Fix spelling errors (#351)

- Fixes some spelling errors
- Adds codespell to the pre-commit hooks

---------

Co-authored-by: Tyler Hutcherson <[email protected]>

Author: abrookins

.pre-commit-config.yaml

@@ -6,3 +6,12 @@ repos:
         entry: bash -c 'make format && make check-sort-imports && make check-types'
         language: system
         pass_filenames: false
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+      - id: codespell
+        name: Check spelling
+        args:
+          - --write-changes
+          - --skip=*.pyc,*.pyo,*.lock,*.git,*.mypy_cache,__pycache__,*.egg-info,.pytest_cache,docs/_build,env,venv,.venv
+          - --ignore-words-list=enginee

docs/user_guide/02_hybrid_queries.ipynb

@@ -1090,7 +1090,7 @@
    "source": [
     "## Non-vector Queries\n",
     "\n",
-    "In some cases, you may not want to run a vector query, but just use a ``FilterExpression`` similar to a SQL query. The ``FilterQuery`` class enable this functionality. It is similar to the ``VectorQuery`` class but soley takes a ``FilterExpression``."
+    "In some cases, you may not want to run a vector query, but just use a ``FilterExpression`` similar to a SQL query. The ``FilterQuery`` class enable this functionality. It is similar to the ``VectorQuery`` class but solely takes a ``FilterExpression``."
    ]
   },
   {
@@ -1448,7 +1448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.12.8"
   },
   "orig_nbformat": 4
  },

docs/user_guide/03_llmcache.ipynb

@@ -1,16 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Semantic Caching for LLMs\n",
-    "\n",
-    "RedisVL provides a ``SemanticCache`` interface to utilize Redis' built-in caching capabilities AND vector search in order to store responses from previously-answered questions. This reduces the number of requests and tokens sent to the Large Language Models (LLM) service, decreasing costs and enhancing application throughput (by reducing the time taken to generate responses).\n",
-    "\n",
-    "This notebook will go over how to use Redis as a Semantic Cache for your applications"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -110,7 +99,7 @@
     "    name=\"llmcache\",                                          # underlying search index name\n",
     "    redis_url=\"redis://localhost:6379\",                       # redis connection url string\n",
     "    distance_threshold=0.1,                                   # semantic cache distance threshold\n",
-    "    vectorizer=HFTextVectorizer(\"redis/langcache-embed-v1\"),  # embdding model\n",
+    "    vectorizer=HFTextVectorizer(\"redis/langcache-embed-v1\"),  # embedding model\n",
     ")"
    ]
   },
@@ -315,12 +304,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Customize the Distance Threshhold\n",
+    "## Customize the Distance Threshold\n",
     "\n",
-    "For most use cases, the right semantic similarity threshhold is not a fixed quantity. Depending on the choice of embedding model,\n",
-    "the properties of the input query, and even business use case -- the threshhold might need to change. \n",
+    "For most use cases, the right semantic similarity threshold is not a fixed quantity. Depending on the choice of embedding model,\n",
+    "the properties of the input query, and even business use case -- the threshold might need to change. \n",
     "\n",
-    "Fortunately, you can seamlessly adjust the threshhold at any point like below:"
+    "Fortunately, you can seamlessly adjust the threshold at any point like below:"
    ]
   },
   {
@@ -930,7 +919,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.12.8"
   },
   "orig_nbformat": 4
  },

docs/user_guide/04_vectorizers.ipynb

@@ -175,7 +175,7 @@
             }
          ],
          "source": [
-            "# openai also supports asyncronous requests, which we can use to speed up the vectorization process.\n",
+            "# openai also supports asynchronous requests, which we can use to speed up the vectorization process.\n",
             "embeddings = await oai.aembed_many(sentences)\n",
             "print(\"Number of Embeddings:\", len(embeddings))\n"
          ]
@@ -495,7 +495,7 @@
             "\n",
             "mistral = MistralAITextVectorizer()\n",
             "\n",
-            "# embed a sentence using their asyncronous method\n",
+            "# embed a sentence using their asynchronous method\n",
             "test = await mistral.aembed(\"This is a test sentence.\")\n",
             "print(\"Vector dimensions: \", len(test))\n",
             "print(test[:10])"

