Document search and question-answering system with RAG pipeline, HTTP service, file storage, database, and a Platform tenant chart whose ModelGateway serves the pipeline's generation and embeddings routes.
Render it
# CLI
npx @nanohype/sdk render-composite document-intelligence --out ./my-app
# SDK
import { LocalSource, renderComposite } from "@nanohype/sdk";
const result = await renderComposite(source, "document-intelligence", variables);
# MCP — from an agent
get_composite({ name: "document-intelligence" })
What it draws together
Variables
Required
ProjectName string- Project name used across all templates Must be lowercase kebab-case.
Optional
LlmProvider string — defaults to gateway- LLM provider the packages deployed on the substrate call: gateway, the Platform ModelGateway, which holds the Bedrock role there, so none of them holds a model credential. The eval harness runs on a CI runner and keeps its own bedrock default
VectorStore string — defaults to pgvector- Vector database backend
IncludeEvals bool — defaults to true- Include evaluation harness
Tenant string — defaults to ${ProjectName}- Owning team: the tenants-<team> namespace the Platform, BudgetPolicy and ModelGateway CRs are applied in, the Platform's spec.tenant, and the team label on the tenant chart's resources and telemetry
Image string — defaults to ${ProjectName}- Container image repository the tenant chart deploys, without a tag; each values-<env>.yaml pins one
AppMetric string — defaults to http- Prefix of the RED series the tenant chart's Grafana dashboard reads: <prefix>_requests_total, <prefix>_errors_total and <prefix>_request_duration_seconds. The default, http, is the prefix apps/api (a ts-service) emits them under