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