From e729f3dc22d6c5fdaa2bb76dae8d94c352e10a16 Mon Sep 17 00:00:00 2001 From: "david.noel@withpigment.com" Date: Sun, 12 Jul 2026 10:50:19 +0200 Subject: [PATCH 1/6] Add v1 external-collaboration mention detector MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Exploratory detector (no pre-filter) that sends each amendment's exposé sommaire to an OpenAI-compatible API (Scaleway, OpenRouter...) and extracts declared collaborations with external entities. - Provider-agnostic via LLM_* env vars (base_url/model/key) - Random or deterministic sampling over the amendements table - Defensive JSON parsing (no reliance on json_object mode) - Rate-limit handling: throttle + exponential backoff on 429 - Writes raw results to analysis/output/ (gitignored), prints a frequency summary of the formulations encountered --- .env.example | 11 + .gitignore | 1 + analysis/__init__.py | 0 analysis/detect_mentions.py | 217 +++++++++++++++ justfile | 4 + pyproject.toml | 1 + uv.lock | 536 ++++++++++++++++++++++++++---------- 7 files changed, 625 insertions(+), 145 deletions(-) create mode 100644 analysis/__init__.py create mode 100644 analysis/detect_mentions.py diff --git a/.env.example b/.env.example index f731d9c..c334ab5 100644 --- a/.env.example +++ b/.env.example @@ -9,3 +9,14 @@ PG_PORT=5432 # Mettre à True pour logguer toutes les requêtes SQL émises par SQLAlchemy. PG_ECHO=False + +# Analyse des amendements via une API OpenAI-compatible (Scaleway, OpenRouter...). +# LLM_API_KEY : clé secrète du provider. +# LLM_BASE_URL : endpoint OpenAI-compatible du provider. +# LLM_MODEL : identifiant exact du modèle (confirmer via la liste des modèles). +# +# OpenRouter : LLM_BASE_URL=https://openrouter.ai/api/v1 LLM_MODEL=qwen/qwen3-30b-a3b +# Scaleway : LLM_BASE_URL=https://api.scaleway.ai/v1 LLM_MODEL=qwen3-30b-a3b +LLM_API_KEY= +LLM_BASE_URL=https://openrouter.ai/api/v1 +LLM_MODEL=qwen/qwen3-30b-a3b diff --git a/.gitignore b/.gitignore index 46e1244..21f684d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ # Project ignore data/* +analysis/output/ # From https://github.com/github/gitignore/blob/main/Python.gitignore diff --git a/analysis/__init__.py b/analysis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/analysis/detect_mentions.py b/analysis/detect_mentions.py new file mode 100644 index 0000000..91987ec --- /dev/null +++ b/analysis/detect_mentions.py @@ -0,0 +1,217 @@ +"""Détection exploratoire (v1, sans pré-filtre) des mentions de collaboration externe. + +On envoie l'exposé sommaire de chaque amendement à un modèle (API OpenAI-compatible : +Scaleway, OpenRouter...) et on lui demande de repérer les passages où l'auteur déclare +avoir « travaillé avec », « été inspiré par », etc. une entité externe (lobby, +association, syndicat, entreprise, fédération professionnelle, ONG...). + +Cette version tourne sur un sous-ensemble et écrit les résultats bruts dans un fichier +JSONL, sans rien persister en base : l'objectif est d'observer les formulations réelles +avant de figer une table d'analyse. + +Usage: + uv run python -m analysis.detect_mentions --limit 50 + uv run python -m analysis.detect_mentions --limit 100 --offset 100 +""" + +import argparse +import json +import os +import time +from pathlib import Path + +from dotenv import load_dotenv +from openai import OpenAI +from openai import RateLimitError +from sqlalchemy import text + +from etl.database import get_engine + +OUTPUT_DIR = Path("analysis/output") + +SYSTEM_PROMPT = """\ +Tu analyses des amendements de l'Assemblée nationale française. On te donne l'exposé +sommaire d'un amendement (le texte par lequel l'auteur justifie sa proposition). + +Ta tâche : repérer chaque passage où l'auteur déclare que l'amendement a été élaboré, +travaillé, inspiré, proposé ou suggéré EN LIEN AVEC UN ACTEUR EXTERNE — par exemple un +lobby, une association, un syndicat, une entreprise, une fédération professionnelle, une +ONG, un think tank, un collectif citoyen. Signale aussi les cas ambigus. + +Ne compte PAS