HumBiOT AI RAG
HumBiOT is an innovative project leveraging cutting-edge AI to transform how the Humboldt Institute manages biodiversity-related queries. By implementing Retrieval-Augmented Generation (RAG) techniques and the state-of-the-art LLaMa 3.1 language model, humBiOT seamlessly combines advanced natural language processing with real-time information retrieval. This enables highly accurate, contextual, and automated responses to public inquiries received through both web forms and institutional email.
Built on a robust AWS Bedrock infrastructure and integrating technologies like Elastic search and RESTful APIs, humBiOT automates repetitive processes, accelerates information access, and ensures data-driven, scientifically grounded answers. The project not only revolutionizes citizen service and operational efficiency, but also explores new frontiers for AI in ecological research and biodiversity conservation
System Architecture & Core Technologies
HumBiOT is built on a robust Retrieval-Augmented Generation (RAG) framework, combining the LLaMa 3.1 language model with a high-performance information retrieval engine. The architecture leverages an AWS-based cloud infrastructure to ensure scalability and security, and provides seamless integration with institutional platforms such as web forms and email systems.
- Language Model: The LLaMa 3.1 model is trained and fine-tuned with biodiversity-specific datasets, ensuring contextually relevant and scientifically rigorous responses.
- Retrieval Engine: Technologies like Elasticsearch or FAISS enable the system to swiftly retrieve and cross-reference relevant documents from the Humboldt Institute’s knowledge base.
- API Ecosystem: RESTful APIs, built with Python frameworks (Flask, FastAPI, Django), mediate user interactions from web and email channels to backend AI services.
- Frontend and Backend: User interfaces are developed with modern web frameworks such as React or Angular, while backend microservices handle business logic and data exchange.
- Email Automation: Integration with SMTP/IMAP APIs facilitates the automated extraction, processing, and response to institutional emails.
Task
Develop a fine-tuned RAG chatbot for biodiversity in the south american region