Module 1 · AI
📖 3 min read · By ['[“HungHM15”]']

Assignment: Human-in-the-Loop & Persistence#

Assignment Metadata#

Field

Description

Assignment Name

Human-in-the-Loop & Persistence

Course

LangGraph and Agentic AI

Project Name

hitl-persistence-agent

Estimated Time

150 minutes

Framework

Python 3.10+, LangGraph, LangChain, SQLite/PostgreSQL, FAISS

Learning Objectives#

By completing this assignment, you will be able to:

  • Implement interrupt patterns for human approval workflows

  • Configure different checkpointer backends (MemorySaver, SQLiteSaver, PostgresSaver)

  • Design confirmation UIs for critical operations

  • Build cache tools using vector stores for response caching

  • Apply state update patterns for manual corrections

Problem Description#

Extend the FPT Customer Chatbot from Assignment 04 with:

  1. Human-in-the-Loop Confirmation: Require user approval before ticket/booking operations

  2. Response Caching: Store RAG/IT support responses in FAISS for follow-up queries

  3. Persistent State: Enable long-running conversations with SQLite checkpointer

Technical Requirements#

Environment Setup#

  • Python 3.10 or higher

  • Required packages:

    • langgraph >= 0.2.0

    • langchain >= 0.1.0

    • faiss-cpu >= 1.7.0

    • sentence-transformers >= 2.2.0

Prerequisite#

  • Completed Assignment 04 (FPT Multi-Agent Chatbot)

Tasks#

Task 1: Interrupt Before Tool Execution (35 points)#

  1. Configure interrupt_before for sensitive tools:

    • All ticket creation/update/cancel operations

    • All booking creation/update/cancel operations

  2. Implement confirmation flow:

    • Detect pending tool state via graph.get_state()

    • Generate human-readable confirmation message

    • Parse user response (“y” to continue, other to cancel)

  3. Handle user responses:

    • “y”: Resume graph execution with app.invoke(None, config)

    • Other: Update state to cancel and return appropriate message

  4. Create confirmation message generator that:

    • Extracts tool name and arguments from pending state

    • Formats readable summary for user review

    • Includes clear instructions for approval/rejection

Task 2: Cache Tool Implementation (35 points)#

  1. Create cache_tool that:

    • Stores all RAG and IT Support responses in FAISS vectorstore

    • Indexes by query embedding for similarity search

    • Stores metadata: timestamp, query_type, source_agent

  2. Implement cache lookup in orchestrator:

    • Before calling RAG/IT tools, check cache for similar queries

    • Use similarity threshold (e.g., 0.85) to determine cache hit

    • Return cached response if found, otherwise proceed to tool

  3. Add cache management:

    • TTL-based invalidation (e.g., 24 hours)

    • Manual cache clear capability

    • Cache statistics logging

Task 3: Checkpointer Configuration (20 points)#

  1. Replace MemorySaver with SQLiteSaver:

    • Configure persistent storage in checkpoints.db

    • Test conversation resumption after process restart

  2. Implement thread management:

    • List active threads

    • View checkpoint history for a thread

    • Delete old threads (cleanup)

  3. Document migration path to PostgresSaver for production

Task 4: Testing & Validation (10 points)#

  1. Test interrupt workflow:

    • Create ticket → Confirm “y” → Verify ticket created

    • Create booking → Reject with “n” → Verify booking NOT created

  2. Test cache functionality:

    • Query IT support → Verify cached

    • Similar follow-up query → Verify cache hit

    • After TTL → Verify cache miss

  3. Test persistence:

    • Start conversation → Stop process → Resume → Verify context retained

Submission Requirements#

Required Deliverables#

  • Source code extending Assignment 04

  • README.md with setup instructions

  • Demo video or screenshots showing:

    • Interrupt confirmation workflow

    • Cache hit/miss scenarios

    • Conversation persistence across restarts

Submission Checklist#

  • interrupt_before configured for all sensitive tools

  • Confirmation message clearly shows pending action

  • Cache tool stores and retrieves responses correctly

  • SQLiteSaver enables conversation persistence

  • Code runs without errors

Evaluation Criteria#

Criteria

Points

Interrupt workflow implementation

35

Cache tool with FAISS

35

Checkpointer configuration

20

Testing & validation

5

Code quality and documentation

5

Total

100

Hints#

  • Use app.get_state(config) to inspect current state including pending tool calls

  • The next field in state shows which node(s) are pending

  • For FAISS caching, use sentence-transformers for consistent embeddings

  • SQLiteSaver requires context manager: with SqliteSaver.from_conn_string(...) as saver:

References#