Finding a good hospital or doctor can be complex, and internet searches or personal references often make this process inefficient. To tackle this, I developed a conversational AI Agent that automates the retrieval of essential healthcare information, including hospital details, wait times, customer reviews, visits, and doctor profiles. The system integrates OpenAI's GPT-4 and LLaMA 3 models for advanced natural language processing and uses LangChain to manage conversational flows. Neo4j graph databases effectively model the complex relationships between entities, while Retrieval-Augmented Generation (Graph-RAG) to extract relevant data and generate appropriate responses. This solution embeds data efficiently, streamlining decision-making for users.

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Developed multi-agent AI system for automated content creation using crew.ai and Llama 3 via Groq API. Implemented 3-agent sequential workflow: Content Planner, Writer, and Editor.
Skills Applied: Python, crew.ai, LangChain, Groq API
Key Features:
- Agent-specific roles and goals
- Task-based workflow: planning, writing, editing
- LLM integration for intelligent content generation
- Crew orchestration for seamless agent collaboration
Outcomes: - Streamlined content creation
- Improved SEO optimization
- Consistent quality and brand adherence

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