AI Dashboard Template

Accepts uploaded documents (PDF, TXT, Markdown) and automatically chunks them into semantically meaningful segments, then generates vector embeddings using Vercel AI SDK's embedding models. The pipeline stores both raw text chunks and their embeddings in a vector database, enabling semantic search without manual preprocessing. Uses streaming ingestion to handle large document batches without blocking the UI.

Executes semantic search by converting user queries to embeddings using the same model as the document corpus, then performs vector similarity search (cosine distance or dot product) against the stored embeddings to retrieve top-K most relevant chunks. Results are ranked by similarity score and returned with metadata (source document, chunk position) for attribution. Supports filtering by document source or metadata tags before similarity ranking.

Stores chat conversations in a database (PostgreSQL, MongoDB, or Vercel KV) with timestamps and metadata, allowing users to resume previous conversations or search conversation history. Implements efficient retrieval of conversation threads and optional summarization of long conversations to manage storage and context window usage. Supports both user-initiated saves and automatic persistence.

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 Provides a dashboard view of document freshness and allows marking documents as 'verified' or 'outdated'.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 chat interface where user messages trigger a RAG pipeline: query embedding → vector search → context retrieval → LLM prompt augmentation → streaming response. Uses Vercel AI SDK's streaming primitives to send response tokens to the client in real-time, creating a perceived low-latency chat experience. Context from retrieved documents is injected into the system prompt with source attribution, and the LLM generates responses grounded in the knowledge base.

Provides a web UI for administrators to view uploaded documents, monitor embedding status, delete or re-index documents, and configure RAG parameters (chunk size, similarity threshold, context window). Uses server-side API endpoints to manage the vector database and document metadata store. Includes real-time status indicators for ingestion pipelines and search performance metrics (query latency, retrieval quality).

Abstracts LLM provider selection (OpenAI, Anthropic, Ollama, or others) behind Vercel AI SDK's unified interface, allowing developers to swap providers by changing environment variables without code changes. Implements streaming response handling for all providers using a consistent API, and includes automatic fallback or provider selection based on model availability. Supports both chat and completion models with configurable temperature, max tokens, and system prompts.

Automatically manages the context window by calculating token counts for user messages, retrieved documents, and system prompts, then truncating or prioritizing context to fit within the LLM's maximum token limit. Uses token counting APIs from the LLM provider to ensure accurate calculations. Implements strategies like retrieving fewer documents, summarizing context, or using a sliding window of conversation history to maximize relevant context while staying within limits.

Automatically extracts metadata from uploaded documents (title, author, creation date, document type) using LLM-based extraction or regex patterns, then allows administrators to add custom tags for categorization. Metadata is stored alongside embeddings and used for filtering search results, organizing the knowledge base, and providing context attribution in chat responses. Supports both automatic extraction and manual tagging via the admin dashboard.

Tracks which documents and chunks were used to generate each chat response, then displays source citations in the UI with links to original documents or specific sections. Implements citation formatting (e.g., '[1] Document Title, page X') and includes metadata about retrieval score and chunk position. Supports both inline citations (within response text) and reference lists (at the end of response).

Collects metrics on search performance (query latency, retrieval quality, embedding generation time) and displays them in the admin dashboard. Tracks metrics like average retrieval score, number of queries per hour, and cache hit rates. Uses server-side logging and optional integration with observability platforms (Vercel Analytics, Datadog, or custom logging) to identify performance bottlenecks and optimize the RAG pipeline.

Allows administrators to upload multiple documents simultaneously (via drag-and-drop or file picker) and tracks ingestion progress for each file. Uses server-side streaming or background jobs to process documents asynchronously, preventing UI blocking. Provides real-time progress indicators (percentage complete, estimated time remaining) and error handling for individual file failures without stopping the batch.