langchain-fireworks[patch]: Add ruff bandit rules to linter (#31796)

- Add ruff bandit rules
- Address a s113 error

Author: mdrxy

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -288,7 +288,7 @@ def lc_secrets(self) -> dict[str, str]:
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object."""
+        """Get the namespace of the LangChain object."""
         return ["langchain", "chat_models", "fireworks"]
 
     @property
@@ -327,7 +327,7 @@ def is_lc_serializable(cls) -> bool:
     )
     """Fireworks API key.
     
-    Automatically read from env variable `FIREWORKS_API_KEY` if not provided.
+    Automatically read from env variable ``FIREWORKS_API_KEY`` if not provided.
     """
 
     fireworks_api_base: Optional[str] = Field(
@@ -338,8 +338,8 @@ def is_lc_serializable(cls) -> bool:
     request_timeout: Union[float, tuple[float, float], Any, None] = Field(
         default=None, alias="timeout"
     )
-    """Timeout for requests to Fireworks completion API. Can be float, httpx.Timeout or 
-        None."""
+    """Timeout for requests to Fireworks completion API. Can be ``float``,
+    ``httpx.Timeout`` or ``None``."""
     streaming: bool = False
     """Whether to stream the results or not."""
     n: int = 1
@@ -636,8 +636,8 @@ def bind_functions(
 
         Assumes model is compatible with Fireworks function-calling API.
 
-        NOTE: Using bind_tools is recommended instead, as the `functions` and
-            `function_call` request parameters are officially marked as deprecated by
+        NOTE: Using bind_tools is recommended instead, as the ``functions`` and
+            ``function_call`` request parameters are officially marked as deprecated by
             Fireworks.
 
         Args:
@@ -699,10 +699,10 @@ def bind_tools(
                 :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
             tool_choice: Which tool to require the model to call.
                 Must be the name of the single provided function,
-                "auto" to automatically determine which function to call
-                with the option to not call any function, "any" to enforce that some
+                ``'auto'`` to automatically determine which function to call
+                with the option to not call any function, ``'any'`` to enforce that some
                 function is called, or a dict of the form:
-                {"type": "function", "function": {"name": <<tool_name>>}}.
+                ``{"type": "function", "function": {"name": <<tool_name>>}}``.
             **kwargs: Any additional parameters to pass to
                 :meth:`~langchain_fireworks.chat_models.ChatFireworks.bind`
         """
@@ -760,11 +760,11 @@ def with_structured_output(
 
             method: The method for steering model generation, one of:
 
-                - "function_calling":
+                - ``'function_calling'``:
                     Uses Fireworks's `tool-calling features <https://docs.fireworks.ai/guides/function-calling>`_.
-                - "json_schema":
+                - ``'json_schema'``:
                     Uses Fireworks's `structured output feature <https://docs.fireworks.ai/structured-responses/structured-response-formatting>`_.
-                - "json_mode":
+                - ``'json_mode'``:
                     Uses Fireworks's `JSON mode feature <https://docs.fireworks.ai/structured-responses/structured-response-formatting>`_.
 
                 .. versionchanged:: 0.2.8
@@ -793,6 +793,7 @@ def with_structured_output(
                 - ``"parsing_error"``: Optional[BaseException]
 
         Example: schema=Pydantic class, method="function_calling", include_raw=False:
+
             .. code-block:: python
 
                 from typing import Optional
@@ -826,6 +827,7 @@ class AnswerWithJustification(BaseModel):
                 # )
 
         Example: schema=Pydantic class, method="function_calling", include_raw=True:
+
             .. code-block:: python
 
                 from langchain_fireworks import ChatFireworks
@@ -854,6 +856,7 @@ class AnswerWithJustification(BaseModel):
                 # }
 
         Example: schema=TypedDict class, method="function_calling", include_raw=False:
+
             .. code-block:: python
 
                 # IMPORTANT: If you are using Python <=3.8, you need to import Annotated
@@ -884,6 +887,7 @@ class AnswerWithJustification(TypedDict):
                 # }
 
         Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
+
             .. code-block:: python
 
                 from langchain_fireworks import ChatFireworks
@@ -897,9 +901,9 @@ class AnswerWithJustification(TypedDict):
                             'answer': {'type': 'string'},
                             'justification': {'description': 'A justification for the answer.', 'type': 'string'}
                         },
-                       'required': ['answer']
-                   }
-               }
+                        'required': ['answer']
+                    }
+                }
 
                 llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)
                 structured_llm = llm.with_structured_output(oai_schema)
@@ -913,6 +917,7 @@ class AnswerWithJustification(TypedDict):
                 # }
 
