A C++20 command-line pipeline that samples city records from local JSON, enriches each with Wikipedia context, and generates bilingual brewery names and descriptions via a local GGUF model or a deterministic mock.

How It Fits The Main App
Tech Stack
Build
Model
Run
Architecture
Generated Output
Language Generation Quality
- Known Issues
Tested Hardware
Repo Layout
Code Tour
Fixture Strategy
Next Steps

How It Fits The Main App

The pipeline is a data ingestion layer. It sits outside the web app runtime and produces seed records the app imports at startup or during a dedicated seed step.

Planned app area	Pipeline contribution
Brewery discovery and management	Sampled city records, localized names, long-form descriptions
Beer reviews and ratings	Stable brewery fixtures with enough context to anchor review pages
Social follow relationships	Repeatable brewery entities for feeds, follows, and saved lists
Geospatial brewery experiences	Latitude, longitude, and country-level metadata

Tech Stack

C++20
CMake 3.24+
Boost.JSON, Boost.ProgramOptions, Boost.DI
spdlog
libcurl
SQLite amalgamation fetched and compiled via CMake FetchContent
llama.cpp

The build fetches Boost.DI, spdlog, llama.cpp, and SQLite via CMake. Metal is enabled on Apple Silicon; CUDA or HIP/ROCm is detected on Linux when the toolkit is present.

Code Style: Modern C++20 throughout - RAII for ownership, std::unique_ptr for injected dependencies, std::optional for parse outcomes, std::span for read-only views over generated city data, structured bindings in pipeline loops. Formatting follows the Google C++ Style Guide via .clang-format with a narrow column limit and two-space indentation.

Build

Requirements: C++20 compiler, CMake 3.24+, libcurl, Boost (JSON and ProgramOptions). SQLite is fetched from the upstream amalgamation, so no system SQLite package is required.

cmake -S . -B build
cmake --build build

Model

Skip this step if you only need --mocked.

mkdir -p models
curl -L \
  -o models/google_gemma-4-E4B-it-Q6_K.gguf \
  https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF/resolve/main/google_gemma-4-E4B-it-Q6_K.gguf?download=true

Run

Run from build/ so the copied locations.json and prompts/ are available. Each run also writes a fresh dated SQLite file such as biergarten_seed_2026-04-19T15-30-45.123456Z.sqlite into the working directory.

./biergarten-pipeline --mocked
./biergarten-pipeline --model models/google_gemma-4-E4B-it-Q6_K.gguf --temperature 1.0 --top-p 0.95 --top-k 64 --n-ctx 8192 --seed -1

CLI Flags

Flag	Purpose
`--mocked`	Deterministic mock generator, no model required.
`--model, -m`	Path to a GGUF file. Required unless `--mocked` is set.
`--temperature`	Sampling temperature. Default: `1.0`.
`--top-p`	Nucleus sampling. Default: `0.95`.
`--top-k`	Top-k sampling. Default: `64`.
`--n-ctx`	Context window size. Default: `8192`.
`--seed`	Random seed. Default: `-1` (random at runtime).
`--help, -h`	Print usage and exit.

--mocked and --model are mutually exclusive. Omitting both exits with an error before the pipeline starts. Sampling flags are ignored when --mocked is set.

The post-build step copies prompts/ into build/prompts/. Rebuild after editing prompts/system.md.

Architecture

Pipeline Stages

Stage	Implementation
Load	`JsonLoader::LoadLocations()` reads `locations.json` into typed `Location` records.
Sample	`BiergartenDataGenerator::QueryCitiesWithCountries()` samples up to 50 locations per run.
Enrich	`WikipediaService` fetches city and beer context. Keeps going when a lookup fails.
Generate	`MockGenerator` or `LlamaGenerator` produces brewery names and descriptions in English and the local language.
Store	`SqliteExportService` writes each successful brewery into a fresh dated `.sqlite` database with normalized location and brewery tables.
Log	`spdlog` writes results and warnings to the console.

