Chat With PDF by Copilot.us

Accepts multiple PDF files simultaneously and creates searchable vector embeddings or text indices for each document, enabling parallel processing of content across files. The system likely uses PDF parsing libraries (PyPDF2, pdfplumber, or similar) to extract text, then chunks content into semantic segments and embeds them using language model APIs or local embedding models for retrieval-augmented generation (RAG).

Maintains conversation history while retrieving relevant passages from indexed PDFs and attributing responses to specific source documents and page numbers. Uses semantic similarity matching (likely cosine distance on embeddings) to rank candidate chunks, then passes top-K results to an LLM with a prompt template that instructs the model to cite sources and acknowledge when information spans multiple documents.

Converts natural language queries into embeddings and performs vector similarity search across all indexed PDFs to retrieve the most relevant passages, regardless of keyword matching. Uses approximate nearest neighbor (ANN) search algorithms (likely FAISS, Pinecone, or Weaviate) to efficiently find top-K similar chunks from potentially thousands of embedded segments across multiple documents.

Constructs LLM prompts dynamically by injecting retrieved PDF passages as context, using a template-based approach that formats source material for the language model. The system likely implements a prompt chain that retrieves relevant chunks, formats them with document metadata, and passes them to the LLM with instructions to answer based on provided context and cite sources.

Maintains conversation history, user queries, and retrieved context across multiple turns within a single session, allowing the LLM to reference previous exchanges and build on prior context. Likely uses in-memory session storage or database-backed state to persist conversation metadata, retrieved passages, and user preferences across requests.

Processes multiple uploaded PDFs concurrently rather than sequentially, extracting text, chunking content, and generating embeddings in parallel to reduce total ingestion time. Likely uses async/await patterns or thread pools to parallelize I/O-bound PDF parsing and API calls for embedding generation across files.

Interprets ambiguous or incomplete user queries by expanding them into more specific search terms or asking clarifying questions before retrieving from PDFs. May use the LLM to rephrase queries, generate related search terms, or suggest interpretations when a query is vague, improving retrieval accuracy without requiring users to manually refine their questions.

Extracts text from PDFs while attempting to preserve document structure (headings, lists, tables, sections), enabling more accurate chunking and context retrieval. Uses PDF parsing libraries that recognize structural elements rather than treating PDFs as flat text, improving semantic understanding of document organization.