QueryCrafter

🌐 LangGraph Research & Answering System

This project is an intelligent research pipeline built using LangGraph, LangChain, Streamlit, and Hugging Face. It allows users to enter a topic, fetches real-time web data using Tavily, and generates a concise answer using a HuggingFace-hosted language model.

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🚀 Features

• 🔍 Real-time Web search using Tavily API
• 🧠 Research summarization and Answer generation using Hugging Face LLMs (GPT-2 or GPT-Neo)
• 🛠 Modular agents using LangGraph
• 🖥️ Interactive Streamlit UI

🧱 Tech Stack

• LangGraph
• LangChain
• Tavily
• Hugging Face Transformers
• Streamlit

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🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/AshNicolus/QueryCrafter.git
cd QueryCrafter

2. Setup Environment

Install required packages:

pip install -r requirements.txt

Create a .env file:

TAVILY_API_KEY=your_tavily_api_key
HUGGINGFACEHUB_API_TOKEN=your_huggingface_token

3. Run the App

streamlit run app.py

4. Input Your Query

• Enter your research topic in the UI.
• Wait for the system to fetch web results and generate an answer.

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