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🚀 Built a Basic RAG and an Advanced RAG System from Scratch — Without LangChain
To understand how RAG works internally, I first built a Basic RAG Pipeline:
Basic RAG
PDF → Extraction → Chunking → Embeddings
→ FAISS Search → Top-K Chunks
→ Prompt → LLM → Answer
It works well for simple questions, but struggles with exact keywords, IDs, and complex queries.
So I built an Advanced RAG Pipeline:
Advanced RAG
PDF → Extraction → Chunking → Embeddings
→ Dense + Sparse Index
→ Query Understanding
→ Dense Retrieval + Sparse Retrieval
→ Reciprocal Rank Fusion (RRF)
→ Cross-Encoder Reranking
→ Noise Filtering
→ Context Fusion
→ Prompt → LLM → Answer
Key Improvements
✅ Semantic Search (FAISS)
✅ Exact Keyword Search (BM25)
✅ Hybrid Retrieval
✅ Cross-Encoder Reranking
✅ Duplicate Removal
✅ Better Context Selection
✅ Reduced Hallucinations
One of the biggest lessons from this project:
A better RAG system isn't always about using a better LLM. It's often about building a better retrieval pipeline.
Tech Stack: Python • Streamlit • FAISS • Sentence Transformers • BM25 • Cross-Encoder Reranker • Groq
#RAG #GenAI #LLM #AIEngineering #MachineLearning #Python #FAISS #BuildInPublic
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