comme mention le simple fait de citer un acteur (« comme le rappelle +l'INSEE »). Cherche une déclaration de COLLABORATION ou d'INSPIRATION revendiquée par +l'auteur de l'amendement. + +Réponds UNIQUEMENT avec un objet JSON valide de la forme : +{ + "mentions": [ + { + "citation": "", + "formulation": "", + "entite": "", + "type_entite": "", + "externe": + } + ] +} +Si aucune mention, renvoie {"mentions": []}. +""" + +USER_TEMPLATE = 'Exposé sommaire de l\'amendement :\n\n"""\n{expose}\n"""' + + +def get_config(): + api_key = os.getenv("LLM_API_KEY") + base_url = os.getenv("LLM_BASE_URL") + model = os.getenv("LLM_MODEL") + if not api_key: + raise SystemExit( + "LLM_API_KEY manquante. Renseigne la clé du provider dans le fichier .env." + ) + return api_key, base_url, model + + +def fetch_amendements(limit: int, offset: int, random_sample: bool = False): + """Return a sample of (uid, numero, expose) with a non-trivial exposé sommaire. + + Par défaut, échantillon ordonné par `numeroOrdreDepot` (déterministe, paginable + via offset). Avec random_sample=True, tirage aléatoire sur tout le corpus — utile + en phase d'observation car les mentions sont rares et concentrées nulle part. + """ + order_by = "random()" if random_sample else '"numeroOrdreDepot"' + query = text( + 'SELECT uid, "numeroOrdreDepot", "exposeSommaire" ' + "FROM amendements " + 'WHERE "exposeSommaire" IS NOT NULL AND length("exposeSommaire") > 40 ' + f"ORDER BY {order_by} " + "LIMIT :limit OFFSET :offset" + ) + with get_engine().connect() as conn: + rows = conn.execute(query, {"limit": limit, "offset": offset}).all() + return rows + + +def parse_json(content: str) -> dict: + """Extract a JSON object from a model reply, tolerating fences and prose. + + Les modèles OpenRouter ne supportent pas tous le mode `json_object` ; on + n'impose donc aucun `response_format` et on récupère l'objet JSON à la main. + """ + if not content or not content.strip(): + # Réponse vide du modèle : on considère qu'il n'y a pas de mention. + return {"mentions": []} + text = content.strip() + if text.startswith("```"): + # Retire un éventuel bloc ```json ... ``` + text = text.split("```", 2)[1] if text.count("```") >= 2 else text + text = text.removeprefix("json").strip() + start, end = text.find("{"), text.rfind("}") + if start != -1 and end != -1 and end > start: + text = text[start : end + 1] + return json.loads(text) + + +def detect(client: OpenAI, model: str, expose: str, max_retries: int = 4) -> dict: + """Ask the model to extract collaboration mentions from one exposé sommaire. + + Réessaie sur 429 (rate limit) avec un backoff exponentiel plafonné : utile sur + les modèles `:free` d'OpenRouter, très limités en requêtes/minute. + """ + for attempt in range(max_retries + 1): + try: + response = client.chat.completions.create( + model=model, + temperature=0, + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": USER_TEMPLATE.format(expose=expose)}, + ], + ) + break + except RateLimitError: + if attempt == max_retries: + raise + wait = min(2**attempt, 30) + print(f" 429 rate-limited, nouvelle tentative dans {wait}s...") + time.sleep(wait) + content = response.choices[0].message.content + return parse_json(content) + + +def run(limit: int, offset: int, random_sample: bool = False, delay: float = 4.0): + load_dotenv() + api_key, base_url, model = get_config() + client = OpenAI(api_key=api_key, base_url=base_url) + + rows = fetch_amendements(limit, offset, random_sample) + print(f"Analyse de {len(rows)} amendements (modèle: {model})...") + + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + out_path = OUTPUT_DIR / "mentions_sample.jsonl" + + formulations: dict[str, int] = {} + nb_avec_mention = 0 + + with out_path.open("w", encoding="utf-8") as out: + for i, (uid, numero, expose) in enumerate(rows, start=1): + if i > 1: + time.sleep(delay) + try: + result = detect(client, model, expose) + mentions = result.get("mentions", []) + except Exception as e: # noqa: BLE001 - on veut continuer le run + print(f" [{i}/{len(rows)}] {uid}: erreur -> {e}") + out.write( + json.dumps({"uid": uid, "error": str(e)}, ensure_ascii=False) + "\n" + ) + continue + + out.write( + json.dumps( + {"uid": uid, "numero": numero, "mentions": mentions}, + ensure_ascii=False, + ) + + "\n" + ) + + if mentions: + nb_avec_mention += 1 + for m in mentions: + formulation = (m.get("formulation") or "?").strip().lower() + formulations[formulation] = formulations.get(formulation, 0) + 1 + print(f" [{i}/{len(rows)}] {uid}: {len(mentions)} mention(s)") + + print(f"\n{nb_avec_mention}/{len(rows)} amendements avec au moins une mention.") + print("Formulations rencontrées (fréquence) :") + for formulation, count in sorted( + formulations.items(), key=lambda kv: kv[1], reverse=True + ): + print(f" {count:3d} {formulation}") + print(f"\nRésultats détaillés : {out_path}") + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--limit", type=int, default=50, help="Nombre d'amendements à analyser" + ) + parser.add_argument( + "--offset", type=int, default=0, help="Décalage dans le jeu de données" + ) + parser.add_argument( + "--random", + action="store_true", + help="Tirage aléatoire sur tout le corpus (ignore l'offset)", + ) + parser.add_argument( + "--delay", + type=float, + default=4.0, + help="Pause en secondes entre deux appels (throttle anti rate-limit)", + ) + args = parser.parse_args() + run(args.limit, args.offset, args.random, args.delay) + + +if __name__ == "__main__": + main() diff --git a/justfile b/justfile index 2876808..1633147 100644 --- a/justfile +++ b/justfile @@ -21,3 +21,7 @@ all: # Run psql to explore the database psql: psql -h localhost -U postgres -d ipolitics + +# Detect external-collaboration mentions on a sample of amendments (v1, no pre-filter) +detect-mentions limit="50": + uv run python -m analysis.detect_mentions --limit {{limit}} diff --git a/pyproject.toml b/pyproject.toml index f7020fe..717e9c3 100644 --- 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@@ -289,6 +534,7 @@ dependencies = [ requires-dist = [ { name = "dotenv", specifier = ">=0.9.9" }, { name = "httpx", specifier = ">=0.28.1" }, + { name = "openai", specifier = ">=2.45.0" }, { name = "psycopg", extras = ["binary"], specifier = ">=3.3.4" }, { name = "sqlalchemy", specifier = ">=2.0.51" }, ] From 726994fd1f9191aa23cd050d55b86f71f874de14 Mon Sep 17 00:00:00 2001 From: "david.noel@withpigment.com" Date: Sun, 12 Jul 2026 10:53:26 +0200 Subject: [PATCH 2/6] Add amendement_mentions analysis table MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Stores external-collaboration mentions detected in amendments' exposé sommaire. Analysis table (not in ETL_TABLES): survives rebuilds via create_all while amendements/dossiers are dropped and reloaded. amendementUid is a soft reference to amendements.uid (no ForeignKey), matching the RefUid pattern in the Amendement model, so the referential constraint never blocks the drop of the ETL-managed amendements table. One row per mention; provenance kept via modele/createdAt. --- models/__init__.py | 1 + models/amendement_mention.py | 47 ++++++++++++++++++++++++++++++++++++ 2 files changed, 48 insertions(+) create mode 100644 models/amendement_mention.py diff --git a/models/__init__.py b/models/__init__.py index 187aec0..dd7df44 100644 --- a/models/__init__.py +++ b/models/__init__.py @@ -1,2 +1,3 @@ from models.amendement import Amendement # noqa: F401 +from models.amendement_mention import AmendementMention # noqa: F401 from models.dossier import Dossier # noqa: F401 diff --git a/models/amendement_mention.py b/models/amendement_mention.py new file mode 100644 index 0000000..d11a10f --- /dev/null +++ b/models/amendement_mention.py @@ -0,0 +1,47 @@ +from datetime import datetime + +from sqlalchemy import Boolean, DateTime, Text, func +from sqlalchemy.orm import Mapped, mapped_column + +from models.base import Base + + +class AmendementMention(Base): + """Mention de collaboration externe détectée dans l'exposé sommaire d'un amendement. + + Table