides a unified interface for switching between embedding models (OpenAI, Anthropic, Cohere, local models) without changing application code. The abstraction layer handles model-specific API calls, response parsing, and dimension normalization. Supports batch embedding for efficient processing of multiple documents and caching of embeddings to reduce API costs.","intents":["I want to experiment with different embedding models without rewriting code","I need to reduce embedding API costs by caching or using cheaper models","I want to use a local embedding model for privacy without changing my application"],"best_for":["teams evaluating embedding models for production use","organizations with privacy requirements (local embeddings)","cost-conscious teams optimizing LLM spend"],"limitations":["different embedding models produce vectors of different dimensions — switching models requires re-indexing the entire corpus","embedding quality varies significantly between models — cheaper models may hurt search relevance","batch embedding has rate limits — large batches may be throttled by API providers","caching adds complexity — stale embeddings may be returned if documents are updated"],"requires":["API keys for chosen embedding model provider(s)","optional: local embedding model binary (e.g., Ollama) if using local embeddings","vector database supporting the chosen embedding dimension"],"input_types":["text to embed (string or array of strings)","model identifier (string)","optional: embedding parameters (dimension, pooling strategy)"],"output_types":["embedding vector (float array)","embedding dimension (integer)","model metadata (name, version, cost)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_5","uri":"capability://text.generation.language.prompt.engineering.with.retrieved.context","name":"prompt-engineering-with-retrieved-context","description":"Constructs system and user prompts that include retrieved documents as context, with configurable formatting (e.g., markdown, XML tags, structured JSON). Implements prompt templates that guide the LLM to cite sources, avoid hallucination, and stay within the knowledge base scope. Supports dynamic prompt adjustment based on query type (factual, analytical, creative) and document relevance.","intents":["I want the LLM to cite sources when answering questions","I need to prevent the model from making up information outside the knowledge base","I want different answer styles depending on the type of question"],"best_for":["teams building trustworthy Q&A systems with source attribution","organizations with compliance requirements for answer traceability","developers fine-tuning RAG quality through prompt optimization"],"limitations":["prompt injection attacks are possible if user queries are not sanitized — malicious input can override system instructions","LLM may still hallucinate despite context — no guarantee of grounding in retrieved documents","prompt length is limited by model context window — cannot include unlimited retrieved documents","citation accuracy depends on LLM behavior — model may cite documents it didn't actually use"],"requires":["LLM API key (OpenAI, Anthropic, or compatible)","retrieved documents with clear source metadata","optional: prompt validation library to detect injection attempts"],"input_types":["system prompt template (string with placeholders)","user query (string)","retrieved documents (array of objects with content and metadata)","optional: query classification (factual, analytical, etc.)"],"output_types":["formatted prompt (string)","LLM response with citations","confidence score (optional)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_6","uri":"capability://automation.workflow.real.time.document.sync.and.invalidation","name":"real-time-document-sync-and-invalidation","description":"Monitors source documents for changes and automatically re-indexes modified documents without requiring manual intervention. Implements change detection via file timestamps, content hashing, or webhook notifications from document sources. Invalidates stale embeddings and queues documents for re-embedding, with configurable batch sizes and scheduling to avoid overwhelming the embedding API.","intents":["I want my knowledge base to stay in sync with source documents automatically","I need to re-index documents when they change without manual uploads","I want to schedule re-indexing during off-peak hours to reduce costs"],"best_for":["teams with frequently-updated knowledge bases (wikis, documentation)","organizations syncing from external sources (Notion, Confluence, GitHub)","cost-conscious teams optimizing embedding API usage"],"limitations":["change detection adds latency — documents may not be re-indexed immediately after updates","batch scheduling can cause stale embeddings to serve for hours — not suitable for real-time requirements","webhook-based sync requires source system integration — not all document sources support webhooks","no conflict resolution — if a document is updated while re-indexing, the newer version may be lost"],"requires":["document source with change notification capability (file system, API, webhook)","background job queue (e.g., Bull, Temporal, or serverless task scheduler)","embedding API with sufficient rate limits for batch re-indexing"],"input_types":["document source configuration (path, API endpoint, webhook URL)","change detection strategy (timestamp, hash, webhook)","re-indexing schedule (cron expression)"],"output_types":["re-indexing job status (queued, in-progress, completed)","updated embeddings in vector database","sync logs and error reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_7","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation-history-and-context-management","description":"Maintains multi-turn conversation state by storing user messages and assistant responses, with optional summarization of long conversations to fit within LLM context windows. Implements context windowing strategies (e.g., sliding window, summary + recent messages) to balance conversation coherence with token limits. Supports session persistence to resume conversations across browser sessions.","intents":["I want users to have multi-turn conversations that remember previous context","I need to manage context windows so conversations don't exceed token limits","I want to persist conversations so users can resume them later"],"best_for":["teams building conversational knowledge base assistants","applications requiring long-running user interactions","organizations needing conversation audit trails for compliance"],"limitations":["context window management is lossy — summarization may lose important details","session persistence requires external storage — no built-in database","conversation state grows unbounded — old conversations must be archived or deleted","multi-turn RAG is expensive — each turn requires new document retrieval and LLM calls"],"requires":["session storage (database, Redis, or browser localStorage)","LLM API key for summarization (if using summary-based context management)","optional: conversation analytics database"],"input_types":["user message (string)","conversation ID (string)","optional: context window strategy (sliding, summary, etc.)"],"output_types":["updated conversation history (array of messages)","context window summary (string)","session metadata (user ID, timestamp, token count)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_8","uri":"capability://data.processing.analysis.usage.tracking.and.cost.monitoring","name":"usage-tracking-and-cost-monitoring","description":"Logs all API calls (embeddings, LLM completions, vector searches) with token counts, latency, and cost estimates. Aggregates usage metrics by user, document, query type, and time period. Provides dashboards and alerts for cost anomalies, quota overages, and performance degradation. Integrates with billing systems to track actual costs against estimates.","intents":["I need to understand how much my RAG system costs to operate","I want to identify which documents or users are driving the most API usage","I need alerts when costs exceed budget thresholds"],"best_for":["teams managing RAG systems in production with cost constraints","organizations with chargeback models (billing departments per usage)","developers optimizing RAG performance and cost"],"limitations":["cost estimates are approximate — actual billing may differ based on provider pricing changes","logging adds latency — high-volume systems may see 50-100ms overhead per request","no built-in cost optimization recommendations — requires manual analysis","cost tracking is post-hoc — cannot prevent overspending in real-time"],"requires":["analytics database (PostgreSQL, BigQuery, or similar)","API keys for cost estimation (OpenAI pricing API, etc.)","optional: alerting system (email, Slack, PagerDuty)"],"input_types":["API call metadata (model, tokens, latency)","user/document identifiers","cost thresholds and alert rules"],"output_types":["usage metrics (tokens, API calls, cost)","cost breakdown by dimension (user, document, model)","alerts and anomaly reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai-dashboard-template__cap_9","uri":"capability://safety.moderation.access.control.and.document.permissions","name":"access-control-and-document-permissions","description":"Implements role-based access control (RBAC) to restrict which users can search which documents. Supports document-level permissions (public, internal, restricted) and user roles (admin, editor, viewer). Filters search results based on user permissions, preventing unauthorized access to sensitive documents. Integrates with authentication providers (OAuth, SAML, API keys).","intents":["I need to restrict access to sensitive documents based on user roles","I want different users to see different search results based on permissions","I need to audit who accessed which documents"],"best_for":["enterprises with sensitive internal documentation","organizations with compliance requirements (HIPAA, SOC 2)","teams managing knowledge bases across departments with different access levels"],"limitations":["permission checks add latency to search queries — ~50-100ms per query","no fine-grained field-level permissions — only document-level access control","permission changes are not retroactive — existing cached results may violate new permissions","no audit logging of search queries — cannot track which documents users searched for"],"requires":["authentication system (OAuth provider, SAML IdP, or custom auth)","permission database (PostgreSQL, DynamoDB, or similar)","optional: audit logging 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