langchain-nomic[patch]: Add ruff bandit rules to linter (langchain-ai#31805)

- Add ruff bandit rules
- Some formatting

Author: mdrxy

libs/partners/nomic/langchain_nomic/embeddings.py

@@ -10,6 +10,7 @@ class NomicEmbeddings(Embeddings):
     """NomicEmbeddings embedding model.
 
     Example:
+
         .. code-block:: python
 
             from langchain_nomic import NomicEmbeddings
@@ -66,16 +67,16 @@ def __init__(
 
         Args:
             model: model name
-            nomic_api_key: optionally, set the Nomic API key. Uses the NOMIC_API_KEY
+            nomic_api_key: optionally, set the Nomic API key. Uses the ``NOMIC_API_KEY``
                 environment variable by default.
             dimensionality: The embedding dimension, for use with Matryoshka-capable
                 models. Defaults to full-size.
-            inference_mode: How to generate embeddings. One of `remote`, `local`
-                (Embed4All), or `dynamic` (automatic). Defaults to `remote`.
+            inference_mode: How to generate embeddings. One of ``'remote'``, ``'local'``
+                (Embed4All), or ``'dynamic'`` (automatic). Defaults to ``'remote'``.
             device: The device to use for local embeddings. Choices include
-                `cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See
-                the docstring for `GPT4All.__init__` for more info. Typically
-                defaults to CPU. Do not use on macOS.
+                ``'cpu'``, ``'gpu'``, ``'nvidia'``, ``'amd'``, or a specific device
+                name. See the docstring for ``GPT4All.__init__`` for more info.
+                Typically defaults to ``'cpu'``. Do not use on macOS.
         """
         _api_key = nomic_api_key or os.environ.get("NOMIC_API_KEY")
         if _api_key:
@@ -91,8 +92,8 @@ def embed(self, texts: list[str], *, task_type: str) -> list[list[float]]:
 
         Args:
             texts: list of texts to embed
-            task_type: the task type to use when embedding. One of `search_query`,
-                `search_document`, `classification`, `clustering`
+            task_type: the task type to use when embedding. One of ``'search_query'``,
+                ``'search_document'``, ``'classification'``, ``'clustering'``
         """
 
         output = embed.text(

libs/partners/nomic/pyproject.toml

@@ -50,6 +50,7 @@ select = [
     "I",    # isort
     "T201", # print
     "UP",   # pyupgrade
+    "S",    # security
 ]
 ignore = [ "UP007", ]
 
@@ -77,3 +78,9 @@ markers = [
     "compile: mark placeholder test used to compile integration tests without running them",
 ]
 asyncio_mode = "auto"
+
+[tool.ruff.lint.extend-per-file-ignores]
+"tests/**/*.py" = [
+    "S101", # Tests need assertions
+    "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
+]