remove redundant section

Author: tylerhutcherson

python-recipes/RAG/07_user_role_based_rag.ipynb

@@ -477,7 +477,7 @@
       "source": [
         "## 3. Document Management (Using LangChain)\n",
         "\n",
-        "Here, we'll use **LangChain** for document loading, chunking, and vectorizing. Then,. we’ll **store documents** in Redis as JSON. Each document will look like:\n",
+        "Here, we'll use **LangChain** for document loading, chunking, and vectorizing. Then, we’ll **store documents** in Redis as JSON. Each document will look like:\n",
         "\n",
         "```json\n",
         "{\n",
@@ -492,72 +492,6 @@
         "```"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "id": "0429e94c",
-      "metadata": {
-        "id": "0429e94c"
-      },
-      "source": [
-        "### Redis doc index creation\n",
-        "Below we use RedisVL to prepare a search index where we will load our docs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "b33c23e0",
-      "metadata": {
-        "id": "b33c23e0"
-      },
-      "outputs": [],
-      "source": [
-        "from redisvl.index import SearchIndex\n",
-        "from redisvl.schema import IndexSchema\n",
-        "\n",
-        "\n",
-        "schema = IndexSchema.from_dict({\n",
-        "  \"index\": {\n",
-        "    \"name\": \"docs\",\n",
-        "    \"prefix\": \"doc\",\n",
-        "    \"storage_type\": \"json\"\n",
-        "  },\n",
-        "  \"fields\": [\n",
-        "    {\n",
-        "        \"name\": \"doc_id\",\n",
-        "        \"type\": \"tag\",\n",
-        "    },\n",
-        "    {\n",
-        "        \"name\": \"chunk_id\",\n",
-        "        \"type\": \"tag\",\n",
-        "    },\n",
-        "    {\n",
-        "        \"name\": \"allowed_roles\",\n",
-        "        \"path\": \"$.allowed_roles[*]\",\n",
-        "        \"type\": \"tag\",\n",
-        "    },\n",
-        "    {\n",
-        "        \"name\": \"content\",\n",
-        "        \"type\": \"text\",\n",
-        "    },\n",
-        "    {\n",
-        "        \"name\": \"embedding\",\n",
-        "        \"type\": \"vector\",\n",
-        "        \"attrs\": {\n",
-        "            \"dims\": 768,\n",
-        "            \"distance_metric\": \"cosine\",\n",
-        "            \"algorithm\": \"flat\",\n",
-        "            \"datatype\": \"float32\"\n",
-        "        }\n",
-        "    }\n",
-        "  ]\n",
-        "})\n",
-        "\n",
-        "\n",
-        "index = SearchIndex(schema, redis_client=redis_client)\n",
-        "index.create(overwrite=True, drop=False)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "id": "d3cJ5DSP5vXt",
@@ -742,7 +676,7 @@
         "            )\n",
         "        )\n",
         "        \n",
-        "        return results\n"
+        "        return results"
       ]
     },
     {
@@ -824,7 +758,7 @@
         "    4. Return top-K results.\n",
         "    \"\"\"\n",
         "    # 1. Load & validate user roles\n",
-        "    user_obj = User.get(index.client, user_id)\n",
+        "    user_obj = User.get(redis_client, user_id)\n",
         "    if not user_obj:\n",
         "        raise ValueError(f\"User {user_id} not found.\")\n",
         "\n",