Update query interface

README.md

@@ -74,27 +74,27 @@ This would correspond to a dataset that looked something like
 
 With the schema, the RedisVL library can be used to create, load vectors and perform vector searches
 ```python
-import pandas as pd
 
 from redisvl.index import SearchIndex
-from redisvl.query import create_vector_query
+from redisvl.query import VectorQuery
 
-# define and create the index
-index = SearchIndex.from_yaml("./users_schema.yml"))
+# initialize the index and connect to Redis
+index = SearchIndex.from_dict(schema)
 index.connect("redis://localhost:6379")
-index.create()
 
-index.load(pd.read_csv("./users.csv").to_dict("records"))
+# create the index in Redis
+index.create(overwrite=True)
 
-query = create_vector_query(
-    ["user", "age", "job", "credit_score"],
-    number_of_results=2,
+# load data into the index in Redis (list of dicts)
+index.load(data)
+
+query = VectorQuery(
+    vector=[0.1, 0.1, 0.5],
     vector_field_name="user_embedding",
+    return_fields=["user", "age", "job", "credit_score"],
+    num_results=3,
 )
-
-query_vector = np.array([0.1, 0.1, 0.5]).tobytes()
-results = index.search(query, query_params={"vector": query_vector})
-
+results = index.search(query.query, query_params=query.params)
 
 ```
 

conftest.py

@@ -33,4 +33,10 @@ def event_loop():
     except RuntimeError:
         loop = asyncio.new_event_loop()
     yield loop
-    loop.close()
+    loop.close()
+
+@pytest.fixture
+def clear_db():
+    redis.flushall()
+    yield
+    redis.flushall()

docs/api/cache.rst

@@ -0,0 +1,28 @@
+
+********
+LLMCache
+********
+
+SemanticCache
+=============
+
+.. _semantic_cache_api:
+
+.. currentmodule:: redisvl.llmcache.semantic
+
+.. autosummary::
+
+    SemanticCache.__init__
+    SemanticCache.check
+    SemanticCache.store
+    SemanticCache.set_threshold
+    SemanticCache.threshold
+    SemanticCache.index
+    SemanticCache.ttl
+    SemanticCache.set_ttl
+
+
+.. autoclass:: SemanticCache
+   :show-inheritance:
+   :members:
+   :inherited-members:

docs/api/filter.rst

@@ -0,0 +1,59 @@
+******
+Filter
+******
+
+.. _filter_api:
+
+TagFilter
+=========
+
+
+.. currentmodule:: redisvl.query
+
+.. autosummary::
+
+    TagFilter.__init__
+    TagFilter.to_string
+
+
+.. autoclass:: TagFilter
+    :show-inheritance:
+    :members:
+    :inherited-members:
+
+
+
+TextFilter
+==========
+
+
+.. currentmodule:: redisvl.query
+
+.. autosummary::
+
+    TextFilter.__init__
+    TextFilter.to_string
+
+
+.. autoclass:: TextFilter
+    :show-inheritance:
+    :members:
+    :inherited-members:
+
+
+NumericFilter
+=============
+
+
+.. currentmodule:: redisvl.query
+
+.. autosummary::
+
+    NumericFilter.__init__
+    NumericFilter.to_string
+
+
+.. autoclass:: NumericFilter
+    :show-inheritance:
+    :members:
+    :inherited-members:

docs/api/index.md

@@ -9,7 +9,11 @@ myst:
 
 ```{toctree}
 :caption: RedisVL
+:maxdepth: 2
 
-redisvl_api
+searchindex
+query
+filter
+cache
 ```
 

docs/api/query.rst

@@ -0,0 +1,25 @@
+
+*****
+Query
+*****
+
+VectorQuery
+===========
+
+.. _query_api:
+
+.. currentmodule:: redisvl.query
+
+.. autosummary::
+
+    VectorQuery.__init__
+    VectorQuery.set_filter
+    VectorQuery.get_filter
+    VectorQuery.query
+    VectorQuery.params
+
+
+.. autoclass:: VectorQuery
+   :show-inheritance:
+   :members:
+   :inherited-members:

docs/api/redisvl_api.rst → docs/api/searchindex.rst

@@ -1,8 +1,7 @@
 
-***********
-RedisVL API
-***********
-
+*****
+Index
+*****
 
 SearchIndex
 ===========

docs/user_guide/getting_started_01.ipynb

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,15 +189,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m14:26:12\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[17001]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
-      "\u001b[32m14:26:12\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[17001]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. user_index\n"
+      "\u001b[32m00:29:48\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[40909]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
+      "\u001b[32m00:29:48\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[40909]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. user_index\n",
+      "\u001b[32m00:29:48\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[40909]\u001b[0m \u001b[1;30mINFO\u001b[0m 2. my_index\n"
      ]
     }
    ],
@@ -217,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,48 +238,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from redisvl.query import create_vector_query\n",
+    "from redisvl.query import VectorQuery\n",
     "\n",
     "# create a vector query returning a number of results\n",
     "# with specific fields to return.\n",
-    "query = create_vector_query(\n",
-    "    return_fields=[\"users\", \"age\", \"job\", \"credit_score\", \"vector_score\"],\n",
-    "    number_of_results=3,\n",
-    "    vector_field_name=\"user_embedding\"\n",
+    "query = VectorQuery(\n",
+    "    vector=[0.1, 0.1, 0.5],\n",
+    "    vector_field_name=\"user_embedding\",\n",
+    "    return_fields=[\"user\", \"age\", \"job\", \"credit_score\", \"vector_distance\"],\n",
+    "    num_results=3\n",
     ")\n",
     "\n",
-    "# establish a query vector to search against the data in Redis\n",
-    "query_vector = np.array([0.1, 0.1, 0.5], dtype=np.float32).tobytes()\n",
-    "\n",
     "# use the SearchIndex instance (or Redis client) to execute the query\n",
-    "results = index.search(query, query_params={\"vector\": query_vector})"
+    "results = index.search(query.query, query_params=query.params)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Score: 0\n",
-      "Document {'id': 'v1:john', 'payload': None, 'vector_score': '0', 'age': '1', 'job': 'engineer', 'credit_score': 'high'}\n",
+      "Document {'id': 'v1:john', 'payload': None, 'vector_distance': '0', 'user': 'john', 'age': '1', 'job': 'engineer', 'credit_score': 'high'}\n",
       "Score: 0\n",
-      "Document {'id': 'v1:mary', 'payload': None, 'vector_score': '0', 'age': '2', 'job': 'doctor', 'credit_score': 'low'}\n",
+      "Document {'id': 'v1:mary', 'payload': None, 'vector_distance': '0', 'user': 'mary', 'age': '2', 'job': 'doctor', 'credit_score': 'low'}\n",
       "Score: 0.653301358223\n",
-      "Document {'id': 'v1:joe', 'payload': None, 'vector_score': '0.653301358223', 'age': '3', 'job': 'dentist', 'credit_score': 'medium'}\n"
+      "Document {'id': 'v1:joe', 'payload': None, 'vector_distance': '0.653301358223', 'user': 'joe', 'age': '3', 'job': 'dentist', 'credit_score': 'medium'}\n"
      ]
     }
    ],
    "source": [
     "for doc in results.docs:\n",
-    "    print(\"Score:\", doc.vector_score)\n",
+    "    print(\"Score:\", doc.vector_distance)\n",
     "    print(doc)\n"
    ]
   },