Question library
Explained questions for the decisions developers make.
AI chatbot
A chatbot exposes a conversational model through your app. It may perform no tools or retrieval. Ordinary conversation alone does not make it an agent.
Streaming AI chatbot
A streaming chatbot shows safe response pieces and progress as they arrive. Final completion remains a separate event.
RAG chatbot
A RAG chatbot answers conversations using retrieved knowledge. The dialogue interface and retrieval pipeline are separate responsibilities.
AI search
AI search often uses semantic or hybrid retrieval to return relevant results. Generation is optional. A result list can be sufficient.
AI summarizer
A summarizer condenses supplied content while preserving the requested facts. It transforms evidence rather than creating new authoritative facts.
AI extractor
An extractor converts unstructured text/media into defined fields. Unknown fields should remain unknown instead of being guessed.
AI classifier
A classifier selects a category for input. An LLM classifier can handle varied wording with a bounded output label.
Customer-support agent
A support agent combines dialogue with safe tools and knowledge search to resolve requests. It may use a bounded loop or explicit graph.
CRM assistant
A CRM assistant helps staff query customer records, summarize interactions, and prepare updates. It needs staff-scoped data access and explicit write policy.
Email assistant
An email assistant classifies, summarizes, searches, or drafts email. Sending is a separate consequential operation. Incoming email content is untrusted.
Workflow automation and introducing Python/FastAPI
AI workflow automation embeds model tasks inside an ordinary business process. Python/FastAPI adds a service boundary for Python-specific parsing, numerical work, or model execution when needed. Node remains capable of hosted model calls and orchestration.
Atlas, document storage, metadata, and vector search
Atlas is MongoDB's managed database environment; MongoDB Vector Search indexes supported vector fields for similarity retrieval. Source metadata and application records can live alongside chunk documents. A vector/search index is distinct from ordinary…
Conversations, agent state, and user memory
MongoDB collections can store conversation messages, task/run state, approved preferences, and memory records. A LangGraph checkpointer requires its expected integration contract rather than an arbitrary collection called memory. Business claims remain…
Multi-tenancy and comparison with PostgreSQL + pgvector
Multi-tenancy supports several organizations while preventing cross-organization access. MongoDB often fits flexible document records; PostgreSQL provides relational constraints, joins, transactions, and optional row-level security with pgvector. Both require…
Scalability, availability, reliability, and latency
Scalability handles growing load. Availability means the system is accessible when needed; reliability means tasks produce correct, durable outcomes despite failures. Latency measures waiting, including queue time and user-visible completion.
Queues, caching, databases, and vector indexes
Queues decouple arrival from processing. Caches reduce repeated eligible work. Business databases store authoritative records; vector indexes store derived retrieval representations. Each has a distinct consistency and recovery contract.
Providers, failover, security, multi-tenancy, and observability
Provider failover chooses an allowed alternative after eligible failure. Multi-tenancy scopes records and operations; security enforces trust boundaries. Observability connects execution paths to their outcomes and resource costs.
Design 1: RAG chatbot
A conversational interface over an eligible knowledge corpus. It combines a document pipeline with a request-time answer pipeline.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.