Objective:

Develop a RAG chatbot to provide accurate, context-aware responses for business analytics blogs.

Key Features:

  • Real-time retrieval of relevant blog content using vector search for enhanced accuracy.
  • Multi-LLM integration via OpenRouter to dynamically select the best model for response generation.
  • Serverless deployment on Google Cloud Functions for scalability and cost efficiency.
  • Automated vector storage updates every weekend to maintain up-to-date knowledge.

Results:

  • Accurate and context-aware chatbot responses based on real-time retrieval.
  • Serverless deployment ensures dynamic scaling with minimal overhead.
  • Fast and precise vector-based document retrieval using Pinecone.
  • Optimized chatbot efficiency with dynamic LLM selection.
  • Continuous knowledge updates through automated vector database refresh.

Customization for IT Staff:

  • Adaptable for internal IT documentation retrieval, improving support response accuracy.
  • Can be extended to analyze and respond to IT helpdesk queries dynamically.
  • AI-driven knowledge base assists in enhancing IT support efficiency.

Timeline 6 Weeks:

  • Document Ingestion & Vector Storage: 1 week
  • Chatbot Development & Query Processing: 2 weeks
  • Performance Optimization & Security: 2 weeks
  • Deployment & Maintenance: 1 week

Techstack:

Python, Cloud Functions, Cloud Schedulers, Flask, Langchain, pinecone, vertex AI, OpenRouter