LangChain Project 6: Build a High-Performance Multi-PDF RAG Engine (FAISS + MiniLM)

Simple “chat with PDF” demos are cute… until you try multiple documents, speed, and accuracy.
So in this episode of the LangChain Builder Series, we rebuild our earlier PDF RAG tool into a high-performance, production-style Multi-PDF Retrieval System.

This version can ingest multiple PDFs at once, generate fast MiniLM embeddings, and store everything in a persistent FAISS index — so you don’t re-embed files every run.
Result: faster retrieval, better relevance, and a scalable architecture you can reuse for real knowledge bases.

And yes — it’s still fully local: Llama 3 via Ollama, powered by LangChain + Streamlit.
No cloud. No API keys. Private by design.

What you’ll build + learn in this episode:
✔ Multi-PDF ingestion + processing (real-world document flows)
✔ MiniLM embeddings for speed + efficiency
✔ FAISS vector indexing for fast similarity search
✔ Persistent vector storage (no repeated embedding)
✔ Retrieval tuning: chunking, k-results, search params, rerank-style thinking
✔ Prompt tuning for higher quality RAG answers
✔ A polished Streamlit UI for multi-document chat + search

TIMESTAMPS -
00:00 Why simple RAG breaks in real life
00:35 What we’re building: Multi-PDF high-performance RAG
01:25 Architecture overview (Ingest → Embed → Index → Retrieve → Answer)
02:15 Multi-PDF ingestion + preprocessing
03:25 Chunking strategy upgrades (quality + speed)
04:30 MiniLM embeddings: why faster ≠ worse
05:40 Building a persistent FAISS index (no re-embedding)
06:50 Retrieval flow: top-k chunks + relevance tuning
08:05 Prompt tuning for better grounded answers
09:10 Connecting to local Llama 3 (Ollama)
10:00 Streamlit UI: multi-doc workflow + chat experience
11:10 Performance tips + common failure modes
12:05 Wrap-up + next upgrades

By the end, you’ll have a scalable Multi-PDF RAG engine you can adapt for enterprise knowledge bases, internal AI search, research tools, and document assistants.

Want to build real AI systems that retrieve knowledge, reason with context, and execute workflows like agents?
Start here:
👉 https://www.niit.com/india/course/building-agentic-ai-systems/?utm_source=yt&utm_medium=video&utm_campaign=langchain_builder_series_ep6_25feb26&utm_content=description_link

👇 Comment below:
Should the next upgrade be citations + source highlighting, or hybrid search (BM25 + vectors)?
#NIIT #UnlockWithNIIT #LangChain #RAGPipeline #FAISS #MiniLM #VectorIndexing #Llama3 #Ollama #LocalLLM #MultiPDFRAG #Embeddings #AIEngineering #GenAI #OpenSourceAI

Видео LangChain Project 6: Build a High-Performance Multi-PDF RAG Engine (FAISS + MiniLM) канала NIIT