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metadata
title: EdSummariser
emoji: 📚
colorFrom: red
colorTo: indigo
sdk: docker
sdk_version: latest
pinned: false
license: apache-2.0
short_description: Ed-Assistant summary your learning journey with Agentic RAG

StudyBuddy (EdSummariser)

Live demo
System Architecture

StudyBuddy is an end-to-end Retrieval-Augmented Generation (RAG) app for learning from your own documents.

  • Ingestion: PDF/DOCX parse → optional image captions → chunk to cards → embed → store.
  • Retrieval: filename detection → per-file relevance classification (NVIDIA) → vector search (Mongo Atlas or local cosine) with retries and summary fallbacks.
  • Reasoning: context-only answering; per-user recent-memory mixing (classification + semantic); key rotation and robust HTTP for LLMs.

Key Endpoints (FastAPI)

  • Auth: POST /auth/signup, POST /auth/login
  • Projects: POST /projects/create, GET /projects, GET /projects/{id}, DELETE /projects/{id}
  • Upload: POST /upload, GET /upload/status
  • Data: GET /files, GET /file-summary, GET /cards
  • Chat: POST /chat → { answer, sources, relevant_files }
  • Report: POST /report (Gemini CoT filter + write), POST /report/pdf
  • Health: GET /healthz, GET /rag-status, GET /test-db

High level flow:

  1. Upload PDF/DOCX → parse pages → extract images → BLIP captions → merge → chunk into cards → embed → store.
  2. Chat request → detect any filenames in the question → preload filenames + summaries.
  3. NVIDIA marks per-file relevance. Any filenames explicitly mentioned are always included.
  4. Vector search restricted to relevant files. If no hits: retry with mentioned files only, then with all files. If still no hits but summaries exist, return those summaries.
  5. Compose answer with strict guardrails to “answer from context only.” Summarize the Q/A and store in per-user LRU memory.

Project Structure 

app.py                  # FastAPI app, routes, chat/report flows, ingestion orchestration
static/                 # Minimal UI (index.html, styles, scripts)
memo/                   # Memory system (LRU + helpers)
utils/
  api/                  # Model router, key rotator
  ingestion/            # Parsing, captioning, chunking
  rag/                  # Embeddings + RAG store (Mongo + vector search)
  service/              # Summarizer, PDF generation (dark IDE-like code blocks)
  logger.py             # Tagged logging
Dockerfile
requirements.txt

Quick Start 

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
export MONGO_URI="mongodb://localhost:27017"
uvicorn app:app --reload --host 0.0.0.0 --port 8000

Open: http://localhost:8000/static/ • Health: GET /healthz

Configuration

  • MONGO_URI (required), MONGO_DB (default: studybuddy)
  • ATLAS_VECTOR=1 to enable Atlas Vector Search, MONGO_VECTOR_INDEX (default: vector_index)
  • EMBED_MODEL (default: sentence-transformers/all-MiniLM-L6-v2)
  • NVIDIA_API_1..5, GEMINI_API_1..5 (key rotation); model overrides via GEMINI_SMALL|MED|PRO, NVIDIA_SMALL

Retrieval Strategy (concise)

  1. Detect mentioned filenames (e.g., JADE.pdf).
  2. Classify file relevance (NVIDIA) and restrict search.
  3. Vector search → on empty hits, retry with mentions-only → all files → fallback to file-level summaries.
  4. Answer from context only; store compact memory summaries.

Notes

  • PDF export renders code blocks with a dark IDE-like theme and lightweight syntax highlighting; control characters are stripped to avoid square artifacts.
  • CORS is open for the demo UI; restrict for production.

Docs

Report Generation

Memo Dir

Utils Dir

Routes Dir

Agent Assignment

Manage ingestion_python as a git subtree (brief)

Remotes (set once):

git remote add hfi1 https://huggingface.co/spaces/BinKhoaLe1812/StuddyBuddy_Ingestion1
git remote add hfi2 https://huggingface.co/spaces/BinKhoaLe1812/StuddyBuddy_Ingestion2
git remote add hfi3 https://huggingface.co/spaces/BinKhoaLe1812/StuddyBuddy_Ingestion3

Push ingestion_python to a Space:

# push current ingestion_python contents to hfi1 main
git subtree push --prefix=ingestion_python hfi1 main

# (alternatives)
git subtree push --prefix=ingestion_python hfi2 main
git subtree push --prefix=ingestion_python hfi3 main

Pull updates from a Space into the folder:

git fetch hfi1
git subtree pull --prefix=ingestion_python hfi1 main --squash

Links:

License

Apache-2.0