Capstone Project: Production-Grade Multimodal RAG Platform

This is where all your learning comes together. You will build a multimodal RAG system that handles a diverse set of real-world data and provides a secure, verifiable, and snappy user experience.

Project Vision

Build a "Multimodal Knowledge Portal" for a fictional company called Aether Intelligence. This portal must allow employees to query the company's video archives, technical manuals, and financial dashboards.

Core Requirements

1. Ingestion Pipeline

Handle at least 3 data types: PDF, Image (Diagram), and Audio/Video (Transcript).
Implement a Conditioning step that removes noise and adds metadata.

2. Processing and Storage

Implement Layout-Aware OCR for at least one scanned document.
Use Chroma as the vector database with a persistent storage backend.
Design a Metadata Schema that supports department-level filtering.

3. Retrieval and Ranking

Implement Hybrid Search (Vector + Keyword) using LangChain.
Implement a Re-Ranking step using a Cross-Encoder (local or hosted).

4. Generation and Verification

Use Claude 3.5 Sonnet (via Bedrock or API) for the generation.
Implement Source Citations in every response.
Add a Verification Loop that flags potential hallucinations.

5. Production Operations

Create a FastAPI wrapper for the retrieval and generation logic.
Implement Audit Logging for every query.
Create a Deployment Plan (Dockerized).

Deliverables

Architecture Diagram: A clear flow chart showing the ingestion and retrieval layers (Mermaid format).
Data Flow Documentation: Explain how a raw video file becomes a searchable context chunk.
Retrieval Strategy: Document your choice of embedding model, re-ranker, and similarity threshold.
Final System Demo: A working API or CLI that can accurately answer a set of predefined questions about your provided data.

Evaluation Criteria

Metric	Passing Grade
Faithfulness	> 0.90 (Answers are supported by docs).
Response Latency	< 10 seconds (including retrieval + generation).
Citation Accuracy	100% (Every claim matches its ID).
Security	Metadata filter is applied correctly for unauthorized queries.

Final Thoughts

Building a production RAG system is a journey of continuous improvement. The capstone is your first "Production-Ready" version. Good luck!