📚 Retrieval-Augmented Generation (RAG) Pipeline using LangChain + FAISS + OpenAI

This repository contains a Jupyter Notebook implementation of an end-to-end RAG pipeline built using:

🔹 LangChain – document loading, chunking, retrieval, prompting
🔹 FAISS – vector indexing + similarity search
🔹 OpenAI – embeddings + LLM generation
🔹 Python – easy-to-follow step-based workflow

This project demonstrates how Large Language Models can be grounded with retrieved knowledge to generate accurate, context-aware answers without hallucination.

🔥 What this project does

Step	Description	Tools Used
1. Data Ingestion	Reads PDF/Text/Markdown documents	LangChain Loaders
2. Chunking	Splits text into manageable overlapping chunks	RecursiveCharacterTextSplitter
3. Embeddings	Converts chunks into numerical vectors	OpenAIEmbeddings
4. Vector Storage	Indexes embeddings for similarity search	FAISS
5. Retrieval	Retrieves context relevant to a query	MMR / k-NN search
6. Augmentation	Injects retrieved chunks into prompt as context	PromptTemplate
7. Generation	Produces final grounded answer	ChatOpenAI
8. Source Transparency	Returns text chunks used for the answer	return_source_documents=True

The entire flow is implemented inside a single .ipynb notebook for easy demonstration.

📁 Project Structure
📦 RAG-LangChain-FAISS
│
├── data/ # Place your documents here
├── store/ # FAISS index will be saved/loaded here automatically
├── RAG_Pipeline.ipynb # Main notebook containing full implementation
└── README.md # (this file)
The first run requires a document inside data/.
If the folder is empty, the notebook automatically generates a sample file.

🚀 Setup Instructions

1. Clone the project
   git clone
   cd RAG-LangChain-FAISS
   

2. Create virtual environment (recommended)
   python -m venv .venv
   .venv\Scripts\activate # Windows
   

3. Install dependencies
   pip install -r requirements.txt

4. Add your OpenAI API key
   setx OPENAI_API_KEY "sk-xxxxx"
   

Restart terminal afterward.

▶ Run the Notebook
jupyter notebook
Open RAG_Pipeline.ipynb
Run each cell in order to execute the full pipeline.

🧠 How the pipeline works – Conceptual Flow
📄 Documents (.pdf/.txt/.md)
│
[1] Load & Ingest
│
[2] Split into Chunks
│
[3] Create Embeddings (OpenAI)
│
[4] Store in FAISS Vector Index
│
User asks question
│
[5] Retrieve Similar Chunks (MMR)
│
[6] Augment Prompt w/ Context
│
[7] Generate Answer (LLM)
↓
🧩 Final context-grounded answer

🌟 Key Features ✔ No hallucinations — answers come from your data
✔ Local vector search (FAISS) for speed
✔ Works fully inside Jupyter (no backend server needed)
✔ Supports TXT, PDF, Markdown ingestion
✔ Transparent — prints retrieved context chunks
✔ Great starter template for production RAG systems

📌 Ideal Use Cases
🔹QA on internal knowledge documents
🔹University/Policy/Manual based answering systems
🔹Company private knowledge bots
🔹PDF summarization + question answering
🔹Personal research assistant