Building a Local, Privacy‑First RAG Pipeline with LangChain: From Embeddings to Hybrid Retrieval

As part of my broader project to build a completely local, privacy‑first AI assistant, I’ve been exploring how to design a robust Retrieval‑Augmented Generation (RAG) pipeline using LangChain, LangGraph, and local LLMs. My goal is to create a model‑agnostic system that runs entirely on‑device, supports multimodal inputs, and uses a persistent vector store for fast, repeatable retrieval.

This notebook represents a major milestone in that journey. It walks through the full lifecycle of building a RAG system—from embedding text, to loading and chunking documents, to constructing a persistent FAISS vector store, to hybrid retrieval strategies that combine semantic and keyword search. The entire workflow is implemented using local models (Qwen embeddings) and open‑source components.

All code for this workflow is available in the public GitHub repository. I encourage you to clone it, run the notebook end‑to‑end, and contribute improvements or extensions.

github repo

The full notebook with all the steps, is available here:

RAG-techniques-LangChain

Clone the repo, open the Jupyter notebook, and step through the code.

Notebook Step by Step Walkthrough

RAGWorkflow

Final Thoughts

This notebook covers the full spectrum of RAG techniques—from basic embeddings to advanced semantic chunking, persistent vector stores, multi‑document ingestion, external retrieval, and hybrid search strategies. It forms the backbone of a local, privacy‑first AI assistant capable of:

  • Running entirely on‑device
  • Using local LLMs and embedding models
  • Maintaining a persistent, scalable knowledge base
  • Combining semantic and keyword retrieval for high‑quality answers

All code is available in the public GitHub repository. I invite you to:

RAG-techniques-LangChain
  • Clone the repo
  • Run the notebook end‑to‑end
  • Experiment with your own documents
  • Submit pull requests with improvements
  • Share ideas for extending the system

This is an ongoing project, and I’m excited to continue refining the architecture, adding multimodal capabilities, and integrating LangGraph for agentic workflows. Follow along and help build the next generation of privacy‑first AI tooling.


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