AI Dashboard Template

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.

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.

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.

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 Provides a dashboard view of document freshness and allows marking documents as 'verified' or 'outdated'.

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.

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.

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.

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.

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.

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.

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.

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).