the-biergarten-app/docs/pipeline

title, last-updated, tags

title	last-updated	tags
Biergarten Pipeline — C++ CLI for generating seed data	2026-09-02	pipeline cpp llm seed-data docker

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 plus locale-grounded user profiles via a local GGUF model or a deterministic mock.

This pipeline produces AI-generated data. It is not a source of truth for brewing techniques, cultural representation, or local-language accuracy. See ETHICS-AND-KNOWN-ISSUES.md for full documentation of limitations, hallucination patterns, and bias.

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Table of contents

• How it fits the main app
• Quick start
  • Build
  • Model
  • Run
• Docker / RunPod
• Architecture
  • Pipeline Stages
  • Key Components
  • Runtime Behaviour
• Generated output
• Tech stack
• Tested hardware
• Fixture strategy
• Repo layout
• Code tour
• Next steps

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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
User accounts and profiles	Locale-grounded names, bios, and an auth-ready email/date-of-birth pair for seeding real accounts

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Quick start

Build

Requirements: C++20 compiler, CMake 3.31+, OpenSSL, 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

CMake automatically detects whether a compatible llama.cpp installation is present on the system (libllama, libggml, libggml-base, and llama.h visible on the default search paths). If found, it links against those libraries and skips the FetchContent build. If not found, it fetches and builds llama.cpp from source at tag b9012. No additional flags are required in either case.

Metal is enabled automatically on Apple Silicon. CUDA or HIP/ROCm is detected automatically on Linux when the relevant toolkit is present.

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 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 \
  --prompt-dir prompts \
  --location-count 25 \
  --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. Selects the local Llama.cpp generator.
--openai	Use the OpenAI API (Chat Completions) for brewery/user data. Requires the OPENAI_API_KEY environment variable.
--openai-model	OpenAI model ID used when --openai is set. Default: gpt-4o-mini.
--prompt-dir	Directory containing prompt files (for example, BREWERY_GENERATION.md). Required unless --mocked is set.
--output, -o	Directory for generated SQLite artifacts. Default: output.
--log-path	Path for application logs. Default: pipeline.log.
--location-count	Number of cities to sample from locations.json per run. Default: 10.
--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).
--n-gpu-layers	Number of model layers to offload to GPU. Default: 0.
--help, -h	Print usage and exit.

--mocked, --model, and --openai are mutually exclusive. Omitting all three exits with an error before the pipeline starts. Sampling flags are ignored unless --model is set (Llama).

The post-build step copies prompts/ into build/prompts/. Rebuild after editing any prompt file.

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Docker / RunPod

The tooling/pipeline/runpod/ directory contains a GPU-ready container configuration for running the pipeline on RunPod or any Docker host with an NVIDIA GPU.

How it works

The container uses a two-stage build. The builder stage installs CMake/Ninja, clones the matching llama.cpp release tag for its headers only (installed into /usr/local/include), and copies prebuilt shared libraries (libllama, libggml, and CUDA/CPU backend plugins) from ghcr.io/ggml-org/llama.cpp:full-cuda into /usr/local/lib. With both headers and libraries present, CMake's system-library detection (see Build above) finds them and skips the FetchContent source build, keeping image build times short.

The runtime stage copies the compiled binary, the same prebuilt shared libraries, and config/prompt assets into a slim CUDA runtime image. It sets LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH so the dynamic linker resolves libllama/libggml at startup, and also co-locates libggml-cuda.so and the CPU backend plugins next to the binary for ggml_backend_load_all()'s dlopen scan.

Build the image

Run from the tooling/pipeline/ directory (the CMake project root), not from inside runpod/, so the COPY . . step picks up the full project context.

docker build -t biergarten-pipeline:latest -f runpod/Dockerfile .

To monitor the full build output and confirm CMake selects the system llama.cpp:

docker build \
  --progress=plain \
  --no-cache \
  -t biergarten-pipeline:latest \
  -f runpod/Dockerfile \
  . 2>&1 | tee build.log

Look for [biergarten] Found system llama.cpp — skipping FetchContent in the output to confirm the fast path was taken.

Run the container

The container always runs the model-backed path; there is no --mocked container mode (use a native build for that; see Quick start). The entrypoint, runpod/start.sh, downloads the GGUF model automatically if it is not already present at the configured path.

docker run --rm \
  --runtime=nvidia \
  -v "$PWD/models:/workspace/models" \
  -v "$PWD/output:/workspace/output" \
  -v "$PWD/logs:/workspace/logs" \
  biergarten-pipeline:latest

By default this downloads google_gemma-4-E4B-it-Q6_K.gguf to ./models/ on first run if it isn't already there. To use a pre-downloaded model, place it at that path first; see Model.

Environment variables

Variable	Purpose
BIERGARTEN_MODEL_PATH	GGUF model path. Default: /workspace/models/google_gemma-4-E4B-it-Q6_K.gguf.
BIERGARTEN_OUTPUT_DIR	SQLite output directory. Default: /workspace/output.
BIERGARTEN_LOG_PATH	Log file path. Default: /workspace/logs/pipeline.log.
BIERGARTEN_GL_LAYERS	GPU layers to offload (--n-gpu-layers). Default: 40.
BIERGARTEN_TEMPERATURE, BIERGARTEN_TOP_P, BIERGARTEN_TOP_K, BIERGARTEN_N_CTX, BIERGARTEN_SEED	Optional sampling overrides, unset by default (binary defaults apply).
BIERGARTEN_EXTRA_ARGS	Additional raw CLI args appended verbatim.

