mirror of https://github.com/Monadical-SAS/reflector.git synced 2026-03-21 22:56:47 +00:00

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Igor Loskutov 608a3805c5 chore: remove completed PRD, rename setup doc, drop response_format tests

- Remove docs/01_ollama.prd.md (implementation complete)
- Rename local-dev-setup.md -> standalone-local-setup.md
- Remove TestResponseFormat class from test_llm_retry.py

2026-02-10 16:14:33 -05:00

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2	Standalone Local Setup

Standalone Local Setup

The goal: a clueless user clones the repo, runs one script, and has a working Reflector instance locally. No cloud accounts, no API keys, no manual env file editing.

git clone https://github.com/monadical-sas/reflector.git
cd reflector
./scripts/setup-local-dev.sh

The script is idempotent — safe to re-run at any time. It detects what's already set up and skips completed steps.

Prerequisites

Docker / OrbStack / Docker Desktop (any)
Mac (Apple Silicon) or Linux
16GB+ RAM (32GB recommended for 14B LLM models)

What the script does

1. LLM inference via Ollama (implemented)

Mac: starts Ollama natively (Metal GPU acceleration). Pulls the LLM model. Docker containers reach it via host.docker.internal:11434.

Linux: starts containerized Ollama via docker-compose.standalone.yml profile (ollama-gpu with NVIDIA, ollama-cpu without). Pulls model inside the container.

Configures server/.env:

LLM_URL=http://host.docker.internal:11434/v1
LLM_MODEL=qwen2.5:14b
LLM_API_KEY=not-needed

The current standalone script for this step is scripts/setup-local-llm.sh. It will be folded into the unified setup-local-dev.sh once the other steps are implemented.

2. Environment files

The script would copy .env templates if not present and fill defaults suitable for local dev (localhost postgres, redis, no auth, etc.).

The exact set of env defaults and whether the script patches an existing .env or only creates from template has not been decided yet. A follow-up research pass can determine what's safe to auto-fill vs. what needs user input.

3. Transcript storage

Production uses AWS S3. Local dev needs an alternative.

Options include MinIO in docker-compose (S3-compatible, zero config), a filesystem-backed storage backend (if one exists in the codebase), or skipping storage for dev if the pipeline can function without it. This depends on what TRANSCRIPT_STORAGE_BACKEND supports beyond aws — needs investigation.

4. Transcription and diarization

Production uses Modal.com (cloud GPU) or self-hosted GPU servers.

The codebase has a TRANSCRIPT_BACKEND=whisper option for local Whisper. Whether this runs acceptably on CPU for short dev recordings, and whether diarization has a local fallback, is unknown. For a minimal local setup, it may be sufficient to skip transcription and only test the LLM pipeline against already-transcribed data.

5. Docker services

docker compose up -d postgres redis server hatchet hatchet-worker-cpu hatchet-worker-llm web

Frontend included in compose (web service). Everything comes up in one command.

6. Database migrations

docker compose exec server uv run alembic upgrade head

Idempotent (alembic tracks applied migrations).

7. Health check

Verifies:

Server responds at http://localhost:1250/health
LLM endpoint reachable from inside containers
Frontend serves at http://localhost:3000

What's NOT covered

These require external accounts and infrastructure that can't be scripted:

Live meeting rooms — requires Daily.co account, S3 bucket, IAM roles
Authentication — requires Authentik deployment and OAuth configuration
Production deployment — see Deployment Guide

Current status

Step 1 (Ollama/LLM) is implemented and tested. Steps 2-7 need a separate research and implementation pass each.

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