🚀 Wikipedia Chatbot using LangChain, FastAPI, and OpenAI

📌 Project Overview

This project is an AI-powered Wikipedia Chatbot that allows users to ask natural language questions and receive intelligent answers. The chatbot is built using LangChain for query orchestration, Pinecone as a vector database for semantic search, OpenAI’s GPT model as the language model, FastAPI for backend integration, and Streamlit for an interactive UI.

🛠 Tech Stack & Workflow

🔹 1. LangChain for Graph Orchestration

• Handles the logic for processing user queries and retrieving relevant Wikipedia information.
• Uses retrieval-augmented generation (RAG) to improve responses by combining vector search with LLM capabilities.

🔹 2. Pinecone as the Vector Database

• Stores Wikipedia embeddings for fast and efficient similarity search.
• Enables semantic search to fetch relevant documents based on query meaning rather than exact words.

🔹 3. OpenAI GPT for Answer Generation

• Uses OpenAI’s LLM to generate human-like responses based on the retrieved Wikipedia content.
• Enhances answers by rephrasing and structuring the information for clarity.

🔹 4. FastAPI for Backend Integration

• Provides a lightweight, high-performance REST API to handle user queries.
• Exposes a /wiki_search/ endpoint to process user questions and return intelligent responses.

🔹 5. Streamlit for Chatbot UI

• Creates an interactive chat-based UI where users can type questions and receive instant responses.
• Maintains a conversation history for better user experience.

🔄 How It Works

1️⃣ User enters a question in the chatbot UI.
2️⃣ FastAPI sends the query to LangChain for processing.
3️⃣ Pinecone searches for the most relevant Wikipedia passages.
4️⃣ OpenAI LLM refines the response using the retrieved information.
5️⃣ Streamlit displays the answer in an intuitive chat format.

🌟 Key Features

✅ Fast & Accurate – Combines vector search with LLM intelligence.
✅ Conversational UI – Uses Streamlit for an engaging chatbot experience.
✅ Scalable Architecture – Built with modular components (FastAPI, Pinecone, OpenAI, LangChain).
✅ Real-time Responses – Ensures low latency for quick query resolution.

📸 Screenshot

--- ### 🚀 Setup Instructions

1. Clone the repository

   git clone https://github.com/themagedigest/Wiki-Chatbot.git
   cd Wiki-Chatbot

2. Create virtual environment

   python3 -m venv virtualenv

3. Activate the environment

   source virtualenv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Run the FastAPI backend

   python3 -m uvicorn api:app --reload

6. Start the Streamlit UI

   python3 -m streamlit run bot.py