--prompt-dir is hardcoded to /app/prompts inside the container and is not configurable via environment variable.

RunPod deployment

Use a GPU pod template. Mount persistent storage for /workspace/models, /workspace/output, and /workspace/logs. See tooling/pipeline/runpod/pod-template.yaml for a starter template; set the environment variables listed in Environment variables to match your run.

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Architecture

Pipeline stages

Stage	Implementation
Load	ICuratedDataService (CuratedJsonDataService) reads locations.json, personas.json, forenames-by-country.json, and surnames-by-country.json (paths supplied via a CuratedDataFilePaths DTO at construction) into typed records, caching each after its first load. --mocked runs use MockCuratedDataService's fixed in-memory dataset instead.
Sample	BiergartenPipelineOrchestrator::QueryCitiesWithCountries() samples --location-count locations per run (default 10).
Enrich	WikipediaEnrichmentService fetches brewing and beer-related context. Keeps going when a lookup fails. --mocked runs use MockEnrichmentService instead and skip Wikipedia entirely.
Generate Users	GenerateUsers() samples a persona and a forename/surname pair per enriched city (skipping countries with no name data), then MockGenerator or LlamaGenerator produces a username, bio, and activity weight around the sampled name.
Generate Breweries	MockGenerator, LlamaGenerator, or OpenAIGenerator produces brewery names and descriptions in English and the local language, then IAddressService::ReverseGeocode() resolves a street address for the brewery's jittered coordinates.
Store	SqliteExportService writes each successful user and brewery into a fresh dated .sqlite database with normalized cities, breweries, brewery_addresses, users, and user_addresses tables.
Log	spdlog writes results and warnings to the console.

If name sampling, enrichment, or generation fails for a city, that city is skipped and the pipeline continues. GenerateUsers() runs before GenerateBreweries() in BiergartenPipelineOrchestrator::Run().

Key components

Component	Description
src/main.cc	Argument parsing and the Boost.DI composition root.
CuratedJsonDataService	Implements ICuratedDataService; takes a CuratedDataFilePaths DTO (locations/personas/forenames/surnames paths) in its constructor, parses and validates curated location, persona, and forename/surname JSON, and memoizes each result after its first load on a given instance. MockCuratedDataService is the in-memory substitute (4 fixed locations, 3 personas, and name data for US/DE/FR/BE) used in --mocked runs.
WikipediaEnrichmentService	Queries Wikipedia extracts, caches results, and returns empty context on failure. MockEnrichmentService is the no-op substitute used in --mocked runs.
LlamaGenerator	Formats prompts for Gemma 4, validates JSON output for both GenerateBrewery and GenerateUser, and retries malformed responses up to three times with corrective feedback in the retry prompt. The token budget is fixed across attempts; it is not raised automatically on truncation.
MockGenerator	Uses stable hash-based output so the same city/persona/name input always produces the same brewery or user.
NominatimAddressService	Reverse-geocodes each brewery's jittered coordinates into a street address (address_line1, postal_code) via the public Nominatim API, respecting its one-request-per-second usage policy. Falls back to a placeholder address_line1 ("Address unavailable") and empty postal_code when the lookup fails or returns no usable street address. MockAddressService is the no-network substitute ("123 Mock Street", "00000") used in --mocked runs. Only breweries get a resolved street address; generated users carry a coordinate pair with no address fields.
SqliteExportService	Creates a dated SQLite file per run and persists each successful user and brewery into normalized tables.
Brewery/user payloads	Brewery payloads include English and local-language name and description fields. User payloads carry a sampled first/last name and gender, an LLM-generated username/bio/activity weight, and a programmatically generated (not LLM-authored) unique email and date of birth.

Runtime behaviour

Component / behavior	Details
WikipediaEnrichmentService	Fetches two Wikipedia extracts per city: a generic "brewing" extract and a "beer in {country}" extract. It does not currently query a city- or region-specific page. Each query string is cached after its first successful (or empty) lookup.
GetLocationContext()	Returns an empty string when the web client is unavailable or when lookup/parsing fails.
LlamaGenerator	Validates model output as structured JSON. On validation failure it retries up to three times, replaying the previous error message in the next prompt so the model can self-correct. All runs to date have produced valid output on the first pass; the retry path is kept for resilience.
MockGenerator	Uses stable hashes for repeatable output in demos and Storybook runs.
NominatimAddressService.ReverseGeocode()	Sleeps one second after every request to respect Nominatim's usage policy, and returns std::nullopt (logged as a warning, not a skipped city) if the request fails, the response has no address object, or neither house_number nor road is present — the brewery is still generated and exported, just with a placeholder address.
CuratedJsonDataService	Memoizes each of LoadLocations(), LoadPersonas(), LoadForenamesByCountry(), and LoadSurnamesByCountry() independently the first time each is called, since BiergartenPipelineOrchestrator owns a single ICuratedDataService instance for the whole run; later calls return the cached result instead of re-parsing.
GenerateUsers()	Samples a forename/surname pair per city via SampleName(), keyed by the city's ISO 3166-1 code. Countries present in locations.json but absent from either name fixture (currently KE, SE, SG, TH, VN, ZA) are skipped, the same way a failed enrichment or generation call skips a city; see ETHICS-AND-KNOWN-ISSUES.md's Names-by-Country Dataset section.

