| import pandas as pd |
| import json |
| import os |
|
|
| |
| ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| INPUT_DIR = os.path.join(ROOT_DIR, "data", "input") |
| OUTPUT_JSONL = os.path.join(ROOT_DIR, "data", "metiers_rag.jsonl") |
| OUTPUT_PARQUET = os.path.join(ROOT_DIR, "data", "metiers_rag.parquet") |
|
|
| def prepare_expert_rag_jsonl(max_words=300): |
| print(f"🚀 Démarrage de la préparation (Racine: {ROOT_DIR})") |
| |
| try: |
| df_metiers = pd.read_csv(os.path.join(INPUT_DIR, "gem_metiers.csv")) |
| df_liens = pd.read_csv(os.path.join(INPUT_DIR, "gem_metiers_competences.csv")) |
| df_dict_comp = pd.read_csv(os.path.join(INPUT_DIR, "gem_competences.csv")) |
| |
| df_verbatims = pd.DataFrame(columns=["id_metier", "verbatim"]) |
| v_path = os.path.join(INPUT_DIR, "metiers_verbatims.csv") |
| if os.path.exists(v_path): |
| df_verbatims = pd.read_csv(v_path) |
| print(f"✅ {len(df_verbatims)} verbatims trouvés.") |
| |
| except Exception as e: |
| print(f"❌ Erreur : {e}") |
| return |
|
|
| comp_defs = {} |
| for _, row in df_dict_comp.iterrows(): |
| c_id = row['Id interne Neobrain Compétence'] |
| comp_defs[c_id] = {i: row.get(f'Description Niveau {i} Compétence', '') for i in range(1, 5)} |
|
|
| rag_items = [] |
| |
| for _, metier in df_metiers.iterrows(): |
| code_m = metier['Code Métier'] |
| nom_m = metier['Métier collaborateur'] |
| famille = metier['Famille métier'] |
| |
| comp_du_metier = df_liens[df_liens['Code Métier'] == code_m].sort_values(by='Poids de la compétence', ascending=False) |
| |
| header = f"# Fiche Métier : {nom_m}\n\n" |
| header += f"- **Code Métier** : {code_m}\n" |
| header += f"- **Famille** : {famille}\n\n" |
|
|
| v_list = df_verbatims[df_verbatims['id_metier'] == code_m]['verbatim'].tolist() |
| if v_list: |
| header += "## Missions quotidiennes (Verbatims)\n" |
| for v in v_list: |
| header += f"- \"{v}\"\n" |
| header += "\n" |
|
|
| header += "## Profil de compétences (Attentes)\n\n" |
|
|
| comp_lines = [] |
| for groupe, df_g in comp_du_metier.groupby('Groupe de compétences', sort=False): |
| comp_lines.append(f"### {groupe}") |
| for _, row_c in df_g.iterrows(): |
| nom_c = row_c['Nom de la compétence'] |
| val = row_c['Niveau requis pour le métier'] |
| niv_req = int(float(val)) if pd.notna(val) else 0 |
| c_id = row_c['Id interne Neobrain Compétence'] |
| desc_niveau = comp_defs.get(c_id, {}).get(niv_req, "") |
| |
| line = f"- **{nom_c}** (Niveau {niv_req})" |
| if pd.notna(desc_niveau) and desc_niveau != "": |
| line += f" : {desc_niveau}" |
| comp_lines.append(line) |
| comp_lines.append("") |
|
|
| current_chunk_comps = [] |
| current_word_count = len(header.split()) |
| chunk_id = 1 |
|
|
| def add_item(cid, text, midx): |
| rag_items.append({ |
| "id": f"{cid}_{midx}" if midx > 1 else cid, |
| "title": nom_m, |
| "text": text, |
| "metadata": {"famille": famille, "chunk": midx, "source": "OPT-NC Expert Ref 2025"} |
| }) |
|
|
| for line in comp_lines: |
| line_words = len(line.split()) |
| if current_word_count + line_words > max_words and current_chunk_comps: |
| add_item(code_m, header + "\n".join(current_chunk_comps), chunk_id) |
| chunk_id += 1 |
| current_chunk_comps = [line] |
| current_word_count = len(header.split()) + line_words |
| else: |
| current_chunk_comps.append(line) |
| current_word_count += line_words |
|
|
| if current_chunk_comps: |
| add_item(code_m, header + "\n".join(current_chunk_comps), chunk_id) |
|
|
| |
| os.makedirs(os.path.dirname(OUTPUT_JSONL), exist_ok=True) |
| |
| |
| with open(OUTPUT_JSONL, "w", encoding="utf-8") as f: |
| for item in rag_items: |
| f.write(json.dumps(item, ensure_ascii=False) + "\n") |
| |
| |
| df_final = pd.DataFrame(rag_items) |
| df_final['famille'] = df_final['metadata'].apply(lambda x: x['famille']) |
| df_final['source'] = df_final['metadata'].apply(lambda x: x['source']) |
| df_final['chunk_index'] = df_final['metadata'].apply(lambda x: x['chunk']) |
| df_final.drop(columns=['metadata']).to_parquet(OUTPUT_PARQUET, index=False) |
| |
| print(f"✨ Terminé !") |
| print(f"📄 JSONL : {OUTPUT_JSONL}") |
| print(f"📦 PARQUET : {OUTPUT_PARQUET}") |
|
|
| if __name__ == "__main__": |
| prepare_expert_rag_jsonl() |
|
|
