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-# airline-faq-rag
+# Airline FAQ RAG Project
+
+Welcome to the **Airline FAQ RAG Project**! This repository houses an innovative pet project exploring the creation of airline-related FAQ data using an AI agent and building a Retrieval-Augmented Generation (RAG) application to provide accurate and context-aware responses. Whether you're an AI enthusiast, a developer, or someone curious about natural language processing (NLP) and information retrieval, this project offers insights into data generation, embedding strategies, and RAG system optimization.
+
+## 🎯 Project Overview
+
+This project has two primary components:
+
+1. **FAQ Data Generation Agent**: An AI-driven agent that generates high-quality, airline-related FAQ data based on predefined topics, exploring the impact of prompt engineering on output quality.
+2. **RAG Application**: A Retrieval-Augmented Generation system that leverages the generated FAQ data to answer queries, with experiments on different chunking strategies to optimize retrieval performance.
+
+The goal is to build a foundation for an end-to-end conversational AI agent for airline customer support, starting with a robust RAG system.
+
+## 🚀 Features
+
+- **Dynamic FAQ Generation**: Automatically creates comprehensive airline FAQs covering topics like booking, baggage, cancellations, and in-flight services.
+- **RAG Implementation**: Combines retrieval and generation to provide accurate, context-aware answers to user queries.
+- **Chunking Experiments**: Evaluates multiple chunking strategies (e.g., full FAQ file vs. question-answer pair chunking) to optimize embedding and retrieval performance.
+- **Prompt Engineering Insights**: Explores how different prompts affect the quality and relevance of generated FAQ data.
+- **Scalable Design**: Lays the groundwork for extending the system into a fully autonomous airline support agent.
+
+## 📂 Repository Structure
+
+- `data_generation/`: Scripts for generating airline FAQ data using an AI agent.
+- `rag_application/`: Implementation of the RAG system, including embedding creation and retrieval logic.
+- `data/`: Sample FAQ datasets and embeddings.
+- `experiments/`: Notebooks and scripts comparing chunking strategies and their performance.
+- `docs/`: Additional documentation and analysis of findings.
+
+## 🛠️ Getting Started
+
+### Prerequisites
+- Python 3.8+
+- Libraries: `transformers`, `faiss`, `numpy`, `pandas`, `langchain` (or your preferred RAG framework)
+- Optional: GPU for faster embedding generation
+
+### Installation
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/your-username/airline-faq-rag.git
+   cd airline-faq-rag
+   ```
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Generate FAQ data:
+   ```bash
+   python data_generation/generate_faq.py --topics "booking,baggage,cancellations"
+   ```
+4. Run the RAG application:
+   ```bash
+   python rag_application/run_rag.py
+   ```
+
+## 💡 Key Components
+
+### 1. FAQ Data Generation
+The FAQ generation agent creates airline-related question-answer pairs based on user-specified topics (e.g., booking, baggage, cancellations). Key features:
+- **Prompt Engineering**: Experimented with various prompts to control tone, detail, and accuracy. For example:
+  - Prompt 1: "Generate 10 FAQs about airline baggage policies in a formal tone."
+  - Prompt 2: "Create concise FAQs for airline cancellations with customer-friendly language."
+- **Learnings**:
+  - Specific prompts with clear instructions (e.g., "include examples") yield more relevant and detailed FAQs.
+  - Iterative prompt refinement improves output consistency and reduces hallucination.
+  - Adding context (e.g., airline-specific policies) enhances realism but requires careful prompt design to avoid bias.
+
+### 2. RAG Application
+The RAG system retrieves relevant FAQ answers for user queries using embeddings and generates responses. Key experiments:
+- **Embedding Strategies**:
+  - **Full FAQ File Embedding**: Treated the entire FAQ dataset as a single document, creating one embedding per file.
+  - **Question-Answer Pair Chunking**: Split FAQs into individual question-answer pairs, creating embeddings for each pair.
+  - **Custom Chunking**: Grouped related FAQs (e.g., all baggage-related questions) into chunks to balance context and granularity.
+- **Performance Evaluation**:
+  - **Full FAQ Embedding**: Fast but less precise, as it struggles with fine-grained retrieval for specific questions.
+  - **Question-Answer Pair Chunking**: Best performance for precise queries (e.g., "What is the baggage allowance?"), with higher relevance scores in retrieval.
+  - **Custom Chunking**: Improved context for complex queries but increased retrieval latency.
+- **Findings**: Question-answer pair chunking outperformed other methods in precision and recall, making it the preferred approach for this use case.
+
+## 🔍 Key Learnings
+- **Prompt Sensitivity**: Small changes in prompt wording significantly affect FAQ quality. For example, specifying "customer-friendly" vs. "formal" tones altered the output's readability and tone.
+- **Chunking Matters**: Fine-grained chunking (question-answer pairs) improves retrieval accuracy but requires careful indexing to manage scale.
+- **Embedding Trade-offs**: Dense embeddings (e.g., using BERT-based models) offer better semantic understanding but are computationally expensive compared to sparse methods.
+- **Scalability Challenges**: Large FAQ datasets require efficient indexing (e.g., FAISS) to maintain low-latency retrieval.
+
+## 🚀 Next Steps
+To evolve this project into an end-to-end airline support agent:
+1. **Context-Aware Generation**: Integrate user context (e.g., booking details) into the RAG pipeline for personalized responses.
+2. **Multi-Turn Conversations**: Enhance the agent to handle follow-up questions and maintain conversation history.
+3. **Real-Time Data Integration**: Incorporate live airline data (e.g., flight status APIs) to provide dynamic answers.
+4. **Model Fine-Tuning**: Fine-tune the language model on airline-specific data to improve response accuracy and domain knowledge.
+5. **Evaluation Metrics**: Implement automated evaluation (e.g., BLEU, ROUGE, or human-in-the-loop feedback) to quantify response quality.
+6. **Deployment**: Package the RAG system as a web or mobile app for real-world testing.
+
+## 🤝 Contributing
+We welcome contributions! Feel free to:
+- Submit pull requests for new features or bug fixes.
+- Share ideas for improving FAQ generation or RAG performance.
+- Add new chunking strategies or prompt templates to the experiments folder.
+
+Please read `CONTRIBUTING.md` for guidelines.
+
+## 📜 License
+This project is licensed under the MIT License. See `LICENSE` for details.
+
+## 📬 Contact
+For questions or feedback, reach out via GitHub Issues or email at [[email protected]].
+
+---
+
+✈️ *Let's build the future of airline customer support together!*