Process flow - activity diagram

See Activity diagram.

Architectural overview - class diagram

See Class diagram.

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Generated output

Each successful run stores a BreweryRecord pair with the source location and a BreweryResult payload, and a UserRecord pair with the source location and a UserResult 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.
address_line1	Street address resolved via reverse geocoding, or a placeholder if resolution failed.
postal_code	Postal code resolved via reverse geocoding, or empty if resolution failed.

Field	Meaning
first_name	Sampled forename, copied from the curated name data (not LLM-invented).
last_name	Sampled surname, copied from the curated name data (not LLM-invented).
gender	Gender associated with the sampled forename in the source dataset.
username	LLM-generated handle.
bio	LLM-generated short biography.
activity_weight	Relative check-in/activity weight, reserved for a future J-curve activity profile.
email	Unique @thebiergarten.app address, generated programmatically from the sampled name.
date_of_birth	Randomized date of birth (age 19-48), generated programmatically.

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

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Tech stack

• C++20
• CMake 3.31+
• Boost.JSON, Boost.ProgramOptions, Boost.DI
• spdlog
• cpp-httplib (with OpenSSL)
• SQLite amalgamation fetched and compiled via CMake FetchContent
• llama.cpp (auto-detected from system install or fetched via FetchContent)
• Docker with NVIDIA CUDA 12.6 base image for GPU container builds
• RunPod for cloud GPU inference

The build fetches Boost.DI, spdlog, and SQLite via CMake. llama.cpp is fetched only when a system installation is not detected. Metal is enabled on Apple Silicon; CUDA or HIP/ROCm is detected on Linux when the toolkit is present.

Code style: The code follows the Google C++ Style Guide, except that indentation is 3 spaces instead of Google's 2, and uses C++20 throughout.

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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

x86_64 Linux (Docker / RunPod, NVIDIA CUDA)

Host	RunPod GPU pod
Base	nvidia/cuda:12.6.3-devel-ubuntu24.04
Model	Gemma 4 E4B Q6_K
Inference	llama.cpp prebuilt CUDA backends via dlopen

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Fixture strategy

• --mocked for stable fixtures, repeatable screenshots, and Storybook runs. MockCuratedDataService swaps in for CuratedJsonDataService, so no fixture files need to be present on disk.
• --model when geographically grounded content matters for demos.
• Keep locations.json structured enough to support discovery and future filtering.
• personas.json, forenames-by-country.json, and surnames-by-country.json are curated/vendored fixture data, not LLM-generated; see ETHICS-AND-KNOWN-ISSUES.md's Names-by-Country Dataset section for provenance.
• Treat SQLite output as seed material for the app's brewery and user domains, not production data.

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Repo layout

Path	Purpose
tooling/pipeline/includes/	Public headers and shared models.
tooling/pipeline/src/	Implementation files.
tooling/pipeline/locations.json	Curated city input copied into the build tree.
tooling/pipeline/personas.json	Curated user persona archetypes copied into the build tree.
tooling/pipeline/forenames-by-country.json	Vendored (CC0) forename data by ISO 3166-1 country code.
tooling/pipeline/surnames-by-country.json	Vendored (CC0) surname data by ISO 3166-1 country code.
tooling/pipeline/prompts/	System prompts used by the model-backed path.
tooling/pipeline/runpod/	Dockerfile, launcher, and RunPod pod template.
docs/pipeline/diagrams/	Architecture and pipeline diagrams.
docs/pipeline/ETHICS-AND-KNOWN-ISSUES.md	Ethics, bias, hallucination analysis, mitigations.

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Code tour

Paths below are relative to tooling/pipeline/.

Path	Purpose
src/main.cc	Argument parsing and DI composition root.
src/biergarten_pipeline_orchestrator/	Orchestration, sampling, logging, and export.
src/services/curated_data/	CuratedJsonDataService, the file-backed ICuratedDataService, and MockCuratedDataService, the in-memory ICuratedDataService used in --mocked runs.
src/services/enrichment/wikipedia/	Enrichment service and cache.
src/services/sqlite/	SQLite export implementation.
src/data_generation/llama/	Local inference, prompt loading, and output validation.
src/data_generation/mock/	Deterministic fallback.
runpod/	Container build and runtime launcher.

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Next steps

The pipeline currently produces city-aware brewery and user records and dated SQLite exports.

The next passes add additional fixture types so the app can exercise the full brewery and social domains without live data.

For the detailed engineering breakdown of what's needed to reach the architecture in diagrams/planned/, see ROADMAP.md.

Testing

• 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.

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.