docs/user_guide/05_hash_vs_json.ipynb

@@ -282,7 +282,7 @@
     "from redisvl.query import VectorQuery\n",
     "from redisvl.query.filter import Tag, Text, Num\n",
     "\n",
-    "t = (Tag(\"credit_score\") == \"high\") & (Text(\"job\") % \"enginee*\") & (Num(\"age\") > 17)\n",
+    "t = (Tag(\"credit_score\") == \"high\") & (Text(\"job\") % \"enginee*\") & (Num(\"age\") > 17)  # codespell:ignore enginee\n",
     "\n",
     "v = VectorQuery(\n",
     "    vector=[0.1, 0.1, 0.5],\n",

docs/user_guide/07_message_history.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Large Language Models are inherently stateless and have no knowledge of previous interactions with a user, or even of previous parts of the current conversation. While this may not be noticable when asking simple questions, it becomes a hinderance when engaging in long running conversations that rely on conversational context.\n",
+    "Large Language Models are inherently stateless and have no knowledge of previous interactions with a user, or even of previous parts of the current conversation. While this may not be noticeable when asking simple questions, it becomes a hindrance when engaging in long running conversations that rely on conversational context.\n",
     "\n",
     "The solution to this problem is to append the previous conversation history to each subsequent call to the LLM.\n",
     "\n",
@@ -276,7 +276,7 @@
    "source": [
     "You can adjust the degree of semantic similarity needed to be included in your context.\n",
     "\n",
-    "Setting a distance threshold close to 0.0 will require an exact semantic match, while a distance threshold of 1.0 will include everthing."
+    "Setting a distance threshold close to 0.0 will require an exact semantic match, while a distance threshold of 1.0 will include everything."
    ]
   },
   {

pyproject.toml

@@ -75,6 +75,7 @@ dev = [
     "types-pyopenssl",
     "testcontainers>=4.3.1,<5",
     "cryptography>=44.0.1 ; python_version > '3.9.1'",
+    "codespell>=2.4.1,<3",
 ]
 docs = [
     "sphinx>=4.4.0",
@@ -118,3 +119,5 @@ asyncio_mode = "auto"
 [tool.mypy]
 warn_unused_configs = true
 ignore_missing_imports = true
+exclude = ["env", "venv", ".venv"]
+

redisvl/extensions/cache/llm/semantic.py

@@ -385,7 +385,7 @@ def check(
         .. code-block:: python
 
             response = cache.check(
-                prompt="What is the captial city of France?"
+                prompt="What is the capital city of France?"
             )
         """
         if not any([prompt, vector]):
@@ -476,7 +476,7 @@ async def acheck(
         .. code-block:: python
 
             response = await cache.acheck(
-                prompt="What is the captial city of France?"
+                prompt="What is the capital city of France?"
             )
         """
         aindex = await self._get_async_index()
@@ -588,7 +588,7 @@ def store(
         .. code-block:: python
 
             key = cache.store(
-                prompt="What is the captial city of France?",
+                prompt="What is the capital city of France?",
                 response="Paris",
                 metadata={"city": "Paris", "country": "France"}
             )
@@ -656,7 +656,7 @@ async def astore(
         .. code-block:: python
 
             key = await cache.astore(
-                prompt="What is the captial city of France?",
+                prompt="What is the capital city of France?",
                 response="Paris",
                 metadata={"city": "Paris", "country": "France"}
             )

redisvl/extensions/constants.py

@@ -1,7 +1,7 @@
 """
 Constants used within the extension classes SemanticCache, BaseMessageHistory,
 MessageHistory, SemanticMessageHistory and SemanticRouter.
-These constants are also used within theses classes corresponding schema.
+These constants are also used within these classes' corresponding schemas.
 """
 
 # BaseMessageHistory

redisvl/extensions/message_history/base_history.py

@@ -60,7 +60,7 @@ def get_recent(
         raw: bool = False,
         session_tag: Optional[str] = None,
     ) -> Union[List[str], List[Dict[str, str]]]:
-        """Retreive the recent conversation history in sequential order.
+        """Retrieve the recent conversation history in sequential order.
 
         Args:
             top_k (int): The number of previous exchanges to return. Default is 5.

redisvl/extensions/message_history/semantic_history.py

@@ -248,7 +248,7 @@ def get_recent(
         raw: bool = False,
         session_tag: Optional[str] = None,
     ) -> Union[List[str], List[Dict[str, str]]]:
-        """Retreive the recent message history in sequential order.
+        """Retrieve the recent message history in sequential order.
 
