Project 8: Movie Recommendation Graph Agent

Build a LangChain agent that traverses a knowledge graph to recommend movies with explanations.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12-18 hours
Language Python or JavaScript
Prerequisites Graph basics, recommendation logic
Key Topics knowledge graphs, traversal, explainability

1. Learning Objectives

By completing this project, you will:

  1. Model movie data as a graph.
  2. Query and traverse the graph for recommendations.
  3. Integrate graph search as a tool.
  4. Explain recommendations with graph paths.
  5. Evaluate recommendation relevance.

2. Theoretical Foundation

2.1 Graph-Based Recommendations

Graphs model relationships (actors, genres, directors). Traversal reveals relevant neighbors and paths.

3. Project Specification

3.1 What You Will Build

A recommendation agent that uses graph traversal to suggest movies and explains the path used.

3.2 Functional Requirements

  1. Graph schema for movies, people, genres.
  2. Traversal tool for graph queries.
  3. Ranking of candidate movies.
  4. Explanations with path evidence.
  5. Feedback capture for tuning.

3.3 Non-Functional Requirements

  • Deterministic ranking for identical inputs.
  • Transparent explanations based on graph paths.
  • Session state for multi-turn recs.

4. Solution Architecture

4.1 Components

Component Responsibility
Graph Store Nodes/edges for movies
Graph Tool Query and traversal
Ranker Score candidate movies
Explainer Render path-based rationale

5. Implementation Guide

5.1 Project Structure

LEARN_LANGCHAIN_PROJECTS/P08-movie-graph-agent/
├── src/
│   ├── graph.py
│   ├── traverse.py
│   ├── ranker.py
│   ├── explain.py
│   └── chat.py

5.2 Implementation Phases

Phase 1: Graph schema (3-4h)

  • Build nodes and edges for movies.
  • Checkpoint: graph query returns expected neighbors.

Phase 2: Traversal + ranking (4-6h)

  • Implement traversal-based candidate selection.
  • Checkpoint: recommendations vary with preferences.

Phase 3: Explanations + feedback (3-5h)

  • Add path-based explanations.
  • Capture user feedback.
  • Checkpoint: explanations cite graph paths.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit traversal path length constraints
Integration ranking stable top-3 results
Regression explain path-based rationale

6.2 Critical Test Cases

  1. Graph path exists for each recommendation.
  2. Ranking changes with user preferences.
  3. Explanation references actors/genres correctly.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Over-connected graph irrelevant recs add edge weights
Weak explanations no path evidence log traversal paths
Slow traversal latency spikes limit depth or precompute

8. Extensions & Challenges

Beginner

  • Add a small static movie dataset.
  • Add genre-only recommendations.

Intermediate

  • Add weighting for recency or ratings.
  • Add hybrid vector + graph ranking.

Advanced

  • Add graph embeddings.
  • Add real-time updates from user feedback.

9. Real-World Connections

  • Streaming platforms use graph signals for recommendations.
  • Knowledge graphs power explainable discovery.

10. Resources

  • Graph databases (Neo4j, Neptune) docs
  • Recommendation systems references
  • LangChain graph tooling

11. Self-Assessment Checklist

  • I can model data in a graph schema.
  • I can traverse graphs for recommendations.
  • I can explain results with path evidence.

12. Submission / Completion Criteria

Minimum Completion:

  • Graph-based recommendations
  • Path-based explanations

Full Completion:

  • Ranked results
  • Feedback capture

Excellence:

  • Hybrid ranking or embeddings
  • Real-time graph updates

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LANGCHAIN_PROJECTS.md.