Chatbot Not Properly Implemented – Migrate Logic to Backend with Vector Database #62
Description
Description
The chatbot feature was improperly implemented in the frontend, resulting in it being non-functional and unsuitable for production use. The current approach places the entire chatbot logic (response generation, context handling, and API calls) on the client side, which introduces critical problems:
- No mechanism for persisting conversations or maintaining context over time.
- API keys exposed directly in the frontend code, causing a serious security vulnerability.
- Inability to handle complex or context-rich queries effectively.
- Suboptimal performance, especially on low-power devices, due to running heavy processing in the browser.
- The implementation lacks scalability and flexibility needed for future AI/NLP feature expansions.
The chatbot was never fully functional or reliable due to these architectural flaws and must be completely redesigned.
Impact
- Chatbot responses are inconsistent or fail altogether.
- Public exposure of sensitive API credentials.
- No conversational memory or context awareness.
- Poor user experience on low-end devices due to client-side computation load.
- Cannot scale or support advanced NLP capabilities moving forward.
Fix Required
- Re-architect the chatbot by moving all logic from frontend (React) to the backend (Node.js or Python).
- Integrate a vector database (e.g., Pinecone, Weaviate, or FAISS) for storing and retrieving contextual embeddings, enabling semantic search over conversation history.
- Develop a secure and efficient REST or WebSocket API for frontend to request chatbot responses.
- Implement persistent conversation memory through embeddings stored in the vector DB, allowing context-aware replies.
- Ensure API keys and sensitive logic are fully protected on the server side.
