adds content filtering notebook and corresponding dataset

README.md

@@ -94,6 +94,11 @@ For further insights on enhancing RAG applications with dense content representa
 
 ## Recommendation systems
 
+| Recipe | Description |
+| --- | --- |
+| [/recommendation-systems/content_filtering.ipynb](python-recipes/recommendation-systems/content_filtering.ipynb) | Intro content filtering example with redisvl |
+
+### See also
 An exciting example of how Redis can power production-ready systems is highlighted in our collaboration with [NVIDIA](https://developer.nvidia.com/blog/offline-to-online-feature-storage-for-real-time-recommendation-systems-with-nvidia-merlin/) to construct a state-of-the-art recommendation system.
 
 Within [this repository](https://github.com/redis-developer/redis-nvidia-recsys), you'll find three examples, each escalating in complexity, showcasing the process of building such a system.

python-recipes/RAG/01_redisvl.ipynb

@@ -439,7 +439,7 @@
         "        'chunk_id': i,\n",
         "        'content': chunk.page_content,\n",
         "        # For HASH -- must convert embeddings to bytes\n",
-        "        'text_embedding': array_to_buffer(embeddings[i])\n",
+        "        'text_embedding': array_to_buffer(embeddings[i], dtype='float32')\n",
         "    } for i, chunk in enumerate(chunks)\n",
         "]\n",
         "\n",

python-recipes/RAG/04_advanced_redisvl.ipynb

@@ -574,7 +574,7 @@
         "        'chunk_id': f'{i}',\n",
         "        'proposition': proposition,\n",
         "        # For HASH -- must convert embeddings to bytes\n",
-        "        'text_embedding': array_to_buffer(prop_embeddings[i])\n",
+        "        'text_embedding': array_to_buffer(prop_embeddings[i], dtype=\"float32\")\n",
         "    } for i, proposition in enumerate(propositions)\n",
         "]\n",
         "\n",