You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
motief/ARCHITECTURE.md

128 lines
6.2 KiB

---
title: ARCHITECTURE
---
## Overview
- Small Python project that collects, stores and presents Dutch parliamentary motions (Tweede Kamer). It ingests votes via OData API, stores motions in a DuckDB file, generates short human-readable summaries using an LLM client, and exposes a Streamlit UI for users to vote and view matching results.
## Tech stack
- Language: Python (single-project repository)
- Data: DuckDB (file: data/motions.db)
- Web / UI: Streamlit (app.py, pages/)
- HTTP: requests (ai_provider.py, api_client.py)
- LLM: QWEN (via OpenRouter) / OpenAI-compatible client (ai_provider.py). Prefer QWEN via OpenRouter where possible.
- Analysis: scipy (SVD), scikit-learn (clustering), umap-learn (dimensionality reduction)
- Visualization: Plotly
- Packaging: pyproject.toml
## Top-level layout (annotated)
./
- app.py — Streamlit UI entrypoint (Home.py routing)
- Home.py — Thin wrapper with minimal logic
- database.py — MotionDatabase: DuckDB schema, insert/query/update, party-match calculations
- api_client.py — TweedeKamerAPI: fetch OData voting records and group into motions
- summarizer.py — MotionSummarizer: LLM integration to generate layman_explanation
- config.py — Config dataclass: central configuration (DATABASE_PATH, API/AI settings, constants)
- ai_provider.py — Lightweight HTTP wrapper around OpenRouter/OpenAI-style backends
- explorer.py — Explorer page logic, tab routing, SVD visualization
- explorer_helpers.py — Pure functions for chart builders, coordinate computation
- data/ — data/motions.db (DuckDB file, ~18GB)
- pyproject.toml — project metadata / dependencies
- .env — environment variables (not printed here)
## Directory structure
- `pages/` — Streamlit pages: 1_Stemwijzer.py, 2_Explorer.py
- `pipeline/` — Data ingestion pipelines: run_pipeline.py, svd_pipeline.py, text_pipeline.py
- `analysis/` — SVD, clustering, trajectory, visualization modules
- `similarity/` — Embedding-based similarity computation
- `scripts/` — Utility scripts for data processing
- `tests/` — Test suite using pytest
- `migrations/` — SQL migration files
## Core components
- Streamlit UI (app.py + pages/)
- Presents the voting UI, reads filtered motions from database, creates sessions, writes user votes
- Explorer page (explorer.py) provides SVD visualization and party trajectory analysis
- Storage (database.py)
- MotionDatabase encapsulates DuckDB schema creation and CRUD for motions and user sessions
- Exposes a module-level instance `db = MotionDatabase()` used across the codebase
- Key responsibilities: insert_motion, get_filtered_motions, create_session, update_user_vote, calculate_party_matches
- Ingestion (api_client.py + pipeline/)
- api_client.py fetches votes via Tweede Kamer OData API and groups records into motions
- pipeline/ orchestrates the full ingestion and analysis workflow
- Summarization (summarizer.py)
- Wraps an OpenRouter/OpenAI-compatible client (QWEN via OpenRouter recommended) to produce short layman explanations and persists them to DB
- Reads motions without layman_explanation and updates rows
- Analysis (analysis/)
- SVD decomposition of voting patterns
- UMAP for visualization
- Clustering for motion grouping
- Trajectory computation for party movement over time
## Data flow (high level)
1. Ingestion
- Pipeline triggers TweedeKamerAPI.get_motions(...)
- Each produced motion dict is passed to MotionDatabase.insert_motion()
- insert_motion writes to DuckDB (data/motions.db)
2. Enrichment
- summarizer.update_motion_summaries() reads motions lacking layman_explanation, calls the LLM client and writes summary text back to the DB
3. Analysis
- pipeline/svd_pipeline.py computes SVD embeddings from vote matrix
- Results stored in svd_vectors table for visualization
4. Presentation / Interaction
- app.py (Streamlit) queries motions via db.get_filtered_motions() and displays them
- Users vote; app.py writes votes into the database via db.update_user_vote()
- app.py calls db.calculate_party_matches() to compute match percentages for parties
## External integrations & dependencies
- Tweede Kamer OData API (api_client.py)
- HTTP (requests)
- DuckDB (database file at data/motions.db)
- Streamlit for UI
- OpenRouter/OpenAI-compatible LLM client (ai_provider.py) — configured with environment variables in config.py
## Configuration
- config.py: central Config dataclass. Observed keys / env variables referenced across the codebase include:
- config.DATABASE_PATH (default "data/motions.db")
- OPENROUTER_API_KEY / other OPENROUTER_* variables used by ai_provider.py
- QWEN_MODEL (or other model identifier) referenced in summarizer.py
- API timeout / batch size constants
- .env file present at repo root (do not commit secrets)
- Packaging metadata: pyproject.toml
## Build, run & development notes
- Install dependencies via the project's Python packaging (pyproject.toml)
- Use `uv add` and `uv run` to manage the dependencies in this directory and run scripts
- Streamlit app: run `uv run streamlit run app.py` from project root to start the UI (app.py is the intended web entrypoint)
- Never use pip directly!
- Run tests: `uv run pytest tests/`
## Tests
- Test suite in `tests/` using pytest
- Run with `uv run pytest tests/`
## Notes / caveats
- Project is synchronous (no async/await patterns detected)
- Many modules rely on module-level singletons (e.g., `db = MotionDatabase()`, `summarizer = MotionSummarizer()`)
- Error handling frequently catches broad Exception and prints to stdout (see database.py, api_client.py)
- Logging is not centralized (print statements used)
## Where to look first (for contributors)
- app.py + pages/ — follow the UI flow and see how votes & sessions are used
- database.py — core data model and calculations
- explorer.py — SVD visualization and party analysis
- api_client.py — OData ingestion logic
- summarizer.py — LLM usage and environment variables
- pipeline/ — how ingestion and analysis is orchestrated