Create scripts/prepare_rag_jsonl.py

scripts/prepare_rag_jsonl.py

@@ -0,0 +1,118 @@
+import pandas as pd
+import json
+import os
+
+# On définit les chemins par rapport à la racine du projet
+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)
+
+    # EXPORT DOUBLE FORMAT
+    os.makedirs(os.path.dirname(OUTPUT_JSONL), exist_ok=True)
+    
+    # 1. JSONL
+    with open(OUTPUT_JSONL, "w", encoding="utf-8") as f:
+        for item in rag_items:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    
+    # 2. PARQUET (On aplatit un peu pour le format colonnaire)
+    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()