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notebooks/11-Adding_Hybrid_Search.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMiRdFt5prnQ9g/oqsPddxG",
+      "authorship_tag": "ABX9TyO362/noWgs82KNvLAlRlkT",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -1229,7 +1229,7 @@
         "import chromadb\n",
         "from llama_index.vector_stores import ChromaVectorStore\n",
         "\n",
-        "# Create your index\n",
+        "# Load the vector store from the local storage.\n",
         "db = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
         "chroma_collection = db.get_or_create_collection(\"mini-llama-articles\")\n",
         "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
@@ -1451,8 +1451,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Define the ServiceCotext object to tie the LLM for generating final answer,\n",
-        "# and the embedding model to help with retrieving related nodes.\n",
+        "# Define a query engine that is responsible for retrieving related pieces of text,\n",
+        "# and using a LLM to formulate the final answer.\n",
         "custom_query_engine = RetrieverQueryEngine(\n",
         "    retriever=custom_retriever,\n",
         "    response_synthesizer=response_synthesizer,\n",