From 6597537307858c4f9118d75e08f0bc21408eed46 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Thu, 17 Aug 2023 19:21:48 +0900 Subject: [PATCH] Fix typo in redis-hybrid-query-examples.ipynb (#642) bellow -> below --- .../vector_databases/redis/redis-hybrid-query-examples.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb b/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb index 0ebf38a..e24a68f 100644 --- a/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb +++ b/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb @@ -521,7 +521,7 @@ "source": [ "## Generate OpenAI Embeddings and Load Documents into the Index\n", "\n", - "Now that we have a search index, we can load documents into it. We will use the dataframe containing the styles dataset loaded previously. In Redis, either the HASH or JSON (if using RedisJSON in addition to RediSearch) data types can be used to store documents. We will use the HASH data type in this example. The cells bellow will show how to get OpenAI embeddings for the different products and load documents into the index." + "Now that we have a search index, we can load documents into it. We will use the dataframe containing the styles dataset loaded previously. In Redis, either the HASH or JSON (if using RedisJSON in addition to RediSearch) data types can be used to store documents. We will use the HASH data type in this example. The cells below will show how to get OpenAI embeddings for the different products and load documents into the index." ] }, {