Capstone: Chat2Action

Chat2Action — ERPNext Task Assistant with RAG Helpdesk

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Real-World Problem Statement

Business owners, team leads, and operations managers often don't have time to open ERP dashboards just to assign, update, or review tasks. Tasks are frequently delegated informally (messages/DMs) or kept in memory, which leads to missed deadlines, duplicated work, and miscommunication.

This mini-capstone builds a chat-based assistant that performs real task operations in ERPNext (Frappe) and also provides a RAG-powered helpdesk that answers "how to use ERPNext tasks" questions grounded in official documentation.

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Project Scope

A) Action Mode (ERPNext Integration — Mandatory)

The chatbot must execute actions in real ERPNext via REST APIs (Frappe).

Task Operations (Doctype: Task)

• Create task (title, assignee required; due date recommended)
• Update task (title, due date, status, assignee)
• Delete/cancel task
• List tasks:
  • by user (assignee)
  • by status (Open / Working / Completed, etc.)
  • by due date window (today/this week)

User Resolution

• Mention-based assignee selection (e.g., "Ali") mapped to ERPNext users

ERP Adapter Layer

• A dedicated service/module wrapping ERPNext calls:
  • create_task(), update_task(), cancel_or_delete_task(), list_tasks()
  • get_users() (for assignee lookup)

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B) Knowledge Mode (RAG using ERPNext Docs — Mandatory)

The chatbot must answer ERPNext workflow questions using Retrieval-Augmented Generation grounded in ERPNext documentation.

RAG Content Source (Required)

• ERPNext official documentation related to Tasks/Projects (minimum: ~10-20 pages worth of content)

Required RAG Features

• Document ingestion pipeline (download → clean → chunk)
• Vector indexing (FAISS/Chroma/Pinecone—any is fine)
• Retrieval (top-k) + answer generation
• Citations: every answer must cite doc chunk(s) or source URLs
• Hallucination control: if not found in docs, refuse safely

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C) Interface & System Requirements

• REST API backend (FastAPI recommended)
• Web chat UI (React/Streamlit/Gradio; simple but usable)
• Dockerized run (ERPNext + backend + UI via docker-compose)
• Logging + basic error handling
• Basic auth for your backend (simple token/API key is enough)

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Out of Scope (Not Required)

• Invoice generation / Sales Invoice integration
• Training custom ML models
• Building a full ERP system
• Payment systems
• Enterprise SSO
• Advanced analytics dashboards
• Complex multi-tenant permissions (optional bonus)

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Expected Time to Complete

Total Duration: 1-2 weeks (7-12 days)

Recommended Schedule

Day 1–2: ERPNext Setup + API Connectivity

• Run ERPNext via Docker
• Create API key/secret
• Verify: list users, create a Task, update a Task

Day 3–4: Action Mode (Chat → Intent → ERP Adapter)

• Implement intent routing: create/update/delete/list tasks
• Implement disambiguation (multiple matching tasks)
• Add input validation (required fields, dates)

Day 5–6: RAG Pipeline

• Ingest ERPNext doc
• Chunk + embed + index
• Implement Knowledge Mode answers with citations + refusal

Day 7–8: UI

• Chat UI with loading states and clear confirmations
• Show citations in the UI for RAG answers

Day 9–12: Polish + Tests + Documentation

• Logging, error handling, retries
• README + short write-up
• Demo assets

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Deliverables

1) Working Code (GitHub Repository)

Repository must include:

• Backend service (FastAPI)
• ERP adapter module (ERPNext REST wrapper)
• RAG ingestion + retrieval module
• Frontend UI
• Docker Compose setup to run the full stack

Minimum "one command" run:

• docker compose up --build

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2) README Documentation

Must include:

• Project overview (Action Mode + Knowledge Mode)
• Architecture diagram (image or Mermaid)
• Setup instructions (ERPNext + backend + UI)
• Environment variables
• API examples (curl) for task
• Example chat commands (5+)
• RAG explanation (sources, chunking, embeddings, citations)
• Refusal behavior definition (when docs do not contain answer)

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3) Short Technical Write-Up (1–2 pages)

Include:

• Architecture decisions
• ERP integration approach (Task doctype, key endpoints)
• NLP approach (intent + entities + disambiguation)
• RAG pipeline details (chunking, vector DB, retrieval)
• Challenges + solutions
• Future improvements

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4) Demo Assets

Provide either a short demo video (preferred) or screenshots + transcripts.

Must show:

1. Create a Task via chat → visible in ERPNext UI
2. Update a Task via chat → visible in ERPNext UI
3. List tasks for a user/date range via chat
4. Ask an ERPNext question → answered with citations (RAG)
5. Ask an unrelated question → safe refusal ("not found in docs")

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Evaluation Rubric

Sufficient

• ERPNext runs locally via Docker
• Chat can create and list Tasks in ERPNext
• Basic RAG answers at least some "Task" questions with ≥1 citation
• Basic UI exists
• README includes setup steps

Minimum acceptance tests

• "Assign task to Ali due tomorrow" creates a Task in ERPNext
• "How do I assign a task in ERPNext?" returns a grounded answer with citation

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Good (Competent)

• Full Task works reliably (create/update/cancel-or-delete/list)
• Listing filters work (by user + status + due window)
• RAG answers are consistently cited and mostly correct
• Disambiguation implemented (if multiple tasks match)
• Modular code structure (services)
• 5+ test scenarios documented

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Excellent (Exceptional)

• Robust NLP handling (missing fields, multiple intents, better entity extraction)
• RAG upgrades:
  • hybrid retrieval (BM25 + dense) or reranking
  • strong refusal accuracy (low hallucination)
  • multiple citations when needed
• Production polish
  • retries/backoff for ERPNext API
  • automated tests + 10 scenarios
  • an additional system command
• Bonus:
  • voice input STT (optional) integrated cleanly

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Good luck with your challenge! This project combines NLP, RAG, and real-world system integration - skills highly valued in modern AI engineering.