Skip to content

PROJECT_OVERVIEW.md

Latest commit

 

History

History
48 lines (40 loc) · 2.45 KB

PROJECT_OVERVIEW.md

File metadata and controls

48 lines (40 loc) · 2.45 KB
Raw

ContextFlow - Project Overview

Project name

ContextFlow

Purpose

Build a focused, public portfolio project that demonstrates a practical retrieval-augmented generation (RAG) system: ingest text, chunk it, embed it, store vectors in PostgreSQL/pgvector, retrieve relevant context, and produce grounded responses with citations.

Target audience

  • Hiring teams and technical reviewers evaluating fullstack + RAG fundamentals
  • Developers looking for a clear reference implementation of "RAG done sensibly"

What this project demonstrates

  • RAG system design from first principles (ingestion -> retrieval -> grounded output)
  • Modern fullstack engineering with a clean separation of concerns
  • Python/FastAPI backend architecture, data modeling, and API design
  • Manual text ingestion (public-safe MVP)
  • Deterministic text chunking and re-indexing
  • OpenAI embeddings stored in PostgreSQL via pgvector
  • Semantic retrieval using vector similarity search with source filtering and top-K controls
  • Retrieval-only inspection for chunk scores and attribution
  • Grounded response patterns (answer + sources) and safe-by-default thinking

What this project intentionally does not do

  • Production SaaS concerns (multi-tenant auth, billing, admin portals, compliance)
  • Automated ingestion in the MVP (no file uploads/PDF parsing/OCR/crawling)
  • Enterprise integrations in the MVP (no WordPress sync or private connectors)
  • "Magic" prompts or hidden proprietary logic

Public showcase goals

  • Keep the scope understandable in one sitting (reviewer-friendly)
  • Make tradeoffs explicit (why this design, why these constraints)
  • Show end-to-end architecture maturity without overbuilding
  • Provide UI screens that support portfolio walkthroughs: what knowledge exists, what has been indexed, how chunks are retrieved, and how answers cite retrieved context

High-level MVP summary

  • Manual text entry only (paste or type knowledge)
  • Store knowledge as sources, documents, and chunks
  • Index documents into embedded chunks for semantic retrieval
  • Retrieve chunks across all sources or a selected source
  • Return retrieval-only results or grounded outputs with retrieved sources/chunks

Why this project is public-safe

  • No private business data or client content is included or required
  • No proprietary prompts or copied internal business logic
  • Guardrails prevent risky ingestion sources in early phases
  • Designed as a portfolio reference implementation, not an operational product