Architecture Specification

RAG Pipeline.
Mapped in Dimensions.

This diagram visualizes the server-side RAG pipeline that powers AskMe AI. Documents are parsed with pdf-parse, chunked server-side, and embedded via Gemini text-embedding-004 (768-dim vectors). All vectors are stored in Supabase pgvector with IVFFlat indexing for sub-100ms cosine similarity queries. Student queries follow the same embedding path before retrieval and LLM synthesis.

Pipeline Flow Map
Select Pipeline Node
The architecture overview is rendered client-side for visualization. In production, vector synthesis and retrieval run on Supabase pgvector with Gemini text-embedding-004.
Pipeline Node 01

Document Ingestion

Parsing & Cleansing

Ingests raw text and PDF payloads. Uses pdf-parse to extract structured layout data and strip header/footer noise.

Internal Logic Specifications

Outputs pure unicode string representations optimized for chunk parsing algorithms.

STATUS: CALIBRATED
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