docs: manual de producción completo + ajustes seed fallback

- Add production manual (docs/MANUAL_PRODUCCION.md)
- Fix seed script to work without OpenAI API key (zero-vector fallback)
- Fix alembic env to use database_url_str
- Fix pyproject.toml hatch build config

scripts/seed_knowledge.py

@@ -222,7 +222,9 @@ async def seed_knowledge_base() -> None:
     async with AsyncSessionLocal() as session:
         # Create table if not exists
         from sqlalchemy import text
-        await session.execute(text(CREATE_KNOWLEDGE_TABLE_SQL))
+        statements = [s.strip() for s in CREATE_KNOWLEDGE_TABLE_SQL.strip().split(";") if s.strip()]
+        for stmt in statements:
+            await session.execute(text(stmt + ";"))
         await session.commit()
 
     async with AsyncSessionLocal() as session:
@@ -234,20 +236,59 @@ async def seed_knowledge_base() -> None:
         await rag.delete_by_source("catalogo_paquetes")
         await rag.delete_by_source("faq_general")
 
+        # Check if OpenAI is configured
+        from src.config import settings
+        openai_configured = bool(settings.OPENAI_API_KEY.get_secret_value()) and not settings.OPENAI_API_KEY.get_secret_value().startswith("sk-xxxxx")
+
         total = len(SKEEN_KNOWLEDGE)
         for i, item in enumerate(SKEEN_KNOWLEDGE, 1):
-            doc_id = await rag.add_document(
-                content=item["content"],
-                category=item["category"],
-                source=item["source"],
-            )
-            print(f"   [{i}/{total}] {item['category'].upper():12} → {doc_id[:8]}...")
+            try:
+                doc_id = await rag.add_document(
+                    content=item["content"],
+                    category=item["category"],
+                    source=item["source"],
+                )
+                print(f"   [{i}/{total}] {item['category'].upper():12} → {doc_id[:8]}...")
+            except Exception as exc:
+                # Fallback: insert with zero vector if OpenAI fails
+                from sqlalchemy import text
+                import json
+                zero_vector = "[" + ",".join(["0.0"] * settings.VECTOR_DIMENSION) + "]"
+                result = await session.execute(
+                    text("""
+                        INSERT INTO knowledge_chunks (id, content, metadata, category, source, embedding)
+                        VALUES (gen_random_uuid()::text, :content, :metadata, :category, :source, CAST(:embedding AS vector))
+                        RETURNING id
+                    """),
+                    {
+                        "content": item["content"],
+                        "metadata": json.dumps({}),
+                        "category": item["category"],
+                        "source": item["source"],
+                        "embedding": zero_vector,
+                    },
+                )
+                row = result.mappings().first()
+                doc_id = row["id"] if row else "unknown"
+                print(f"   [{i}/{total}] {item['category'].upper():12} → {doc_id[:8]}... (sin embedding)")
 
-    print(f"\n✅ Knowledge base seeded with {total} documents.")
+    if not openai_configured:
+        print(f"\n⚠️  Knowledge base seeded with {total} documents BUT WITHOUT EMBEDDINGS.")
+        print("    Set OPENAI_API_KEY in .env and re-run: python scripts/seed_knowledge.py")
+    else:
+        print(f"\n✅ Knowledge base seeded with {total} documents.")
 
 
 async def verify_search() -> None:
     """Quick verification search."""
+    from src.config import settings
+    openai_configured = bool(settings.OPENAI_API_KEY.get_secret_value()) and not settings.OPENAI_API_KEY.get_secret_value().startswith("sk-xxxxx")
+
+    if not openai_configured:
+        print("\n🔍 Verification search skipped (OpenAI API key not configured).")
+        print("    Re-run after setting OPENAI_API_KEY to test semantic search.")
+        return
+
     print("\n🔍 Running verification searches...")
 
     async with AsyncSessionLocal() as session: