AI Chatbot Stack
AI / MLAdvancedAI product stackLLM-powered chatbot with RAG vector retrieval, WebSocket streaming, multi-provider failover, and persistent chat history. Suitable for production AI assistants and copilot products.
Recommended for: Teams building retrieval-backed assistants
Use Case
AI assistants, RAG chatbots, conversational interfaces
Best Fit Scenarios
- Customer support copilots
- Internal knowledge assistants
- Multi-model chatbot experiences with fallback
Stack Breakdown
Architecture Layers
Components by Category
frontend
backend
database
external
Why This Topology Works
WebSocket gateway provides streaming responses. FastAPI orchestrates RAG retrieval from Pinecone before calling LLM providers. Fallback chain (OpenAI → Anthropic) ensures availability.
Scaling Notes
FastAPI workers scale per-request. Pinecone handles vector similarity search at scale. Token budget controls cost per conversation.
Observability
Token usage tracked per conversation. Latency percentiles on LLM calls. Sentry captures embedding and generation failures.
Typical Bottlenecks
- Frontend rendering and bundle delivery under peak traffic
- Service latency and timeout behavior on critical routes
- Write amplification and query contention on primary stores
Async Flow and Reliability
The flow is mostly synchronous. Add queue-backed workers for long-running or failure-prone operations to protect request latency.
Upgrade Path
Harden each domain with clear ownership, enforce SLO budgets, and adopt multi-region or active-passive failover where downtime costs are high.
Operating Envelope
Complexity is marked as Advanced with an intended scope of AI product stack. Use this as a planning baseline before adapting the template to your reliability and team constraints.