🧠 Building the RAG Wiki Assistant with LangChain, FAISS, and Gemini

Retrieval-Augmented Prompting for Effective Wikipedia Q&A

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🧾 Abstract

RAG Wiki Assistant is a Retrieval-Augmented Generation (RAG) system that combines LangChain, FAISS, and Gemini 2.5 Flash to answer user queries using a Wikipedia-based knowledge base.
It dynamically retrieves relevant information and generates context-rich, grounded responses using verified content rather than model memory.

Key features include:

• Wikipedia content ingestion and vectorization
• Dynamic retrieval and context construction
• Controlled, citation-based generation
• Session persistence and logging

This publication outlines the assistant’s purpose, technical architecture, and potential applications in education, research, and AI development.

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1. 🎯 What Is This About? (Purpose)

The RAG Wiki Assistant is designed to generate accurate, fact-based, and context-aware responses by combining retrieval and generation techniques.
Instead of relying on model hallucination, it grounds its outputs in Wikipedia content, making it suitable for:

• Educational platforms and academic support
• Research summarization and knowledge exploration
• Automated fact-checking systems
• AI explainability and decision-support tools

The assistant ensures every response is relevant, verifiable, and contextually grounded in the retrieved source material.

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2. 💡 Why Does It Matter? (Value & Impact)

Traditional AI assistants tend to produce hallucinated or unverifiable information.
This project solves that issue through retrieval-augmented prompting, ensuring every answer is backed by real, sourced data.

🔑 Key Advantages

• Grounded Responses – All outputs are evidence-based.
• Educational Value – Provides transparent reasoning from real sources.
• Dynamic Topic Retrieval – Instantly adapts to new Wikipedia topics.
• Session Continuity – Maintains context across multi-turn interactions.

In short, RAG Wiki Assistant bridges the gap between retrieval precision and generative fluency, making it a trusted AI companion for learning and discovery.

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3. 🧩 Can I Trust It? (Technical Quality)

🧠 Core Tech Stack

🔧 Reliability Features

• Dynamic Prompt Formatting: Adapts prompts based on the current Wikipedia topic.
• Session Memory: Maintains conversational continuity.
• Error Handling & Logging: Improves robustness and debugging clarity.
• Configurable Parameters: Supports topic customization via .env file.

This modular design makes the assistant robust, scalable, and transparent.

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4. ⚙️ Can I Use It? (Documentation)

Absolutely. The assistant is designed for quick setup and easy customization.

🛠 Key Features

• Dynamic Knowledge Base: Change topics by editing .env variables.
• Session Memory: Retains context across questions.
• Error Handling: Catches and logs retrieval/generation issues.

🚀 Setup Overview

# Clone the repository
git clone https://github.com/Ramee4sure/RAG-Wikipedia-Assitant.git
cd RAG-Wikipedia-Assitant

# Create a virtual environment (optional)
python -m venv .venv
.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Mac/Linux

# Install dependencies
pip install -r requirements.txt

# Run the assistant
python src/app.py

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📘 More Documentation

Full instructions, configuration examples, and environment templates are available in the
GitHub Repository.

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🧰 Technical Implementation

from rag_chain.rag import start_bot

# Initialize and start the assistant
if __name__ == "__main__":
    start_bot()

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🔍 Knowledge Base Creation

1. WikipediaLoader retrieves topic-specific articles.
2. RecursiveCharacterTextSplitter divides them into chunks.
3. HuggingFace Embeddings convert chunks to dense vectors.
4. FAISS indexes them for efficient retrieval.

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🧩 Prompt Engineering

A dynamic LangChain PromptTemplate ensures that the LLM generates answers only from retrieved context.

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🤖 Response Generation

• LangChain manages RAG flow.
• Gemini 2.5 Flash composes the final, context-grounded answer.
• Fallback Handling: If retrieval fails, the model gracefully declines to answer.

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🧠 Enhancements

• Multi-turn session memory for conversational flow.
• Error logging for transparency and traceability.
• Configurable topics and chunking size for scalability.

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🔍 Limitations and Future Directions

Current Limitations

• Dependent solely on Wikipedia content quality.
• Lacks real-time content updates.
• Single-language (English) support.

Future Improvements

• Integration with ArXiv, HuggingFace datasets, and GROQ.
• Real-time data ingestion and update mechanisms.
• Multilingual document retrieval and generation.
• Citation visualization in UI for source tracking.

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📊 Example Output

Query:

“What is Transfer Learning in Machine Learning?”

Response:

“Transfer Learning is a machine learning technique where a model trained on one task is reused as a starting point for another related task. (Source: Wikipedia)”

Out-of-Scope Query:

“I'm sorry, but this question cannot be answered with the provided documents.”

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🧩 Evaluation

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🗂️ Project Structure

RAG-Wikipedia-Assistant/
├── src/
│   ├── scraper/               # Wikipedia text scraper
│   ├── rag_chain/             # RAG pipeline (Gemini + FAISS)
│   └── app.py                 # Entry point with .env creation
│
├── wikipedia_pages/           # Stored Wikipedia documents
├── vectorstore/               # FAISS vector database
├── .env_example               # Configuration template
├── requirements.txt           # Dependency list
└── README.md                  # Documentation

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👥 Authors

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🪪 License

MIT License
Permission is granted, free of charge, to use, copy, modify, and distribute this work with attribution.

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📬 Contact Information

Primary Author: Manas Gaurkar
📧 Email: manas.gaurkar.dev@gmail.com
🌐 GitHub: Ramee4sure
📘 Project: RAG-Wikipedia-Assitant

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🧠 “Retrieval before Generation — because truth matters.”