I built a RAG pipeline from scratch — no LangChain, just FastAPI + FAISS
Santanu Mohanta developed a RAG pipeline using FastAPI and FAISS without relying on frameworks like LangChain. The pipeline processes PDF documents by extracting text, chunking it, and embedding it for efficient querying. The system allows for swappable LLM providers and has been tested with various questions to evaluate its performance.
- ▪The pipeline consists of five Python files totaling around 300 lines of code.
- ▪Text extraction is performed using pypdf, followed by chunking and embedding with MiniLM-L6-v2.
- ▪The FAISS index is used for efficient similarity searches among the embedded chunks.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3959064) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Santanu Mohanta Posted on May 30 I built a RAG pipeline from scratch — no LangChain, just FastAPI + FAISS #python #ai #rag #fastapi Most RAG tutorials I found were either "pip install langchain and you're done" or 50-page academic papers. I wanted something in between — a pipeline I could actually explain in an interview, where I understood every line. So I built one from scratch. No LangChain, no LlamaIndex, no frameworks. Just FastAPI, FAISS, sentence-transformers, and an LLM API.
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