Mixpeek consists of an API service and an Engine service (Ray). It depends on MongoDB, Qdrant, Redis, and S3‑compatible storage. This guide outlines common topologies, configuration, health, and scaling.

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Overview

API

FastAPI app with auth, tenancy, routes, and orchestration

Engine

Ray Serve app for extractors, clustering, and taxonomy execution

Datastores

MongoDB (metadata), Qdrant (vectors), Redis (rate limit/tasks), S3 (artifacts)

Observability

Health checks, logs, metrics, and tasks status

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Architecture

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Configuration

  • ENV, DEBUG, LOG_LEVEL
  • QDRANT_URL, QDRANT_API_KEY
  • MONGO_URI, MONGODB_API_DB
  • REDIS_URL
  • AWS_BUCKET, AWS_REGION

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Health and readiness

  • Route: /v1/health — checks Redis, MongoDB, Qdrant, Celery, Engine
  • Reference: Health API
curl -s https://api.mixpeek.com/v1/health | jq

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Deploy steps (typical)

1

Provision deps

Managed MongoDB, Qdrant, Redis, and S3 bucket with IAM
2

Deploy Engine

Start Ray Serve with configured workers and resource limits
3

Deploy API

Start FastAPI app; point to Engine and datastores via env vars
4

Verify

Call /v1/health and run a smoke: create namespace → collection → retriever → execute

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Scaling and upgrades

Scale Engine

Add Ray workers for extractor and clustering throughput

Scale Qdrant

Increase memory/IO; shard by namespace if needed

API Autoscale

Horizontally scale API; rate limit via Redis to protect backends

Zero‑downtime

Blue/green or rolling restarts; confirm /v1/health before cutover

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Operations references