Renames HybridQuery to AggregateHybridQuery (#112)

* renames HybridQuery to AggregateHybridQuery

* updates minimum redisvl version

Author: justin-cechmanek

python-recipes/vector-search/01_redisvl.ipynb

@@ -82,7 +82,7 @@
       },
       "outputs": [],
       "source": [
-        "%pip install -q \"redisvl>=0.6.0\" sentence-transformers pandas nltk"
+        "%pip install -q \"redisvl>=0.11.0\" sentence-transformers pandas nltk"
       ]
     },
     {
@@ -4431,9 +4431,9 @@
         }
       ],
       "source": [
-        "from redisvl.query import HybridQuery\n",
+        "from redisvl.query import AggregateHybridQuery\n",
         "\n",
-        "hybrid_query = HybridQuery(\n",
+        "hybrid_query = AggregateHybridQuery(\n",
         "    text=user_query,\n",
         "    text_field_name=\"description\",\n",
         "    text_scorer=\"BM25\",\n",
@@ -4482,6 +4482,7 @@
     },
     "kernelspec": {
       "display_name": "Python 3",
+      "language": "python",
       "name": "python3"
     },
     "language_info": {

python-recipes/vector-search/02_hybrid_search.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "Hybrid search is all about combining lexical search with semantic vector search to improve result relevancy. This notebook will cover 3 different hybrid search strategies with Redis:\n",
     "\n",
-    "1. Linear combination of scores from lexical search (BM25) and vector search (Cosine Distance) with the HybridQuery class\n",
+    "1. Linear combination of scores from lexical search (BM25) and vector search (Cosine Distance) with the AggregateHybridQuery class\n",
     "2. Client-Side Reciprocal Rank Fusion (RRF)\n",
     "3. Client-Side Reranking with a cross encoder model\n",
     "\n",
@@ -32,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install sentence-transformers pandas nltk \"redisvl>=0.6.0\""
+    "%pip install sentence-transformers pandas nltk \"redisvl>=0.11.0\""
    ]
   },
   {
@@ -653,18 +653,18 @@
     "\n",
     "Now that our search index is populated and ready, we will build out a few different hybrid search techniques in Redis.\n",
     "\n",
-    "To start, we will use our `HybridQuery` class that accepts a text string and vector to automatically combine text similarity and vector similarity scores."
+    "To start, we will use our `AggregateHybridQuery` class that accepts a text string and vector to automatically combine text similarity and vector similarity scores."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Linear Combination using HybridQuery\n",
+    "## 1. Linear Combination using AggregateHybridQuery\n",
     "\n",
     "The goal of this technique is to calculate a weighted sum of the text similarity score for our provided text search and the cosine distance between vectors calculated via a KNN vector query. Under the hood this is possible in Redis using the [aggregations API](https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/aggregations/), as of `Redis 7.4.x` (search version `2.10.5`), within a single database call.\n",
     "\n",
-    "As of RedisVl 0.5.0 all of this is nicely encapsulated in your `HybridQuery` class, which behaves much like our other query classes."
+    "As of RedisVl 0.5.0 all of this is nicely encapsulated in your `AggregateHybridQuery` class, which behaves much like our other query classes."
    ]
   },
   {
@@ -681,10 +681,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, we will import our `HybridQuery` and understand its parameters.\n",
-    "At a minimum, the `HybridQuery` needs 4 arguments:\n",
+    "First, we will import our `AggregateHybridQuery` and understand its parameters.\n",
+    "At a minimum, the `AggregateHybridQuery` needs 4 arguments:\n",
     "```python\n",
-    "query = HybridQuery(\n",
+    "query = AggregateHybridQuery(\n",
     "    text = \"your query string here\",\n",
     "    text_field_name = \"<name of the text field in the index to do text search in>\",\n",
     "    vector = <bytes or numeric array, ex: [0.1, 0.2, 0.3]>,\n",
@@ -738,11 +738,11 @@
     }
    ],
    "source": [
-    "from redisvl.query import HybridQuery\n",
+    "from redisvl.query import AggregateHybridQuery\n",
     "\n",
     "vector = model.embed(user_query, as_buffer=True)\n",
     "\n",
-    "query = HybridQuery(\n",
+    "query = AggregateHybridQuery(\n",
     "    text=user_query,\n",
     "    text_field_name=\"description\",\n",
     "    vector=vector,\n",
@@ -760,7 +760,7 @@
    "metadata": {},
    "source": [
     "That's it! That is all it takes to perform a hybrid text matching and vector query with RedisVL.\n",
-    "Of course there are many more configurations and things we can do with the `HybridQuery` class. Let's investigate.\n",
+    "Of course there are many more configurations and things we can do with the `AggregateHybridQuery` class. Let's investigate.\n",
     "\n",
     "First, let's look at just the text query part that is being run:"
    ]
@@ -828,7 +828,7 @@
     "# translate our user query to French and use nltk french stopwords\n",
     "french_query_text = \"Film d'action et d'aventure avec de superbes scènes de combat, des enquêtes criminelles, des super-héros et de la magie\"\n",
     "\n",
-    "french_film_query = HybridQuery(\n",
+    "french_film_query = AggregateHybridQuery(\n",
     "    text=french_query_text,\n",
     "    text_field_name=\"description\",\n",
     "    vector=model.embed(french_query_text, as_buffer=True),\n",
@@ -845,7 +845,7 @@
     "    \"then\", \"there\", \"these\", \"they\", \"this\", \"to\", \"was\", \"will\", \"with\"\n",
     "])\n",
     "\n",
-    "stopwords_query = HybridQuery(\n",
+    "stopwords_query = AggregateHybridQuery(\n",
     "    text=user_query,\n",
     "    text_field_name=\"description\",\n",
     "    vector=vector,\n",
@@ -856,7 +856,7 @@
     "print(stopwords_query._build_query_string())\n",
     "\n",
     "# don't use any stopwords\n",
-    "no_stopwords_query = HybridQuery(\n",
+    "no_stopwords_query = AggregateHybridQuery(\n",
     "    text=user_query,\n",
     "    text_field_name=\"description\",\n",
     "    vector=vector,\n",
@@ -919,7 +919,7 @@
     }
    ],
    "source": [
-    "tfidf_query = HybridQuery(\n",
+    "tfidf_query = AggregateHybridQuery(\n",
     "    text=user_query,\n",
     "    text_field_name=\"description\",\n",
     "    vector=vector,\n",
@@ -1333,7 +1333,7 @@
    "source": [
     "def hybrid_query(text, alpha, num_results) -> List[Dict[str, Any]]:\n",
     "\n",
-    "    query = HybridQuery(\n",
+    "    query = AggregateHybridQuery(\n",
     "        text,\n",
     "        text_field_name=\"description\",\n",
     "        vector=model.embed(text, as_buffer=True),\n",