         Args:
             top_k (int): The number of previous exchanges to return. Default is 5.

redisvl/query/filter.py

@@ -624,7 +624,7 @@ def __str__(self) -> str:
         if not self._filter and not self._operator:
             raise ValueError("Improperly initialized FilterExpression")
 
-        # if theres an operator, combine expressions accordingly
+        # if there's an operator, combine expressions accordingly
         if self._operator:
             if not isinstance(self._left, FilterExpression) or not isinstance(
                 self._right, FilterExpression

redisvl/redis/connection.py

@@ -402,7 +402,7 @@ def validate_sync_redis(
             # Fall back to a simple log echo
             # For RedisCluster, echo is not available
             if hasattr(redis_client, "echo"):
-                await redis_client.echo(_lib_name)
+                redis_client.echo(_lib_name)
 
         # Get list of modules
         installed_modules = RedisConnectionFactory.get_modules(redis_client)

redisvl/utils/vectorize/text/custom.py

@@ -41,7 +41,7 @@ class CustomTextVectorizer(BaseVectorizer):
     This vectorizer is designed to accept a provided callable text vectorizer and
     provides a class definition to allow for compatibility with RedisVL.
     The vectorizer may support both synchronous and asynchronous operations which
-    allows for batch processing of texts, but at a minimum only syncronous embedding
+    allows for batch processing of texts, but at a minimum only synchronous embedding
     is required to satisfy the 'embed()' method.
 
     You can optionally enable caching to improve performance when generating
@@ -94,8 +94,8 @@ def __init__(
         Args:
             embed (Callable): a Callable function that accepts a string object and returns a list of floats.
             embed_many (Optional[Callable]): a Callable function that accepts a list of string objects and returns a list containing lists of floats. Defaults to None.
-            aembed (Optional[Callable]): an asyncronous Callable function that accepts a string object and returns a lists of floats. Defaults to None.
-            aembed_many (Optional[Callable]): an asyncronous Callable function that accepts a list of string objects and returns a list containing lists of floats. Defaults to None.
+            aembed (Optional[Callable]): an asynchronous Callable function that accepts a string object and returns a lists of floats. Defaults to None.
+            aembed_many (Optional[Callable]): an asynchronous Callable function that accepts a list of string objects and returns a list containing lists of floats. Defaults to None.
             dtype (str): the default datatype to use when embedding text as byte arrays.
                 Used when setting `as_buffer=True` in calls to embed() and embed_many().
                 Defaults to 'float32'.

tests/integration/test_llmcache.py

@@ -744,7 +744,7 @@ def test_cache_filtering(cache_with_filters):
     )
     assert len(results) == 4
 
-    # test no results are returned if we pass a nonexistant tag
+    # test no results are returned if we pass a nonexistent tag
     bad_filter = Tag("label") == "bad tag"
     results = cache_with_filters.check(
         "test prompt 1", filter_expression=bad_filter, num_results=5

tests/unit/test_aggregation_types.py

@@ -26,7 +26,7 @@ def test_aggregate_hybrid_query():
 
     assert isinstance(hybrid_query, AggregateRequest)
 
-    # Check defaut properties
+    # Check default properties
     assert hybrid_query._text == sample_text
     assert hybrid_query._text_field == text_field_name
     assert hybrid_query._vector == sample_vector

tests/unit/test_filter.py

@@ -37,8 +37,8 @@
         ("==", "tag/with/slashes", "@tag_field:{tag\\/with\\/slashes}"),
         (
             "==",
-            ["hypen-tag", "under_score", "dot.tag"],
-            "@tag_field:{hypen\\-tag|under_score|dot\\.tag}",
+            ["hyphen-tag", "under_score", "dot.tag"],
+            "@tag_field:{hyphen\\-tag|under_score|dot\\.tag}",
         ),
         # ...additional unique cases as desired...
     ],

tests/unit/test_query_types.py

@@ -367,9 +367,9 @@ def test_string_filter_expressions(query):
     assert query.query_string().__contains__("hello world")
 
     # Optional flag
-    query.set_filter("~(@desciption:(hello | world))")
-    assert query._filter_expression == "~(@desciption:(hello | world))"
-    assert query.query_string().__contains__("~(@desciption:(hello | world))")
+    query.set_filter("~(@description:(hello | world))")
+    assert query._filter_expression == "~(@description:(hello | world))"
+    assert query.query_string().__contains__("~(@description:(hello | world))")
 
 
 def test_vector_query_hybrid_policy():