SciSpace

Processes scientific PDF documents through a multi-stage pipeline: document ingestion with layout-aware parsing to preserve structure (tables, figures, citations), chunking with semantic boundaries (section-aware rather than fixed-length), and embedding-based retrieval to match user queries against document content. Uses dense vector similarity search to identify relevant passages, then feeds retrieved context to an LLM for answer generation with source attribution.

Enables querying across multiple uploaded scientific PDFs simultaneously by maintaining separate embedding indices for each document while performing unified semantic search across all indices. Retrieves relevant passages from multiple papers, then uses an LLM with multi-document context to synthesize answers that compare findings, identify contradictions, or trace concept evolution across papers. Maintains document provenance throughout to attribute claims to specific sources.

Allows users to propose hypotheses or claims and automatically verify them against the uploaded paper content. The system retrieves relevant passages from the paper, compares them against the proposed claim, and provides evidence-based assessment of whether the paper supports, contradicts, or remains neutral on the claim. Uses semantic matching and logical reasoning to identify supporting or contradicting evidence, with confidence scores and source citations.

Parses and indexes citation metadata embedded in PDFs (references, in-text citations, author names, publication years) to enable retrieval that understands citation relationships. When a user asks about a concept, the system can identify which papers cite each other, retrieve cited passages in context, and trace citation chains. This allows answering questions like 'what prior work does this paper build on' or 'which papers cite this finding' by leveraging the citation graph structure rather than just semantic similarity.

Implements OCR and layout analysis to extract tables, figures, and captions from scientific PDFs while preserving their spatial relationships and surrounding text context. Uses vision-language models or specialized table parsing to interpret visual content, then indexes both the extracted structured data (table rows/columns) and the visual content itself. Allows users to query about specific figures or tables by asking natural language questions, with the system retrieving both the visual asset and its contextual interpretation.

Maintains conversation history and document context across multiple sessions, allowing users to upload a PDF once and return later to continue asking questions without re-uploading. Implements session management with persistent storage of document embeddings, conversation state, and user-specific context. Uses conversation memory (likely a sliding window or summarization approach) to maintain coherence across long conversations while managing token budget constraints of the underlying LLM.

Allows users to define or select extraction schemas (e.g., 'extract all methodology details', 'extract all numerical results', 'extract author affiliations') and automatically extract structured data from PDFs matching those schemas. Uses prompt engineering or fine-tuned extraction models to map unstructured paper text to structured formats (JSON, CSV, tables). Enables batch extraction across multiple papers using the same schema, producing comparable structured datasets.

Uses embedding-based similarity to recommend related papers from a user's document collection or external databases based on semantic content. When a user uploads a paper or asks about a topic, the system identifies semantically similar papers in the collection and ranks them by relevance. Implements cosine similarity or other distance metrics on document embeddings to find papers covering related methodologies, findings, or theoretical frameworks without requiring explicit keyword matching.

Processes scientific PDFs in multiple languages (not just English) by detecting document language, applying language-specific OCR and text extraction, and using multilingual embedding models to enable cross-language semantic search. Users can ask questions in their preferred language and receive answers from papers in different languages. Implements automatic translation or multilingual embeddings to bridge language gaps without requiring explicit translation steps.

Enables multiple users to simultaneously annotate, highlight, and comment on the same PDF document with real-time synchronization. Annotations are stored server-side and linked to specific passages, allowing users to build shared understanding of papers. Integrates with the QA system so that questions can reference specific annotations ('What does this highlighted passage mean?'). Uses operational transformation or CRDT-based conflict resolution to handle concurrent edits without data loss.

Generates summaries of scientific papers at multiple configurable levels of detail: one-sentence abstract, paragraph-length summary, section-by-section breakdown, or full detailed summary. Uses extractive and abstractive summarization techniques, with the ability to focus on specific aspects (methodology, results, implications, limitations). Summaries are generated on-demand or cached for quick retrieval, and can be customized for different audiences (technical experts vs. general readers).