documentation/future_roadmap.md

1. Current Limitations

a. Limited Omani Knowledge

• Challenge: The current knowledge base includes WHO guidelines, CBT manuals, and limited local resources.
• Impact: May not fully reflect Omani-specific cultural, religious, and social contexts.

b. Latency & Performance Issues

• Challenge: End-to-end latency can be high due to Whisper STT, LLM processing, and retrieval delays.
• Impact: Slower responses may reduce user trust, particularly during crisis interactions.

c. Scalability with ChromaDB

• Challenge: Current vector store (ChromaDB) may not scale for enterprise-level usage.
• Impact: Limits the system’s ability to handle large, multi-lingual, and multi-regional datasets.

d. Privacy Concerns

• Challenge: Handling sensitive mental health conversations requires strict privacy guarantees.
• Impact: Without advanced techniques, risk of data leaks or non-compliance with regulations.

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2. Proposed Solutions

a. Expanding Omani Knowledge

• Partner with clinical specialists in Oman to curate domain-specific resources.
• Add locally relevant CBT material, cultural practices, and religious guidance.
• Build a tiered knowledge base (global → regional → Omani-specific).

b. Latency Optimization

• Backend: Model distillation, caching, and parallelized retrieval.
• Frontend: Optimized Streamlit workflows and pre-fetching common queries.
• Infrastructure: Deploy via Docker + Kubernetes with load balancing.

c. Enterprise-Grade Vector Stores

• Transition from ChromaDB to scalable solutions such as:
  • Pinecone (managed) for enterprise reliability.
  • Weaviate or Milvus (open-source) for privacy-preserving, on-premise deployment.
• Enable multi-region support for cross-border healthcare projects.

d. Privacy & Security Enhancements

• Apply end-to-end encryption for transcripts and logs.
• Incorporate federated learning and differential privacy for safer AI model updates.
• Regular security audits to ensure compliance with international mental health data regulations.

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3. Long-Term Roadmap

Phase 1: Clinical Integration

• Pilot with mental health professionals in Oman.
• Collect feedback on cultural relevance, safety, and accuracy.
• Improve crisis detection with fine-tuned local datasets.

Phase 2: Technical Scaling

• Migrate to enterprise vector databases.
• Optimize latency with edge deployment and GPU acceleration.
• Expand language support (Arabic dialects, Swahili, Urdu).

Phase 3: Global Expansion

• Position chatbot as a global mental health assistant with localized cultural adaptations.
• Build APIs for integration with telehealth platforms and NGOs.
• Establish partnerships with universities and health ministries.

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4. Vision

The OMANI Chatbot aspires to become a trustworthy, culturally sensitive, and scalable AI assistant for mental health support.
By addressing current limitations and implementing future improvements, it can provide timely, empathetic, and safe interactions for individuals in need—starting in Oman, and eventually expanding worldwide.