         Example: schema=Pydantic class, method="json_mode", include_raw=True:
+
             .. code-block::
 
                 from langchain_fireworks import ChatFireworks
@@ -941,6 +946,7 @@ class AnswerWithJustification(BaseModel):
                 # }
 
         Example: schema=None, method="json_mode", include_raw=True:
+
             .. code-block::
 
                 structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)

libs/partners/fireworks/langchain_fireworks/embeddings.py

@@ -10,60 +10,63 @@
 class FireworksEmbeddings(BaseModel, Embeddings):
     """Fireworks embedding model integration.
 
-     Setup:
-         Install ``langchain_fireworks`` and set environment variable
-         ``FIREWORKS_API_KEY``.
+    Setup:
 
-         .. code-block:: bash
+        Install ``langchain_fireworks`` and set environment variable
+        ``FIREWORKS_API_KEY``.
 
-             pip install -U langchain_fireworks
-             export FIREWORKS_API_KEY="your-api-key"
+        .. code-block:: bash
 
-     Key init args — completion params:
-         model: str
-             Name of Fireworks model to use.
+            pip install -U langchain_fireworks
+            export FIREWORKS_API_KEY="your-api-key"
+
+    Key init args — completion params:
+        model: str
+            Name of Fireworks model to use.
 
     Key init args — client params:
         fireworks_api_key: SecretStr
             Fireworks API key.
 
-     See full list of supported init args and their descriptions in the params section.
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
+
+        .. code-block:: python
 
-     Instantiate:
-         .. code-block:: python
+            from langchain_fireworks import FireworksEmbeddings
 
-             from langchain_fireworks import FireworksEmbeddings
+            model = FireworksEmbeddings(
+                model='nomic-ai/nomic-embed-text-v1.5'
+                # Use FIREWORKS_API_KEY env var or pass it in directly
+                # fireworks_api_key="..."
+            )
 
-             model = FireworksEmbeddings(
-                 model='nomic-ai/nomic-embed-text-v1.5'
-                 # Use FIREWORKS_API_KEY env var or pass it in directly
-                 # fireworks_api_key="..."
-             )
+    Embed multiple texts:
 
-     Embed multiple texts:
-         .. code-block:: python
+        .. code-block:: python
 
-             vectors = embeddings.embed_documents(['hello', 'goodbye'])
-             # Showing only the first 3 coordinates
-             print(len(vectors))
-             print(vectors[0][:3])
+            vectors = embeddings.embed_documents(['hello', 'goodbye'])
+            # Showing only the first 3 coordinates
+            print(len(vectors))
+            print(vectors[0][:3])
 
-         .. code-block:: python
+        .. code-block:: python
 
-             2
-             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+            2
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
 
+    Embed single text:
 
-     Embed single text:
-         .. code-block:: python
+        .. code-block:: python
 
-             input_text = "The meaning of life is 42"
-             vector = embeddings.embed_query('hello')
-             print(vector[:3])
+            input_text = "The meaning of life is 42"
+            vector = embeddings.embed_query('hello')
+            print(vector[:3])
 
-         .. code-block:: python
+        .. code-block:: python
 
-             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
     """
 
     client: OpenAI = Field(default=None, exclude=True)  # type: ignore[assignment] # :meta private:
@@ -76,7 +79,7 @@ class FireworksEmbeddings(BaseModel, Embeddings):
     )
     """Fireworks API key.
     
-    Automatically read from env variable `FIREWORKS_API_KEY` if not provided.
+    Automatically read from env variable ``FIREWORKS_API_KEY`` if not provided.
     """
     model: str = "nomic-ai/nomic-embed-text-v1.5"
 

libs/partners/fireworks/langchain_fireworks/llms.py

@@ -4,7 +4,7 @@
 from typing import Any, Optional
 
 import requests
-from aiohttp import ClientSession
+from aiohttp import ClientSession, ClientTimeout
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -22,15 +22,17 @@
 class Fireworks(LLM):
     """LLM models from `Fireworks`.
 
-    To use, you'll need an API key which you can find here:
-    https://fireworks.ai This can be passed in as init param
-    ``fireworks_api_key`` or set as environment variable ``FIREWORKS_API_KEY``.
+    To use, you'll need an `API key <https://fireworks.ai>`__. This can be passed in as
+    init param ``fireworks_api_key`` or set as environment variable
+    ``FIREWORKS_API_KEY``.
 
