Develop an Advanced Ai-Powered Knowledge Assistant with Llms and Rag

We are seeking an experienced ai engineer or development team to build an advanced ai-powered knowledge assistant. This platform will enable users to upload various document types (PDF, Word, text), ask questions in natural language, and receive accurate, context-aware answers directly from their data.

The core of the system will leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architecture, and vector databases to ensure intelligent document understanding and precise response generation. Semantic search capabilities, powered by vector embeddings, will be crucial for efficient information retrieval. The assistant must also maintain conversational memory and context to provide a seamless user experience.

The project requires a modern, responsive web interface for user interaction and robust secure authentication to support multiple users. The final product should be a scalable, production-ready solution.

Key features include:
- Document upload and analysis (PDF, Word, text).
- Natural language querying.
- Accurate answer generation with document references.
- Semantic search using vector embeddings.
- Conversational memory and context retention.
- Modern, responsive web interface.
- Multi-user support with secure authentication.

The ideal candidate will have proven expertise in ai engineering, llm application development, rag architecture, backend development, and intelligent automation. Familiarity with the specified technology stack is essential.