README.md

metadata

title: CineMatch AI (Powered by Modal & Nebius)
emoji: 🎬
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 6.0.1
app_file: app.py
pinned: false
license: mit
short_description: Multi-Agent RAG for Semantic Movie Discovery
tags:
  - mcp-in-action-track-01
  - agents
  - rag
  - modal
  - nebius
  - search

🎬 CineMatch AI: Semantic Movie Discovery via Multi-Agent RAG

🏆 Submission for MCP 1st Birthday Hackathon

Track: MCP in Action Special Category: Modal Innovation Award

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🚀 Elevator Pitch

Describe your story, find its cinematic soulmates. CineMatch AI is a creative partner for movie discovery. Instead of keywords, users describe their own original story ideas, dreams, or real-life situations (50+ words). A team of 5 Autonomous Agents collaborates to refine this narrative, retrieve semantic matches from a vector database (FAISS running on Modal GPU), and expertly justify why these movies fit the user's concept.

Crucially, the system features a Shadow Evaluation Agent (AgentOps) that runs silently in the background via MCP, auditing recommendations for hallucinations and logical coherence.

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🛠️ Architecture & MCP Integration

The system uses a Hierarchical Multi-Agent Architecture orchestrated on Modal (Serverless GPU) and powered by Llama-3.3-70B (via Nebius AI).

The Agentic Pipeline:

1. 👮‍♂️ Coordinator Agent: Validates input and manages the conversation state machine. Uses Context Engineering to guide users.
2. 📝 Editor & 🎞️ Critic Agents: Refine the user's raw text into a professional synopsis optimized for embedding.
3. 🔍 Retriever Agent (MCP Tool User):
   • Acts as a bridge to our Vector Database.
   • Calls the semantic search tool (hosted on Modal) which queries a FAISS index of thousands of movies.
   • Uses all-MiniLM-L6-v2 for embedding generation.
4. 🏆 Expert Agent: Performs parallel evaluation of candidates and generates persuasive justifications.
5. 🕵️ Shadow Evaluator (AgentOps):
   • Demonstrates the power of MCP for monitoring.
   • Runs asynchronously to audit the expert's output against ground truth metadata, detecting hallucinations without disrupting the UX.

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🔌 Dual-Layer Architecture (MCP Integration)

CineMatch AI features a unique dual-layer approach to balance speed and standardization:

1. Direct RPC Layer: Used by the Gradio Web UI for low-latency user interactions. It communicates directly with Modal functions via optimized RPC calls.
2. MCP Layer (tools/): A fully compliant Model Context Protocol implementation. This allows external agents (like Claude Desktop or other MCP clients) to discover and connect to our Movie Search Engine as a standard tool, fulfilling the hackathon's interoperability requirements.

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🏗️ Technical Stack

• Infrastructure: Modal (Serverless Python, GPU A10G for FAISS/Embeddings).
• LLM: Meta Llama 3.3 70B Instruct (via Nebius AI).
• Protocol: Agents communicate using structured tool calls (MCP pattern).
• Interface: Gradio 6.

🎥 Demo Video

[LINK TO YOUR VIDEO HERE]

🔗 Social Media Post

[LINK TO YOUR SOCIAL MEDIA POST HERE]

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🚀 How to Use

1. Enter a detailed description of a story, dream, or life situation (e.g., "A story about a lonely botanist on Mars who learns to communicate with rocks. He was ..." (50+ words)).
2. Watch the agents collaborate in real-time.
3. Receive 3 highly relevant movie recommendations with expert justifications.
4. (Optional) Check the logs to see the Shadow Evaluator's quality audit.