d'ANALYSE (pas alimentée par l'ETL) : elle n'est donc pas listée dans + ETL_TABLES et survit aux rebuilds. Un amendement peut porter plusieurs mentions, + d'où une clé primaire de substitution et une ligne par mention. + + `amendementUid` est une référence molle vers `amendements.uid` (pas de ForeignKey) : + la table `amendements` étant recréée à chaque rebuild, une contrainte référentielle + bloquerait son drop. On suit ici la même logique que les RefUid du modèle Amendement. + + Les champs métier reprennent le schéma produit par le détecteur + (analysis/detect_mentions.py). + """ + + __tablename__ = "amendement_mentions" + + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + + # Référence molle vers amendements.uid (indexée pour les jointures applicatives). + amendementUid: Mapped[str] = mapped_column(index=True) + + # Passage exact recopié depuis l'exposé sommaire. + citation: Mapped[str] = mapped_column(Text) + # Expression déclencheuse, ex. « travaillé avec », « en lien avec ». + formulation: Mapped[str | None] + # Nom de l'entité citée, ou NULL si non nommée. + entite: Mapped[str | None] + # lobby|association|syndicat|entreprise|federation_professionnelle|ong| + # think_tank|collectif_citoyen|organe_public|autre|inconnu + typeEntite: Mapped[str | None] + # True si acteur d'intérêt privé/externe, False si institution publique. + externe: Mapped[bool | None] = mapped_column(Boolean) + + # Provenance : le modèle varie pendant le POC, on trace ce qui a produit la ligne. + modele: Mapped[str | None] + createdAt: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now() + ) From 9368440dbe7924e4f76ee22b1ed5065172ffff89 Mon Sep 17 00:00:00 2001 From: "david.noel@withpigment.com" Date: Sun, 12 Jul 2026 11:07:17 +0200 Subject: [PATCH 3/6] Persist detected mentions to amendement_mentions table Add --persist flag to the detector: on top of the JSONL output it writes each mention to the amendement_mentions table via SQLAlchemy. Idempotent per amendment (delete-then-insert), commit per row so a partial run keeps what it found. The just recipe now forwards extra flags (--random/--persist). --- analysis/detect_mentions.py | 104 ++++++++++++++++++++++++++---------- justfile | 5 +- 2 files changed, 79 insertions(+), 30 deletions(-) diff --git a/analysis/detect_mentions.py b/analysis/detect_mentions.py index 91987ec..c4cef40 100644 --- a/analysis/detect_mentions.py +++ b/analysis/detect_mentions.py @@ -23,9 +23,11 @@ from dotenv import load_dotenv from openai import OpenAI from openai import RateLimitError -from sqlalchemy import text +from sqlalchemy import delete, text +from sqlalchemy.orm import Session from etl.database import get_engine +from models.amendement_mention import AmendementMention OUTPUT_DIR = Path("analysis/output") @@ -138,48 +140,89 @@ def detect(client: OpenAI, model: str, expose: str, max_retries: int = 4) -> dic return parse_json(content) -def run(limit: int, offset: int, random_sample: bool = False, delay: float = 4.0): +def persist_mentions(session: Session, uid: str, mentions: list[dict], model: str): + """Réécrit les mentions d'un amendement en base (delete puis insert). + + Idempotent : un re-run sur le même amendement remplace ses lignes, donc une + mention disparue lors d'une nouvelle passe est bien supprimée. + """ + session.execute( + delete(AmendementMention).where(AmendementMention.amendementUid == uid) + ) + for m in mentions: + externe = m.get("externe") + session.add( + AmendementMention( + amendementUid=uid, + citation=str(m.get("citation") or ""), + formulation=m.get("formulation"), + entite=m.get("entite"), + typeEntite=m.get("type_entite"), + externe=externe if isinstance(externe, bool) else None, + modele=model, + ) + ) + session.commit() + + +def run( + limit: int, + offset: int, + random_sample: bool = False, + delay: float = 4.0, + persist: bool = False, +): load_dotenv() api_key, base_url, model = get_config() client = OpenAI(api_key=api_key, base_url=base_url) rows = fetch_amendements(limit, offset, random_sample) - print(f"Analyse de {len(rows)} amendements (modèle: {model})...") + dest = "base + JSONL" if persist else "JSONL" + print(f"Analyse de {len(rows)} amendements (modèle: {model}, sortie: {dest})...") OUTPUT_DIR.mkdir(parents=True, exist_ok=True) out_path = OUTPUT_DIR / "mentions_sample.jsonl" formulations: dict[str, int] = {} nb_avec_mention = 0 + session = Session(get_engine()) if persist else None + + try: + with out_path.open("w", encoding="utf-8") as out: + for i, (uid, numero, expose) in enumerate(rows, start=1): + if i > 1: + time.sleep(delay) + try: + result = detect(client, model, expose) + mentions = result.get("mentions", []) + except Exception as e: # noqa: BLE001 - on veut continuer le run + print(f" [{i}/{len(rows)}] {uid}: erreur -> {e}") + out.write( + json.dumps({"uid": uid, "error": str(e)}, ensure_ascii=False) + + "\n" + ) + continue - with out_path.open("w", encoding="utf-8") as out: - for i, (uid, numero, expose) in enumerate(rows, start=1): - if i > 1: - time.sleep(delay) - try: - result = detect(client, model, expose) - mentions = result.get("mentions", []) - except Exception as e: # noqa: BLE001 - on veut continuer le run - print(f" [{i}/{len(rows)}] {uid}: erreur -> {e}") out.write( - json.dumps({"uid": uid, "error": str(e)}, ensure_ascii=False) + "\n" + json.dumps( + {"uid": uid, "numero": numero, "mentions": mentions}, + ensure_ascii=False, + ) + + "\n" ) - continue - out.write( - json.dumps( - {"uid": uid, "numero": numero, "mentions": mentions}, - ensure_ascii=False, - ) - + "\n" - ) + if session is not None: + persist_mentions(session, uid, mentions, model) - if mentions: - nb_avec_mention += 1 - for m in mentions: - formulation = (m.get("formulation") or "?").strip().lower() - formulations[formulation] = formulations.get(formulation, 0) + 1 - print(f" [{i}/{len(rows)}] {uid}: {len(mentions)} mention(s)") + if mentions: + nb_avec_mention += 1 + for m in mentions: + formulation = (m.get("formulation") or "?").strip().lower() + formulations[formulation] = formulations.get(formulation, 0) + 1 + print(f" [{i}/{len(rows)}] {uid}: {len(mentions)} mention(s)") + finally: + if session is not None: + session.close() print(f"\n{nb_avec_mention}/{len(rows)} amendements avec au moins une mention.") print("Formulations rencontrées (fréquence) :") @@ -209,8 +252,13 @@ def main(): default=4.0, help="Pause en secondes entre deux appels (throttle anti rate-limit)", ) + parser.add_argument( + "--persist", + action="store_true", + help="Écrit aussi les mentions dans la table amendement_mentions", + ) args = parser.parse_args() - run(args.limit, args.offset, args.random, args.delay) + run(args.limit, args.offset, args.random, args.delay, args.persist) if __name__ == "__main__": diff --git a/justfile b/justfile index 1633147..282e65b 100644 --- a/justfile +++ b/justfile @@ -23,5 +23,6 @@ psql: psql -h localhost -U postgres -d ipolitics # Detect external-collaboration mentions on a sample of amendments (v1, no pre-filter) -detect-mentions limit="50": - uv run python -m analysis.detect_mentions --limit {{limit}} +# Extra flags pass through, e.g.: just detect-mentions 100 --random --persist +detect-mentions limit="50" *ARGS: + uv run python -m analysis.detect_mentions --limit {{limit}} {{ARGS}} From 108dd9121694b962fba65853bc4c5c5d1a3be6c6 Mon Sep 17 00:00:00 2001 From: "david.noel@withpigment.com" Date: Sun, 12 Jul 2026 11:27:04 +0200 Subject: [PATCH 4/6] Tweak detector docstrings --- analysis/detect_mentions.