-    Fireworks AI API reference: https://readme.fireworks.ai/
+    `Fireworks AI API reference <https://readme.fireworks.ai/>`__
 
     Example:
+
         .. code-block:: python
             response = fireworks.generate(["Tell me a joke."])
+
     """
 
     base_url: str = "https://api.fireworks.ai/inference/v1/completions"
@@ -48,40 +50,40 @@ class Fireworks(LLM):
     )
     """Fireworks API key.
     
-    Automatically read from env variable `FIREWORKS_API_KEY` if not provided.
+    Automatically read from env variable ``FIREWORKS_API_KEY`` if not provided.
     """
     model: str
-    """Model name. Available models listed here: 
-        https://readme.fireworks.ai/
-    """
+    """Model name. `(Available models) <https://readme.fireworks.ai/>`__"""
     temperature: Optional[float] = None
     """Model temperature."""
     top_p: Optional[float] = None
-    """Used to dynamically adjust the number of choices for each predicted token based 
-        on the cumulative probabilities. A value of 1 will always yield the same 
-        output. A temperature less than 1 favors more correctness and is appropriate 
-        for question answering or summarization. A value greater than 1 introduces more 
-        randomness in the output.
+    """Used to dynamically adjust the number of choices for each predicted token based
+    on the cumulative probabilities. A value of ``1`` will always yield the same output.
+    A temperature less than ``1`` favors more correctness and is appropriate for
+    question answering or summarization. A value greater than ``1`` introduces more 
+    randomness in the output.
     """
     model_kwargs: dict[str, Any] = Field(default_factory=dict)
-    """Holds any model parameters valid for `create` call not explicitly specified."""
+    """Holds any model parameters valid for ``create`` call not explicitly specified."""
     top_k: Optional[int] = None
     """Used to limit the number of choices for the next predicted word or token. It 
-        specifies the maximum number of tokens to consider at each step, based on their 
-        probability of occurrence. This technique helps to speed up the generation 
-        process and can improve the quality of the generated text by focusing on the 
-        most likely options.
+    specifies the maximum number of tokens to consider at each step, based on their 
+    probability of occurrence. This technique helps to speed up the generation process
+    and can improve the quality of the generated text by focusing on the most likely
+    options.
     """
     max_tokens: Optional[int] = None
     """The maximum number of tokens to generate."""
     repetition_penalty: Optional[float] = None
-    """A number that controls the diversity of generated text by reducing the 
-        likelihood of repeated sequences. Higher values decrease repetition.
+    """A number that controls the diversity of generated text by reducing the likelihood
+    of repeated sequences. Higher values decrease repetition.
     """
     logprobs: Optional[int] = None
     """An integer that specifies how many top token log probabilities are included in 
-        the response for each token generation step.
+    the response for each token generation step.
     """
+    timeout: Optional[int] = 30
+    """Timeout in seconds for requests to the Fireworks API."""
 
     model_config = ConfigDict(
         extra="forbid",
@@ -132,7 +134,7 @@ def _call(
             prompt: The prompt to pass into the model.
 
         Returns:
-            The string generated by the model..
+            The string generated by the model.
         """
         headers = {
             "Authorization": f"Bearer {self.fireworks_api_key.get_secret_value()}",
@@ -148,7 +150,9 @@ def _call(
 
         # filter None values to not pass them to the http payload
         payload = {k: v for k, v in payload.items() if v is not None}
-        response = requests.post(url=self.base_url, json=payload, headers=headers)
+        response = requests.post(
+            url=self.base_url, json=payload, headers=headers, timeout=self.timeout
+        )
 
         if response.status_code >= 500:
             raise Exception(f"Fireworks Server: Error {response.status_code}")
@@ -196,7 +200,10 @@ async def _acall(
         payload = {k: v for k, v in payload.items() if v is not None}
         async with ClientSession() as session:
             async with session.post(
-                self.base_url, json=payload, headers=headers
+                self.base_url,
+                json=payload,
+                headers=headers,
+                timeout=ClientTimeout(total=self.timeout),
             ) as response:
                 if response.status >= 500:
                     raise Exception(f"Fireworks Server: Error {response.status}")

libs/partners/fireworks/pyproject.toml

@@ -52,7 +52,7 @@ disallow_untyped_defs = "True"
 target-version = "py39"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "T201", "UP"]
+select = ["E", "F", "I", "T201", "UP", "S"]
 ignore = [ "UP007", ]
 
 [tool.coverage.run]
@@ -65,3 +65,9 @@ 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
+]