If enrichment or generation fails for a city, that city is skipped and the pipeline continues.

Key Components

src/main.cc - argument parsing and Boost.DI composition root.
JsonLoader - validates curated location input.
WikipediaService - queries Wikipedia extracts, caches results, returns empty context on failure.
LlamaGenerator - formats prompts for Gemma 4, validates JSON output, retries malformed responses up to three times. If output looks truncated, the retry raises the token budget before trying again.
MockGenerator - stable hash-based output so the same city input always produces the same brewery.
SqliteExportService - creates a dated SQLite file per run and persists each successful brewery into normalized tables.
Brewery payloads include English and local-language name and description fields.

Runtime Behaviour

WikipediaService queries city, country, and beer-related Wikipedia extracts using its configured lookup, then caches the first successful response per query string. The fetched extract text is included in the prompt as context for generation.

GetLocationContext() returns an empty string when the web client is unavailable or when lookup/parsing fails.

LlamaGenerator validates model output as structured JSON. The retry path exists as a safety hatch for cases where the reasoning block consumes available token budget and compresses the JSON output space. All runs to date have produced valid output on the first pass; the path is kept for resilience.

MockGenerator uses stable hashes for repeatable output in demos and Storybook runs.

Process Flow - Activity Diagram

Architectural Overview - Class Diagram

Generated Output

Each successful run stores a GeneratedBrewery pair with the source location and a BreweryResult payload. The same generated records are also written to a fresh SQLite export file named with the current UTC timestamp.

Field	Meaning
`name_en`	Brewery name in English.
`description_en`	Brewery description in English.
`name_local`	Brewery name in the local language.
`description_local`	Brewery description in the local language.

The log dump also includes city, country, state or province, ISO subdivision code, latitude, and longitude for each entry.

Consumer Data Shape

Field	Why it matters
`city`, `state_province`, `country`	Human-readable location labels and page headings
`iso3166_1`, `iso3166_2`	Filtering, regional grouping, locale matching
`latitude`, `longitude`	Map pins and nearby brewery views
`local_languages`	Locale-aware copy selection
`name_en`, `description_en`	Default English display content
`name_local`, `description_local`	Local-language display content
`region_context`	Richer copy for cards and detail pages

Language Generation Quality

The generation pipeline passes local language codes to the model to retrieve a translated description_local.

Output quality is reliable for high-resource languages such as French, though it may struggle with regional variants and idiomatic phrasing. This can be seen with these data points:

[
  {
    "city": "Kinshasa",
    "state_province": "Kinshasa",
    "iso3166_2": "CD-KN",
    "country": "Democratic Republic of the Congo",
    "iso3166_1": "CD",
    "latitude": -4.4419,
    "longitude": 15.2663,
    "local_languages": ["fr-CD", "ln"]
  },
  {
    "city": "Paris",
    "state_province": "Île-de-France",
    "iso3166_2": "FR-IDF",
    "country": "France",
    "iso3166_1": "FR",
    "latitude": 48.8566,
    "longitude": 2.3522,
    "local_languages": ["fr-FR"]
  },
  {
    "city": "Abidjan",
    "state_province": "Abidjan",
    "iso3166_2": "CI-AB",
    "country": "Ivory Coast",
    "iso3166_1": "CI",
    "latitude": 5.36,
    "longitude": -4.0083,
    "local_languages": ["fr-CI"]
  },
  {
    "city": "Montreal",
    "state_province": "Quebec",
    "iso3166_2": "CA-QC",
    "country": "Canada",
    "iso3166_1": "CA",
    "latitude": 45.5017,
    "longitude": -73.5673,
    "local_languages": ["fr-CA"]
  },
  {
    "city": "Brussels",
    "state_province": "Brussels-Capital Region",
    "iso3166_2": "BE-BRU",
    "country": "Belgium",
    "iso3166_1": "BE",
    "latitude": 50.8503,
    "longitude": 4.3517,
    "local_languages": ["fr-BE", "nl-BE"]
  }
]

Output sample: ./out-sample/french-cities.example

Known Issues

Low-Resource Language Hallucination

For languages such as Welsh (Wales), Maori (Aotearoa/New Zealand), or Sicilian (Sicily, Italy), the model can generate text that looks syntactically plausible but is semantically incoherent. This comes from limited training-data coverage rather than prompt engineering.

