Open Notebook

Converts uploaded documents (PDFs, text files, web content) into natural-sounding audio narration using text-to-speech synthesis with support for multiple voice profiles, speaking rates, and language detection. The system processes document content through a TTS pipeline that handles formatting preservation, paragraph segmentation, and voice assignment rules to generate coherent multi-voice audio outputs suitable for podcast-style consumption.

Automatically generates structured, interactive notebooks from uploaded documents by parsing content into sections, extracting key concepts, and creating executable cells with explanations. Uses LLM-based content understanding to identify logical breakpoints, generate markdown documentation, and suggest code examples or visualizations that correspond to document concepts, creating a Jupyter-like interface without manual cell creation.

Indexes uploaded documents using vector embeddings and enables semantic search queries that find relevant content by meaning rather than keyword matching. Implements a RAG (Retrieval-Augmented Generation) pipeline where documents are chunked, embedded using a transformer model, stored in a vector database, and retrieved based on cosine similarity to query embeddings, with optional re-ranking for result quality.

Generates concise summaries of documents using LLM-based abstractive summarization that understands semantic meaning and extracts key facts, entities, and relationships. Implements multi-level summarization (document-level, section-level, paragraph-level) with configurable summary length and style, optionally extracting structured data like key concepts, citations, and metadata using prompt engineering or few-shot examples.

Enables conversational Q&A where users ask questions about uploaded documents and receive answers grounded in document content. Implements a retrieval-augmented generation (RAG) loop that retrieves relevant document excerpts via semantic search, passes them as context to an LLM, and generates answers with citations back to source documents. Maintains conversation history for multi-turn interactions with context carryover.

Analyzes relationships and differences across multiple documents by performing semantic comparison, identifying contradictions, and synthesizing insights across sources. Uses LLM-based analysis to create cross-document summaries, comparison matrices, and synthesis reports that highlight agreements, disagreements, and complementary information across the document collection. Implements document clustering and relationship mapping to visualize how documents relate to each other.

Exports generated notebooks and content to multiple formats including Jupyter (.ipynb), markdown, PDF, HTML, and custom formats. Implements format-specific rendering pipelines that preserve code executability, formatting, and interactivity where applicable. Supports batch export of multiple notebooks with consistent styling and optional template application for branded output.

Enables sharing of generated notebooks with team members through shareable links, collaborative editing, and version history tracking. Implements a version control layer that tracks changes to notebooks, allows reverting to previous versions, and supports branching for experimental modifications. Integrates with Git or similar systems for source control and enables commenting/annotation on specific cells or sections.

Allows users to define custom prompts and templates for document processing tasks including summarization, extraction, and notebook generation. Implements a prompt library system where users can create, test, and version prompts, with variable substitution for dynamic content. Supports few-shot example management and prompt chaining for complex multi-step analysis workflows.

Processes multiple documents in batch mode with configurable workflows that apply consistent transformations across all files. Implements job queuing, progress tracking, and error handling for large-scale document processing. Supports scheduling batch jobs to run on a schedule and integrates with external storage systems (S3, Google Drive) for input/output management.