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/analysis/detect_mentions.py b/analysis/detect_mentions.py index c4cef40..e8ae39d 100644 --- a/analysis/detect_mentions.py +++ b/analysis/detect_mentions.py @@ -1,14 +1,9 @@ -"""Détection exploratoire (v1, sans pré-filtre) des mentions de collaboration externe. +"""Détection exploratoire (v1, utilsant uniquement un llm) des mentions de collaboration externe dans les amendements. -On envoie l'exposé sommaire de chaque amendement à un modèle (API OpenAI-compatible : -Scaleway, OpenRouter...) et on lui demande de repérer les passages où l'auteur déclare +On envoie l'exposé sommaire de chaque amendement à un modèle (API OpenAI-compatible) et on lui demande de repérer les passages où l'auteur déclare avoir « travaillé avec », « été inspiré par », etc. une entité externe (lobby, association, syndicat, entreprise, fédération professionnelle, ONG...). -Cette version tourne sur un sous-ensemble et écrit les résultats bruts dans un fichier -JSONL, sans rien persister en base : l'objectif est d'observer les formulations réelles -avant de figer une table d'analyse. - Usage: uv run python -m analysis.detect_mentions --limit 50 uv run python -m analysis.detect_mentions --limit 100 --offset 100 @@ -141,10 +136,9 @@ def detect(client: OpenAI, model: str, expose: str, max_retries: int = 4) -> dic def persist_mentions(session: Session, uid: str, mentions: list[dict], model: str): - """Réécrit les mentions d'un amendement en base (delete puis insert). + """Ecrit ou réécrit les mentions d'un amendement en base (delete puis insert). - Idempotent : un re-run sur le même amendement remplace ses lignes, donc une - mention disparue lors d'une nouvelle passe est bien supprimée. + Idempotent : un re-run sur le même amendement remplace ses lignes existantes. """ session.execute( delete(AmendementMention).where(AmendementMention.amendementUid == uid) From da1c9acda4f654fe4917574a96979587a473a0d2 Mon Sep 17 00:00:00 2001 From: "david.noel@withpigment.com" Date: Sun, 12 Jul 2026 13:36:19 +0200 Subject: [PATCH 5/6] Document DB setup and the mention-detection analysis in the README - Add the missing setup steps (.env copy, starting Postgres via docker compose) - Document the external-collaboration mention detector: LLM_* env config, detect-mentions usage and flags, JSONL/DB outputs - Explain that amendement_mentions is an analysis table that survives rebuilds - Fix the stale Dossier model example (was still named User) --- README.md | 88 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 85 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a661458..9800e8f 100644 --- a/README.md +++ b/README.md @@ -35,10 +35,25 @@ L'API propose de nombreux endpoints. - [Installation d'UV](https://docs.astral.sh/uv/) ### Setup + +1. Installer les dépendances : ```bash uv sync ``` +2. Créer le fichier `.env` à partir de l'exemple, puis l'adapter si besoin : +```bash +cp .env.example .env +``` +Il contient la connexion PostgreSQL (`PG_*`) et la configuration du modèle de langage +utilisé par l'analyse des amendements (`LLM_*`, voir plus bas). + +3. Démarrer PostgreSQL (instance locale définie dans `docker-compose.yml`, sur le port `5432`) : +```bash +docker compose up -d db +``` +Les valeurs par défaut de `.env.example` correspondent à ce conteneur (`postgres`/`postgres`, base `ipolitics`). + ### Usages #### Exécuter des commandes @@ -112,12 +127,12 @@ Voici une partie du fichier `./data/dossiers.json` Je veux rajouter le champ `chambre` dans la DB et faire en sorte que l'ETL l'ajoute de lui-même. 1. Rajouter le champ dans le modèle -``` -class User(Base): +```python +class Dossier(Base): __tablename__ = "dossiers" uid: Mapped[str] = mapped_column(primary_key=True) - titre: Mapped[str] = mapped_column(String(500)) + titre: Mapped[str] = mapped_column(String(1000)) dataset: Mapped[int] chambre: Mapped[str] = mapped_column(String(5)) # <-------- nouvelle colonne qui porte le même nom que le champ du fichier json ``` @@ -125,3 +140,70 @@ class User(Base): Le champ doit porter le même nom sinon l'ETL ne sera pas capable de le trouver. 