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update pipeline docs

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Aaron Po
2026-06-20 18:03:39 -04:00
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docs/pipeline/README.md
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@@ -94,24 +94,27 @@ Each run writes a fresh dated SQLite file such as
./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. Required unless `--mocked` is set. |
| `--prompt-dir` | Directory containing prompt files (e.g. `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`. |
| `--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. |
| Flag | Purpose |
| ------------------ | ---------------------------------------------------------------------------------------------------- |
| `--mocked` | Deterministic mock generator, no model required. |
| `--model, -m` | Path to a GGUF file. Required unless `--mocked` is set. |
| `--prompt-dir` | Directory containing prompt files (e.g. `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` and `--model` are mutually exclusive. Omitting both exits with an
error before the pipeline starts. Sampling flags are ignored when `--mocked` is
@@ -130,17 +133,20 @@ NVIDIA GPU.
### How it works
The container uses a two-stage build. The first stage pulls prebuilt
`libllama`, `libggml`, and backend plugin libraries (including `libggml-cuda.so`
and the CPU variant plugins) from `ghcr.io/ggml-org/llama.cpp:full-cuda`. The
second stage copies those libraries into `/usr/local/lib` and runs `ldconfig` so
the dynamic linker and `dlopen` calls from `ggml_backend_load_all()` can resolve
the CUDA backend plugin at runtime. llama.cpp headers are cloned at the matching
tag and installed into `/usr/local/include`. CMake auto-detects both and skips
the FetchContent source build entirely, keeping image build times short.
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](#build) above) finds them and skips the
FetchContent source build, keeping image build times short.
`GGML_BACKEND_PATH` is set to `/usr/local/lib` so llama.cpp knows where to scan
for backend plugins.
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:$LD_LIBRARY_PATH` so the dynamic linker
resolves `libllama`/`libggml` at startup, and also co-locates
`libggml-cuda.so` and the CPU backend plugins next to the binary for
`ggml_backend_load_all()`'s `dlopen` scan.
### Build the image
@@ -165,44 +171,46 @@ docker build \
Look for `[biergarten] Found system llama.cpp — skipping FetchContent` in the
output to confirm the fast path was taken.
### Run in mocked mode
### Run the container
No model or GPU required. Useful for validating the pipeline logic and SQLite
export path.
```bash
docker run --rm \
-e BIERGARTEN_MODE=mocked \
-v "$PWD/output:/workspace/output" \
-v "$PWD/logs:/workspace/logs" \
biergarten-pipeline:latest
```
### Run in live mode
Mount your GGUF model before starting. The container validates the model path
before launching the binary.
The container always runs the model-backed path; there is no `--mocked`
container mode (use a native build for that — see [Quick Start](#quick-start)).
The entrypoint, `runpod/start.sh`, downloads the GGUF model automatically if
it is not already present at the configured path.
```bash
docker run --rm \
--runtime=nvidia \
-e BIERGARTEN_MODE=live \
-e GGML_BACKEND_PATH="/usr/local/lib/libggml-cuda.so" \
-v "$PWD/models:/workspace/models" \
-v "$PWD/output:/workspace/output" \
-v "$PWD/logs:/workspace/logs" \
biergarten-pipeline:latest
```
The model must be present at `./models/google_gemma-4-E4B-it-Q6_K.gguf` on the
host. See [Model](#model) above for the download command.
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](#model) above.
#### 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`. Set `BIERGARTEN_MODE=live` in the
template environment. See `tooling/pipeline/runpod/pod-template.yaml` for a
starter template.
`/workspace/output`, and `/workspace/logs`. See
`tooling/pipeline/runpod/pod-template.yaml` for a starter template — set the
environment variables listed above to match your run.
---
@@ -213,8 +221,8 @@ starter template.
| 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. |
| 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 | `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. |
@@ -226,11 +234,13 @@ pipeline continues.
- `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.
- `WikipediaEnrichmentService` — queries Wikipedia extracts, caches results,
returns empty context on failure. `MockEnrichmentService` is the no-op
substitute used in `--mocked` runs.
- `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.
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` — 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
@@ -240,18 +250,18 @@ pipeline continues.
### 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.
`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. 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.
`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.
@@ -292,7 +302,6 @@ code, latitude, and longitude for each entry.
| `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 |
---
@@ -368,29 +377,31 @@ Silicon; CUDA or HIP/ROCm is detected on Linux when the toolkit is present.
## 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 prompts used by the model-backed path. |
| `diagrams/` | Architecture and pipeline diagrams. |
| `tooling/pipeline/runpod/` | Dockerfile, launcher, and RunPod pod template. |
| `ETHICS-AND-KNOWN-ISSUES.md` | Ethics, bias, hallucination analysis, mitigations. |
| 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/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. |
---
## Code Tour
Paths below are relative to `tooling/pipeline/`.
- `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/biergarten_pipeline_orchestrator/` — orchestration, sampling, logging,
and export.
- `src/services/enrichment/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.
- `tooling/pipeline/runpod/` — container build and runtime launcher.
- `runpod/` — container build and runtime launcher.
---
@@ -398,7 +409,9 @@ Silicon; CUDA or HIP/ROCm is detected on Linux when the toolkit is present.
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.
the full brewery domain without live data. For the detailed engineering
breakdown of what's needed to reach the architecture in
[`diagrams/planned/`](./diagrams/planned/), see [ROADMAP.md](./ROADMAP.md).
### Testing — Very High Priority