When Coral AI reads a PDF, it uses `pdf_analyzer` tools to break the document into semantic chunks for lightning-fast vector search.
Core Architecture
How It Works Under The Hood
The Deep Semantic Extraction module is built on a highly optimized C++ and Python bridge. By bypassing standard Windows UI restrictions, Coral AI directly interfaces with system memory, native Win32 APIs, and DOM structures to achieve near-zero latency execution.
Vectorized Indexing
Embeds the entire PDF into a local index so queries return exact page context.
Clause Retrieval
Ask 'What does clause 4.2 say?' and it jumps to the exact paragraph.
High-Volume Summarization
Capable of condensing 500-page manuals into bulleted executive summaries.
Citation Mapping
Responses include exactly which page and paragraph the answer was pulled from.
This module does not operate in isolation. It is dynamically invoked by the Coral PlannerAgent via JSON-RPC, allowing it to be chained endlessly with vision and memory modules.