🤖 RAG-IT Chatbot (GPT-3.5 + LangChain)

A production-level IT Support Chatbot built using Retrieval-Augmented Generation (RAG), OpenAI’s GPT-3.5, and LangChain. This project enables intelligent, context-aware question answering based on internal knowledge documents, providing fast and accurate tech support solutions.

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🧠 Key Features

• 🗂️ Contextual QA via RAG: Combines LLM reasoning with document retrieval
• 🔍 Semantic Search: Finds relevant IT support docs using vector similarity
• 🧑‍💻 Interactive Chat Interface: Real-time conversations powered by GPT-3.5
• 🏗️ Modular LangChain Architecture: Easily extendible with new tools or APIs
• 🛡️ Private Knowledge Base: Keeps internal data secure and queryable

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🔧 Tech Stack

• LLM Backend: OpenAI GPT-3.5
• Framework: LangChain
• Vector Store: FAISS / ChromaDB
• Embedding Model: OpenAI or HuggingFace Transformers
• Frontend: Streamlit (optional)
• Utilities: dotenv, tiktoken, PyMuPDF, Python

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2. Install Dependencies

pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in the root directory with the following:

OPENAI_API_KEY=your_openai_key

4. Ingest Knowledge Base

python ingest.py

This step processes documents in /data and builds the vector index.

5. Launch the Chatbot

python langchain_app.py