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
Scope Notes
Intended for Teams building retrieval-backed assistants at Advanced complexity and AI product stack scope. Treat the graph as a starting shape rather than a fixed stack: keep the topology and the signals below, then swap providers to match your platform.
Review Checklist
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
The WebSocket gateway holds the streaming session while FastAPI orchestrates retrieval and generation. Pinecone grounds answers in retrieved context, Redis holds the short-lived conversation window, and PostgreSQL is the durable transcript of record. Anthropic is the failover when OpenAI errors.
Scaling Notes
FastAPI workers scale per request. Redis holds context with a TTL so long sessions cannot grow unbounded, and Pinecone scales by namespace. Retrieval is on the request path; only generation and fallback are long-running.
Observability
Sentry tracks embedding and generation failures. Track token usage and latency percentiles per provider so failover and cost regressions are visible.
Typical Bottlenecks
- LLM provider latency and quota limits
- Context window growth on long conversations
- Vector retrieval quality versus index freshness
Async Flow and Reliability
The streaming answer is generated on the request path, but retrieval, provider calls and failover are isolated so a slow provider degrades to the fallback instead of failing the conversation.
Upgrade Path
Add authentication on the WebSocket handshake and a document ingestion worker that keeps Pinecone in sync, then put a guardrail layer between FastAPI and the providers for PII redaction and token budgets.