ChatPDF

Accepts PDF files (via upload or URL) and converts them into a vector embedding space using a multi-stage pipeline: PDF text extraction (handling layouts, tables, images), chunking into semantic segments, and embedding via a dense retrieval model. The embeddings are stored in a vector database indexed for fast similarity search, enabling subsequent retrieval-augmented generation without re-processing the source document.

Implements a multi-turn chat interface where each user query is encoded into the same embedding space as the ingested PDF, retrieved against the vector index to fetch relevant chunks, and passed as context to an LLM (likely GPT-4 or Claude) for response generation. The system maintains conversation history to support follow-up questions and context carryover across turns, with citations mapping responses back to source PDF pages.

Automatically suggests relevant questions based on document content, helping users discover insights they might not have thought to ask about. The system analyzes the ingested PDF to identify key topics, entities, and relationships, then generates a list of suggested questions that users can click to execute. This enables exploratory document analysis without requiring users to formulate queries from scratch.

Supports uploading and indexing multiple PDFs in a single operation, with progress tracking and error handling for failed ingestions. The system queues documents for processing, indexes them in parallel, and provides a unified interface for querying across the entire batch. Useful for processing document collections without manual per-file uploads.

Executes similarity search queries against the vector index of an ingested PDF, returning ranked chunks (paragraphs, sections, or sentences) sorted by cosine similarity to the query embedding. Supports filtering by metadata (page number, section heading) and configurable chunk size/overlap to balance context preservation with retrieval precision. Results include page numbers and excerpt text for manual inspection.

Allows users to upload and index multiple PDFs, then query across all documents simultaneously by retrieving relevant chunks from each indexed PDF and synthesizing a unified response. The system tracks which document each retrieved chunk originates from, enabling comparative analysis (e.g., 'compare the warranty terms in Contract A vs Contract B') and cross-document citation.

Extracts structured information (tables, forms, key-value pairs) from PDFs by combining layout-aware PDF parsing with LLM-based entity extraction. The system identifies tabular and form-like structures, converts them to structured formats (JSON, CSV), and makes them queryable via the chat interface. Supports extraction of specific fields or entire data structures with type inference.

Automatically tracks and attributes every response to specific source pages and chunks within the ingested PDF. When the LLM generates an answer, the system maps it back to retrieved chunks and includes page numbers, section headings, and excerpt text in the response metadata. Users can click through to view the original context in the PDF viewer.

Maintains a persistent conversation history across multiple turns, storing user queries and LLM responses server-side. The system uses conversation context to inform subsequent retrievals and responses, enabling follow-up questions that implicitly reference earlier discussion without requiring users to re-state context. History is associated with the user account and PDF document.

Provides an embedded or linked PDF viewer that displays the source document alongside the chat interface. When a response includes citations or when a user clicks on a retrieved chunk, the viewer automatically scrolls to and highlights the relevant section in the PDF, enabling visual verification of answers against source content.

Exposes REST or GraphQL APIs for programmatic PDF upload, indexing, and querying, enabling integration with external applications and workflows. Developers can submit PDFs via API, retrieve search results, and execute chat queries without using the web interface. API responses include structured metadata (page numbers, chunk IDs, confidence scores) for downstream processing.

Automatically generates summaries of ingested PDFs by querying the LLM with prompts designed to extract key points, main arguments, and conclusions. The system can produce summaries at different levels of detail (executive summary, detailed outline, bullet points) and can focus on specific topics or sections. Summaries are grounded in the document via citations.