2. Exécuter `just all` + +# Analyse : détection des mentions de collaboration externe + +Objectif : repérer les amendements dont l'exposé sommaire déclare une collaboration ou une +inspiration avec une **entité externe** (lobby, syndicat, association, entreprise, fédération +professionnelle, ONG…) — formulations du type « travaillé avec… », « en concertation avec… », +« inspiré de… ». + +Version **v1 exploratoire, sans pré-filtre** : chaque amendement est soumis à un modèle de langage +(`analysis/detect_mentions.py`). + +## Prérequis + +1. La base doit être alimentée (table `amendements` peuplée) — voir les sections ETL ci-dessus. +2. Configurer l'accès au modèle de langage dans `.env`. Le script utilise le SDK OpenAI contre + **n'importe quelle API compatible OpenAI** (Scaleway, OpenRouter…) via trois variables : + +```dotenv +LLM_API_KEY=... # clé du provider +LLM_BASE_URL=https://openrouter.ai/api/v1 # endpoint OpenAI-compatible +LLM_MODEL=qwen/qwen3-30b-a3b # identifiant exact du modèle +``` + +Changer de provider = changer ces trois lignes, rien d'autre. + +## Lancer une analyse + +```bash +# Sur les 50 premiers amendements (par ordre de dépôt) +just detect-mentions + +# Sur 100 amendements tirés aléatoirement dans tout le corpus +just detect-mentions 100 --random + +# Idem, en persistant les résultats dans la table amendement_mentions +just detect-mentions 100 --random --persist + +# Équivalent sans just : +uv run python -m analysis.detect_mentions --limit 100 --random --persist +``` + +Options (`uv run python -m analysis.detect_mentions --help`) : + +| Option | Effet | +| ----------- | --------------------------------------------------------------------------- | +| `--limit N` | Nombre d'amendements à analyser (défaut : 50). | +| `--offset N`| Décalage dans l'échantillon déterministe (ignoré avec `--random`). | +| `--random` | Tirage aléatoire sur tout le corpus (utile car les mentions sont rares). | +| `--delay S` | Pause en secondes entre deux appels (throttle anti rate-limit, défaut : 4). | +| `--persist` | Écrit aussi les mentions dans la table `amendement_mentions`. | + +## Sorties + +- **JSONL brut** : `analysis/output/mentions_sample.jsonl` (une ligne par amendement, dossier + gitignoré). Réécrit à chaque exécution. +- **Récap console** : nombre d'amendements avec mention et fréquence des formulations rencontrées. +- **Base** (avec `--persist`) : table `amendement_mentions`, une ligne par mention détectée + (`amendementUid`, `citation`, `formulation`, `entite`, `typeEntite`, `externe`, `modele`, + `createdAt`). L'écriture est idempotente par amendement (une nouvelle passe remplace ses lignes). + +## Tables d'analyse et rebuild + +`amendement_mentions` est une **table d'analyse** : elle n'est pas listée dans `ETL_TABLES` +(`etl/database.py`) et **survit donc à un `db-rebuild`**, contrairement à `amendements`/`dossiers` +qui sont détruites puis rechargées. Sa colonne `amendementUid` est une référence *molle* vers +`amendements.uid` (pas de `ForeignKey`), afin qu'aucune contrainte ne bloque le drop de la table ETL. +Pour ajouter une nouvelle analyse, créer un modèle sur ce principe (hors `ETL_TABLES`). From e85dea91af104cf8c0d57cb0471f7813a5892f37 Mon Sep 17 00:00:00 2001 From: DavidNO Date: Sun, 12 Jul 2026 13:42:49 +0200 Subject: [PATCH 6/6] Update README for versioning and API configuration --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9800e8f..3c1f499 100644 --- a/README.md +++ b/README.md @@ -148,7 +148,7 @@ inspiration avec une **entité externe** (lobby, syndicat, association, entrepri professionnelle, ONG…) — formulations du type « travaillé avec… », « en concertation avec… », « inspiré de… ». -Version **v1 exploratoire, sans pré-filtre** : chaque amendement est soumis à un modèle de langage +Version **v1 exploratoire** : chaque amendement est soumis à un modèle de langage (`analysis/detect_mentions.py`). ## Prérequis @@ -159,11 +159,11 @@ Version **v1 exploratoire, sans pré-filtre** : chaque amendement est soumis à ```dotenv LLM_API_KEY=... # clé du provider -LLM_BASE_URL=https://openrouter.ai/api/v1 # endpoint OpenAI-compatible -LLM_MODEL=qwen/qwen3-30b-a3b # identifiant exact du modèle +LLM_BASE_URL=... # endpoint OpenAI-compatible +LLM_MODEL=... # identifiant exact du modèle ``` -Changer de provider = changer ces trois lignes, rien d'autre. +Changer de provider = changer ces trois lignes. ## Lancer une analyse