SearchPlus

Accepts PDF files and converts them into a queryable vector representation through document parsing and embedding generation. The system extracts text from PDFs (handling multi-page documents), chunks content into semantically meaningful segments, and generates dense vector embeddings that enable semantic search across the document corpus. This approach allows fast retrieval of relevant passages without requiring full document re-reading on each query.

Enables natural language questions about PDF content through a chat interface that performs semantic search over embedded documents. User queries are converted to embeddings, matched against document vectors using similarity metrics (likely cosine distance), and relevant passages are retrieved and fed into an LLM context window for synthesis and answer generation. The system maintains conversation history to enable follow-up questions and contextual refinement.

Maintains conversation state across multiple uploaded PDFs, allowing users to ask questions that implicitly reference content from different documents or compare information across sources. The system tracks which documents are active in the session, manages embedding indices for each document, and routes queries to appropriate document vectors while maintaining a unified conversation history. This enables cross-document reasoning within the constraints of the LLM context window.

Provides free tier access to core PDF chat functionality with implicit usage quotas (document count, query volume, or storage limits), removing friction for trial users while monetizing through premium tier upgrades. The system likely tracks usage metrics per user session and enforces soft or hard limits that trigger upgrade prompts. Premium pricing structure exists but is not transparently communicated, creating uncertainty about cost-benefit analysis.

Stores uploaded PDFs and their vector embeddings within a user session, enabling document reuse across multiple queries without re-uploading. The system maintains session state (document metadata, embedding indices, conversation history) in backend storage, likely with session expiration after inactivity. Users can reference previously uploaded documents in follow-up queries within the same session, but persistence across sessions is unclear.

Delivers fast responses to document queries through optimized vector search and retrieval-augmented generation pipeline. The system likely uses pre-computed embeddings, efficient similarity search algorithms (HNSW or similar), and streaming response generation to minimize end-to-end latency. Minimal lag between query submission and response generation suggests careful optimization of chunking strategy, embedding model selection, and LLM inference.