RAG Chatbot

About the App

Our RAG-powered chatbot is designed to deliver accurate, context-aware, and real-time responses for business analytics content. By combining vector-based document retrieval with multi-LLM integration, the chatbot ensures that every answer is relevant, precise, and aligned with the latest information.

Deployed on Google Cloud Functions, the app runs in a serverless environment that scales effortlessly while keeping costs low. Automated weekly vector storage updates guarantee a continuously refreshed knowledge base, ensuring responses stay up to date.

Beyond business analytics, the chatbot can be customized for IT teams to improve documentation access, streamline support queries, and enhance overall knowledge management. With its blend of AI-driven intelligence, scalable architecture, and flexible integration, this solution is built to empower organisations with smarter, faster, and more reliable insights.

Objective:
Build a RAG-based chatbot that delivers precise and context-aware answers for business analytics blogs.

Core Features:

• Real-time retrieval of relevant blog articles through vector search to improve accuracy.

• Integration with multiple LLMs via OpenRouter to automatically choose the most suitable model for each response.

• Serverless deployment on Google Cloud Functions to ensure scalability and cost-effectiveness.

• Automated updates to the vector database every weekend to keep information current.

Expected Outcomes:

• Reliable, context-aware chatbot responses powered by real-time content retrieval.

• Serverless infrastructure provides auto-scaling with low maintenance overhead.

• Rapid and accurate document retrieval using Pinecone vector search.

• Higher efficiency through dynamic LLM model selection.

• Consistently refreshed knowledge base with automated updates.

Customisation for IT Teams:

• Configurable for internal IT document retrieval to enhance support accuracy.

• Extendable to handle IT helpdesk queries with dynamic responses.

• AI-powered knowledge management to boost IT support performance.

Project Timeline – 6 Weeks:

• Document Processing & Vector Storage: 1 week

• Chatbot Development & Query Handling: 2 weeks

• Security & Performance Optimization: 2 weeks

• Deployment & Ongoing Maintenance: 1 week

Technology Stack:
Python, Google Cloud Functions, Cloud Scheduler, Flask, LangChain, Pinecone, Vertex AI, OpenRouter