the-biergarten-app/pipeline/README.md

# Biergarten Pipeline

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.

---

## Table of Contents

- [How It Fits the Main App](#how-it-fits-the-main-app)
- [Tech Stack](#tech-stack)
- [Build](#build)
- [Model](#model)
- [Run](#run)
- [Architecture](#architecture)
  - [Pipeline Stages](#pipeline-stages)
  - [Key Components](#key-components)
  - [Runtime Behaviour](#runtime-behaviour)
- [Generated Output](#generated-output)
- [Language Generation Quality](#language-generation-quality)
  - [Known Issues](#known-issues)
- [Tested Hardware](#tested-hardware)
- [Repo Layout](#repo-layout)
- [Code Tour](#code-tour)
- [Fixture Strategy](#fixture-strategy)
- [Next Steps](#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
- llama.cpp

The build fetches Boost.DI, spdlog, and llama.cpp 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).

```bash
cmake -S . -B build
cmake --build build
```

---

## Model

> Skip this step if you only need `--mocked`.

```bash
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.

```bash
./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](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. |
| 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.
- 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

![An activity diagram](./diagrams/activity-diagram.svg)

### Architectural Overview — Class Diagram

![A class diagram](./diagrams/class-diagram.svg)

---

## Generated Output

Each successful run stores a `GeneratedBrewery` pair with the source location and a `BreweryResult` payload.

| 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:

```json
[
  {
    "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](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.
- `src/services/wikipedia/` — enrichment service and cache.
- `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. The next passes add SQLite output and additional fixture types so the app can exercise the full brewery domain without live data.

### SQLite Output _(Highest Importance)_

Write generated records to a SQLite database for downstream OLTP seeding. Normalized schema with foreign keys between locations and breweries. Output replaces the current log-only result so the pipeline functions as a proper ingestion layer.

### 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.