From 23a0efbbda82742753c0ea8a5d6ba9a549928e93 Mon Sep 17 00:00:00 2001
From: phact <estevezsebastian@gmail.com>
Date: Fri, 10 Oct 2025 09:58:21 -0400
Subject: [PATCH] one time migration script for existing indexes

---
 scripts/migrate_embedding_model_field.py | 104 +++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 scripts/migrate_embedding_model_field.py

diff --git a/scripts/migrate_embedding_model_field.py b/scripts/migrate_embedding_model_field.py
new file mode 100644
index 00000000..e482c6ca
--- /dev/null
+++ b/scripts/migrate_embedding_model_field.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""
+Migration script to add embedding_model field to existing OpenSearch index.
+Run this once to fix the field type from text to keyword.
+"""
+import asyncio
+import sys
+from opensearchpy import AsyncOpenSearch
+from opensearchpy._async.http_aiohttp import AIOHttpConnection
+
+# Add parent directory to path to import config
+sys.path.insert(0, '/home/tato/Desktop/openrag/src')
+
+from config.settings import (
+    OPENSEARCH_HOST,
+    OPENSEARCH_PORT,
+    OPENSEARCH_USERNAME,
+    OPENSEARCH_PASSWORD,
+    INDEX_NAME,
+)
+from utils.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+async def add_embedding_model_field():
+    """Add embedding_model as keyword field to existing index"""
+
+    # Create admin OpenSearch client
+    client = AsyncOpenSearch(
+        hosts=[{"host": OPENSEARCH_HOST, "port": OPENSEARCH_PORT}],
+        connection_class=AIOHttpConnection,
+        scheme="https",
+        use_ssl=True,
+        verify_certs=False,
+        ssl_assert_fingerprint=None,
+        http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
+        http_compress=True,
+    )
+
+    try:
+        # Check if index exists
+        exists = await client.indices.exists(index=INDEX_NAME)
+        if not exists:
+            logger.error(f"Index {INDEX_NAME} does not exist")
+            return False
+
+        # Get current mapping
+        mapping = await client.indices.get_mapping(index=INDEX_NAME)
+        current_props = mapping[INDEX_NAME]["mappings"].get("properties", {})
+
+        # Check if embedding_model field exists
+        if "embedding_model" in current_props:
+            current_type = current_props["embedding_model"].get("type")
+            logger.info(f"embedding_model field exists with type: {current_type}")
+
+            if current_type == "keyword":
+                logger.info("Field is already correct type (keyword)")
+                return True
+            else:
+                logger.warning(
+                    f"Field exists with wrong type: {current_type}. "
+                    "Cannot change field type on existing field. "
+                    "You need to reindex or use a different field name."
+                )
+                return False
+
+        # Add the field as keyword
+        logger.info("Adding embedding_model field as keyword type")
+        new_mapping = {
+            "properties": {
+                "embedding_model": {"type": "keyword"}
+            }
+        }
+
+        response = await client.indices.put_mapping(
+            index=INDEX_NAME,
+            body=new_mapping
+        )
+
+        logger.info(f"Successfully added embedding_model field: {response}")
+
+        # Verify the change
+        updated_mapping = await client.indices.get_mapping(index=INDEX_NAME)
+        updated_props = updated_mapping[INDEX_NAME]["mappings"]["properties"]
+
+        if "embedding_model" in updated_props:
+            field_type = updated_props["embedding_model"].get("type")
+            logger.info(f"Verified: embedding_model field type is now: {field_type}")
+            return field_type == "keyword"
+        else:
+            logger.error("Field was not added successfully")
+            return False
+
+    except Exception as e:
+        logger.error(f"Error adding embedding_model field: {e}")
+        return False
+    finally:
+        await client.close()
+
+
+if __name__ == "__main__":
+    success = asyncio.run(add_embedding_model_field())
+    sys.exit(0 if success else 1)