Advanced_RAG provides a modular pipeline for retrieval-augmented generation tasks, including document loaders, vector index builders, and chain managers. Users can configure different vector databases (FAISS, Pinecone), customize retriever strategies (similarity search, hybrid search), and plug in any LLM to generate contextual answers. It also supports evaluation metrics and logging for performance tuning and is designed for scalability and extensibility in production environments.

The Django RAG Llama3 Multi-AGI Code Generation API unifies retrieval-augmented generation with a coordinated set of AI agents based on Llama3 to streamline website development. It allows users to submit project requirements via REST endpoints, triggers a requirement analysis agent, invokes frontend and backend code generator agents, and performs automated validation. The system can integrate custom knowledge bases, enabling precise code templates and context-aware components. Built on Django's REST framework, it provides easy deployment, scalability, and extensibility. Teams can customize agent behaviors, adjust model parameters, and extend the retrieval corpus. By automating repetitive coding tasks and ensuring consistency, it accelerates prototyping and reduces manual errors while offering full visibility into each agent's contributions throughout the development lifecycle.