Project 2: Building a RAG-Based Knowledge Assistant
Project 2: Building a RAG-Based Knowledge Assistant
This project focuses on building a knowledge-based AI assistant capable of answering domain-specific queries.
1) Project Goal
- Upload documents
- Convert into embeddings
- Store in vector database
- Retrieve relevant chunks
- Generate contextual responses
2) Architecture Components
- Embedding model
- Vector database
- Retrieval engine
- LLM response layer
3) Implementation Steps
- Document chunking strategy
- Generate embeddings
- Store in Qdrant or Pinecone
- Implement similarity search
- Integrate with LLM
4) Optimization
- Metadata filtering
- Hybrid search
- Latency tuning
5) Learning Outcome
This project teaches enterprise-level RAG system architecture.