Proposed Mitigations

Prevention via allowlist: introduce a high-resource language allowlist. If a location's code is unlisted, skip description_local generation and fall back to English.
Upstream sanitization: strip known low-resource language codes from the locations.json payload before generation.
Downstream flagging: add a description_local_confidence column to the SQLite schema so downstream applications can filter or flag potentially hallucinated text by language tier.

Tested Hardware

ARM macOS - M1 Pro


Host	MacBook Pro 14" (2021)
CPU	Apple M1 Pro (8-core)
GPU	Apple M1 Pro (14-core integrated)
Memory	16 GB
Model	Gemma 4 E4B
Inference	llama.cpp with Metal

x86_64 Linux - NVIDIA RTX 2000


Host	ThinkPad P1 Gen 7 (Fedora 43)
CPU	Intel Core Ultra 7 155H
GPU	NVIDIA RTX 2000 Ada Generation
Memory	32 GB
Model	Gemma 4 E4B
Inference	llama.cpp with CUDA 12.x

Repo Layout

Path	Purpose
`includes/`	Public headers and shared models.
`src/`	Implementation files.
`locations.json`	Curated city input copied into the build tree.
`prompts/`	System prompt used by the model-backed path.
`diagrams/`	Architecture and pipeline diagrams.

Code Tour

src/main.cc - argument parsing and DI composition root.
src/biergarten_data_generator/ - orchestration, sampling, logging, and export.
src/services/wikipedia/ - enrichment service and cache.
src/services/sqlite/ - SQLite export implementation.
src/data_generation/llama/ - local inference, prompt loading, output validation.
src/data_generation/mock/ - deterministic fallback.

Fixture Strategy

--mocked for stable fixtures, repeatable screenshots, and Storybook runs.
--model when geographically grounded content matters for demos.
Keep locations.json structured enough to support discovery and future filtering.
Treat SQLite output as seed material for the app's brewery domain, not production data.

Next Steps

The pipeline currently produces city-aware brewery records and dated SQLite exports. The next passes add additional fixture types so the app can exercise the full brewery domain without live data.

Testing (Very High Importance)

Unit test JSON validation and retry logic against malformed, truncated, and empty model outputs.
Integration test the enrichment pipeline with missing context, short context, and fake context inputs.
Adversarial context tests: feed plausible but geographically incorrect Wikipedia extracts and verify the model does not silently blend them with training data.
Verify bilingual enrichment behaviour when only an English extract is available versus when both extracts are present.
Confirm the retry path is reachable when the reasoning block consumes available token budget.

Beer Generation

Generate catalog entries with style, ABV, IBU, color, aroma notes, and food pairing hints. Link beers back to breweries and cities. Keep style coverage wide enough to exercise search, sort, and category filters.

User Generation

Generate user profiles with stable names, bios, locale hints, and preference signals. Include stable IDs for downstream fixture joins. Keep output deterministic for screenshots while allowing larger randomized batches.

Check-In System

Produce timestamped check-in events between users and breweries. Use a J-curve activity profile - a small set of users accounts for most check-ins, the rest appear occasionally. Add bursty behaviour around weekends and travel periods.

Beer Ratings

Generate rating events with a strong positive skew and a long tail of lower scores. Avoid uniform distributions. Attach timestamps and user IDs so the app can compute averages, trends, and per-style comparisons.

README.md

Biergarten Pipeline

Table of Contents