📄 Automated Research Report Generation System

This project is an AI-powered application that automates the process of generating structured research reports using Retrieval-Augmented Generation (RAG). It integrates:

• A vector-based semantic search system
• Groq-accelerated language models for synthesis
• A clean Streamlit UI for end-to-end interaction

---

Deployed link

https://huggingface.co/spaces/akankshax/automated-report-generation

🧠 Problem Statement

In research-intensive domains, generating detailed reports is time-consuming and error-prone. Researchers manually sift through large volumes of data, documents, and literature scattered across formats and locations.

This project solves that by automating the process through:

• Intelligent document ingestion
• Semantic search using embeddings
• Research report generation via Groq-powered LLMs

---

🎯 Features

• 📚 Multi-source Document Ingestion (PDF, DOCX, TXT)
• 🔍 Semantic Search with FAISS
• 🤖 Groq-hosted LLMs for fast and cost-effective synthesis
• 📝 Structured Report Output: Abstract, Literature Review, Methodology, Findings, Conclusion
• ⚡ Streamlit-based UI
• ✅ Modular and easy-to-extend

---

🛠️ Tech Stack

Component	Technology
Embeddings	text-embedding-3-small via Groq
Vector DB	FAISS
LLM	llama3-8b-8192 via Groq
UI	Streamlit
File Handling	PyPDF2, python-docx
Environment	Python 3.10+
Deployment	Hugging Face
Tracing	LangSmith

---

🚀 How to Run

1. Clone the repo

git clone https://github.com/yourusername/research-report-generator.git
cd research-report-generator

2. Install dependencies

pip install -r requirements.txt

3. Add your .env file

GROQ_API_KEY=your_groq_key
GROQ_API_BASE=https://api.groq.com/openai/v1
EMBEDDING_MODEL=text-embedding-3-small
LANGSMITH_API_KEY=your_lansmith_key

4. Run the embedding pipeline

python build_embeddings.py

5. Launch the Streamlit app

streamlit run streamlit_app.py

Example Query

"The role of LLMs in automating academic research workflows"

Output:

✅ Abstract

✅ Literature Review

✅ Methodology

✅ Findings

✅ Conclusion