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Project Deep DiveRAGLangChainFAISSStreamlitLocal LLM

Local RAG with Streamlit, LangChain, and FAISS — No API Keys Required

End-to-end local RAG pipeline using LangChain, FAISS vector search, and GPT-2 — precursor to DocuMind with Streamlit UI for retrieval-grounded question answering.

4 min readBy Drake Talley
rag-streamlit-langchain project preview

Project Summary

Fully local RAG with FAISS indexing, LangChain chunking, GPT-2 generation, and Streamlit UI — no cloud API keys. Conceptual ancestor of the DocuMind production stack.

Technical deep dive

This repository implements a fully local Retrieval-Augmented Generation (RAG) pipeline using LangChain, FAISS vector search, and a GPT-2 language model — with no external API keys required. It predates my production DocuMind stack but captures the same core insight: grounding answers in retrieved context beats unconstrained generation for factual tasks. The Streamlit UI makes the retrieval-and-generation loop visible, which is ideal for demos, teaching, and SEO around terms like local RAG pipeline, FAISS LangChain tutorial, and offline LLM question answering.

Architecture overview

Local RAG loop: retrieve relevant chunks, then condition generation on retrieved text only.

Key design decisions

  • FAISS in-memory vector index for fast cosine similarity without a vector database server
  • LangChain document loaders and text splitters for chunking with configurable overlap
  • GPT-2 as a lightweight local LLM — no OpenAI or cloud inference dependency
  • Streamlit frontend exposing query input, retrieved sources, and generated response
  • End-to-end runnable on a laptop with modest GPU or CPU inference

When to use this vs DocuMind

CriteriaRAG Streamlit (this repo)DocuMind (production)
Vector storeFAISS in-memoryChromaDB with persistent collections
LLM backendGPT-2 localOllama (llama3, embeddings)
APIStreamlit onlyFastAPI + Next.js UI
CitationsBasic source displayStructured SourceCitation objects
Best forLearning, quick demosProduction RAG reference

Setup

git clone https://github.com/cdtalley/rag-streamlit-langchain
cd rag-streamlit-langchain
pip install -r requirements.txt
streamlit run app.py

Key Features & Capabilities

  • FAISS in-memory vector index for cosine similarity search
  • LangChain document loaders and configurable text splitters
  • GPT-2 local generation conditioned on retrieved context
  • Streamlit UI exposing query, sources, and generated answers

Tech Stack & Components

PythonLangChainFAISSGPT-2StreamlitHugging Face

Getting Started

1.Run locally

Install dependencies and launch Streamlit.

git clone https://github.com/cdtalley/rag-streamlit-langchain
pip install -r requirements.txt
streamlit run app.py

Frequently asked questions

Does this RAG project require OpenAI or cloud APIs?
No. It uses FAISS for vector search and GPT-2 for local text generation. The entire pipeline runs offline after initial model download.
How is this different from DocuMind?
This is a lightweight learning/demo stack with FAISS and Streamlit. DocuMind is a production reference with ChromaDB, Ollama embeddings, FastAPI, citation grounding, and dual library collections — documented at draketalley.ai/blog/documind-local-first-rag-platform.
What documents can I index?
Plain text and common document formats supported by LangChain loaders. Chunk size and overlap are configurable in the notebook/app configuration.
Can I swap GPT-2 for a larger model?
Yes — replace the LangChain LLM wrapper with any Hugging Face or local model compatible with your hardware. DocuMind demonstrates the Ollama-based production pattern.