🧠 Agentic-RAG-Explorer Autonomous Research Assistant
Agentic-RAG-Explorer is a sophisticated research framework powered by autonomous AI agents. By combining Retrieval-Augmented Generation (RAG) with intelligent web-browsing capabilities, the system performs deep-dive research in real-time. It integrates a human-in-the-loop (HITL) architecture to ensure that every stage of data collection remains under user oversight, guaranteeing high-precision results.
🎯 Core Capabilities Autonomous Agentic Workflow: Leverages LangGraph to manage complex reasoning chains and automated research paths using the ReAct framework.
Dynamic Information Retrieval: Integrates with the Tavily API to extract high-quality, context-aware information from the web.
State Management: Utilizes LangGraph’s MemorySaver to provide robust session persistence and checkpointing across multi-step research tasks.
Human-in-the-Loop (HITL) Control: Provides a managed breakpoint in the workflow, allowing users to verify and curate source material prior to the synthesis phase.
🛠️ Tech Stack Frameworks: Python, LangChain, LangGraph.
AI Models: OpenAI GPT-4o-mini.
Frontend: Streamlit.
Search Engine: Tavily API.
⚙️ Setup and Installation Prerequisites: Ensure you have Python installed, then create and activate a virtual environment.
- Install Dependencies:
Bash pip install -r requirements.txt 2. Environment Configuration: Create a .env file in the root directory and provide your API credentials:
קטע קוד OPENAI_API_KEY=your_openai_api_key TAVILY_API_KEY=your_tavily_api_key 3. Launch the Application:
Bash streamlit run app.py The application will be accessible at http://localhost:8501.
💡 Use Cases This assistant is engineered to handle complex research tasks, including:
Analyzing emerging technological frameworks.
Synthesizing data-driven scientific reviews.
Comparative analysis of architectures and systems.
This project is provided for professional development and educational purposes.